text:
The ads in the race for sheriff have gone from discourse of crime and leadership to bedroom talk, and candidate Scott Israel is fuming.
In the video above, Israel's wife, Susan, denounces the attack campaign. An enemy of the Israel candidacy posted three YouTube videos of a young woman claiming that she was 17 and that she and Israel had an affair, that she had an abortion, and that she just now realized he was married with triplets.
"Do you really think I'm stupid?'' the unidentified woman asks.
Israel asked YouTube to take down the videos, which he called defamatory. YouTube declined, as you'll see in Israel's campaign statement on the jump.
I'll post her videos on the jump page, with the Scott Israel campaign statement about them. As a side note, there is mention on the YouTube page of Israel's "illegitimate'' child. The child that was the subject of a paternity suit against Israel was conceived before he was married, public records show.
Israel's wife says the claims are "despicable lies.'' It's disgusting, hurtful and it's absolutely false.''
The videos did not come from the sheriff's campaign, but Israel blames him directly.
Israel's campaign says the woman is quite obviously a bad actress reading from cue cards, and also looks about a decade older than she says she is.
Here's the statement from the Israel campaign:
Democrat candidate for Broward County Sheriff Scott J. Israel today demanded incumbent Republican Sheriff Al Lamberti call on his supporters to take down YouTube videos in which an actress falsely accuses Israel of marital infidelity, statutory rape, and paying for an abortion.
"These are false and vicious smears far outside the bounds of decency in any political campaign. Al Lamberti needs to call on his supporters to take down this dishonest, filth now," said Israel.
"Taunting my wife Susan and mocking our three children -- making all these false allegations -- is truly despicable," said Israel.
"I have never met the woman in this video. These are sleazy and anonymous attacks, as the woman in them doesn't ever reveal her name," said Israel. The videos also were posted on YouTube under a fake YouTube member name of "Barry Israel" and the YouTube pages each display false disclaimers indicating the videos came from Israel's own campaign.
The woman's video messages are internally inconsistent. She first implies she had a fairly recent six-month affair in the original video, then says she "worked" with Israel, then implies in a subsequent video that she is just 17-years-old even though the actress is clearly a decade older than her claimed age.
Even worse, it is readily apparent in one video that the actress is reading from cue cards – and doing a rather poor job of hiding her poor acting work. At other times, she is giggling, winking and playing with her long hair in the videos.
An attorney acting on behalf of Israel contacted YouTube's legal department last week to request the deletion of the defamatory videos. YouTube Legal responded: "We do not remove content due to allegations of defamation. … We recommend that you pursue any claims you may have directly against the person who posted the content. If your lawsuit results in a decision against the person who posted the content, and if that court order requires us to remove the content from our service, we will respond accordingly."
Israel has also asked that people with any information about the identity of the actress to please contact his campaign. "I plan to sue for defamation once we can identify the 'Jane Doe' who appears in the video," said Israel.
Political advertisement paid for and approved by Scott Israel, Democrat, for Broward County Sheriff
Also, here's Israel's wife's statement:
Dear Neighbor,
Sheriff Al Lamberti and his supporters have sunk to a new low, attacking my family and falsely accusing my husband Scott Israel of marital infidelity and statutory rape in an on-line video.
I know politics is dirty, but this is too much! These lies must be refuted.
As a wife and mother, I have to speak out. Al Lamberti has gone too far.
Lamberti has smeared my husband Scott with a video that attacks my family, and even mocks our children. He falsely accuses my husband of having an affair and of rape and of paying for some woman's abortion.
My husband has never met the woman making these outrageous charges in this vile video. She's clearly an actress or model and reading from cue cards. Scott is a good and honest man, a great father, and wonderful husband. These attacks are despicable lies.
I decided to make my own video to expose these lies and tell people how I feel about this outrageous attack on my husband and my family.
You can see my video here:
Hiring some cheap actress to make false claims of marital infidelity against my husband, and posting it online, is low even for a Republican like Lamberti. The "woman" in the video doesn't say her name or location.
It's an assault on our family, and on my husband of 41 years. It's disgusting and hurtful.
Lamberti is also lying about my husband's 30 year record in law enforcement in his TV ads. Lamberti knows the internal affairs files are not "missing" but were legally expunged because the claims were investigated and proven false. My husband is a real cop -- not some slick politician like Lamberti.
These are Republican lies, the same kind that Mitt Romney and the Radical Right tells about President Obama, the First Lady, and his record.
Sheriff Lamberti should be ashamed. But there is a reason he is doing these desperate acts.
Lamberti doesn't want to talk about the report on Channel 10 about his own top HR administrator in BSO embezzling almost $2 million in taxpayer money right under his nose. Why were there no controls in place? Who was watching the store?
Lamberti doesn't want to talk about his ties to convicted ponzi criminal Scott Rothstein. Rothstein and his associates spent $225,000 to elect Republican Al Lamberti sheriff in 2008. BSO actually helped Rothstein flee the country when his $1.2 billion ponzi scam collapsed. Lamberti's top executive officer at BSO personally drove Rothstein to his private jet getaway in a BSO car and helped him load a duffle bag with $16 million in cash onto the plane!
Lamberti doesn't want to talk about the giant no-bid BSO contracts he rigged for his big GOP contributors in return for tens of thousands in campaign contributions. It is a shady deal that is currently under investigation by FDLE! Thanks to Al Lamberti, Broward taxpayers paid more for each police car than any other county in Florida -- but Lamberti did generously reward his rich donors.
Worst of all, until recently Lamberti's top paid political advisor was right-wing radio talk show host Bill Lewis -- who turned out to be a convicted armed cocaine trafficker who spent 11 years in federal prison. Lamberti fired Lewis only after Lewis assaulted his wife in front of their three children and sprayed her in the face with Lysol, and she called 911. Even then, Lamberti only fired Lewis after his criminal record was exposed in the media. Now Lamberti says he has no responsibility for checking the background of the people who work for him. Even though BSO regulations say BSO employees - including Lamberti - cannot associated with convicted felons.
Next time you see a TV ad lying about Scott Israel, please remember who is paying for it and recognize these are more lies from a Republican who will say anything. My husband Scott is a good man and is qualified to bring integrity and professional law enforcement leadership back to the Broward County Sheriff's office.
Thanks for taking the time to read this.


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BACKGROUND IMAGE: iSTOCK/GETTY IMAGES
- FeatureSRM gets smarter
- FeatureSurvey Says: Users thirst for capacity planning tools
- FeatureRounding up remote offices
- FeatureCapacity, throughput demands grow unabated
- FeatureSnapshot: Bare metal restore used by half of storage managers
- FeaturePump up array performance
- FeatureEncryption appliances reviewed
- ArticleTop tape libraries revealed
- FeatureTape security... with compromises
- FeatureCut big backups down to size
- FeatureNo more dodging storage security
iSTOCK/GETTY IMAGES
Pump up array performance
As data wends it ways from hosts, through the fabric and to the array, it encounters many potential bottlenecks. But that also means there are a number of ways to improve storage subsystem performance. The key to the tuning process is a thorough understanding of your application requirements.
To get the best performance from your array, you need to tune it to match its workload.
AN I/O REQUEST STARTS at a host running an application or a service, travels through layers of its operating system to a host bus adapter (HBA) and then hops through the SAN fabric until it reaches the storage subsystem. When the storage subsystem responds, this course is reversed. This circuitous route offers plenty of performance-killing bottlenecks, as well as opportunities to optimize the performance of your storage subsystem to provide better service for all I/O commands.
I/O performance optimization activities for SAN-attached arrays can occur at the following three distinct times:
- Before the subsystem is purchased when choices about drives, interfaces, cache and data replication features are made.
- At configuration time when choices are made related to RAID level, RAID segment and stripe size; which drives to use to support a RAID group; which LUNs to assign to a RAID group; as well as cache options for the subsystem, RAID group and/or LUN.
- At runtime when the subsystem services its assigned applications, choices can be made or modified for cache options, LUN-to-controller affinity, application-to-LUN assignment and LUN-to-RAID group assignment.
I/O workloads
Subsystem performance tuning starts with knowing your hosts' I/O workloads. I/O characteristics such as typical I/O transfer length, read-to-write ratio, random-to-sequential I/O ratio, I/O operations/sec, and megabytes read/sec and written/sec need to be monitored and recorded. All data recovery and availability requirements must also be considered. Workload characteristics should measure activity at the I/O subsystem and not just what's driven by the application; a host's buffer can often capture some I/O activity and never present it to the subsystem. Workload characteristics can also change once a subsystem is tuned to support it. Workload monitoring is an ongoing endeavor that should be done multiple times before and during the life of a subsystem.
Workload monitoring tools are available from a number of vendors. For open systems, EMC Corp., Hitachi Data Systems (HDS), IBM Corp., Sun Microsystems Inc. and other vendors offer performance monitors as standalone products or bundled with their storage products. For mainframes, resource measurement facility-based products provide performance data. Some databases, such as Oracle and Microsoft SQL Server, offer special-purpose performance monitors. Operating systems such as AIX, HP-UX, Mac OS X, Red Hat Linux, Windows NT/Server 2003 and others have special-purpose performance/activity monitors that can display this sort of information on a host basis.
Some storage resource management (SRM) applications such as Computer Associates (CA) International Inc.'s BrightStor, Hewlett-Packard (HP) Co.'s AppIQ and others have features or components that provide workload performance statistics. Lacking these tools, open-systems administrators can use iostat and/or sar commands to gather device statistics on a device-by-device basis for a single host. Gathering and merging this information from all hosts generating a workload may produce sufficient results for workload measurement.
From a subsystems perspective, most workloads exhibit a time-dependent mixture of activities; however, primary workloads generally fall into the following three dimensions:
- Highly sequential workloads require a high degree of read and/or write activities.
- Highly random workloads demand a high number of read and/or write operations/sec with no perceived pattern in the block numbers accessed.
- High-throughput workloads access large blocks of data and push multiple megabytes/sec or gigabytes/sec to or from a subsystem.
- High-transaction or low-throughput workloads demand a relatively high number of small blocks of data from a subsystem.
- High read-to-write workloads demand a relatively low amount of write activity from a subsystem.
Most workloads can be characterized within these three dimensions. A video production house, for example, might have a high-sequential, highthroughput workload that's characterized by a low read-to-write ratio (e.g., lots of video being written). On the other hand, a video server environment may exhibit similar needs for high sequential/throughput, but a relatively high read-to-write ratio (lots of video being read). Database workloads are more complex. Indices may be considered low throughput and high random, and the read-to-write ratio can be high or low depending on update frequency. Database table access varies between write and read based on the size of rows, the frequency of updates and the amount of ad hoc queries (sequential vs. random).
Pre-purchase consideration
Array performance optimization begins when you're deciding on configuration options and before a purchase is made. Let's start with drives.
Disk drives come in a number of interfaces (Serial ATA [SATA], Serial-attached SCSI [SAS] or Fibre Channel [FC]), capacities (gigabytes) and speeds (rpm). Storage arrays typically include drives with three or more capacity points (73GB, 146GB and 300GB) and at two or more speeds (10,000 rpm or 15,000 rpm).
High random I/O workloads need high rpm drives. These drives typically offer more I/O per drive spindle than lower rpm drives. High-sequential workloads can probably get by with slower rpm drives because sequential I/O doesn't seek as much. For high-throughput workloads, high rpm drives may offer higher MB/sec transfer rates than lower rpm drives, but this often isn't an issue because even slower drives have enough bandwidth to sustain typical workloads. Faster drives typically cost approximately 50% more than slower drives at similar capacities and interface types, so price may be an additional factor to consider.
Tune-up tips for arrays
- Split a "hot" subset of LUNs across controllers and multiple RAID groups.
- Some applications are cache friendly (highly sequential) and some aren't (highly random), and can adversely affect each other when run on the same subsystem. If available, use partitioning to isolate applications and increase performance.
- Make sure there are enough Fibre Channel pipes between the host(s) and the subsystem to support the workload.
- If you have a high-throughput workload, your host bus adapter transfer size should match or exceed the LUN's segment size.
- Array performance optimization begins when you're deciding which subsystem configuration options to buy.
- High-random with low-throughput workloads can get by with today's SATA drives because transfer rates aren't much of a concern.
- Spreading the LUNs across a number of RAID groups or a larger RAID stripe size (and therefore more spindles) yields more balanced activity against all drive spindles.
- For highly sequential, high-throughput and high-write workloads, disabling write-back caching (enabling write-through) can improve performance. In write-through mode, write data bypasses the cache and goes straight to the subsystem's disk.
Today, FC rules for high throughput and any highly sequential workloads where data transfer is a dominant activity, although some SATA vendors may quibble with this statement considering the new SATA 3Gb/sec transfer rates. High-random with low-throughput workloads can get by with today's SATA drives because transfer rates aren't much of a concern. However, highly random workloads need faster drives that are available only with FC. Some drive vendors have recently introduced fast drives supporting SAS and FC, so it won't be long before fast SAS drives show up in storage subsystems.
Capacity is mostly irrelevant to I/O performance, but the number of spindles isn't. Highly random workloads can take advantage of more spindles because much of the I/O time is drive seek activity), so if you have the option, buy more spindles. For highly sequential workloads, the number of spindles isn't a concern because sequential I/O requires minimal seek activity. In any case, you must ensure that the set of spindles can support your minimum throughput requirements.
Most I/O subsystems offer configuration options from two to 100 or more front-end interfaces. This is mostly an availability and connectivity concern for highly random workloads, but may be a real concern for high-throughput workloads. Those types of workloads will have high MB/sec rates and you may need to overconfigure host paths to sustain the workload.
Cache is another important pre-purchase consideration because it can have a major impact on I/O performance. Highly sequential workloads require lots of cache because that's where most of the data will be read. Cache read-ahead algorithms try to predict I/O requests by pre-staging data into cache. However, cache is a zero-sum game; when something goes in, something else gets bumped out. The more cache you have, the more pre-staged data is maintained and performance is improved by not having to fetch data from disk. But cache memory isn't cheap and should be increased only if the application requires a larger cache to run properly.
High write activity, for example, may require more cache because with some controllers write activity may be mirrored between clustered controllers and cached before being written back to disk. These options will be discussed more later. Before purchasing an array, you must determine if you'll use write-back caching and/or write mirroring, and then size your cache accordingly. In addition, on some high-end subsystems, asynchronous remote mirroring of I/O activity consumes additional cache. On midrange systems, remote replication takes less cache, as data and meta data are typically held on disk only while being replicated. Sometimes onboard replication of data (point-intime copies) uses cache to retain updates while the copy is active.
Configuring LUNs and RAID groups
Most I/O subsystems map LUNs to specific RAID groups, and the RAID level you use to support those LUNs affects performance. Also, in most midrange systems LUNs are typically assigned to a preferred controller in a cluster of controllers, although this can change during runtime. If there are LUNs reserved for specific controllers, dividing LUN I/O activity across multiple controllers can balance the controller workload.
Most subsystems support several RAID levels. RAID 5 and RAID 1 are typical, and RAID 5+0 (or 50) and 1+0 (10) may also be available. The choice of RAID level is usually based on the workload's read-to-write ratio and randomness. Although the write penalty for RAID 5 keeps getting smaller, it still exists. For most highly random, write-heavy workloads, it's probably wise to use RAID 1. For sequential workloads with low or high write activity, RAID 5 will suffice because sequential writes have less of a write penalty than random I/O. Throughput isn't a big factor when choosing a RAID level as long as the stripe can sustain the workload. However, cost is a factor to consider: RAID 1 consumes twice the physical drive capacity to support a LUN. RAID 5 can require anywhere from 8% to 33% more physical drive capacity to support a LUN, depending on the stripe size.
Some subsystems let you select the RAID segment or chunk size. For high-throughput workloads, if you have a predictable transfer size you can set the RAID segment size equal to or greater than your transfer size and gain some optimization of I/O performance. RAID stripe size is another parameter that some subsystems allow you to configure using RAID x+0 (or x0) levels. The +0 indicates that a LUN can span more than one RAID group. For example, you have two RAID 5 groups with a three data and one parity drive configuration using RAID 5+0 with two RAID groups; this requires six data drives to support this LUN. However, one RAID 5 group with six data drives and one parity drive can support it equally as well. On some subsystems, a RAID group is spread across all spindles in a subsystem so it is, by definition, an x+0 RAID level. Larger stripe size helps when dealing with "hot" LUNs. Hot LUNs exhibit more I/O activity than the typical LUN in your subsystem. By spreading the LUN's workload across a number of RAID groups or a larger RAID stripe size (and therefore more spindles), you get more balanced activity against all drive spindles.
Runtime performance tuning
Some cache settings may be specified during configuration, but most cache settings should be left to default values and then tweaked at runtime once actual I/O workloads can be observed. Some subsystems have cache settings that can be assigned to LUNs, some have cache settings only for the subsystem level and others are somewhere in between. The cache options discussed later in this article may not apply to a specific LUN, but may need to be specified for higher LUN groupings instead.
Write back vs. write through
There are a number of ways to tune the subsystem cache to optimize I/O performance. For high-write workloads, write-back caching can be an effective technique to optimize performance. For highly sequential, high-throughput and high-write workloads, however, disabling write-back caching (enabling write-through) can improve performance. In writethrough mode, write data bypasses the cache and goes straight to the subsystem's disk.
Another cache option is write mirroring, which keeps controllers in a cluster pair in synch with respect to the disk images they maintain. Write mirroring can impact performance for high-write workloads because all write data has to be transferred to the other controller's cache. There are also data availability implications to disabling write mirroring that need to be considered.
For a highly sequential workload, read-ahead caching is a must. For these workloads, it's important to specify a read-ahead cache amount that's roughly equal to the data transferred during the time it takes to perform one disk-read operation (for 15K rpm FC drives at 2Gb/sec, this could be 1.5MB to 3MB). Most highend subsystems optimize this value in real-time, taking into consideration current I/O workload characteristics and cache size.
A few subsystems let you specify how much cache to devote to write activity vs. read activity. These settings aren't necessarily intuitive, but start with your overall subsystem's I/O workload read-to-write ratio and use the read percent vs. the write percent as a starting point. A better approach for most workloads may be to let the subsystem handle this dynamically, as write activity can be highly time dependent (normal work vs. backup activity).
As discussed previously, LUNs may be mapped to controllers and as you monitor your I/O workload, there may be some controller bottlenecks. Once you discover which LUNs are causing the problems, move them to the alternate controller to balance the workload. Some subsystems do this automatically.
In addition to write mirroring, there are other data availability, recovery and subsystem feature issues that impact performance. Many subsystems offer some form of copy-on-write technology that copies a block when it's written to retain replication. This may cause every write to invoke multiple I/Os, so using this feature will impact write performance.
Asynchronous remote replication may consume cache on high-end subsystems and back-end bandwidth on midrange subsystems. With high-end subsystems, write data is typically retained in the cache until it's copied to the remote location. For midrange systems, data is quickly flushed from the cache and then read back only as it's being copied to remote subsystems. So for midrange systems, remote replication doesn't consume lots of cache, but it does produce additional back-end I/O activity. In either case, anything that consumes cache or bandwidth may impact overall subsystem performance.
Using synchronous vs. asynchronous remote replication can cause additional performance hits. For synchronous mirroring, the original write I/O operation is held up until the data is copied to the remote location. Depending on distance and other network factors, this can take a considerable amount of time for each write operation. For asynchronous mirrors, the original I/O isn't held up.
As discussed, some subsystems mitigate the problem of "hot" LUNs automatically or via x+0 RAID group levels. If your subsystem doesn't do this or you haven't used x+0 striping, you may wish to monitor LUN activity to see if there's a particular subset driving a majority of your I/O. If that's the case, splitting the "hot" subset of LUNs across controllers and multiple RAID groups often yields better performance.
Some applications are cache friendly (highly sequential) and some aren't (highly random). When run together on the same subsystem, these applications may have an adverse effect on one another and slow performance. Some subsystems can be partitioned to isolate counterproductive workload combinations. The partitioning splits one physical subsystem into two or more logical subsystems that can be dedicated to support a specific workload. Some subsystems do this by dedicating a portion of cache to a set of LUNs. Others partition by splitting up the entire subsystem--cache, processors and data paths. Yet another method is to split the workload across multiple subsystems.
In some cases, the fabric in front of your subsystem may be a performance bottleneck. For heavy throughput workloads, make sure there are enough FC pipes between the host(s) and the subsystem to support the workload. And remember that HBA parameters need to be in sync with your workload. If you have a high-throughput workload, its HBA transfer size should match or exceed the LUN's segment size. If the HBA transfer size is below the LUN's segment size, the workload won't perform as well.
There are many ways to improve the performance of storage subsystems. Of course, some subsystems may offer more alternatives than presented here, and some array controllers automatically optimize some aspects of subsystem performance such as how cache is configured. Sophisticated caching algorithms have existed in the mainframe arena for many years and for at least a decade for open systems. The bottom line for performance tuning is knowing your application requirements.

subset: commoncrawl


text:
The purpose of the Office of the Diaconate is to recruit, select, educate, and form men who, through the sacrament of Holy Orders, are called to service as deacons for the Diocese of Camden. It also provides ongoing support for the ordained men by offering programs for continuing education, spiritual growth, and community throughout their ministry.
The office serves deacons, families of deacons, deacon candidates and aspirants, pastors, and parishioners.
The ministry value of this department as part of the Vicariate for Clergy is to prepare and support the deacons who, filled with sacramental grace, carry out the three-fold ministry of the liturgy, the word, and service to God's people in their assigned parishes and/or Diocesan positions.


text:
Gun Safety
August 8, 2016
Gun Safety
- Since 1903, the earliest year of data available, the per capita rate of gun accident deaths has decreased 94 percent, from 3.1 to 0.18 deaths per 100,000 population.[1]
- Over the last 20 years, annual numbers of gun accident deaths have decreased by more than half.[2] Concurrently, Americans have acquired over 150 million new guns and today own approximately 350 million guns, an all-time high.[3]
- Guns are involved in 0.4 percent of accidental deaths among the total population and 1.3 percent among children.[4] Today, the odds are more than a million to one against a child in the U.S. dying in a gun accident.
- Voluntary education decreases accidents. NRA firearm safety programs are conducted by more than 125,000 NRA Certified Instructors and Range Safety Officers nationwide, reaching over one million program participants annually.[5] Youngsters learn firearm safety in NRA programs offered through civic groups such as the Boy Scouts, the Jaycees, and the American Legion, and schools.
- NRA's Eddie Eagle GunSafe® program teaches children pre-K through 3rd grade that if they see a gun, they should not touch it, and should tell a grown-up. Since 1988, Eddie has been used by 26,000 schools, civic groups, and law enforcement agencies to reach more than 27 million children.[6]
- NRA's Law Enforcement Division's 13,000 Law Enforcement Firearm Instructors train law enforcement agency firearm instructors nationwide.[7]
- Gun control supporters' claim that there are fewer motor vehicle accident deaths than the grand total of firearm suicides, criminal homicides, self-defense homicides, law enforcement homicides, and accidental deaths is not only incorrect, it is "apples-to-oranges" to the point of bordering on the frivolous. Comparing accidents to accidents alone, motor vehicle traffic accident deaths outnumber firearm accident deaths 57 to one, and those involving firearms have decreased at a faster rate than those involving motor vehicles.[8]
- Gun control supporters want the manufacture of firearms subject to consumer products regulations, and say that such regulations should result in handguns and "assault weapons" being banned.
Current data – In 2014, the most recent year of data available, of 138,593 accidental deaths in the U.S., 38,718 (28%) were due to drug poisoning, 32,834 (24%) to motor vehicle traffic accidents (excluding pedal cyclists), 31,959 (23%) to falls, 6,580 (5%) to suffocation, 3,406 (2.5%) to drowning, 3,314 (2.4%) to non-drug poisoning, 2,701 (1.9%) to fire/flame, 2,156 (1.6%) to medical mistakes, 1,625 (1.2%) to natural/environmental factors, 902 (0.7%) involved pedal cycles, 586 (0.4%) involved guns, and 13,812 (10%) were due to other factors.
Of 3,899 accidental deaths among children, 1,178 (30%) were due to suffocation, 1,083 (28%) to motor vehicle traffic accidents (excluding pedal cyclists), 647 (17%) to drowning, 247 (6%) to fire/flame, 85 (2%) to natural/environmental factors, 68 (1.7%) to poisoning, 53 (1.4%) to falls, 50 (1.3%) involved guns, 48 (1.2%) involved pedal cycles, 39 (1.0%) involved medical mistakes, and 401 (10%) were due to other factors.
Motor vehicles and guns – For propaganda purposes, gun control supporters compare the number of deaths involving motor vehicles, 99.3% of which are accidental, to those involving guns, 1.7% of which are accidental, hoping that the latter will eventually outnumber the former. In 2012, a news outlet owned by anti-gun activist Michael Bloomberg predicted, "[b]y 2015, firearm fatalities will probably exceed traffic fatalities for the first time."[9]
In 2016, an offshoot of the National Coalition to Ban Handguns[10] claimed, "science-based regulations have dramatically reduced deaths from motor vehicles in recent decades. It's well past time that we regulate firearms for health and safety just like all other consumer products."[11]By "regulate," the group means, "ATF [Bureau of Alcohol, Tobacco, Firearms and Explosives] should be empowered to operate as a health and safety agency with the ability to . . . restrict the availability of specific firearms, classes of firearms and firearm products . . . Take immediate action to stop the sale and distribution of firearms or firearms products found to be 'imminent hazards' . . . no new versions of assault weapons . . . Handguns should be banned from future sale except for military and law-enforcement personnel."[12]
However, the claim that motor vehicle accidents have declined because the federal government dictates how vehicles should be built, and that the same should be done to guns, is dubious. Gun accident deaths, without the government regulation that gun control supporters propose, have decreased at a much greater rate than accidental deaths involving heavily regulated motor vehicles. From 1981 to 2014 (the earliest and most recent years of data available from the CDC), the annual number of motor vehicle traffic accident deaths decreased 33%, while the annual number of gun accident deaths decreased 69%. Also, while motor vehicle accident deaths remained steady during the 1990s, and increased slightly after the turn of the century, they decreased sharply in 2008 and 2009, concurrent with the recession, and have remained steady thereafter.
In the 1990s, gun control supporters claimed motor vehicle accident deaths decreased from 1968 to 1991 because of driver licensing and vehicle registration laws. However, most such laws were imposed between the world wars, and motor vehicle accident deaths increased sharply after 1930. Also, between 1968 and 1991 the motor vehicle accident death rate dropped only 37% with registration and licensing, while the firearm accident death rate dropped 50% without those restrictions. Gun control supporters want registration and licensing only to acquire records necessary to allow the government know who has guns, in the event that a gun confiscation law is imposed. The anti-gun activist group now known as the Brady Campaign once said that registration was the second step in its three-step plan for the confiscation of handguns.[13]
The CAP law myth: Gun control supporters used to point to a study by the Harborview Injury Prevention and Research Center (active in the Handgun Epidemic Lowering Plan, or HELP, Network) claiming that "Child Access Prevention" (CAP) laws (which under some circumstances criminalize leaving a gun accessible to a child who obtains and misuses it), imposed in 12 states between 1989 and 1993, decreased firearm accident deaths among children.[14]However, firearm accident deaths among children began declining in the mid-1970s, not in 1989, when "CAP" laws were first imposed.[15] Also, such accidents have decreased nationwide, not only in "CAP" states. And the study failed to note that also in 1989, NRA's Eddie Eagle GunSafe® Program was introduced nationwide.
[1] National Safety Council, Injury Facts 2000 Edition, page 40, and CDC, WISQARS data query website.
[2] Note 1, CDC.
[3] Philip Cook and Jens Ludwig, Guns in America: Results of a Comprehensive National Survey on Firearms Ownership and Use, Police Foundation, 1996, estimated 192 million privately owned firearms in 1994. See also Bureau of Alcohol, Tobacco, Firearms and Explosives, Annual Firearms Manufacturers and Export Reports and Firearm Commerce in the United States 2014, and NICS data.
[4] Note 2.
[5] For more on NRA training programs, visit http://training.nra.org//.
[6] For more on the Eddie Eagle program, visit https://eddieeagle.nra.org/.
[7] For more information about NRA Law Enforcement training programs, visit http://le.nra.org/.
[8] Note 2.
[9] Chris Christoff and Ilan Kolet, American Gun Deaths to Exceed Traffic Fatalities by 2015.
[10] Violence Policy Center (VPC), formed in 1988 as the New Right Watch (renamed VPC in 1988 or 1989) by former NCBH staffer Josh Sugarmann, author of Every Handgun is Aimed at You: The Case for Banning Handguns.
[11] VPC, Gun Deaths Surpass Motor Vehicle Deaths in 21 State and the District of Columbia, January 11, 2016.
[12] VPC, Cease Fire: A Comprehensive Strategy to Reduce Firearms Violence, 1998.
[13] National Council to Control Handguns director Pete Shields, in The New Yorker, "A Reporter At Large: Handguns," July 26, 1976. "The first problem is to slow down the increasing number of handguns being produced and sold in this country. The second problem is to get handguns registered. And the final problem is to make the possession of all handguns and all handgun ammunition—except for the military, policemen, licensed security guards, licensed sporting clubs, and licensed gun collectors—totally illegal." NCCH was renamed Handgun Control, Inc. in 1979 and renamed Brady Campaign to Prevent Gun Violence in 2001.
[14] Journal of the American Medical Association, Oct. 1, 1997.
[15] Note 2 and National Safety Council, annual Accident Facts reports 1976 and thereafter.
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PING with Jennifer Granick
PING with Jennifer Granick from the October 2005 issue of Information Security magazine.
Ciscogate, the summer's high-profile security event between former ISS researcher, Mike Lynn, and Cisco over the disclosure of a flaw in the IOS router operating system, stirred heavy emotions around responsible vulnerability disclosure and whether there is security in obscurity. Attorney Jennifer Granick, who represented Lynn (a civil case was settled), is an advocate of responsible disclosure. Granick, executive director of the Center for Internet and Society at Stanford University, provides some perspective on this case and what issues it may raise for the future of cyberlaw.
What has this case done for full disclosure advocates?
It's one of those situations that has gotten people talking about it. My view is skewed because it happened at Black Hat where most are advocates of disclosure. Most in the security community feel the amount of information Mike disclosed was completely responsible. In fact, some would say it was not full disclosure; it would have been if he had released exploit code. He explained the problem. I think in some ways, this has been good for advocates and bad for opponents because it suggests companies are overprotective of information since everybody I've spoken with believes Mike's information didn't put people in danger.
With the ubiquity of the product, Cisco's easygoingness about encouraging people to patch, the seriousness of the flaw, the ease of fixing it and the fact that the amount of information he put out there would not allow people to readily replicate it -- put all those factors together and I think most people would agree the way he handled it was responsible.
Yet Cisco didn't like it. I think it becomes a battle of perception; what one person thinks is useful information may be too useful to another.
Cisco charged that Mike had crossed the line and provided too much information, including trade secrets. What does the law say about trade secrets in this regard?
There were no trade secrets at stake here. Mike didn't have the source code, he had the binaries, the product that they distribute. Trade-secret laws are about protecting people in a fiduciary relationship of trust from disclosing private information that is economically valuable. The idea is if an insider gets information, they must keep it secret. It's about misappropriation of trade secrets.
This is not something that is supposed to stop the public from tinkering with products that are publicly available. It's like saying you can't look at the motor of your car and figure out how it works because the fuel-injection system is proprietary. It would be an issue if an insider from GM were talking to a competitor about a trade secret. The same responsibility does not bind the general public. Think about the limitation on people's liberties or on the economy if we could not figure out how to make machines work better or interoperate with each other.
Trade secret law says to target insiders and people who misappropriate.
Would this case have made the same splash if Mike was an independent researcher and not employed by ISS prior to his presentation? It definitely would have had the same splash if he were independent. Independent researchers are always making presentations of this kind at shows like Black Hat or CANSEC West. This made a bigger splash because the lawsuit involved Cisco. I think they mishandled their PR.
One of the things about the substance of his presentation: You can't depend on a title to give you credibility. The credibility of your hypothesis is based on the quality of your work. You have to show enough quality of work to get people to believe what you're talking about. I think he succeeded in convincing people he was not kidding; this is real.
As an advocate of full disclosure, what do you believe is a proper means of disclosing information?
It really depends; it really is in the eye of the beholder. There are a number of factors that must be considered: Are there patches available? How long have they been out? What kind of information are you disclosing? Is there proof-of-concept code? Are you describing the problem in English?
The point is, in a computer context, there is no security through secrecy about flaws. If one person has found it, chances are others have as well. With computers, it's cheap and easy to keep trying to find a flaw until you do. It's expensive and difficult to attack a castle. You may attack a castle 10 or 100 times to find a weakness, but there's a cost in lives. With computers, it may take 1,000 attacks to find a weakness, but you can keep it a secret. It's costless to keep it a secret. I agree that I'm an advocate of full disclosure, but I don't think there should be exploit code thrown left and right, but to keep it secret does not make it valuable.
Full disclosure debates do stir some emotions.
It does stir passions, but to some extent, the debate is settled. Most are in favor of responsible disclosure, where no exploit code is released, but perhaps enough details are provided that others may be able to replicate it themselves.
Mike disclosed less than that level of information. It's not easy (to develop an exploit from his presentation) unless you're an expert in routers. People generally believe this works and that it's for the greater good. The economics of this may eventually change. Companies that are no good with security problems may be better at it. In the end, it's a matter of customer awareness. Again with cars, don't tell me there's a problem with my car, tell me a tire might blow out. We need to know consequences.
In the end, is Ciscogate going to be a good or bad thing for security?
For Mike, it's bad thing. It's very stressful, expensive and painful to be sued. I don't know, maybe the community is demonstrating its frustration with ISS' and Cisco's approach and this may show that this isn't the way to appear like a responsible vendor to purchasers of their products. It may be valuable in that, next time, people won't shoot the messenger. Personally, for Mike, it's not good. Overall, it's good for the security. Cisco routers have a problem and they can be fixed with a patch. People know a lot more about router security.
What issues has Ciscogate raised for the future?
This case raises a lot of issues that we'll see again in cyberlaw. It will be interesting to see if EULAs deprive someone of the right to reverse engineer a patch. It will call into question what is a legitimate trade secret. What amount of disclosure is responsible and the audience to which disclosures are made will be a real issue; whether it helps good guys or bad guys and the fragmentation of the security community in terms of which parts are trustworthy with this kind of information. This is the tip of the iceberg.


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LCA and Multiple Imputed Datasets
Mplus Discussion
Missing Data Modeling
Jon Heron
posted on Monday, July 17, 2006 - 7:05 am
I've just fitted a 5-class LCA model on 10 imputed datasets.
The results are nonsense.
I think that the reason for this is that
"Parameter estimates are averaged over the set of analyses"
means that the resulting parameters for class-1 are the average of the parameters for the class-1's for each of the 10 datasets. Hence, if the ordering of the 5-classes is different within each dataset then everything gets jumbled.
If so, I'm wondering how to get round this. Are the same random starts used within each dataset?
many thanks
Linda K. Muthen
posted on Monday, July 17, 2006 - 8:39 am
To avoid label switching, use user-specified starting values. You can take these from the analysis of one of the imputed data sets.
Jon Heron
posted on Tuesday, July 18, 2006 - 12:11 am
thanks Linda
Stephen Gilman
posted on Friday, June 13, 2008 - 10:08 pm
I wonder if there is any update to the above postings regarding the ability to either save (or even view) the class probabilities for an LCA model with multiply imputed datasets. If it's not possible to save the class probabilities, then I am not sure how to determine which observations should be classified into which class.
Thanks very much for your help,
Linda K. Muthen
posted on Saturday, June 14, 2008 - 4:16 pm
It would not make sense to save the average of the class probabilities. What you should do is run the analysis with all of the parameters fixed to the values of the average estimates from the multiple imputation analysis and obtain latent class probabilities from that model. Use one data set. It doesn't matter which one because all of the parameters are fixed.
posted on Tuesday, January 12, 2010 - 11:30 am
Dear Linda,
I was looking for a way to save the class probabilities for an LCA model with multiply imputed datasets when I came across the postings above. I understand your answer from 6/14/08 in principle, but I don't know how to translate it into practice. My model has three classes, and I�m running Mplus 5.21.
When you say �all the parameters fixed to the average estimates� are you talking about the mean intercept and slope for each latent class? Do you have any code you can share?
Thank you in advance for your help.
Linda K. Muthen
posted on Tuesday, January 12, 2010 - 4:22 pm
If the model run with multiple imputations has 10 model parameters, then these 10 parameters must be fixed in the MODEL command to the values estimated under multiple imputation. Then run the analysis as a regular analysis with any one of the imputed data sets and ask for CPROBABILITIES in the SAVEDATA command.
posted on Wednesday, January 13, 2010 - 9:58 am
Thank you for your reply; as you can tell I'm pretty new to Mplus and still confused. This is the code for the analysis with the imputation:
File is C:\MPLUS\imp.dat;
Type = IMPUTATION;
Names are
id dv0 dv1 dv2;
Missing are all (-9);
Idvariable is id;
Classes = C(2);
Type = mixture;
Starts 5000 100;
i s | dv0@0 dv1@1 dv2@2;
tech9 tech11;
Results is C:\MPLUS\imp.dat;
If I understand correctly, I should rerun the model on one of the datasets adding something to the MODEL statement to set the values of dv0 dv1 and dv2 to the estimates obtained by imputation. The imputation output reports an estimated intercept and slope for each of the dv's, separateley for class 1 and class 2. Are these the values I should use for the revised MODEL statement? Can you please show me how the code would look like?
Thank you again.
Linda K. Muthen
posted on Wednesday, January 13, 2010 - 2:58 pm
Please send the full output from the multiple imputation analysis and your license number to
Sharon Ghazarian
posted on Thursday, March 25, 2010 - 8:48 am
Can the runall utility be used for lca/lta analyses to enable the use of multiple imputation datasets instead of having to fix model parameters?
Linda K. Muthen
posted on Thursday, March 25, 2010 - 11:19 am
I don't understand your question. If you have multiple imputation data sets, you should use TYPE=IMPUTATION in the DATA command.
Sharon Ghazarian
posted on Thursday, March 25, 2010 - 11:44 am
I am using type=mixture for my analyses and so I get an error message when I use type=imputation as well. The error says that imputation cannot be used with type=mixture.
Linda K. Muthen
posted on Thursday, March 25, 2010 - 12:33 pm
Please send your full output and license number to
. IMPUTATION and MIXTURE can be used together.
Jon Heron
posted on Wednesday, May 19, 2010 - 4:33 am
Hi there,
I've returned to the task analysing a latent class model across a number of imputed datasets (created using ICE in Stata).
I see I started this thread nearly 4 years ago - wish i'd sorted this problem out then
Anyway, I have 20 datasets. I've fitted the 4-class model to the first one and then used the resulting parameters as starting values for all the parameters in the combined analysis with type=imputation. Despite the starting values, the ordering of classes between datasets changes so that the result bears no resemblance to those obtained were I to analyse each dataset in succession.
As I plan to do my covariate analysis in Stata, I could probably make do without the results of the model averaged over my 20 datasets, provided I can get hold of the posterior probabilities for each of my 20 models - I expect I can then use these in Stata within the MIM routine. I see that savedata does not work with imputation, so do I have any alternative to fitting the same model to each dataset myself and combining the outputted results?
many thanks, Jon
Bengt O. Muthen
posted on Wednesday, May 19, 2010 - 4:44 pm
In Version 6 you can do ICE within Mplus using the Sequential imputation option (see the V6 UG pp. 463-464). Also, V6 Type=Imputation now has the default of automatically using starting values from the first imputation analysis for subsequent imputations.
The class switching would seem to happen if you have substantial amounts of missingness and/or a couple of classes that are really close.
Since you want to do a "covariate analysis" at the end it sounds like you plan to use posterior probs (or most likely class) as DVs, in which case it might be better to use the V6 approach of Plausible values for the latent classes. This is a research topic, however.
By the way, you might also find the new V6 OUTPUT option SVALUES useful.
Jon Heron
posted on Thursday, May 20, 2010 - 1:10 am
Thanks Bengt,
my plan was to do ICE within Stata and them compare the results with those obtained through ICE + Mplus
I do have substantial missingness - 3,000 complete cases, another 4,500 with partial missing data, however even the largest class representing 80% of the sample can occur as either class 1, 2, 3 or 4 when I use the starting values from dataset 1 and apply them to my other 19 datasets individually.
I've now written some Stata code to impute using ICE, run an LCA on each dataset using Mplus and Rich Jones' "runmplus", import all the posterior probs back into Stata and set up a stacked dataset so I can use MIM (module to analyse and manipulate multiply imputed datasets) based on the posterior probabilities.
This left me 2 problems to solve
1] Sorting the resulting classes within each dataset so that c1 always has the same interpretation
2] Having a sense of what the LCA model is, averaged over the 20 datasets. I guess I can save out the model parameters from each dataset and average them - I don't even need to look at parameter variances if I jsut want to picture the profiles.
Thanks for the heads-up about the new features - sounds like I've only been scratching the surface of what V6 can do.
Jon Heron
posted on Thursday, May 20, 2010 - 1:23 am
I think saving out the model parameters will solve both of these problems - I can use the parameters themselves to tell me the class ordering from each dataset
Jon Heron
posted on Thursday, May 20, 2010 - 5:02 am
If I might carry on this dialogue with myself while you guys are still asleep, I am wondering about the potential problems of not have the latent class posterior probabilities present whilst performing the imputation.
As I understand it, there is an assumption that the data in your imputation model has a correlation of zero with any data not present in the dataset so if you then bring along something new you can get biased estimates of association between the new variable/construct and the stuff you've been imputing.
Seems to me that if the new variable is deterministically derived like a principle component then there's no problem, but I wonder if there may be a problem if you are deriving a latent factor or a latent class measure.
Jenny Chang
posted on Thursday, July 01, 2010 - 5:45 pm
Hi dear, I would like to use MPLUS 5 to estimate a same LCA model for 10 datasets simultaneously so as to save time.but the result have to be independent with each other.
As a new hand, I am not quite clear about the discussion above.Is it the same matter? Could you present an example of the sentence? Thank you!(email:
Linda K. Muthen
posted on Friday, July 02, 2010 - 11:14 am
I think the easiest way to do this is using a DOS bat file.
John Woo
posted on Monday, September 27, 2010 - 7:14 pm
I have a follow up question to your answer to the very first question of this thread.
You said,
"To avoid label switching, use user-specified starting values. You can take these from the analysis of one of the imputed data sets."
If I use manual starting values, as you suggest, then how can I make sure that the result is the global maximum instead of local?
If my "ice" from STATA produced five sets of imputed data, then should I obtain five sets of starting values from running each model separately, and then run the TYPE=IMPUTATION model five times using each set of starting values? If I did and if the results come out different for each set of starting values, what criteria do I use to pick the best model?
Thank you in advance for your help.
Linda K. Muthen
posted on Tuesday, September 28, 2010 - 9:56 am
I would run one data set where the best loglikelihood is replicated. I would then use those parameter estimates for the TYPE=IMPUTATION analsyis. To be certain that you replicate the best loglikelihood in each data set, you would need to analyze each one separately.
Simone Schmidt
posted on Thursday, December 09, 2010 - 6:02 am
I am conducting a 3 class LCA and I�ve got missings on the covariates. Therefore I�m using multiple imputation (m=10). Imputation seems to work well but when using the datalist.dat with type=imputation, for the regression model I only get class 1 and class 2 vs. class 3 in the output. The Alternative Parameterization is not provided so I do not get class 1 and class 3 vs. class 2 etc. (I had the same problem when using the FIML procedure)It works well when doing the analyses seperately for all the 10 imputed datasets - is it ok to pool the results by myself by calculating the means of the estimates?
Linda K. Muthen
posted on Thursday, December 09, 2010 - 12:19 pm
You can pool the parameter estimates but not the standard errors of the parameter estimates.
Oxnard Montalvo
posted on Tuesday, January 22, 2013 - 5:53 pm
Hi, in regards to the comments made a few posts above by Jon Heron, about all covariates for the analysis model being included in the imputation model, is this in fact the case for latent variables?
Does that mean we have to use the H0 approach, to ensure the latent variables of interest will be included, or is the H1 imputation general enough to also cover and latent correlations?
Bengt O. Muthen
posted on Tuesday, January 22, 2013 - 6:45 pm
I don't think you have to do H0 latent variable - based imputation just because you have a latent variable model in mind. An H1 model would work fine. It is not wrong; just not using the underlying structure. An exception might be when the latent variable is a latent class variable, in which case imputing under a single-class model would be using a wrong model.
I hope that is what you were asking.
Oxnard Montalvo
posted on Thursday, January 24, 2013 - 2:50 pm
Hi Bengt,
Yes to a certain extent - I was planning on using latent class models for my analysis.
Raghav Ramachandran
posted on Thursday, September 24, 2015 - 2:31 pm
Drs. Muthen,
I am implementing an LCA model with covariates, including interactions between pairs of covariates, using multiply imputed data (the multiple imputation-FCS algorithm was run in SAS v9.3). However, when I use type=imputation in the data command to calculate pooled parameter estimates over all ten of my imputed data sets, the values do not equal the average of the same parameters over each of the 10 data sets. I've noticed that the LCA item-response probabilities that I obtain from the pooled analysis also differ widely from the analyses of individual imputed data sets. I'd really appreciate suggestions on how to resolve this issue when using type=imputation. Thanks for the help.
Linda K. Muthen
posted on Thursday, September 24, 2015 - 5:22 pm
They should be the average. You would need to send some evidence that they don't along with your license number to
posted on Wednesday, June 05, 2019 - 2:14 pm
I am estimating a latent class model and then for the estimated classes I run a multinomial logit model, while accounting for missing data through multiple imputation. Is it possible to do everything in one step? Would you recommend to first do multiple imputation and then LCA and multinomial model?
Many thanks
Tihomir Asparouhov
posted on Wednesday, June 05, 2019 - 6:33 pm
I think you can do it in one step without multiple imputation using algo=integration; integration=montecarlo.
Alternatively, you can do multiple imputation, in one step together with the LCA estimation, following the setup described in User's guide example 11.6.
A third method is to do the multiple imputation first as in User's Guide example 11.5 followed by LCA model estimation as in User's guide example 13.13.
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Born Anton Zaslavski in Saratov, Russia but growing up in Kaiserslautern, Germany, Grammy-winning electronic producer Zedd broke onto the European dance scene in 2010 with a remix of Skrillex's "Scary Monsters and Nice Sprites." Though known for his glitchy, pulsing production, Zedd's musical journey began in the acoustic realm. Raised in a musical family, the future producer cut his teeth on the piano at four years old and eventually moved on to drums and played in a rock band before being drawn into the digital world in 2009. Zedd built a name for himself with his remixes of... Read more


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PORTLAND, Ore. — Police say the gunman who killed two people and himself in a shooting rampage at an Oregon mall was 22 years old and used a stolen rifle from someone he knew.
Clackamas County Sheriff Craig Roberts says that Jacob Tyler Roberts had armed himself with an AR-15 semiautomatic rifle and had several fully loaded magazines.
Roberts says the rifle jammed during the 22-year-old's attack, but he managed to get it working again.
The chief says authorities don't yet have a motive.
A law enforcement official has told The Associated Press the shooter did not have a criminal record. The official spoke on condition of anonymity because he was not authorized to discuss details of an ongoing criminal investigation.
Two people were killed, and another was wounded.


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There is weird a phenomenon happening in my kitchen drawer. Somehow, every single item from my flatware is different from the next. I have to say that I don't remember ever buying a table set. Forks, spoons and knives were given to me by family members or friends after moving out of my parents' place years ago. Many of these utensils probably come from the high school cafeteria where I used to borrow them to eat my lunch and would forget to bring back. I would like, for once, to own one of these nice table sets that you use when mom and dad come for diner.
I also own an incredible amount of screw drivers. I have to say that I love buying tools. The problem is that I also bought two multi-bit screwdrivers, two electric drills and a Dremel... I don't need my old tools anymore. Also, since they were bad quality, the Philips head all got stripped and I only use the flat ones to open paint cans.
Here's how to merge two problems into one cool solution.
- Acrylic handle screwdrivers
- An old table set
- A metal saw
- A heat gun
- A clamp
The first step is to heat the metal part of the screwdriver. Make sure the air doesn't get on the plastic handle because it will melt like ice. It should take about 30 seconds on full power.
Squeeze the metal part in a clamp and pull on the handle using gloves. My little sister could do it.
This is actually the same process as the manufactured one... but a bit more manual.
Repeat step 1 and 2 for all unwanted tools.
Use a Sharpie marker to indicate where you'll have to cut the flatware.
You can use a steel cutters, a metal saw, a buffer, a band saw... or whatever you have that can cut steel.
Sand down the excess steel. The flatware handle should be about 2 mm larger than the hole in the acrylic handle.
Use a heat gun or put the flatware in the oven at 300 degrees (no microwave...duh). It should take about 1 minute with the heat gun. Use glove because it's HOT. Be careful not to leave it for too long because the flatware might turn brown and get stained by impurities.
While still hot, insert the flatware in the proper handle. Don't breathe the fumes of the acrylic melting or you'll have a big headache. Let everything cool and harden for 30 minutes.
I used SUGRU to fill the holes left in the handle. Sugru is a kind of silicone paste that hardens in a few hours to leave you the time to do a clean job. It also comes in different colors. If you are experimented with epoxy, this would also do a great finish.
Congratulations, you've just made your own handyman place setting. Remember that acrylic won't survive to the dishwasher. It's better to wash these utensils manually.


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- Online at playphone.com or any other site operated by Playphone (the "Sites"); and
- When you call, email or otherwise communicate with Playphone.
This Privacy Policy does not apply to games or other applications from third-parties that may be available through the Service (our "Partners") and does not apply to game portals Playphone has developed specifically for wireless providers, e.g., Verizon Games.
Please read this Privacy Policy carefully and if you have any questions, feel free to contact us. By accessing the Service, Apps, or Sites, you are accepting the practices described in this Privacy Policy. Please note that we may modify the Privacy Policy from time to time. It is your responsibility to ensure that you regularly review our Privacy Policy, posted at www.playphone.com, as continued usage of the Service, Apps, or Sites indicates your acceptance of such modifications.
Information we collect
We collect and record the information you have provided. We may also collect your email address, as applicable, in accordance with the granted permissions on your Device. Other than names, phone numbers and email addresses, we don't generally collect any personally identifying information about our users, with the exception of any credit card, debit card, prepaid card, scratch card, gift card or other information provided by you for billing and payment purposes and Activity Information, described below. We may offer you the opportunity to submit other information about yourself (such as gender, age, occupation, hobbies, interests, location, etc.) or you may provide information through the Service or Apps. Such information shall also be covered by this Privacy Policy. Any actions you perform or information you provide that can be seen by other users of the Service, Apps, or Sites or any Partners, is considered public and should be performed and provided at your own risk and discretion.
When you use a social network service ("SNS") in connection with your use of the Service, we may record information regarding the use of that SNS and we may collect other information that is visible on the SNS in accordance with the relevant terms of use associated with the provider of the SNS.
Note that since some Playphone software is served through a SNS, it is your responsibility to read and accept the terms and privacy policy of such SNS prior to continuing. When you invite new members into your network, match contacts or send messages through the Service, we securely collect and maintain on secure servers the information associated with those messages, including email addresses, phone numbers and content.
The Service and Sites may contain links to other websites or services. We are of course not responsible for the privacy practices of other sites or service. We encourage you to be aware when you leave the Service or Site to read the privacy statements of each and every web site or application that collects personal information. This Privacy Policy applies solely to information collected by Playphone on the Service, Sites, or in any Apps.
You should also be aware that most content delivered through the Service originates with another content provider. Such other content providers may place "cookies" on your browser, or otherwise gather information about you, including by asking you to provide information through the Service, Apps or Sites.
Finally, in order to provide the best possible service and to allow us to make certain internal reports and make recommendations, we automatically collect information about your use of the Service, Apps, or Sites, such as internet protocol addresses, browser type, browser language, referring/exit pages and URLs, other browser history, platform type, number of clicks, domain names, landing pages, pages viewed and the order of those pages, the amount of time spent on particular pages, the date and time, or other similar information ("Activity Information").
By using the Service, you consent to our retaining all the above-described information in anonymous form as long as Playphone sees fit.
The Service is not directed at children, and Playphone does not knowingly collect or solicit personal information from anyone under the age of 13 or knowingly allow such persons to register or participate in the Service or Apps. If you are under 13, please do not attempt to register for the Service or send any information about yourself to us, including your name, address, telephone number, or email address. No one under age 13 may provide any personal information to Playphone. In the event that we learn that we have collected personal information from a child under age 13 we will take all reasonable measures to delete that information and to not use such information for any purpose (except where necessary to protect the safety of the child or others or where required by law). If you believe that we might have any information from or about a child under the age of 13, please contact us at email@example.com.
Use and sharing of your information
We do not sell, rent, or share your personally identifiable information ("personal information") to any third party, except as set forth below. We also may use certain information about you that is not personally identifiable.
We may use and share public information collected and your Activity Information to determine the kinds of content that you would like to view and to assist any advertisers in targeting their advertising.
We also use your name, email address, phone number and other information on our system to notify you of new product releases, service notifications, and to solicit your feedback and input. Those emails will contain links for preference management and where appropriate, unsubscribe links should you decide you do not want to receive further emails.
You understand and agree that we may access, preserve, and disclose any information we may have about you and the contents of your account if required to do so by law or in a good faith belief that such access, preservation or disclosure is reasonably necessary to avoid liability, to comply with legal process, such as a search warrant, subpoena, statute, or court order, to protect our rights and property, or that of our affiliates or the public, or enforce this Privacy Policy or the Playphone End User Terms of Service (or any other applicable Playphone terms or policies). Playphone is not required to question or contest the validity of any search warrant, subpoena or other similar governmental request that Playphone receives.
We also may use non-personally identifiable information and certain technical information about your phone, computer and your access of the Service (including your phone number and internet protocol address) in order to operate, maintain and manage the Service.
Playphone may disclose information about you, including Activity Information, to its partners in order to provide the Service, Apps or Sites, resolve maintenance problems and correct errors in the Service, to communicate with you about the Service, Apps, or Sites, to provide you with advertising and promotional information in connection with the Service, Apps, or Sites and to enhance your experience with Playphone. However, we do not give our partners an independent right to share this information. For more information on automatic collection of information and advertising, see "Cookies" and "Third-party advertising companies," below.
Due to our contractual obligations with our Partners and other third parties and the need to share information to deliver and support the Service, we cannot provide you with the opportunity to opt-out of sharing information (whether personally identifiable information or other information) with these third parties.
Playphone, certain of its Partners, and each of their contractors and employees may view the information that you transmit to provide the Service, Apps, or Sites and otherwise perform the tasks set forth in this Privacy Policy.
You may decide to provide us with another person's email address so that person may be invited to join the Service or so that we may facilitate your communication with other people through the Service (such as when you upload an address book).
You may also provide us with another person's contact information for purposes of delivering information through a SNS. We use this information to contact and, if applicable, remind that person that he or she has been invited to join the Service or has received content through a SNS. All invitees are provided with the option not to receive further invitations from Playphone. We may disclose such information if required to do so in order to comply with legal process, as set forth above.
Safeguarding your information
Playphone follows generally accepted industry security standards to safeguard and help prevent unauthorized access, maintain data security and correctly use your personal information. However, no commercial method of information transfer over the Internet or electronic data storage is known to be 100% secure. As a result, Playphone cannot guarantee the absolute security of that information during its transmission or its storage in Playphone's systems. Be advised that if you send messages or post material containing personal information, that information may be collected and used by others.
Upon termination of your account, Playphone will remove your profile and other personal information from view. Playphone, however, may (but is not obligated to) keep information about you that is on its system following your termination, and this Privacy Policy will continue to apply to such information. Please contact Playphone if you have any questions about your personal information.
Transfer of information
We reserve the right to transfer your personal information in the event of a transfer of ownership of Playphone or the acquisition of substantially all the assets of Playphone related to the business which collected such information, such as acquisition by or merger with another company. In such an event, Playphone will notify users who have provided email addresses when information about those users is transferred and becomes subject to a different privacy policy.
A cookie is a small text file recorded on your hard drive that identifies you as a unique user and contains certain information about your particular login session and/or your use of the Service or Sites. The Service or Sites may send cookies to your computer when you access them, or otherwise view content through the Service, Apps or Sites. The Service and Sites use cookies to improve quality, make recommendations, and make the Service, Apps, or Sites easier to use for you. The "help" feature on most browsers will tell you how to prevent your browser from accepting new cookies, how to have the browser notify you when you receive new cookies or how to disable cookies altogether. If you reject cookies, you may not be able to participate in certain activities on the Services or Sites.
Third-party advertising companies
Playphone may use third-party advertising companies to serve ads on our Service, Apps, or Sites. Our advertisers may use third-party advertising technology to target interest-based advertising on the Service, Apps, or Sites. In the course of advertisements being served to you, a unique third-party cookie or cookies may be placed on your computer. Similarly, third-party advertising companies may provide us with pixel tags to help manage and optimize our online advertising. Pixel tags enable us to recognize a browser's cookie when a browser visits the site on which the beacon is located, and to learn which banner ads bring users to a given site.
Playphone does not otherwise provide any personally identifiable information to third-party advertising companies.
Do not track
Do Not Track (DNT) is a privacy preference that users can set in some web browsers, allowing users to opt out of tracking by websites and online services. At the present time, the World Wide Web Consortium (W3C) has not yet established universal standards for recognizable DNT signals and therefore, the Service and Sites do not recognize DNT.
Can I change my mind?
If you want to withdraw consent to our use or storage of your personal information or which to opt-out from receiving email or other communications from Playphone, please send e-mail to email@example.com. Recognize that withdrawing your consent may limit or preclude your use of the Service or Apps.
Your California privacy rights
This Privacy Policy describes how we may share your information, including your personally identifiable information, for marketing and other purposes: "Use and sharing of your personal information." The Privacy Policy applies to all users of the Playphone Service, Apps, or Sites, including California residents.
How to contact us
If you have any questions or concerns about this Privacy Policy, please feel free to e-mail us at email@example.com.


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Caitlin Rea has assisted on both of Harvard's goals this season (photo courtesy Gil Talbot).
CAMBRIDGE, Mass. – After going 1-1 in the first two games of the season, the Harvard field hockey team will take on Providence and No. 17 Boston College this weekend. The Crimson will travel to Providence Saturday, before hosting BC Sunday as part of the Harvard Student Rewards program.
at Providence, Saturday, 1 p.m.
Live Stats
Boston College, Sunday, 2 p.m.
Live Stats | Video | Directions
Last Time Out
After two games, sophomores Catriona McDonald and Caitlin Rea and junior Caroline McNeill are tied for the Harvard team-lead in points with two apiece. McDonald and McNeill have both tallied goals, while Rea assisted on each score in the opening weekend. The Crimson bested Holy Cross, 1-0, in head coach Tjerk van Herwaarden's first career game, but slipped to Sacred Heart, 2-1 (2 OT), on Labor Day.
Protecting the Cage
Cynthia Tassopoulos and Jenn Hatfield split the first two games at goalie for the Crimson. Tassopoulos shutout Holy Cross while making four saves and Hatfield had an impressive 15-save showing during the 98-minute double overtime game against Sacred Heart.
Student Rewards
Sunday's matchup with Boston College is part of the Harvard Student Rewards program. Free frisbees will be given to all students in attendance. Also, students may swipe their ID for entry into the Student Rewards grand prize.
About Providence (1-2)
The Friars picked up their first win of the season on Labor Day, a 6-2 rout of Kent State, after starting the season 0-2. Providence took No. 25 Maine and No. 12 Boston University to overtime, but gave up a golden goal in each contest. Sophomore Daniella Bernasconi (2 goals) and junior Caitlin Malone (1 goal, 2 assists) lead the Friars with four points. Like the Crimson, Providence has also utilized two goalies this season with sophomore Deidra Clymer logging 159:31 minutes in two starts and senior Kathi Weidman getting 61:01 minutes in the cage with one start. Clymer has given up 1.76 goals per game and has a .733 save percentage, while Weidman has allowed 2.29 goals per contest and has a .667 save percentage.
Series History – Harvard vs. Providence
Harvard will look to break an 11-11-1 tie in the all-time series against Providence. The Friars have won the last four meetings between the two teams dating back to 2004. Before Providence's recent streak, Harvard had won the previous eight matchups between 1997-2004.
About No. 17 Boston College (3-1)
The Eagles were off to a 3-0 start with wins over No. 20 Michigan State, No. 25 Maine and Quinnipiac, but most recently dropped a 2-1 overtime decision to No. 19 Massachusetts. Junior Virgynia Muma leads all Eagle scorers with three goals, including two game-winners. Freshman Leah Settipane has handled the goalkeeper duties, allowing 0.75 goals per game with a .857 save percentage. BC has taken advantage of quick starts throughout the season by outshooting (28-10), outscoring (5-0) and getting more corners (17-3) than their opponents in the first half. The second half hasn't been as friendly to the Eagles as they have been outshot (21-16), have less corners (12-8) and are only +1 (3-2) in goal differential during the final 35 minutes.
Series History – Harvard vs. Boston College
Boston College has reeled off seven straight wins over the Crimson to take a 12-7-1 advantage in the all-time series. Harvard's last win over BC, a 2-1 victory, came on October 22, 2003.
Up Next
Harvard opens Ivy League play September 15 against rival Yale at Jordan Field.


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Crystal Silver Grey SW6803
- Datasheet (英語)
- Crystal Metal Pearl Pigments (英語)
Guangzhou Yortay Fine Chemicals Co., Ltd.目前只在以下作出標識的區域展示其產品資料:
Crystal Silver Grey SW6803 forms part of the crystal titanium series of pigments that are inorganic, with a light to dark grey shine and a particle diameter of 5-25µm. The pigment shows good stability, has a strong metallic effect, high hiding power with brighter colors than the conventional recolored series. It can be used in car surface and refinished paints, in interior and exterior wall paints as well as gravure, screen and offset inks.
Guangzhou Yortay Fine Chemicals Co. Ltd. is a pearlescent pigment manufacturer with a research and development team that provides service for the different needs of customers. Yortay Chemicals produces high performance pigments for the graphic art and inks industry at home and abroad. Their products are sold to more than 40 countries and they include conventional industrial grade and special effect grade pearlescent pigments.
產品類型 Categories
- 顏料/顏料分散體 ~ 珠光般 / 干涉顏料 ~ 顏色
Guangzhou Yortay Fine Chemicals Co., Ltd.
希望在賽百庫經銷商/貿易商板塊進行展示推廣?請立即聯絡我們 !


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Had the farce of its development not abruptly dead-ended in May of last year, April 1, 2010 was to have been the date Duke Nukem Forever went to gold master - that is, was at last finished.
The detail was revealed over Twitter by former Take-Two Interactive producer Jason Bergman on April 1, so, naturally, a lot of people thought it was a hoax. Bergman in followup tweets vowed it was not, and that the team deliberately chose April Fool's day as the gold date - likely for the meltdown it would have caused in the gaming press.
(I don't even want to imagine being on shift for such a thing. First, it's this game. There would be enormous pressure on both sides: Not publishing, or going with it. Then throw in that it's April Fool's, and the people confirming the rumor have had, let's say, limited credibility on this subject in the past. Though to announce Duke Nukem Forever is on the way and have gaming news outlets deliberately not publish that is admittedly hilarious.)
The Cut Scene reached out to Bergman and was assured by Bergman the April 1, 2010 date was true. That would have meant we'd have finally had Duke Nukem Forever by late Spring or early Summer.
Today was the Day 'Duke Nukem Forever' was Scheduled to Go Gold - Really [Cut Scene]


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Andrea Clarke
Class of 2014
Major/Minor: Major: English
Minor: Drama
Campus Involvement
- Drama Department Productions
- Cater Society of Junior Fellows
- Independent Playhouse
- Running Club
Hometown? Baltimore, MD
High School? Mount de Sales Academy
What are you up to this summer? I am interning at two different radio stations in Baltimore City: 98Rock and WYPR. At 98Rock, I will be working mostly in the promotions department, and at WYPR, I am a creative intern for "The Signal," which is a daily show that focuses on the Maryland arts scene. I am very excited to explore my hometown through these two jobs and hope to see as many Orioles games as possible!
Tell us about your study abroad experience. In Fall 2012 I studied abroad in England at Royal Holloway University of London. During that time I was able to travel to Scotland, Paris, and The Netherlands. One of my favorite things about living overseas was the massive English breakfast: eggs, ham, sausage, potatoes, mushrooms, tomatoes, baked beans, toast, and tea!!
Favorite Chestertown hangout? Plaza Tapatia, the local Mexican restaurant. All-you-can-eat chips and salsa and solid margaritas. Plus they make you wear a huge sombrero if it's your birthday!
Acting the Part:
Through the Cater Society of Junior Fellows, Andrea traveled to Maroua, Cameroon to develop and teach a camp for girls. Andrea's camp was designed to encourage self-awareness, assertiveness, and creativity in girls in a society where women are typically disrespected.