Miracle-dz/UniFAR-FTUs huggingface dataset:
A simple Maxwellian optical system to investigate the photoreceptors contribution to pupillary light reflex

The three classes of photoreceptors cones, rods, and intrinsically photosensitive retinal ganglion cells (ipRGCs) contribute to the pupillary light reflex (PLR). The Silent Substitution technique has been proposed to analyze the contribution of individual photoreceptor to PLR. Through the usage of properly selected pairs of light stimuli, this approach allows stimulating a single class of photoreceptors while keeping the activation of all the others constant. In this way, it is possible to understand the single photoreceptors class effect on both image-forming and non-image-forming functions of the human eyes. In this work, a simple approach to perform the Silent Substitution technique is presented and tested. The instrumentation has been designed with four primaries RGBY fiber-coupled LEDs and a double lens system to achieve a Maxwellian-View like optical system. Preliminary tests were conducted on three volunteers in which the PLRs induced by melanopic and chloropic stimulation were measured. The preliminary results confirm the expectations, the light-adapted pupil diameter is principally regulated by the activation level of the melanopsin-expressing ipRGCs photoreceptors, while conedriven induced pupil responses to peak in color contrast are transitory, reverting to the light-adapted baseline pupil diameter more rapidly in respect to melanopsin counterpart.
Beyond vaccination: A Cross-Sectional Study of the importance of Behavioral and Native Factors on COVID-19 Infection and Severity.

The COVID-19 pandemic has a major impact on a global scale. Understanding the innate and lifestyle-related factors influencing the rate and severity of COVID-19 is important for making evidence-based recommendations. This cross-sectional study aimed at establishing a potential relationship between human characteristics and vulnerability/resistance to SARS-CoV-2. We hypothesize that the impact of virus is not the same due to cultural and ethnic differences. A cross-sectional study was performed using an online questionnaire. The methodology included a development of a multi-language survey, expert evaluation and data analysis. Data was collected using a 13-item pre-tested questionnaire based on a literature review. Data was statistically analyzed using the logistic regression. For a total of 1125 respondents, 332 (29.5%) were COVID-19 positive, among them 130 (11.5%) required home-based treatment, and 14 (1.2%) intensive care. The significant factors included age, physical activity and health status all found to have a significant influence on the infection (p < 0.05). The severity of infection was associated with preventive measures and tobacco (p < 0.05). This suggests the importance of behavioral factors compared to innate ones. Apparently, the individual behavior is mainly responsible for the spread of the virus. Adopting a healthy lifestyle and scrupulously observing preventive measures including vaccination would greatly limit the probability of infection and prevent the development of severe COVID-19.
COVID-19 and its clinical severity are associated with alterations of plasma sphingolipids and enzyme activities of sphingomyelinase and ceramidase

In the current pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; COVID-19), a better understanding of the underlying mechanisms is essential to reduce morbidity and mortality and treat post-COVID-19 disease. Here, we analyzed alterations of sphingolipids and their metabolizing enzymes in 125 men and 74 women tested positive for SARS-CoV-2 and hospitalized with mild, moderate or severe symptoms or after convalescence. The activities of acid and neutral sphingomyelinases (ASM, NSM), which hydrolyze sphingomyelin to ceramide, were significantly increased in COVID-19 patients, while the activity of neutral ceramidase (NC), which hydrolyzes ceramide to sphingosine, was reduced. These alterations could each contribute to elevated ceramide levels in patients. Accordingly, liquid chromatography tandem-mass spectrometry (LC-MS/MS) yielded increased levels of ceramides 16:0 and 18:0 with highest levels in severely affected patients and similar effects for dihydroceramides 16:0 and 18:0, whereas levels of (dihydro-)ceramides 24:0 were reduced. Furthermore, sphingomyelin 20:0; 22:0 and 24:0 as substrates of ASM and NSM as well as their dihydrosphingomyelin counterparts were reduced in patients as well as sphingosine-1-phosphate further downstream of NC activity. Effects of NSM, NC, ceramides and sphingomyelins remained significant after Bonferroni correction. SARS-CoV-2 antibody levels in convalescent patients were associated with age but none of the sphingolipid parameters. Based on our data, COVID-19 is associated with a dysregulation of sphingolipid homeostasis in a severity-dependent manner, particularly focused around a reduction of sphingomyelins and an accumulation of ceramides by increased enzyme activities leading to ceramide elevation (ASM, NSM) combined with a decreased activity of enzymes (NC) reducing ceramide levels. The potential of a combined sphingolipid/enzyme pattern as a diagnostic and prognostic marker and therapeutic target deserves further exploration.
Understanding data storage and ingestion for large-scale deep recommendation model training: industrial product

Datacenter-scale AI training clusters consisting of thousands of domain-specific accelerators (DSA) are used to train increasingly-complex deep learning models. These clusters rely on a data storage and ingestion (DSI) pipeline, responsible for storing exabytes of training data and serving it at tens of terabytes per second. As DSAs continue to push training efficiency and throughput, the DSI pipeline is becoming the dominating factor that constrains the overall training performance and capacity. Innovations that improve the efficiency and performance of DSI systems and hardware are urgent, demanding a deep understanding of DSI characteristics and infrastructure at scale. This paper presents Meta's end-to-end DSI pipeline, composed of a central data warehouse built on distributed storage and a Data PreProcessing Service that scales to eliminate data stalls. We characterize how hundreds of models are collaboratively trained across geo-distributed datacenters via diverse and continuous training jobs. These training jobs read and heavily filter massive and evolving datasets, resulting in popular features and samples used across training jobs. We measure the intense network, memory, and compute resources required by each training job to preprocess samples during training. Finally, we synthesize key takeaways based on our production infrastructure characterization. These include identifying hardware bottlenecks, discussing opportunities for heterogeneous DSI hardware, motivating research in datacenter scheduling and benchmark datasets, and assimilating lessons learned in optimizing DSI infrastructure.
Low Frequency Experimental Analysis of Dry Friction Damping in Cable Isolators

Mechanical vibrations normally produce adverse effects in structures, machinery and people. One of the most used methods of vibration control is vibration isolation. Amongst the different configurations of isolators, wire rope springs, also known as cable isolators are used for their high capacities of energy storage and dissipation, which is based on dry friction. As a result, they are used in extreme applications such as aeronautical, military, naval, and others involving high vibration and shock levels. An experimental analysis of the quantification of dry friction damping is presented in this paper, estimating the damping by two methods, namely a low frequency sinusoidal input to obtain the hysteresis loops, then a broadband frequency excitation in order to estimate the modal damping. It is found that there is an optimum value of deflection and load on the springs that produce the highest energy dissipation; a similar trend is observed in the two methods considered. This will give more insight into understanding the mechanism of energy dissipation and use of this information to improve the design of vibration isolators.
On minimum phase transformation and filter design

Minimum-phase finite impulse response filters are widely used in practice, and much research has been devoted to the design of such filters. However, for the important case of Chebyshev filters there is a curious mismatch between current best practice and well-established theoretical principles. The paper shows that this difference can be understood through analysis of the time-domain factorization of a suitable extended matrix. This analysis explains why the definition of a factorable linear phase filter must be revised. The time domain analysis of factorization suggests initial values leading to fast and accurate convergence of iterative algorithms for the design of minimum-phase finite impulse response filters. Numerical results are provided to demonstrate that a significant improvement in filter tap accuracy is obtained when the well-established theoretical principles are correctly applied to the design of minimum phase finite impulse response filters.
Two for One & One for All: Two-Sided Manipulation in Matching Markets

Strategic behavior in two-sided matching markets has been traditionally studied in a "one-sided" manipulation setting where the agent who misreports is also the intended beneficiary. Our work investigates "two-sided" manipulation of the deferred acceptance algorithm where the misreporting agent and the manipulator (or beneficiary) are on different sides. Specifically, we generalize the recently proposed accomplice manipulation model (where a man misreports on behalf of a woman) along two complementary dimensions: (a) the two for one model, with a pair of misreporting agents (man and woman) and a single beneficiary (the misreporting woman), and (b) the one for all model, with one misreporting agent (man) and a coalition of beneficiaries (all women). Our main contribution is to develop polynomial-time algorithms for finding an optimal manipulation in both settings. We obtain these results despite the fact that an optimal one for all strategy fails to be inconspicuous, while it is unclear whether an optimal two for one strategy satisfies the inconspicuousness property. We also study the conditions under which stability of the resulting matching is preserved. Experimentally, we show that two-sided manipulations are more frequently available and offer better quality matches than their one-sided counterparts.
Device-Independent Certification of Maximal Randomness from Pure Entangled Two-Qutrit States Using Non-Projective Measurements

While it has recently been demonstrated how to certify the maximal amount of randomness from any pure two-qubit entangled state in a device-independent way, the problem of optimal randomness certification from entangled states of higher local dimension remains open. Here we introduce a method for device-independent certification of the maximal possible amount of 2log23 random bits using pure bipartite entangled two-qutrit states and extremal nine-outcome general non-projective measurements. To this aim, we exploit a device-independent method for certification of the full Weyl–Heisenberg basis in three-dimensional Hilbert spaces together with a one-sided device-independent method for certification of two-qutrit partially entangled states.
Comment on Causal Networks and Freedom of Choice in Bell's Theorem

: Bell inequalities may only be derived, if hidden variables do not depend on the experimental settings. The stochastic independence of hidden and setting variables is called: freedom of choice, free will, measurement independence (MI) or no conspiracy. By imbedding the Bell causal structure in a larger causal network the authors correctly prove, that one can explain and quantify possible violation of MI without evoking super-determinism. They assume the independence of the variables that causally determine the settings and investigate how they might become correlated with hidden variables (e.g., when the cosmic photons enter the laboratory). Using their extended causal networks they derive a contextual probabilistic model on which their further correct results are based. The authors seem to ignore that contextual probabilistic model may be derived directly using only probabilistic concepts and incorporating correctly setting dependent variables describing measuring instruments. In these contextual probabilistic models experimenters’ freedom of choice is not compromised and the results of Bell Tests including an apparent violation of Einsteinian non-signaling may be explained in a locally causal way. Talking about freedom of choice is misleading and is rooted in incorrect understanding of Bayes Theorem. We explain why MI should be called noncontextuality and why its violation in Bell Tests confirms only the contextual character of quantum observables. Therefore, contextuality and not experimenters’ freedom of choice are important resources in quantum information.
APack: Off-Chip, Lossless Data Compression for Efficient Deep Learning Inference

Data accesses between on- and off-chip memories account for a large fraction of overall energy consumption during inference with deep learning networks. We present APack, a simple and effective, lossless, off-chip memory compression technique for fixed-point quantized models. APack reduces data widths by exploiting the non-uniform value distribution in deep learning applications. APack can be used to increase the effective memory capacity, to reduce off-chip traffic, and/or to achieve the desired performance/energy targets while using smaller off-chip memories. APack builds upon arithmetic coding, encoding each value as an arithmetically coded variable length prefix, plus an offset. To maximize compression ratio a heuristic software algorithm partitions the value space into groups each sharing a common prefix. APack exploits memory access parallelism by using several, pipelined encoder/decoder units in parallel and keeps up with the high data bandwidth demands of deep learning. APack can be used with any machine learning accelerator. In the demonstrated configuration, APack is placed just before the off-chip memory controller so that he rest of the on-chip memory and compute units thus see the original data stream. We implemented the APack compressor and decompressor in Verilog and in a 65nm tech node demonstrating its performance and energy efficiency. Indicatively, APack reduces data footprint of weights and activations to 60% and 48% respectively on average over a wide set of 8-bit quantized models. It naturally adapts and compresses models that use even more aggressive quantization methods. When integrated with a Tensorcore-based accelerator, APack boosts the speedup and energy efficiency to 1.44X and 1.37X respectively.
Tsallis statistics and thermofractals: applications to high energy and hadron physics

We study the applications of non-extensive Tsallis statistics to high energy and hadron physics. These applications include studies of $pp$ collisions, equation of state of QCD, as well as Bose-Einstein condensation. We also analyze the connections of Tsallis statistics with thermofractals, and address some of the conceptual aspects of the fractal approach, which are expressed in terms of the renormalization group equation and the self-energy corrections to the parton mass. We associate these well-known concepts with the origins of the fractal structure in the quantum field theory.
Object Detection in Aerial Images: What Improves the Accuracy?

Object detection is a challenging and popular computer vision problem. The problem is even more challenging in aerial images due to significant variation in scale and viewpoint in a diverse set of object categories. Recently, deep learning-based object detection approaches have been actively explored for the problem of object detection in aerial images. In this work, we investigate the impact of Faster R-CNN for aerial object detection and explore numerous strategies to improve its performance for aerial images. We conduct extensive experiments on the challenging iSAID dataset. The resulting adapted Faster R-CNN obtains a significant mAP gain of 4.96% over its vanilla baseline counterpart on the iSAID validation set, demonstrating the impact of different strategies investigated in this work.
Assembling a Cyber Range to Evaluate Artificial Intelligence / Machine Learning (AI/ML) Security Tools

In this case study, we describe the design and assembly of a cyber security testbed at Oak Ridge National Laboratory in Oak Ridge, TN, USA. The range is designed to provide agile reconfigurations to facilitate a wide variety of experiments for evaluations of cyber security tools -- particularly those involving AI/ML. In particular, the testbed provides realistic test environments while permitting control and programmatic observations/data collection during the experiments. We have designed in the ability to repeat the evaluations, so additional tools can be evaluated and compared at a later time. The system is one that can be scaled up or down for experiment sizes. At the time of the conference we will have completed two full-scale, national, government challenges on this range. These challenges are evaluating the performance and operating costs for AI/ML-based cyber security tools for application into large, government-sized networks. These evaluations will be described as examples providing motivation and context for various design decisions and adaptations we have made. The first challenge measured end-point security tools against 100K file samples (benignware and malware) chosen across a range of file types. The second is an evaluation of network intrusion detection systems efficacy in identifying multi-step adversarial campaigns -- involving reconnaissance, penetration and exploitations, lateral movement, etc. -- with varying levels of covertness in a high-volume business network. The scale of each of these challenges requires automation systems to repeat, or simultaneously mirror identical the experiments for each ML tool under test. Providing an array of easy-to-difficult malicious activity for sussing out the true abilities of the AI/ML tools has been a particularly interesting and challenging aspect of designing and executing these challenge events.
A H\"ormander-Mikhlin theorem for high rank simple Lie groups

We establish regularity conditions for $L_p$-boundedness of Fourier multipliers on the group von Neumann algebras of higher rank simple Lie groups. This provides a natural H\"ormander-Mikhlin criterion in terms of Lie derivatives of the symbol and a metric given by the adjoint representation. In line with Lafforgue/de la Salle's rigidity theorem, our condition imposes certain decay of the symbol at infinity. It refines and vastly generalizes a recent result by Parcet, Ricard and de la Salle for $\SL$. Our approach is partly based on a sharp local H\"ormander-Mikhlin theorem for arbitrary Lie groups, which follows in turn from recent estimates by the authors on singular nonToeplitz Schur multipliers. We generalize the latter to arbitrary locally compact groups and refine the cocycle-based approach to Fourier multipliers in group algebras by Junge, Mei and Parcet. A few related open problems are also discussed.
AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models

Recently, large pre-trained models have significantly improved the performance of various Natural LanguageProcessing (NLP) tasks but they are expensive to serve due to long serving latency and large memory usage. To compress these models, knowledge distillation has attracted an increasing amount of interest as one of the most effective methods for model compression. However, existing distillation methods have not yet addressed the unique challenges of model serving in datacenters, such as handling fast evolving models, considering serving performance, and optimizing for multiple objectives. To solve these problems, we propose AutoDistill, an end-to-end model distillation framework integrating model architecture exploration and multi-objective optimization for building hardware-efficient NLP pre-trained models. We use Bayesian Optimization to conduct multi-objective Neural Architecture Search for selecting student model architectures. The proposed search comprehensively considers both prediction accuracy and serving latency on target hardware. The experiments on TPUv4i show the finding of seven model architectures with better pre-trained accuracy (up to 3.2% higher) and lower inference latency (up to 1.44x faster) than MobileBERT. By running downstream NLP tasks in the GLUE benchmark, the model distilled for pre-training by AutoDistill with 28.5M parameters achieves an 81.69 average score, which is higher than BERT_BASE, DistillBERT, TinyBERT, NAS-BERT, and MobileBERT. The most compact model found by AutoDistill contains only 20.6M parameters but still outperform BERT_BASE(109M), DistillBERT(67M), TinyBERT(67M), and MobileBERT(25.3M) regarding the average GLUE score. By evaluating on SQuAD, a model found by AutoDistill achieves an 88.4% F1 score with 22.8M parameters, which reduces parameters by more than 62% while maintaining higher accuracy than DistillBERT, TinyBERT, and NAS-BERT.
Oxygen-enhanced Extremely Metal-poor Damped Lyα Systems: A Signpost of the First Stars?

We present precise abundance determinations of two near-pristine damped Lyα systems (DLAs) to assess the nature of the [O/Fe] ratio at [Fe/H] < −3.0 (i.e., <1/1000 of the solar metallicity). Prior observations indicate that the [O/Fe] ratio is consistent with a constant value, [O/Fe] ≃ +0.4, when −3 < [Fe/H] < −2, but this ratio may increase when [Fe/H] ≲ −3. In this paper, we test this picture by reporting new, high-precision [O/Fe] abundances in two of the most metal-poor DLAs currently known. We derive values of [O/Fe] = +0.50 ± 0.10 and [O/Fe] = +0.62 ± 0.05 for these two z ≃ 3 near-pristine gas clouds. These results strengthen the idea that the [O/Fe] abundances of the most metal-poor DLAs are elevated compared to DLAs with [Fe/H] ≳ −3. We compare the observed abundance pattern of the latter system to the nucleosynthetic yields of Population III supernovae (SNe), and find that the enrichment can be described by a (19–25) M⊙ Population III SN that underwent a (0.9–2.4) × 1051 erg explosion. These high-precision measurements showcase the behavior of [O/Fe] in the most metal-poor environments. Future high-precision measurements in new systems will contribute to a firm detection of the relationship between [O/Fe] and [Fe/H]. These data will reveal whether we are witnessing a chemical signature of enrichment from Population III stars and allow us to rule out contamination from Population II stars.
Text Style Transfer for Bias Mitigation using Masked Language Modeling

It is well known that textual data on the internet and other digital platforms contain significant levels of bias and stereotypes. Various research findings have concluded that biased texts have significant effects on target demographic groups. For instance, masculine-worded job advertisements tend to be less appealing to female applicants. In this paper, we present a text-style transfer model that can be trained on non-parallel data and be used to automatically mitigate bias in textual data. Our style transfer model improves on the limitations of many existing text style transfer techniques such as the loss of content information. Our model solves such issues by combining latent content encoding with explicit keyword replacement. We will show that this technique produces better content preservation whilst maintaining good style transfer accuracy.