As time passes your faces shape and texture begins to change. Lines become more pronounced and your skin can start to sag. A great solution to this is a good facelift which can reverse the signs of aging and leave you with healthy, younger looking skin. There are two types of facelift. The mini facelift which focuses on the lower cheeks, jawline and neck and the full facelift which focuses on the upper and lower cheeks, jawline, mouth, neck and midface. Both are performed under anesthesia and use surgery to tighten and reposition loose neck muscles. The mini facelift has a shorter recovery time than a full facelift making it a better choice for those who want a short downtime.
Is Facial Plastic Surgery a Good Option for Me?
With all the different types of procedures that can be performed on the face it is easy to get overwhelmed with the amount of choices not to mention the added task of finding a good facial plastic surgeon in San Francisco. Depending on your concerns about your face your surgeon will assess your face structure then develop a plan that is tailored specifically to you that will help address your concerns. Your doctor will also explain any risks involved with each procedure and whether or not they would be good for you.
A typical facial plastic surgery can last between 2 to 5 hours depending on what needs to be done and how skilled your doctor is. Mini facelifts are done under either local anesthesia, IV sedation, or general anesthesia while full facelifts are performed under IV sedation or general anesthesia.
When it comes to your face you should choose a doctor that specializes in facial procedures. A facial plastic surgeon is a doctor who performs surgery on the face in the deepest levels. They are experts in the field of facial anatomy and understand the nervous structure, vascular structure and facial tissues. They know how to keep surgery safe while achieving great aesthetic results. A good facial plastic surgeon in San Francisco is Dr. Michael Echavez. Dr. Echavez received his degree from UC San Francisco and has worked for years as a plastic surgeon with concentration on facial plastic surgery. He is a board certified surgeon and spends some of his time at his practice training other surgeons and is published in in various media.
So don't let the passage of time or any imperfections you may notice when looking in the mirror bother you. With a surgeon like Dr. Echavez you will never have to worry about them and can rest assured that your face is in the best hands possible.
What' Are the Benefits of Botox?
What Does Voluma Do for You?


This is seven year old Staffie Cross DYSON. He enjoys being around other dogs and walks well on a lead.
Dyson is a happy lad waiting patiently for someone to offer him a loving new home.
For further information on him, please contact us on 07931220094.
Dyson is on trial in a new home.


When a person slips and falls while on another person's property, they may assume that the property owner is automatically responsible to pay for their damages.
Unfortunately, North Carolina's laws covering slips and falls are far more complicated. Despite the fact that slip and fall injuries are among the most common types of personal injury cases, they can be very difficult to prove.
A Burlington slip and fall lawyer understands the laws as they apply to slip and fall cases. Combining this knowledge with the facts of an individual's case, an experienced personal injury attorney can form a comprehensive legal strategy to obtain compensation for an individual's injuries.
Most slips and falls are the result of an accident. Because of this, they are pursued under the legal theories of premises liability and negligence. Premises liability is the concept that property owners have a legal responsibility to keep their land well- maintained and safe for visitors.
This can mean that they must repair any structural defects, such as a broken stair, but also must keep the area clear of any temporary hazards, like spills or ice.
In order for an injured person to win a slip and fall case, they must be able to prove that the property owner was negligent in their care for the property.
Duty – In some scenarios, a person has a legal responsibility to protect another person. This is known as a duty of care. This is never automatic, but people assume this duty when they invite others onto their property. Shop owners open for business, people having a house guest, or even inviting a delivery person onto their land creates a duty for the property owner to protect these visitors.
Breach – A breach of the duty of care happens when the property owner fails to protect the invited party. In slip and fall cases, this usually happens when a spill is not cleaned or cordoned off. The question of whether a breach happened is the central issue in most slip and fall cases. The amount of time since the spill, any actions taken by the property owner, and how obvious the spill was, are all pieces of evidence that will be debated.
Cause – The breach of the duty of care needs to be the cause of the plaintiff's injuries. Medical documentation is the best evidence for this element, especially if the treating doctor states in their records that the injury was caused by the fall.
Scope – The injuries suffered need to be foreseeable given the facts of the case. Since falls can result in almost any type of injury, this element is not usually in dispute.
Damages – The plaintiff needs a true physical harm to prevail. Any non-economic damages, such as pain and suffering or lost time at work can be added on, but the claim must be rooted in a physical injury.
People may believe that if they are injured due to a slip and fall on another's property, especially a business, that their claim for damages will be simple. In truth, the law is far more complicated than proving that a person fell on another's land. Questions will arise over how exactly the fall happened, at what time of day, and even if the injured person was responsible for the accident.
Burlington slip and fall lawyers represent injured individuals in their fights with insurance companies to answer these questions and demonstrate that the property owner is responsible for the accident.
By gathering the evidence, applying the law, and negotiating from a position of strength, most cases settle without needing to go to court. However, if a trial is necessary, litigators will present the case to the jury with force and clarity. There is a limited time to file a claim so do not delay; contact today.


1) Have measurements made by a tailor or sewer.
2) Take Measurements over tee shirt and comfortable pants, belt must be horizontal and even with top of hip bone 1-1/2" below belly button.
3) Make all measurements using cloth tape to center of belt.
4) Show exact measurements. We will add "ease" as required.
BE SURE to take all measurements over well-fitting underwear; DO NOT measure over baggy clothes.


text:
B.D.S. (Adelaide, Australia), 2001
M.D.S. Distinction in Oral & Maxillofacial Surgery (Hong Kong) 2006
F.R.A.C.D.S. (Australia), 2006
F.A.M.S. Oral & Maxillofacial Surgery, 2009
Ph.D. (Netherlands), 2009
Dr Lee is a specialist in Oral and Maxillofacial Surgery (OMS). She obtained her Bachelor of Dental Surgery in the University of Adelaide, Australia in 2001 and graduated with distinction in Master of Dental Surgery (OMS) in the University of Hong Kong, 2006. She later obtained a PhD from the Radboud University of Nijmegen, The Netherlands in 2009. She worked at the National Dental Centre since graduating and as a consultant from 2010 to 2014, contributing to teaching and research. She was also the associate editor for the Proceedings of Singapore Healthcare involving with research and publications and was also appointed as a Clinical Lecturer for the National University of Singapore during this period.
She is a fellow of the Academy of Medicine Singapore, a member of the Singapore Dental Association and the Association of Oral and Maxillofacial Surgeons. She was president for the Association of Oral and Maxillofacial Surgeons, Singapore from 2009-2010. Her special interests are in dentoalveolar surgery and in dental implants. She loves her work and interacting with patients. Her hobbies are in photography and in appreciating nature and landscapes.
Area of Specialty

redpajama_set_name: RedPajamaCommonCrawl


We have to battle in the LU Top Down Knock Down.
I'm usually online under another alt, just tell me which channels you're always in.
Timezone is Berlin (GMT +1 DST). Sadly I won't be able to come on so late during the next days. ._.
Okay, what time are you usually online?
Oh well, i guess that my grammatical errors could remember the Slavic Grammar structure but nope.
I come from Italy, Sardinia to be precise.
And you? where do you come from?
Sorry if i post there but your Private Message space it's full.
It's practically identical to normal trust but with expanded roles lists to add the new roles.
So that people doesn't have to check the code to examine them.
Psst, I'm not sure if you noticed, but your signature says "You don't have to be legendary" and then there's a Xerneas. Just pointing it out.
matt ayala was banned by Earl of Lemongrab for 1 day!
I know that we need to battle soon. I have been just hoping that I wouldn't need to battle at 4AM (or 6AM). But I guess I need to try being online next night. Coinflip win wouldn't be nice.
How do you likez my new fusions? (My thread) I know, You haven't seen them.
ROTOM IS STRONGEST BIRD. FEAR BIRDS!!!
Is it alright if we battle on PS seeing htat my Mac can't read PO?
FALCON SAY ON FRIDAY EVENING. FALCON HAS TOO MANY EXAMS DUE TOMORROW. /rant over. What timezone are you?
Not today. Gotta sleep. When's the deadline?
Son, I wanna get at least 5 of these pokes by the time we finish lol. Either that or I'll whittle you down to one.
Hey. We are opponents for the XY Forum tour. I am GMT -8, let me know convenient times for you.
Hey Matt! I saw your art shop and wondering if I could add my styles (work with you guys) to it. My signature is an example of my work, also I can provide other e.x. if needed.
It's Old Tongue from Robert Jordan's Wheel of Time series.


text:
Home JUDICIARY National Drug Control Master Plan: Malami Commends Leadership, Staff of NDLEA
National Drug Control Master Plan: Malami Commends Leadership, Staff of NDLEA
The Attorney General of the Federation and Minister of Justice Abubakar Malami (SAN) has commended the leadership of National Drug Law Enforcement Agency (NDLEA) for the renewed vigour and unflinching commitment in the fight against drug abuse in the country.
Abubakar Malami SAN made the assertion at the launch of National Drug Control Master Plan 2021-2025 ( NDCMP)
The AGF, represented by the Special Assistant to the President on Financial Crimes and Compliance, Office of the Attorney General of the Federation and Minister of Justice, Mr.Biodun Aikomo he said events of the past 11 months, including the launch of War Against Drug Abuse ( WADA) Campaign and the impact yielding Offensive Action of NDLEA were attestation to resolve to tackle the drug abuse in the country.
Also Read: UNDP, Japan Supports Nigeria's Virtual Court Facilities In Correctional Centres
Mr Malami said NDCMP 2021- 2025 lunched was strategic to the planned effort to change the drug abuse and trafficking narrative of Nigeria.
" The significance of this occasion cannot be over- emphasized given the strategic and critical importance of the document in addressing the complex issues of production, cultivation, trafficking, sales and abuse of psychotropic drugs and illicit substances.
He noted that the National Drug Control Master Plan was central to the country's approach to tackling the illicit drug conundrum with the overall goal of improved health and security for all Nigerians.
" menace of drug abuse and trafficking is a global problem and Nigeria is not immune to this scourge. Our society is grappling with some of the consequences of the illicit drug menace, including its negative impact on public health, the corrosion of the social fabric and the attendant pernicious effects on the economy.
Since the release of the findings of the National Drug Use Survey 2018, we have had to sit up and come to terms with the grim reality of drug abuse in this country. Having a drug user population of 14.3 million people (14.4%), between 15 and 64 years of age, of which one in every four drug users is female while 376,000 people are high-risk drug users, is a wake-up call for urgent action."
ReportCourt Sentences Primary School Teacher to Life Imprisonment For Raping 5 Year Old Pupil
"We cannot afford to trivialise the situation, especially with incontrovertible evidence that buttresses the fact that Nigeria has moved from a transit country to a consumer country where all sorts of illicit drugs are trafficked in and out of our territories.
The picture is more frightening when you consider that Cannabis Sativa is being cultivated illegally in large quantities and large shipments of cocaine and heroin are coming in from South America and South-East Asia, with the production of Methamphetamine gathering momentum in the country. He disclosed.
Abubakar Malami maintained that the present administration attaches priority to clamping down on drug trafficking and eradicating drug abuse in society.
The Minister pointed out that as a blueprint, this was the fourth strategic document, of a structured and coordinated approach to drug control, developed by Nigeria since 1999.
He explained that the consistent development of the National Drug Control Master Plan (NDCMP) speaks volumes about Nigerias commitment to continuity in her institutional framework for response to the evolving complex drug situation.
" The NDCMP 2021-2025, developed after extensive consultations with key stakeholders at the state and national levels, provides the critically needed focus in Nigerias drug control efforts, by setting out activities and measurable targets for the various government agencies involved in drug control, thereby buttressing the need for inclusive, multifaceted and concerted approach in our drug control efforts."
" I strongly believe that the rigorous implementation of the NDCMP 2021-2025 will bring the desired result in our calculated effort to exert control over the menace and overcome the challenges of the drug problem and related organized crimes in Nigeria.
" I urge all stakeholders responsible for reducing the supply of and the demand for narcotic drugs and psychotropic substances in Nigeria to place a high premium on the implementation of the provisions of this important document. Malami stated
He reminded the significance of collective responsibility to actualise the action points and achieve the goals of NDCMP 2021-2025.
"May I conclude by expressing appreciation to the European Union and the United Nations Office on Drugs and Crime, UNODC, for their roles in funding and facilitating the development of this strategic document. Also, the immeasurable contributions of the Inter-Ministerial Committee Members in the development of this document is highly appreciated". Attorney General of the Federation further said.
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Federal Govt Compiles Names of Prison Inmates for Presidential Pardon


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unless - to define exception handling routines.
stmnt unless stmnt
Similar to the repetition and selection constructs, the unless construct is not really a statement, but a method to define the structure of the underlying automaton and to distinguish between higher and lower priority of transitions within a single process. The construct can appear anywhere a basic Promela statement can appear.
The first statement, generally defined as a block or sequence of basic statements, is called the main sequence. The second statement is called the escape sequence. The guard of either sequence can be either a single statement, or it can be an if , do , or lower level unless construct with multiple guards and options for execution.
The executability of all basic statements in the main sequence is constrained to the non-executability of all guard statements of the escape sequence. If and when one of the guard statements of the escape sequence becomes executable, execution proceeds with the remainder of the escape sequence and does not return to the main sequence. If all guards of the escape sequence remain unexecutable throughout the execution of the main sequence, the escape sequence as a whole is skipped.
The effect of the escape sequence is distributed to all the basic statements inside the main sequence, including those that are contained inside atomic sequences. If a d_step sequence is included, though, the escape affects only its guard statement (that is, the first statement) of the sequence, and not the remaining statements inside the d_step . A d_step is always equivalent to a single statement that can only be executed in its entirety from start to finish.
As noted, the guard statement of an unless construct can itself be a selection or a repetition construct, allowing for a non-deterministic selection of a specific executable escape. Following the semantics model, the guard statements of an escape sequence are assigned a higher priority than the basic statements from the main sequence.
Unless constructs may be nested. In that case, the guard statements from each unless statement take higher priority than those from the statements that are enclosed. This priority rule can be reversed, giving the highest priority to the most deeply nested unless escapes, by using Spin run-time option -J . This option is called -J because it enforces a priority rule that matches the evaluation order of nested catch statements in Java programs.
Promela unless statements are meant to facilitate the modeling of error handling methods in implementation level languages.
Consider the following unless statement:
{ B1; B2; B3 } unless { C1; C2 }
where the parts inside the curly braces are arbitrary Promela fragments. Execution of this unless statement begins with the execution of B1 . Before each statement execution in the sequence B1;B2;B3 , the executability of the first statement, or guard , of fragment C1 is checked using the normal Promela semantics of executability. Execution of statements from B1;B2;B3 proceeds only while the guard statement of C1 remains unexecutable. The first instant that this guard of the escape sequence is found to be executable, control changes to it, and execution continues as defined for C1;C2 . Individual statement executions remain indivisible, so control can only change from inside B1;B2;B3 to the start of C1 in between individual statement executions. If the guard of the escape sequence does not become executable during the execution of B1;B2;B3 , it is skipped when B3 terminates.
Another example of the use of unless is:
A;
:: b1 -> B1
od unless { c -> C };
The curly braces around the main or the escape sequence may be deleted if there can be no confusion about which statements belong to those sequences. In the example, condition c acts as a watchdog on the repetition construct from the main sequence. Note that this is not necessarily equivalent to the construct:
:: c -> break
od;
C; D
if B1 or B2 are non-empty. In the first version of the example, execution of the iteration can be interrupted at any point inside each option sequence. In the second version, execution can only be interrupted at the start of the option sequences.
In the presence of rendezvous operations, the precise effect of an unless construct can be hard to assess. See the semantics definition for details on resolving apparent semantic conflicts.


The decision has been made to join the SDBE MAT by the Governors of St Stephen's. A copy of the letter sent to all stakeholders can be found here.
The Governing Body's priority is to enable St Stephen's to continue to provide an excellent education for our children while protecting the school's role at the heart of our community and retaining its unique qualities and strengths.
A period of formal consultation was held. Please click here for more information.
Click here for more details about the SDBE MAT and the other schools that are part of the Trust.


Features vegetable seeds, annual and perennial flower seed varieties, garden ready transplants, flower bulbs and tubers for the home gardener. Browse through a wide assortment of gardening supplies including seed starting supplies, plant protection, containers and garden tools.
Published every January and available year-round while supply lasts.


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Accepted Manuscript: NeuroKube: An Automated and Autoscaling Neuroimaging Reconstruction Framework using Cloud Native Computing and A.I.
Title: NeuroKube: An Automated and Autoscaling Neuroimaging Reconstruction Framework using Cloud Native Computing and A.I.
The Neuroscience domain stands out from the field of sciences for its dependence on the study and characterization of complex, intertwining structures. Understanding the complexity of the brain has led to widespread advances in the structure of large-scale computing resources and the design of artificially intelligent analysis systems. However, the scale of problems and data generated continues to grow and outpace the standards and practices of neuroscience. In this paper, we present an automated neuroscience reconstruction framework, called NeuroKube, for large-scale processing and labeling of neuroimage volumes. Automated labels are generated through a machine-learning (ML) workflow, with data-intensive steps feeding through multiple GPU stages and distributed data locations leveraging autoscalable cloud-native deployments on a multi-institution Kubernetes system. Leading-edge hardwareand storage empower multiple stages of machine-learning, GPU accelerated solutions. This demonstrates an abstract approach to allocating the resources and algorithms needed to elucidate the highly complex structures of the brain. We summarize an integrated gateway architecture, and a scalable workflowdriven segmentation and reconstruction environment that brings together image big data with state-of-the-art, extensible machinelearning methods.
Madany, Matthew; Marcus, Kyle; Peltier, Steven; Ellisman, Mark H.; Altintas, Ilkay
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2020 IEEE International Conference on Big Data
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A Deep Learning-Based Real-time Seizure Detection System
https://doi.org/10.1109/SPMB50085.2020.9353623
Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))
Obeid, Iyad Selesnick (Ed.)
Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEGmore »channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the "Signal File" and "Visualizer" blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1's (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, "Deep learning for electroencephalogram (EEG) classification tasks: a review," J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, "Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients," Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, "Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms," in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] "CFM Olympic Brainz Monitor." [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, "Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset," J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, "Improved EEG Event Classification Using Differential Energy," in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, "Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors," Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O'Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., "The Temple University Hospital Seizure Detection Corpus," Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, "Automatic seizure detection in the newborn: Methods and initial evaluation," Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9.« less
The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem
Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))
The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert datamore »scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this "Deep Learning Epilepsy Detection Challenge", participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge.« less
Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms
Golmohammadi, Meysam ; Shah, Vinit ; Obeid, Iyad ; Picone, Joseph ( April 2020 , Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications)
Obeid, Iyad ; Selesnick, Ivan ; Picone, Joseph (Ed.)
Scalp electroencephalograms (EEGs) are the primary means by which phy-sicians diagnose brain-related illnesses such as epilepsy and seizures. Au-tomated seizure detection using clinical EEGs is a very difficult machine learning problem due to the low fidelity of a scalp EEG signal. Neverthe-less, despite the poor signal quality, clinicians can reliably diagnose ill-nesses from visual inspection of the signal waveform. Commercially avail-able automated seizure detection systems, however, suffer from unaccepta-bly high false alarm rates. Deep learning algorithms that require large amounts of training data have not previously been effective on this task due to the lack of big data resources necessary for building such models and the complexity of the signals involved. The evolution of big data science, most notably the release of the Temple University EEG (TUEG) Corpus, has mo-tivated renewed interest in this problem. In this chapter, we discuss the application of a variety of deep learning ar-chitectures to automated seizure detection. Architectures explored include multilayer perceptrons, convolutional neural networks (CNNs), long short-term memory networks (LSTMs), gated recurrent units and residual neural networks. We use the TUEG Corpus, supplemented with data from Duke University, to evaluate the performance of these hybrid deep structures. Since TUEG contains a significant amountmore »of unlabeled data, we also dis-cuss unsupervised pre-training methods used prior to training these com-plex recurrent networks. Exploiting spatial and temporal context is critical for accurate disambigua-tion of seizures from artifacts. We explore how effectively several conven-tional architectures are able to model context and introduce a hybrid system that integrates CNNs and LSTMs. The primary error modalities observed by this state-of-the-art system were false alarms generated during brief delta range slowing patterns such as intermittent rhythmic delta activity. A varie-ty of these types of events have been observed during inter-ictal and post-ictal stages. Training models on such events with diverse morphologies has the potential to significantly reduce the remaining false alarms. This is one reason we are continuing our efforts to annotate a larger portion of TUEG. Increasing the data set size significantly allows us to leverage more ad-vanced machine learning methodologies.« less
Issues in the Reproducibility of Deep Learning Results
Jean-Paul, S. ; Elseify, T. ; Obeid, I. ; Picone, J. ( December 2019 , IEEE Signal Processing in Medicine and Biology Symposium (SPMB))
Obeid, I. ; Selesnik, I. ; Picone, J. (Ed.)
The Neuronix high-performance computing cluster allows us to conduct extensive machine learning experiments on big data [1]. This heterogeneous cluster uses innovative scheduling technology, Slurm [2], that manages a network of CPUs and graphics processing units (GPUs). The GPU farm consists of a variety of processors ranging from low-end consumer grade devices such as the Nvidia GTX 970 to higher-end devices such as the GeForce RTX 2080. These GPUs are essential to our research since they allow extremely compute-intensive deep learning tasks to be executed on massive data resources such as the TUH EEG Corpus [2]. We use TensorFlow [3] as the core machine learning library for our deep learning systems, and routinely employ multiple GPUs to accelerate the training process. Reproducible results are essential to machine learning research. Reproducibility in this context means the ability to replicate an existing experiment – performance metrics such as error rates should be identical and floating-point calculations should match closely. Three examples of ways we typically expect an experiment to be replicable are: (1) The same job run on the same processor should produce the same results each time it is run. (2) A job run on a CPU and GPU should producemore »identical results. (3) A job should produce comparable results if the data is presented in a different order. System optimization requires an ability to directly compare error rates for algorithms evaluated under comparable operating conditions. However, it is a difficult task to exactly reproduce the results for large, complex deep learning systems that often require more than a trillion calculations per experiment [5]. This is a fairly well-known issue and one we will explore in this poster. Researchers must be able to replicate results on a specific data set to establish the integrity of an implementation. They can then use that implementation as a baseline for comparison purposes. A lack of reproducibility makes it very difficult to debug algorithms and validate changes to the system. Equally important, since many results in deep learning research are dependent on the order in which the system is exposed to the data, the specific processors used, and even the order in which those processors are accessed, it becomes a challenging problem to compare two algorithms since each system must be individually optimized for a specific data set or processor. This is extremely time-consuming for algorithm research in which a single run often taxes a computing environment to its limits. Well-known techniques such as cross-validation [5,6] can be used to mitigate these effects, but this is also computationally expensive. These issues are further compounded by the fact that most deep learning algorithms are susceptible to the way computational noise propagates through the system. GPUs are particularly notorious for this because, in a clustered environment, it becomes more difficult to control which processors are used at various points in time. Another equally frustrating issue is that upgrades to the deep learning package, such as the transition from TensorFlow v1.9 to v1.13, can also result in large fluctuations in error rates when re-running the same experiment. Since TensorFlow is constantly updating functions to support GPU use, maintaining an historical archive of experimental results that can be used to calibrate algorithm research is quite a challenge. This makes it very difficult to optimize the system or select the best configurations. The overall impact of all of these issues described above is significant as error rates can fluctuate by as much as 25% due to these types of computational issues. Cross-validation is one technique used to mitigate this, but that is expensive since you need to do multiple runs over the data, which further taxes a computing infrastructure already running at max capacity. GPUs are preferred when training a large network since these systems train at least two orders of magnitude faster than CPUs [7]. Large-scale experiments are simply not feasible without using GPUs. However, there is a tradeoff to gain this performance. Since all our GPUs use the NVIDIA CUDA® Deep Neural Network library (cuDNN) [8], a GPU-accelerated library of primitives for deep neural networks, it adds an element of randomness into the experiment. When a GPU is used to train a network in TensorFlow, it automatically searches for a cuDNN implementation. NVIDIA's cuDNN implementation provides algorithms that increase the performance and help the model train quicker, but they are non-deterministic algorithms [9,10]. Since our networks have many complex layers, there is no easy way to avoid this randomness. Instead of comparing each epoch, we compare the average performance of the experiment because it gives us a hint of how our model is performing per experiment, and if the changes we make are efficient. In this poster, we will discuss a variety of issues related to reproducibility and introduce ways we mitigate these effects. For example, TensorFlow uses a random number generator (RNG) which is not seeded by default. TensorFlow determines the initialization point and how certain functions execute using the RNG. The solution for this is seeding all the necessary components before training the model. This forces TensorFlow to use the same initialization point and sets how certain layers work (e.g., dropout layers). However, seeding all the RNGs will not guarantee a controlled experiment. Other variables can affect the outcome of the experiment such as training using GPUs, allowing multi-threading on CPUs, using certain layers, etc. To mitigate our problems with reproducibility, we first make sure that the data is processed in the same order during training. Therefore, we save the data from the last experiment and to make sure the newer experiment follows the same order. If we allow the data to be shuffled, it can affect the performance due to how the model was exposed to the data. We also specify the float data type to be 32-bit since Python defaults to 64-bit. We try to avoid using 64-bit precision because the numbers produced by a GPU can vary significantly depending on the GPU architecture [11-13]. Controlling precision somewhat reduces differences due to computational noise even though technically it increases the amount of computational noise. We are currently developing more advanced techniques for preserving the efficiency of our training process while also maintaining the ability to reproduce models. In our poster presentation we will demonstrate these issues using some novel visualization tools, present several examples of the extent to which these issues influence research results on electroencephalography (EEG) and digital pathology experiments and introduce new ways to manage such computational issues.« less
Enabling Multi-GPU Support in gem5
Yogatama, Bobbi W. ; Sinclair, Matthew D. ; Swift, Michael M. ( May 2020 , gem5 Users Workshop)
In the past decade, GPUs have become an important resource for compute-intensive, general-purpose GPU applications such as machine learning, big data analysis, and large-scale simulations. In the future, with the explosion of machine learning and big data, application demands will keep increasing, resulting in more data and computation being pushed to GPUs. However, due to the slowing of Moore's Law and rising manufacturing costs, it is becoming more and more challenging to add compute resources into a single GPU device to improve its throughput. As a result, spreading work across multiple GPUs is popular in data-centric and scientific applications. For example, Facebook uses 8 GPUs per server in their recent machine learning platform. However, research infrastructure has not kept pace with this trend: most GPU hardware simulators, including gem5, only support a single GPU. Thus, it is hard to study interference between GPUs, communication between GPUs, or work scheduling across GPUs. Our research group has been working to address this shortcoming by adding multi-GPU support to gem5. Here, we discuss the changes that were needed, which included updating the emulated driver, GPU components, and coherence protocol.
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Madany, Matthew, Marcus, Kyle, Peltier, Steven, Ellisman, Mark H., and Altintas, Ilkay. NeuroKube: An Automated and Autoscaling Neuroimaging Reconstruction Framework using Cloud Native Computing and A.I.. Retrieved from https://par.nsf.gov/biblio/10251125. 2020 IEEE International Conference on Big Data . Web. doi:10.1109/BigData50022.2020.9378053.
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Madany, Matthew, Marcus, Kyle, Peltier, Steven, Ellisman, Mark H., & Altintas, Ilkay. NeuroKube: An Automated and Autoscaling Neuroimaging Reconstruction Framework using Cloud Native Computing and A.I.. 2020 IEEE International Conference on Big Data, (). Retrieved from https://par.nsf.gov/biblio/10251125. https://doi.org/10.1109/BigData50022.2020.9378053
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Madany, Matthew, Marcus, Kyle, Peltier, Steven, Ellisman, Mark H., and Altintas, Ilkay. "NeuroKube: An Automated and Autoscaling Neuroimaging Reconstruction Framework using Cloud Native Computing and A.I.". 2020 IEEE International Conference on Big Data (). Country unknown/Code not available. https://doi.org/10.1109/BigData50022.2020.9378053. https://par.nsf.gov/biblio/10251125.
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@article{osti_10251125,
place = {Country unknown/Code not available}, title = {NeuroKube: An Automated and Autoscaling Neuroimaging Reconstruction Framework using Cloud Native Computing and A.I.}, url = {https://par.nsf.gov/biblio/10251125}, DOI = {10.1109/BigData50022.2020.9378053}, abstractNote = {The Neuroscience domain stands out from the field of sciences for its dependence on the study and characterization of complex, intertwining structures. Understanding the complexity of the brain has led to widespread advances in the structure of large-scale computing resources and the design of artificially intelligent analysis systems. However, the scale of problems and data generated continues to grow and outpace the standards and practices of neuroscience. In this paper, we present an automated neuroscience reconstruction framework, called NeuroKube, for large-scale processing and labeling of neuroimage volumes. Automated labels are generated through a machine-learning (ML) workflow, with data-intensive steps feeding through multiple GPU stages and distributed data locations leveraging autoscalable cloud-native deployments on a multi-institution Kubernetes system. Leading-edge hardwareand storage empower multiple stages of machine-learning, GPU accelerated solutions. This demonstrates an abstract approach to allocating the resources and algorithms needed to elucidate the highly complex structures of the brain. We summarize an integrated gateway architecture, and a scalable workflowdriven segmentation and reconstruction environment that brings together image big data with state-of-the-art, extensible machinelearning methods.}, journal = {2020 IEEE International Conference on Big Data}, author = {Madany, Matthew and Marcus, Kyle and Peltier, Steven and Ellisman, Mark H. and Altintas, Ilkay}, }
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The new website for Somerset College is now live! After a successful pitch back at the start of the year we have come to the end of a very rewarding and successful project.
The new site makes use of our powerful 'Engine' Content Management System (CMS) to allow the college to make regular updates to the site via a news, events and blog section. The CMS also enables staff to update courses, downloads, manage and approve new content, manage enquiries, collate statistics and much more.


Headlines have once again highlighted allegations of fraud - Volkswagen's admission that they were designing and installing software in millions of diesel engines to cheat official emissions testing. The consequences, civil, regulatory and criminal, of such actions look likely to be severe.
Fraud is estimated to cost the UK economy £70 billion per annum. Third party current account fraud grew 12 times faster than first party current account fraud last year, and identity theft now represents 69% of all current account fraud (Experian 2015). Certain sectors and geographical locations are more vulnerable to fraudsters. Businesses operating in wholesale and retail trade, manufacturing, financial and insurance services and construction need to take particular care.
The insurance market has a significant problem with reports that every day in the UK insurance fraud worth £3.5 million is uncovered (The Guardian May 2014). However, all sectors are vulnerable, most recently, the phone and broadband provider Talk Talk, which has over 4 million UK customers admitted that banking details and personal information have been accessed by hackers in a recent cyber-attack.
So what is commercial fraud?
Essentially, it involves one party obtaining by dishonest means some type of financial advantage or causing some financial loss to another party. Commercial fraud is greatly varied and can range from diversion of funds, fraudulently creating and approving purchases, to business intermediaries trying to divert funds for their own gain.
A recent case, Credit Agricole Corporation and Investment Bank v Papadimitriou (2015) is significant in recognising that whilst the specific circumstances are all important – where there is a high-value private banking transaction largely conducted offshore involving significant fee income for the bank – the bank must accept it is under a duty to examine the commercial purpose of the proposed transaction as part of its responsibility to conduct due diligence.
Clearly there is a huge ongoing challenge in combating commercial fraud. Money laundering remains a substantial problem, with the International Monetary Fund estimating that around 5% of worldwide currency circulation is laundered money.
The UK's AML (Anti-Money Laundering) and KYC (Know Your Customers) guidelines recommend a risk-based approach to money laundering prevention.
Businesses that seek to protect themselves from the financial and reputational impacts of fraudulent attacks should put in place comprehensive anti-bribery, corruption and anti-money laundering policies respectively. Enshrined in any policy should be a commitment to screening and checking entities against all available data. All potential and existing businesses partners should be subjected to regular checks for any anomalies. It is also essential to proactively monitor for any unusual events, such as unusual transactions, that might be suggestive of fraudulent activity.


Auctions are always fun to poke around in, wishing you had the money to buy some of those cool things. For some reason there seems to be a lot of Civil War stuff on the block lately.
Rock Island Auction has a nice blog entry about the evolution of the Henry rifle. I've mentioned it as well but this is a much more detailed look at the business end of things and the principals involved. One thing that comes through quite clearly is that while Oliver Winchester might not have known much about firearms (he started out in shirts) he was a very savvy businessman.
Their Civil War arms are listed here, but I thought the 1864 Henry worthy of mention. As you can see they fetch high prices.
Heritage Auctions in Dallas has a couple of auctions of interest. One is a Civil War and Militaria auction that has a lot of things like carte de visites of luminaries like Robert E. Lee, George Custer, and other generals; surgical instruments, drums, flags, and much more.
There is also an Arms and Armor auction coming up with numerous Civil War arms and accoutrements, including several rare Henrys.
A Legends of the Wild West auction has photos of Civil War personalities William Quantrill, Bloody Bill Anderson, Jo Shelby and Jesse James.
And finally an Americana auction that features several photos of Abolitionist John Brown, as well the leg shackles he wore during his incarceration at Charles Town prior to his execution.
UPDATE: Forgot to mention that there are also two complete (and working) period Gatling guns for sale. Just the thing for keeping the zombies at bay.


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sportsGolf
10 things to know about the AT&T Byron Nelson's new home TPC Craig Ranch, including its rank among Texas courses
TPC Craig Ranch cracked The Dallas Morning News' top 50 golf courses in Texas.
No. 19: TPC Craig Ranch, McKinney, Private, Tom Weiskopf, opened in 2004, par 71, 7,438 yards(TPC Craig Ranch)
By Mark Kazlowski
With the announcement Tuesday that TPC Craig Ranch will serve as the new home for the AT&T Byron Nelson beginning in 2021, here are 10 things to know:
1. The par-72, 7,438-yard private course opened in 2004 just north of State Highway 121 in McKinney.
2. It is part of the sprawling 2,200-acre Craig Ranch master-planned community developed by David Craig. The Dallas Morning News reported in 2004 the course cost more than $40 million.
3. Tom Weiskopf designed TPC Craig Ranch with PGA Tour player D.A. Weibring as a consultant. According to Weiskopf's website, it is Weiskopf's third design in Texas along with Buffalo Creek in Rockwall and La Cantera in San Antonio.
4. It cost $47,500 to become a charter member of the course in 2004. Avid Golfer magazine estimates it costs $25,000 today with monthly dues of $1,025.
5. TPC Craig Ranch was No. 41 in The Dallas Morning News rankings of Texas courses in 2019.
6. Two tour championships for what is now the Korn Ferry Tour were held at TPC Craig Ranch. Matt Bettencourt won the 2008 Nationwide Tour Championship with a score of 17-under 267. Justin Bolli won the 2012 Web.com Tour Championship with a 16-under 268.
7. The course features Zoysia fairways and bentgrass greens. Rowlett Creek plays a prominent role in the routing.
8. Dallas-based ClubCorp acquired TPC Craig Ranch in January 2019.
9. It is one of 39 courses in the TPC network in the United States.
10. The 12th hole can play as a par 4 or par 5. It is a par 4 from 493-yard Tour tees. It is a par 5 from the 547-yard Tournament and Craig Ranch tees. It is likely the PGA Tour will use the Tour tees, making it a par 71.
'All the stars seemed to line up': AT&T Byron Nelson moves to McKinney's TPC Craig Ranch for 5 years starting in 2021
By Brad Townsend
Find more golf stories from The Dallas Morning News here.
Mark Kazlowski, Mark writes about local golf happenings and edits the Texas Golf section, which ranks the best courses in Texas. It's his goal to play the top 100 courses. He's about halfway there.. Mark is a senior multiplatform editor for SportsDay. He has had several editing roles in the sports department since arriving in 1985 and has seen many changes in SportsDay.
mkazlowski@dallasnews.com @MarkKazlowski
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Proficiency exams are offered for the Fall and Spring Semesters only. We will be offering proficiency testing for HIST 141, 142, 171, and 172. If you are interested in taking a written proficiency exam, please consult the reading list below. An online registration form will be available at this link the week before classes resume. Questions may be directed to the History Department BEFORE the first week of classes. Books are available now via the online Illini Union Bookstore.
Tuesday January 15, at 5 PM. Students are allowed 3 hours to complete the exam.
Exams for all four classes will be available at this session; students must be pre-qualified and may test for a maximum of one class per semester. The Department prohibits the retaking of any proficiency examination which a student has failed. See other restrictions below.
Please check Proficiency Exam Info from Student Code for additional information, and consult this webpage for updates.