Miracle-dz/UniFAR-FTUs huggingface dataset:
LookupFFN: Making Transformers Compute-lite for CPU inference

While GPU clusters are the de facto choice for training large deep neural network (DNN) models today, several reasons including ease of workflow, security and cost have led to efforts investigating whether CPUs may be viable for inference in routine use in many sectors of the industry. But the imbalance between the compute capabilities of GPUs and CPUs is huge. Motivated by these considerations, we study a module which is a workhorse within modern DNN architectures, GEMM based Feed Forward Networks (FFNs), and assess the extent to which it can be made compute- (or FLOP-) lite. Specifically, we propose an alternative formulation (we call it LookupFFN) to GEMM based FFNs inspired by the recent studies of using Locality Sensitive Hashing (LSH) to approximate FFNs. Our formulation recasts most essential operations as a memory look-up, leveraging the trade-off between the two resources on any platform: compute and memory (since CPUs offer it in abundance). For RoBERTa language model pretraining, our formulation achieves similar performance compared to GEMM based FFNs, while dramatically reducing the required FLOP. Our development is complemented with a detailed hardware profiling of strategies that will maximize efficiency -- not just on contemporary hardware but on products that will be offered in the near/medium term future. Code is avaiable at \url{https://github.com/mlpen/LookupFFN}.
The ESTEREL language

The authors present the basics of the ESTEREL reactive model of synchronous parallel systems. The ESTEREL programming style, based on instantaneous communications and decisions, is illustrated through the example of a mouse handler. The ESTEREL formal semantics is described, and it is shown how programs can be compiled into finite state sequential machines for efficient execution. The implementation is described with the ESTEREL environment, including simulation, and verification and validation tools. Some ESTEREL uses in various contexts are reported. >
Access climate data in ESG from the EGI infrastructure

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Access climate data in ESG from the EGI infrastructure Sébastien Denvil, Jérôme Raciazek, Monique Petitdidier, André Gemünd, Horst Schwichtenberg
Household Plastic Waste Mis-Management Effect On Environmental Plastic Pollution.

The world today is facing the challenge of rational resource management and recovery for the huge amount of plastic waste. The lack of technical skills for hazardous waste management, inadequately developed recycling and recovery infrastructure, and, above all, a lack of knowledge about rules and regulations are key factors. Behind this huge accumulation of plastic waste. The severity of plastic pollution has detrimental effects on the environment and the entire ecosystem. In this study, domestic waste is one of the main sources of municipal solid waste including food waste, paper, plastic, rags, metal, and glass from residential areas. The main bottleneck lies in its removal. This study could provide the Jaipur government with background information to determine the future implementation of household waste segregation activities. This study also proposes to focus on community involvement in segregation at source, waste minimization, and recycling as a habit and way of life. Local governments can facilitate this by providing bins for sorting waste and by setting up waste banks and recycling facilities on a larger scale than those currently available. The indiscriminate disposal of plastic waste at an astonishing rate has led to the search for comprehensive, effective, and sustainable remediation studies in search of a practical alternative to the management, disposal, and destroy plastic debris. While there are a number of processes such as incineration, landfilling, and recycling already in place, they are unsustainable, expensive, and have serious impacts on the environment, wildlife, marine, and human health.
emerging therapeutic potential of transdermal clonidine: a prospective as an adaptogen

: Clonidine (CLO) has become popular in fields other than treatment of arterial hypertension. Transdermal (TD) CLO and oral CLO have comparable pharmacodynamics and therapeutic efficacy; the TD form has a reduced frequency of adverse effects. In patients with emesis, pain, gastrointestinal disorders, cognitive impairment, and dysphagia, TD drug delivery is the pharmacokinetic solution. CLO, within the “therapeutic range” of plasma concentration, is an agonist of alpha-2 adrenoreceptors (ATARs). The network of these receptors functions as an adaptive system, a vital link between the sympathoadrenergic system and multiple organs and systems. CLO activates this ATAR network and triggers transformations in cellular biology and in function of organs and systems. These reactions and responses increase resistance to stress/damage and protect the homeostasis. CLO enhances the adaptive response and is, therefore, an adaptogen. CLO and currently recognized phytoadaptogens cause similar changes in cellular signaling. CLO therapy is described by a new CLO-ATAR-adaptation concept. CLO therapy works within the ATAR network territory and makes adaptive sense. This concept explains successes of CLO therapy in diverse fields and its failures. It is ground for a prognosis on emerging therapeutic potential of TD CLO.
Fast and scalable score-based kernel calibration tests

We introduce the Kernel Calibration Conditional Stein Discrepancy test (KCCSD test), a non-parametric, kernel-based test for assessing the calibration of probabilistic models with well-deﬁned scores. In contrast to previous methods, our test avoids the need for possibly expensive expectation approximations while providing control over its type-I error. We achieve these improvements by using a new family of kernels for score-based probabilities that can be estimated without probability density samples, and by using a conditional goodness-of-ﬁt criterion for the KCCSD test’s U-statistic. We demonstrate the properties of our test on various synthetic settings.
Representations and degenerations

Snapshots of modern mathematics from Oberwolfach provide exciting insights into current mathematical research. They are written by participants in the scientiﬁc program of the Mathematisches Forschungsinstitut Oberwolfach (MFO). The snapshot project is designed to promote the understanding and appreciation of modern mathematics and mathematical research in the interested public worldwide. All snapshots are published in cooperation with the IMAGINARY platform and can be found on www.imaginary. org/snapshots and on www.mfo. de/snapshots.
Predicting Coma Recovery After Cardiac Arrest With Residual Neural Networks

Aims: Interpretation of continuous EEG is a demanding task that requires the expertise of trained neurologists. However, these experts are not always available in many medical centers. As part of the 2023 George B. Moody PhysioNet Challenge, we developed a deep learning based method for analyzing EEG data of comatose patients and predicting prognosis following cardiac arrest. Methods: Our approach is a two-step pipeline that consists of a prediction model and a decision-making strategy. The prediction model is a residual neural network (ResNet-18) that extracts features and makes a prediction based on a short 5-minute EEG recording. In the second step, a majority vote over multiple predictions made for several EEG recordings of a patient determines the final prognosis. Results: Based on 10-fold cross-validation on the training set, we achieved a true positive rate (TPR) of 0.41 for predicting poor outcome while keeping the false positive rate below 0.05 at 72 hours after recovery of spontaneous circulation. On the official challenge leaderboard, our team ZIB_Visual scored 0.426 TPR. Conclusion: Our approach, while simple to implement and execute, faced overfitting challenges during the official competition phase. In this paper, we discuss our implementation and potential improvements to address these issues.
Anti-Nutritional and Nutritional Composition of Zobo (Hibiscus Sabdariffa) Enriched with Pineapple and Orange Juice

Zobo (Roselle), a popular traditional Nigerian beverage, is known for its rich taste and potential health benefits. Recently, there has been growing interest in enhancing the nutritional value of Zobo through the addition of fruit juices. This research aimed to assess the Anti-nutritional and nutritional composition of Zobo enriched with pineapple and orange juice. The results revealed that the incorporation of pineapple and orange juice significantly increased the moisture content as expected of a juice product. Crude fibre and carbohydrates were found to increase as pineapple and orange were added. All anti-nutrient contents were also found to be low in all samples. Notably, saponin concentrations in Zobo samples were found to be 5.70±0.02, 4.81±0.09, 4.09±0.03, 4.38±0.54. The presence of Saponin in food is known to kill protozoa and mollusks, and also act as antifungal and antiviral agents. Furthermore, indicates that the panelists accepted the four samples in all of the attributes except sample D. These findings support the potential of Zobo enriched with pineapple and orange juice as a functional beverage with enhanced nutritional qualities.
The mid-Cretaceous greenhouse climate and marine stable oxygen isotope: Insights from general circulation model experiments

Vegetation-climate interactions are thought to have amplified polar warmth during past warm periods. Here, we explore the vegetation-climate interactions in the mid-Cretaceous using a fully coupled ocean-atmosphere general circulation model with a dynamic vegetation component. We run simulations with 1x, 10x and 16x pre-industrial atmospheric pCO2. Results show that forests expand from mid-latitudes to high latitudes as CO2 increases from 1x to 10x and 16x, mainly due to the CO2-induced warming. This expansion of mid-to-high latitude forests are largely supported by the distribution of midCretaceous fossil woods and coal deposits. Globally, the presence of vegetation increases mean annual temperature and precipitation by 0.9 °C and 0.11 mm day relative to bare ground. High-latitude warming induced by the presence of vegetation (~1.9 °C) is less than half of that reported in previous studies. The weaker warming here is mainly due to less pronounced albedo feedbacks, and to a less extent, reduced poleward heat transport via weakening of the meridional overturning circulation. These vegetation-induced climate changes largely resemble CO2-induced changes. However, model results show that high atmospheric pCO2 and vegetation cannot achieve the estimated polar warmth. We therefore conclude that other mechanisms in addition to high atmospheric pCO2 and
Comparability in evolutionary biology: The case of Darwin’s barnacles

Abstract Language change and biological evolution are sufficiently similar that biologists and linguists often face similar challenges in reconstructing paths of historical change connecting different species or languages. Tracing evolutionary change over time requires us to consider how shared features have been modified in different lineages since they shared a common ancestor, and this means we have to be able to establish meaningful comparability between traits. In some cases, we may wish to understand how the same ancestral trait has been modified in each lineage in response to different pressures. But in other cases, we may wish to ask whether particular traits often arise in response to certain circumstances. Biologists must therefore consider different reasons for similarities between species, and choose to compare those traits that are relevant to the story they want to tell. To reconstruct histories of change, we need to compare homologous traits (those similar due to shared ancestry). But comparing analogous traits (independently derived but similar traits) highlights how separate evolutionary lineages can find similar solutions to common problems. I will illustrate the importance of comparability in constructing evolutionary explanations using one of the more obscure yet fascinating examples of Charles Darwin’s scientific researches, his multi-volume taxonomic treatise on barnacles. Darwin faced the challenge of how to explain the evolutionary trajectory of unique and highly modified traits that appear to have no equivalents in related taxa. He did this by tracing the development of unique traits within growing individuals, looking for variation in these strange adaptations between individuals, and comparing them across species that varied in their degree of modification from their ancestor. Using meticulous observations to establish comparability, even in such an incomparable animal as the barnacle, he could reconstruct plausible evolutionary explanations for even the most bizarrely modified traits, such as the presence of parasitic males and the invention of the cement that sticks barnacles to rocks, boats and whales. Nowadays, scientists increasingly rely on DNA evidence to trace evolutionary paths, which brings both advantages and challenges in establishing comparability. Even if you, like most people, are not particularly interested in barnacles, Darwin’s underappreciated taxonomic work is a surprisingly good place to go to if you want to think about the issue of comparability and why it matters to understanding evolution.
Ieee Transactions on Communications, Accepted for Publication on the Outage Capacity of Sensing-enhanced Spectrum Sharing Cognitive Radio Systems in Fading Channels

— Spectrum sharing has received an increasing amount of attention in cognitive radio over the past few years as an effective method of alleviating the spectrum scarcity problem in wireless communications by allowing unlicensed users to use the same spectrum as the licensed users under the condition of protecting the latter from harmful interference using a received interference power constraint at the licensed receivers. In this paper, we study the outage capacity and the truncated channel inversion with fixed rate (TIFR) capacity of a sensing-enhanced spectrum sharing cognitive radio system under two different scenarios, namely with and without missed-detection interference power constraints for the protection of the primary users, for both Rayleigh and Nakagami-m fading channels. In our analysis, we consider various constraints on the capacity that include: (i) average transmit power constraints, (ii) peak interference power constraints, (iii) average interference power constraints and (iv) target detection probability constraints, and derive the power allocation strategy, as well as the TIFR and outage capacity for each scenario. Finally, we provide simulation results, which indicate that the sensing-enhanced spectrum sharing cognitive radio system can achieve higher outage and TIFR capacity compared to the conventional non-sensing spectrum sharing cognitive radio system.
Lung Image Segmentation Using U-Net Architecture

: One of the leading causes of death is lung cancer. The majority of the time, a delayed diagnosis and subsequent therapy result in lung cancer death, claims a study. In a very small percentage of cases where lung cancer is discovered early, it can be treated. Thus, improving the patient's survival depends greatly on the early identification of lung cancer. Based on generative adversarial networks (GANs), this study suggests an approach to lung segmentation accuracy that is more accurate. We may employ GANs and capitalise on their capacity to change images to achieve image segmentation. The ability of generative adversarial networks (GANs) to synthesise globally and locally coherent images with object shapes and textures that are indistinguishable from genuine images is one of the fundamental issues still to be solved. In this study, we offer lung segmentation using U-net, one of the popular deep learning architectures for image segmentation [1]. Therefore, this process is required to remove extra information from lung CT pictures. Based on a limited image collection that includes a few hundred manually segmented lung images, our network accurately segments the data.
HOW DOES YOUR KINDERGARTEN CLASSROOM AFFECT YOUR EARNINGS ? EVIDENCE FROM PROJECT STAR

In Project STAR, 11,571 students in Tennessee and their teachers were randomly assigned to different classrooms within their schools from kindergarten to third grade. This paper evaluates the long-term impacts of STAR using administrative records. We obtain five results. First, kindergarten test scores are highly correlated with outcomes such as earnings at age 27, college attendance, home ownership, and retirement savings. Second, students in small classes are significantly more likely to attend college and exhibit improvements on other outcomes. Class size does not have a significant effect on earnings at age 27, but this effect is imprecisely estimated. Third, students who had a more experienced teacher in kindergarten have higher earnings. Fourth, an analysis of variance reveals significant classroom effects on earnings. Students who were randomly assigned to higher quality classrooms in grades K-3 —as measured by classmates’endof-class test scores —have higher earnings, college attendance rates, and other outcomes. Finally, the effects of class quality fade out on test scores in later grades but gains in non-cognitive measures persist. ∗We thank Josh Angrist, Lisa Barrow, David Card, Gary Chamberlain, Elizabeth Cascio, Janet Currie, Jeremy Finn, Edward Glaeser, Bryan Graham, James Heckman, Caroline Hoxby, Guido Imbens, Thomas Kane, Lawrence Katz, Alan Krueger, Derek Neal, Jonah Rockoff, Douglas Staiger, numerous seminar participants, and anonymous referees for helpful discussions and comments. We thank Helen Bain and Jayne Zaharias at HEROS for access to the Project STAR data. The tax data were accessed through contract TIRNO-09-R-00007 with the Statistics of Income (SOI) Division at the US Internal Revenue Service. We thank the SOI staff, and in particular Nicholas Greenia and Barry W. Johnson, for their invaluable help and guidance in this process. Gregory Bruich, Jane Choi, Jessica Laird, Keli Liu, Laszlo Sandor, and Patrick Turley provided outstanding research assistance. Financial support from the Lab for Economic Applications and Policy at Harvard, the Center for Equitable Growth at UC Berkeley, and the National Science Foundation is gratefully acknowledged.
POVERTY AND THE POLITICAL ECONOMY OF PUBLIC EDUCATION SPENDING: EVIDENCE FROM BRAZIL

Alargebodyofliteraturehasemphasizedtheelitecaptureofdemocraticinstitutionsastheexplanationforthelowlevelsofspendingonpubliceducationinmanylow-incomedemocracies.Thispaper providesanalternativetothatlongstandinghypothesis.Motivatedbynewcross-countryfactsand evidencefromBrazilianmunicipalities,wehypothesizethatmanydemocraticdevelopingcountries mightinvestlessinpubliceducationspendingbecausepoordecisivevoterspreferthegovernment toallocateresourceselsewhere.Onepossibleexplanationisthatlow-incomevoterscouldinstead favorredistributiveprogramsthatincreasetheirincomesintheshortrun,suchascashtransfers.To testforthispossibility,wedesignandimplementanexperimentalsurveyandanincentivizedchoice experimentinBrazil. Theﬁndingsfrombothinterventionssupportourhypothesis. (JEL:C90,H52, I25,O15,P16)
Detection of Hypertension Through Features from Heart Rate Variability and Machine Learning Analysis

Despite the strong association between hypertension (HT) and heart-rate variability (HRV), the ability of HRV to detect HT cases under the coexistence of several pathologies remains unknown. The present study aims to optimize the HT detection using machine- learning (ML) techniques, in 202 5-minute ECG recordings from the MIMIC database. Recordings were classified by blood pressure (BP) into normotensive (NT), prehypertensive (PHT) and hypertensive (HT) with a cut-off BP of < 120/80 mm Hg, < 139/89 mm Hg and 2: 140/90 mm Hg, respectively. Time-, frequency-domain and Poincaré HRV features were explored. Multi-class (MC), between-class comparison (BC) and 1-vs-all analysis was performed. Single- (SF) and multifeature (MF) classification with 10-fold cross-validation and a 20% test set was also performed. Statistically significant differences (p < 0.022) were found in MC and BC for most HRV features. Differences were more prominent in HT group (p < 0.0003). The best MF accuracy for MC was 95% using 6 features. HT and NT detection accuracy was 87.5% and 95%, using 6 and 4 features, respectively, for 1-vs-all analysis. The proposed models can be easily implemented and achieve high classification accuracy. The results suggest the use of HRV to detect HT or HT-prone patients in diseased population.
Patient Experience Journal Patient Experience Journal

Background: Though there are similarities to music therapy, the field of community music in healthcare, while in its infancy, is steadily growing. Objective: This case study explored how semi-formal, active music-making can play a role in illness and recovery and provide patients with a sense of voice, connection, and community, and the efficacy of community music programming in a hospital. Methods: Six participants began and three participants completed a 6-week music class learning the ukulele. Interpretative Phenomenological Analysis (IPA) was used as a method for data analysis from semi-structured pre-questionnaires, transcribed classes, transcribed post-interviews, and weekly questionnaires from both the participants and the facilitator. Results: Emergent and recurrent themes central to the participants’ experiences were discovered: (1) Music as a connector, (2) Music within us external to cancer, (3) Musical experiences interrupted by cancer, (4) Music creates empowerment. Subthemes and individual experiences are also explored. Conclusions: Implications for future research and music’s role in improving the Patient Experience in hospital settings are discussed.
Dartmouth Digital

E(cid:14)cient low-level systems need more control over memory than safe high-level languages usually provide. As a result, run-time systems are typically written in unsafe languages such as C. This report describes an abstract machine designed to give type-safe code more control over memory. It includes complete de(cid:12)nitions and proofs.