The Effect of Caffeine and Choline on Short-term Memory

This study sought to determine whether caffeine combined with choline could improve short-term memory in healthy adults. The study tested the effect of choline (2 gm) alone and in combination with several concentrations of caffeine (25 mg, 50mg and 100mg) on short-term verbal and visual memory and attention. The Wide Range Assessment of Memory and Learning-2 was utilized. Choline 2 gm + caffeine 25 mg group showed significantly (p\u3c0.05) higher overall memory performance whereas memory performance in the choline 2 gm + caffeine 50 mg group was significantly impaired compared to placebo. The data suggest that specific combinations of caffeine and choline can either facilitate or impair short-term memory in adults with normal cognitive function. Future studies of caffeine and choline combinations will test memory performance in subjects with memory impairment

Saturn: An Optimized Data System for Large Model Deep Learning Workloads

Large language models such as GPT-3 & ChatGPT have transformed deep learning (DL), powering applications that have captured the public's imagination. These models are rapidly being adopted across domains for analytics on various modalities, often by finetuning pre-trained base models. Such models need multiple GPUs due to both their size and computational load, driving the development of a bevy of "model parallelism" techniques & tools. Navigating such parallelism choices, however, is a new burden for end users of DL such as data scientists, domain scientists, etc. who may lack the necessary systems knowhow. The need for model selection, which leads to many models to train due to hyper-parameter tuning or layer-wise finetuning, compounds the situation with two more burdens: resource apportioning and scheduling. In this work, we tackle these three burdens for DL users in a unified manner by formalizing them as a joint problem that we call SPASE: Select a Parallelism, Allocate resources, and SchedulE. We propose a new information system architecture to tackle the SPASE problem holistically, representing a key step toward enabling wider adoption of large DL models. We devise an extensible template for existing parallelism schemes and combine it with an automated empirical profiler for runtime estimation. We then formulate SPASE as an MILP. We find that direct use of an MILP-solver is significantly more effective than several baseline heuristics. We optimize the system runtime further with an introspective scheduling approach. We implement all these techniques into a new data system we call Saturn. Experiments with benchmark DL workloads show that Saturn achieves 39-49% lower model selection runtimes than typical current DL practice.Comment: Under submission at VLDB. Code available: https://github.com/knagrecha/saturn. 12 pages + 3 pages references + 2 pages appendi

Reflections on Fees and Fines as Stategraft

In A Theory of Stategraft, Bernadette Atuahene advances the concept of “stategraft” to describe situations in which “state agents transfer property from persons to the state in violation of the state’s own laws or basic human rights.” This Essay delineates the ways in which criminal legal system fees and fines can be characterized as stategraft and explores the value of this concept for social movements. In many ways, the stategraft frame, with its focus on illegality, fits well with much of the litigation and advocacy against unconstitutional fees-and-fines practices that have occurred over the last decade. Exposing illegal practices such as the operation of debtors’ prisons laid the groundwork for a more fundamental critique of the use of the criminal legal system as a revenue generator for the state. The Essay cautions, however, against relying too heavily on illegality to describe what is wrong with fees-and-fines regimes in light of courts’ reluctance to impose robust legal protections against state practices that saddle those who encounter law enforcement with debt. Relying on an illegality critique may make it harder to attack entrenched practices that courts are inclined to bless as legal and obscure more fundamental dynamics of predation and regressive revenue redistribution. At this juncture, calling attention to these structural issues is likely to be more fruitful both as an organizing tactic and as a description of the harms posed by fees and fines

Criminal Justice Debt: A Barrier to Reentry

Many states are impos­ing new and often oner­ous “user fees” on indi­vidu­als with crim­inal convic­­tions. Yet far from being easy money, these fees impose severe – and often hidden – costs on com­munit­ies, taxpay­ers, and indi­gent people convicted of crimes. They create new paths to prison for those unable to pay their debts and make it harder to find employ­ment and hous­ing as well to meet child support oblig­a­tions. This report exam­ines prac­tices in the fifteen states with the highest prison popu­la­tions, which to­gether account for more than 60 percent of all state crim­inal filings. We focused primar­ily on the prolif­er­a­tion of “user fees, ” finan­cial oblig­a­tions imposed not for any tradi­tional crim­inal justice purpose such as punish­ment, deterrence, or rehab­il­it­a­tion but rather to fund tight state budgets. Across the board, we found that states are intro­du­cing new user fees, rais­ing the dollar amounts of exist­ing fees, and intensi­fy­ing the collec­tion of fees and other forms of crim­inal justice debt such as fines and resti­tu­tion. But in the rush to collect, made all the more intense by the fiscal crises in many states, no one is consid­er­ing the ways in which the result­ing debt can under­mine reentry prospects, pave the way back to prison or jail, and result in yet more costs to the public

InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models

Deep learning-based recommender models (DLRMs) have become an essential component of many modern recommender systems. Several companies are now building large compute clusters reserved only for DLRM training, driving new interest in cost- and time- saving optimizations. The systems challenges faced in this setting are unique; while typical deep learning training jobs are dominated by model execution, the most important factor in DLRM training performance is often online data ingestion. In this paper, we explore the unique characteristics of this data ingestion problem and provide insights into DLRM training pipeline bottlenecks and challenges. We study real-world DLRM data processing pipelines taken from our compute cluster at Netflix to observe the performance impacts of online ingestion and to identify shortfalls in existing pipeline optimizers. We find that current tooling either yields sub-optimal performance, frequent crashes, or else requires impractical cluster re-organization to adopt. Our studies lead us to design and build a new solution for data pipeline optimization, InTune. InTune employs a reinforcement learning (RL) agent to learn how to distribute the CPU resources of a trainer machine across a DLRM data pipeline to more effectively parallelize data loading and improve throughput. Our experiments show that InTune can build an optimized data pipeline configuration within only a few minutes, and can easily be integrated into existing training workflows. By exploiting the responsiveness and adaptability of RL, InTune achieves higher online data ingestion rates than existing optimizers, thus reducing idle times in model execution and increasing efficiency. We apply InTune to our real-world cluster, and find that it increases data ingestion throughput by as much as 2.29X versus state-of-the-art data pipeline optimizers while also improving both CPU & GPU utilization.Comment: Accepted at RecSys 2023. 11 pages, 2 pages of references. 8 figures with 2 table

COVID-19 vaccine perspective from adolescents\u27 lens in the US

Introduction The COVID-19 pandemic has presented an unprecedented global health issue. The World Health Organization estimates 773 million confirmed cases and 7 million deaths. Vaccination continues to be the most effective way to prevent COVID-19 and has demonstrated safety and efficacy in all age groups. Though a lot of studies have looked at COVID-19 vaccination acceptance and hesitancy in adults, there is scarce research addressing adolescent vaccination readiness. COVID-19 infection in this age group may result in lost school days, school and community transmission, and loss of productivity for parents. Aim This study aims to determine COVID-19 vaccination rates and factors influencing its acceptance and hesitancy in adolescents in a community setting. Methods A voluntary survey was conducted at a local high school in May 2023. Information was collected about the demographics of adolescents and the educational background of parents/guardians. The survey assessed the COVID vaccine rate, reasons for COVID-19 vaccine acceptance or refusal, number of doses of COVID-19 vaccine and boosters received, prior history of COVID-19 infection, source of information on COVID-19 vaccine, flu vaccine acceptance by the students, and whether they would be willing to take a COVID-19 vaccine booster. Results Four hundred participants, ranging in age from 13 to 19, were surveyed. The vaccination rate in boys was comparable to that in girls. 72% received at least one COVID-19 vaccine, and 66% were considered completely vaccinated. Of those completely vaccinated, 80% had undergone further updated COVID-19 booster vaccinations. Adolescents whose parents/guardians were college graduates had a higher vaccination rate than those whose parents/guardians were not. Caucasians and Asians had a higher vaccination rate compared to African Americans and mixed races. The vaccination rate was not statistically different in adolescents with prior COVID-19 infection versus no prior infection. Flu vaccination was associated with higher COVID-19 vaccination rates. Lack of trust was an important reason for vaccine hesitancy, along with questions about efficacy, concerns about side effects, parent/guardian decisions, and religious reasons. Protecting oneself, family/friends, and community were the major reasons to take the vaccine. Parents/guardians, physicians, peers, television, social media, flyers, and schools were the primary sources that adolescents relied on for information about the COVID-19 vaccination. Conclusion Lower education attainment among parents/guardians, African Americans, and mixed races was associated with lower vaccination rates. Lack of trust in the vaccine, questions about efficacy, and fear of side effects were the most frequently cited reasons for vaccine hesitancy. Parent/guardian influence and religious reasons were other significant reasons for vaccine hesitancy. Flu vaccination was associated with higher COVID-19 vaccination rates. Understanding factors influencing COVID-19 vaccination will allow us to address barriers to COVID-19 vaccination and other vaccinations appropriate for this age group. Educating adolescents in schools, involving local and state health departments to increase awareness about the vaccine, and educating parents and guardians along with the teenagers can help increase the acceptance of the vaccine. These interventions will also positively affect the acceptance of the booster and prepare us for any future pandemics