cssi/SciSciGPT-SciSciNet huggingface dataset:
The CEO Problem With r th Power of Difference and Logarithmic Distortions

The CEO problem has received much attention since first introduced by Berger et al ., but there are limited results on non-Gaussian models with non-quadratic distortion measures. In this work, we extend the quadratic Gaussian CEO problem to two non-Gaussian settings with general $r$ th power of difference distortion. Assuming an identical observation channel across agents, we study the asymptotics of distortion decay as the number of agents and sum-rate, $R_{\textsf {sum}}$ , grow without bound, while individual rates vanish. The first setting is a regular source-observation model with $r$ th power of difference distortion, which subsumes the quadratic Gaussian CEO problem, and we establish that the distortion decays at $\mathcal {O}(R_{\textsf {sum}}^{-r/2})$ when $r \ge 2$ . We use sample median estimation after the Berger-Tung scheme for achievability. The other setting is a non-regular source-observation model, including uniform additive noise models, with $r$ th power of difference distortion for which estimation-theoretic regularity conditions do not hold. The distortion decay $\mathcal {O}(R_{\textsf {sum}}^{-r})$ when $r \ge 1$ is obtained for the non-regular model by midrange estimator following the Berger-Tung scheme. We also provide converses based on the Shannon lower bound for the regular model and the Chazan-Zakai-Ziv bound for the non-regular model, respectively. Lastly, we provide a sufficient condition for the regular model, under which quadratic and logarithmic distortions are asymptotically equivalent by an entropy power relationship as the number of agents grows. This proof relies on the Bernstein-von Mises theorem.
Task Decomposition for MPC: A Computationally Efficient Approach for Linear Time-Varying Systems

Abstract A Task Decomposition method for iterative learning Model Predictive Control (TDMPC) for linear time-varying systems is presented. We consider the availability of state-input trajectories which solve an original task τ1, and design a feasible MPC policy for a new task, τ2, using stored data from τ1. Our approach applies to tasks τ2 which are composed of subtasks contained in τ1. In this paper we formally define the task decomposition problem, and provide a feasibility proof for the resulting policy. The proposed algorithm reduces the computational burden for linear time-varying systems with piecewise convex constraints. Simulation results demonstrate the improved efficiency of the proposed method on a robotic path-planning task.
Optimized Edgebreaker encoding for large and regular triangle meshes

We present a technique aiming to improve the compression of the Edgebreaker CLERS string for large and regular meshes, where regularity is understood as the compactness of the distribution of vertex degrees. Our algorithm uses a specially designed context-based coding to compress the CLERS sequence. It is exceptionally simple to implement and can easily be incorporated into any existing Edgebreaker implementation which uses the Spirale Reversi algorithm for decompression. Even for irregular meshes, it does not carry considerable overhead when compared to the original Edgebreaker encoding. Experimental results show that our procedure is very fast (600000 triangles per second on a PIII 650 MHz for decompression) and leads to compression rates which are, in most cases, superior to those previously reported for large meshes of high regularity.
The Motivated Can Encrypt (Even with PGP)

Existing end-to-end-encrypted (E2EE) email systems, mainly PGP, have long been evaluated in controlled lab settings. While these studies have exposed usability obstacles for the average user and offer design improvements, there exist users with an immediate need for private communication, who must cope with existing software and its limitations. We seek to understand whether individuals motivated by concrete privacy threats, such as those vulnerable to state surveillance, can overcome usability issues to adopt complex E2EE tools for long-term use. We surveyed regional activists, as surveillance of social movements is well-documented. Our study group includes individuals from 9 social movement groups in the US who had elected to participate in a workshop on using Thunderbird+Enigmail for email encryption. These workshops tool place prior to mid-2017, via a partnership with a non-profit which supports social movement groups. Six to 40 months after their PGP email encryption training, more than half of the study participants were continuing to use PGP email encryption despite intervening widespread deployment of simple E2EE messaging apps such as Signal. We study the interplay of usability with social factors such as motivation and the risks that individuals undertake through their activism. We find that while usability is an important factor, it is not enough to explain long term use. For example, we find that riskiness of one's activism is negatively correlated with long-term PGP use. This study represents the first long-term study, and the first in-the-wild study, of PGP email encryption adoption.
Exploring Fault-Energy Trade-offs in Approximate DNN Hardware Accelerators

Systolic array-based deep neural network (DNN) accelerators have recently gained prominence for their low computational cost. However, their high energy consumption poses a bottleneck to their deployment in energy-constrained devices. To address this problem, approximate computing can be employed at the cost of some tolerable accuracy loss. However, such small accuracy variations may increase the sensitivity of DNNs towards undesired subtle disturbances, such as permanent faults. The impact of permanent faults in accurate DNNs has been thoroughly investigated in the literature. Conversely, the impact of permanent faults in approximate DNN accelerators (AxDNNs) is yet under-explored. The impact of such faults may vary with the fault bit positions, activation functions and approximation errors in AxDNN layers. Such dynamacity poses a considerable challenge to exploring the trade-off between their energy efficiency and fault resilience in AxDNNs. Towards this, we present an extensive layer-wise and bit-wise fault resilience and energy analysis of different AxDNNs, using the state-of-the-art Evoapprox8b signed multipliers. In particular, we vary the stuck-at-0, stuck-at-1 fault-bit positions, and activation functions to study their impact using the most widely used MNIST and Fashion-MNIST datasets. Our quantitative analysis shows that the permanent faults exacerbate the accuracy loss in AxDNNs when compared to the accurate DNN accelerators. For instance, a permanent fault in AxDNNs can lead up to 66% accuracy loss, whereas the same faulty bit can lead to only 9% accuracy loss in an accurate DNN accelerator. Our results demonstrate that the fault resilience in AxDNNs is orthogonal to the energy efficiency.
Group B Streptococcus and the vaginal microbiome among pregnant women: a systematic review.

Background Vaginal microbiome studies frequently report diversity metrics and communities of microbiomes associated with reproductive health outcomes. Reports of Streptococcus agalactiae (also known as Group B Streptococcus or GBS), the leading cause of neonatal infectious morbidity and mortality, are notably lacking from the studies of the vaginal microbiome, despite being a known contributor to preterm birth and other complications. Therefore, the purpose of this systematic review was to explore the frequency of GBS reporting in vaginal microbiome literature pertaining to pregnancy and to examine methodological bias that contributes to differences in species and genus-level microbiome reporting. Lack of identification of GBS via sequencing-based approaches due to methodologic or reporting bias may result incomplete understanding of bacterial composition during pregnancy and subsequent birth outcomes. Methodology A systematic review was conducted following the PRISMA guideline. Three databases (PubMed, CINAHL, and Web of Science) were used to identify papers for review based on the search terms "vaginal microbiome", "pregnancy", and "16S rRNA sequencing". Articles were evaluated for methods of DNA extraction and sequencing, 16S region, taxonomy classification database, number of participants or vaginal specimens, and pregnancy trimester. Results Forty-five research articles reported employing a metagenomic approach or 16S approach for vaginal microbiome analysis during pregnancy that explicitly reported taxonomic composition and were included in this review. Less than 30% of articles reported the presence of GBS (N = 13). No significant differences in methodology were identified between articles that reported versus did not report GBS. However, there was large variability across research methods used for vaginal microbiome analysis and species-level bacterial community reporting. Conclusion Considerable differences in study design and data formatting methods may contribute to underrepresentation of GBS, and other known pathogens, in existing vaginal microbiome literature. Previous studies have identified considerable variation in methodology across vaginal microbiome studies. This study adds to this body of work because in addition to laboratory or statistical methods, how results and data are shared (e.g., only analyzing genus level data or 20 most abundant microbes), may hinder reproducibility and limit our understanding of the influence of less abundant microbes. Sharing detailed methods, analysis code, and raw data may improve reproducibility and ability to more accurately compare microbial communities across studies.
Improved diagnosis of tibiofemoral cartilage defects on MRI images using deep learning

Abstract Introduction MRI is the modality of choice for cartilage imaging, however, its diagnostic performance is variable. Objectives We aimed to evaluate whether deep learning can be utilized to accurately identify cartilage defects when applied to the interpretation of knee MRI images and compare deep learning's performance to that of an orthopaedic trainee and an orthopaedic surgeon. Methods We analyzed data from patients who underwent knee MRI evaluation and subsequent arthroscopic knee surgery (207 with-, 90 without cartilage defect). Patients' arthroscopic findings were compared to preoperative MRI images to verify the presence or absence of isolated tibiofemoral cartilage defects. For each patient, the most representative MRI image slice of the patient's condition was selected (defect or no-defect) from a coronal- and sagittal view. We developed three convolutional neural networks (CNNs) to analyze the images: CNN-1 trained on the images of the sagittal and coronal views; CNN-2 trained on the images of the sagittal view; CNN-3 trained on the images of the coronal view. We implemented image-specific saliency maps to visualize the CNNs decision-making process. The same test dataset images were then provided to an experienced orthopaedic surgeon and an orthopaedic trainee. Results Saliency maps demonstrated that the CNNs learned to focus on the clinically relevant areas of the MRI. The CNN-1 achieved higher performance (sensitivity-86.96, specificity-100%, positive predictive value [PPV]-100%, negative predictive value [NPV]-66.67%) than the orthopaedic surgeon (sensitivity-82.61%, specificity-83.33%, PPV- 95%, NPV-55.56%), Conclusions CNN can be used to enhance the diagnostic performance of MRI in identifying isolated tibiofemoral cartilage defects.
Association of statin use with clinical outcomes in patients with triple-negative breast cancer.

523Background: Statins have been shown to target pathways related to breast cancer carcinogenesis, specifically in more aggressive breast cancer subtypes such as triple negative breast cancer (TNBC...
Insulin/IGF‐1 Enhances Intestinal Epithelial Crypt Proliferation through PI3K/Akt, and Not ERK Signaling in Obese Humans

The intestinal epithelium is continuously regenerated through proliferation and differentiation of stem cells located in the intestinal crypts. Obesity affects this process and results in greater stem cell proliferation and altered tissue growth and function. Obesity-induced high levels of insulin and insulin-like growth factor-1 in the stem cell niche are found to impact proliferation in rodents indicating that insulin and insulin-like growth factor-1 receptors may play a role in modulating intestinal epithelial stem cell proliferation. To determine whether insulin or insulin-like growth factor-1 can induce proliferation in human intestinal epithelial stem cells, and if two downstream insulin and insulin-like growth factor-1 receptor signaling pathways, PI3K/Akt and ERK, are involved, we used primary small intestinal epithelial crypts isolated from obese humans and investigated (1) the effect of insulin or insulin-like growth factor-1 on crypt proliferation, and (2) the effect of insulin and insulin-like growth factor-1 signaling inhibitors on insulin or insulin-like growth factor-1-induced proliferation. We found that insulin and insulin-like growth factor-1 enhanced the proliferation of crypt cells, including intestinal epithelial stem cells. Inhibition of the PI3K/Akt pathway attenuated insulin and insulin-like growth factor-1-induced proliferation, but inhibition of the ERK pathway had no effect. These results suggest that the classical metabolic PI3K pathway and not the canonical proliferation ERK pathway is involved in the insulin/insulin-like growth factor-1-induced increase in crypt proliferation in obese humans, which may contribute to abnormal tissue renewal and function. Impact statement This study investigates if insulin or insulin-like growth factor-1 (IGF-1) induces intestinal epithelial proliferation in humans, and if insulin and IGF-1 receptor signaling is involved in this process in obesity. Although obesity-induced high levels of insulin and IGF-1 in the stem cell niche are found to impact the proliferation of intestinal epithelial stem cells in rodents, we are the first to investigate this effect in humans. We found that insulin and IGF-1 enhanced the proliferation of intestinal crypts (including stem cells and other crypt cells) isolated from obese humans, and PI3K/Akt, and not ERK signaling was involved in insulin or IGF-1-induced proliferation. The imbalance in signaling between PI3K/Akt and ERK pathways may point to a pathway-specific impairment in insulin/IGF-1 receptor signaling. We propose that this may contribute to reciprocal relationships between insulin/IGF-1 receptor resistance and intestinal epithelial proliferation that leads to abnormal tissue renewal and function.
Measuring Social Biases in Grounded Vision and Language Embeddings

We generalize the notion of measuring social biases in word embeddings to visually grounded word embeddings. Biases are present in grounded embeddings, and indeed seem to be equally or more significant than for ungrounded embeddings. This is despite the fact that vision and language can suffer from different biases, which one might hope could attenuate the biases in both. Multiple ways exist to generalize metrics measuring bias in word embeddings to this new setting. We introduce the space of generalizations (Grounded-WEAT and Grounded-SEAT) and demonstrate that three generalizations answer different yet important questions about how biases, language, and vision interact. These metrics are used on a new dataset, the first for grounded bias, created by augmenting standard linguistic bias benchmarks with 10,228 images from COCO, Conceptual Captions, and Google Images. Dataset construction is challenging because vision datasets are themselves very biased. The presence of these biases in systems will begin to have real-world consequences as they are deployed, making carefully measuring bias and then mitigating it critical to building a fair society.
Health workers’ behavior, patient reporting and reputational concerns: lab-in-the-field experimental evidence from Kenya

We examine the effectiveness of accountability systems that rely on patient reporting in Kenyan health clinics. Patients and health care providers from public and private health clinics participate in a lab-in-the field experiment focusing on the relationship of trust between patient and provider. Patients decide whether to trust providers, providers have discretion over their reciprocity, and patients can complain. We compare the effectiveness of: (1) a client reporting system where patients’ complaints are disclosed to the providers’ professional peers, possibly leading to non-monetary penalties, (2) a system where complaints lead to monetary penalties, and (3) a system that, like a standard complaint box, attaches no tangible consequences to complaints. Overall, our findings suggest that citizen reporting systems that leverage peer pressure and reputational concerns can improve service delivery.
Data-driven inference of high-accuracy isostable-based dynamical models in response to external inputs

Isostable reduction is a powerful technique that can be used to characterize behaviors of nonlinear dynamical systems using a basis of slowly decaying eigenfunctions of the Koopman operator. When the underlying dynamical equations are known, previously developed numerical techniques allow for high-order accuracy computation of isostable reduced models. However, in situations where the dynamical equations are unknown, few general techniques are available that provide reliable estimates of the isostable reduced equations, especially in applications where large magnitude inputs are considered. In this work, a purely data-driven inference strategy yielding high-accuracy isostable reduced models is developed for dynamical systems with a fixed point attractor. By analyzing steady-state outputs of nonlinear systems in response to sinusoidal forcing, both isostable response functions and isostable-to-output relationships can be estimated to arbitrary accuracy in an expansion performed in the isostable coordinates. Detailed examples are considered for a population of synaptically coupled neurons and for the one-dimensional Burgers’ equation. While linear estimates of the isostable response functions are sufficient to characterize the dynamical behavior when small magnitude inputs are considered, the high-accuracy reduced order model inference strategy proposed here is essential when considering large magnitude inputs.
Fizeau drag in graphene plasmonics

Dragging of light by moving media was predicted by Fresnel1 and verified by Fizeau’s celebrated experiments2 with flowing water. This momentous discovery is among the experimental cornerstones of Einstein’s special relativity theory and is well understood3,4 in the context of relativistic kinematics. By contrast, experiments on dragging photons by an electron flow in solids are riddled with inconsistencies and have so far eluded agreement with the theory5–7. Here we report on the electron flow dragging surface plasmon polaritons8,9 (SPPs): hybrid quasiparticles of infrared photons and electrons in graphene. The drag is visualized directly through infrared nano-imaging of propagating plasmonic waves in the presence of a high-density current. The polaritons in graphene shorten their wavelength when propagating against the drifting carriers. Unlike the Fizeau effect for light, the SPP drag by electrical currents defies explanation by simple kinematics and is linked to the nonlinear electrodynamics of Dirac electrons in graphene. The observed plasmonic Fizeau drag enables breaking of time-reversal symmetry and reciprocity10 at infrared frequencies without resorting to magnetic fields11,12 or chiral optical pumping13,14. The Fizeau drag also provides a tool with which to study interactions and nonequilibrium effects in electron liquids. Direct infrared nano-imaging of plasmonic waves in graphene carrying high current density reveals the Fizeau drag of plasmon polaritons by fast-moving quasi-relativistic electrons.
Demanded abstract interpretation

We consider the problem of making expressive static analyzers interactive. Formal static analysis is seeing increasingly widespread adoption as a tool for verification and bug-finding, but even with powerful cloud infrastructure it can take minutes or hours to get batch analysis results after a code change. While existing techniques offer some demand-driven or incremental aspects for certain classes of analysis, the fundamental challenge we tackle is doing both for arbitrary abstract interpreters. Our technique, demanded abstract interpretation, lifts program syntax and analysis state to a dynamically evolving graph structure, in which program edits, client-issued queries, and evaluation of abstract semantics are all treated uniformly. The key difficulty addressed by our approach is the application of general incremental computation techniques to the complex, cyclic dependency structure induced by abstract interpretation of loops with widening operators. We prove that desirable abstract interpretation meta-properties, including soundness and termination, are preserved in our approach, and that demanded analysis results are equal to those computed by a batch abstract interpretation. Experimental results suggest promise for a prototype demanded abstract interpretation framework: by combining incremental and demand-driven techniques, our framework consistently delivers analysis results at interactive speeds, answering 95% of queries within 1.2 seconds.
Consumers object to algorithms making morally relevant tradeoffs because of algorithms’ consequentialist decision strategies

Why do consumers embrace some algorithms and find others objectionable? The moral relevance of the domain in which an algorithm operates plays a role. The authors find that consumers believe that algorithms are more likely to use maximization (i.e. attempting to maximize some measured outcome) as a decision-making strategy than human decision makers (Study 1). Consumers find this consequentialist decision strategy to be objectionable in morally relevant tradeoffs and disapprove of algorithms making morally relevant tradeoffs as a result (Studies 2, 3a, & 3b). Consumers also object to human employees making morally relevant tradeoffs when they are trained to make decisions by maximizing outcomes, consistent with the notion that their objections to algorithmic decision makers stem from concerns about maximization (Study 4). The results provide insight into why consumers object to some consumer relevant algorithms while adopting others.
High Frame Rate Video Quality Assessment using VMAF and Entropic Differences

The popularity of streaming videos with live, high-action content has led to an increased interest in High Frame Rate (HFR) videos. In this work we address the problem of frame rate dependent Video Quality Assessment (VQA) when the videos to be compared have different frame rate and compression factor. The current VQA models such as VMAF have superior correlation with perceptual judgments when videos to be compared have same frame rates and contain conventional distortions such as compression, scaling etc. However this framework requires additional pre-processing step when videos with different frame rates need to be compared, which can potentially limit its overall performance. Recently, Generalized Entropic Difference (GREED) VQA model was proposed to account for artifacts that arise due to changes in frame rate, and showed superior performance on the LIVE-YT-HFR database which contains frame rate dependent artifacts such as judder, strobing etc. In this paper we propose a simple extension, where the features from VMAF and GREED are fused in order to exploit the advantages of both models. We show through various experiments that the proposed fusion framework results in more efficient features for predicting frame rate dependent video quality. We also evaluate the fused feature set on standard non-HFR VQA databases and obtain superior performance than both GREED and VMAF, indicating the combined feature set captures complimentary perceptual quality information.
A database of pediatric drug effects to evaluate ontogenic mechanisms from child growth and development

Abstract Adverse drugs effects (ADEs) in children are common and may result in disability and death. However, current pediatric drug safety methods have not gone beyond event surveillance to identify and evaluate potential biological mechanisms. Children undergo an evolutionarily conserved and physiologically dynamic process of growth and maturation that can alter pharmacokinetics and pharmacodynamics. Our hypothesis is that temporal patterns of drug event reporting are reflective of dynamic mechanisms from child growth and development. We generated a database of 460,837 pediatric ADEs using generalized additive models (GAMs) that we have previously shown identify dynamic risk estimates of adverse drug events1. We identified 19,438 significant drug-event risks where drug risks corresponded with physiological development throughout childhood. Our results identified known pediatric drug effects and risk dynamics across child development that were not known previously. For example, we identified significant risk dynamics of montelukast-induced psychiatric disorders, including enriched risk (Odds Ratio 8.77 [2.51, 46.94]) within the second year of life. We developed a data-driven time- series clustering approach resulting in up to 95.2% precision and 97.8% sensitivity for categorizing risk dynamics across development stages for all ADEs including known but previously development-agnostic pediatric drug effects. We found that our real-world evidence may contain biologically-relevant underpinnings as well, where risk dynamics of CYP enzyme substrates were dependent on the enzyme’s expression across childhood. We curated this database for the research community to enable, for the first time, evaluation of real-world hypotheses of adverse drug effects across child growth and development.
The Calculation of Transport Phenomena in Electromagnetically Levitated Metal Droplets

A mathematical representation has been developed for the electromagnetic force field, the fluid flow field, the temperature field (and for transport controlled kinetics) in a levitation melted metal droplet. The technique of mutual inductances was employed for the calculation of the electromagnetic force field, while the turbulent Navier-Stokes equations and the turbulent convective transport equations were used to represent the fluid flow field, the temperature field and the concentration field. The governing differential equations, written in spherical coordinates, were solved numerically. The computed results were found to be in good agreement with measurements reported in the literature, regarding the lifting force and the average temperature of the specimen.
The Broad Spectrum Mixed-Lineage Kinase 3 Inhibitor URMC-099 Prevents Acute Microgliosis and Cognitive Decline in a Mouse Model of Perioperative Neurocognitive Disorders

Background: Perioperative neurocognitive disorders (PND) are serious complications that afflict up to 50% of surgical patients and for which there are no disease-modifying therapeutic options. We developed the "broad spectrum" mixed lineage kinase 3 inhibitor URMC-099 to inhibit pathological innate immune responses that underlie neuroinflammation-associated cognitive dysfunction. Here, we test the hypothesis that prophylactic treatment with URMC-099 can prevent surgery-induced neuroinflammation and cognitive impairment in mice following orthopedic surgery-induced PND. Methods: We performed orthopedic surgery by fracturing the tibia of the left-hind limb with intramedullary fixation under general anesthesia and analgesia. We administered URMC-099 (10 mg/kg, i.p.) prophylactically via three intraperitoneal injections. We used two-photon microscopy and CLARITY with light-sheet microscopy to define surgery-induced changes in microglial dynamics and morphology. We assessed cognitive dysfunction following surgery using the "What-Where-When" task and the Memory Load Object Discrimination task. In surgically-treated samples, we analyzed fracture callouses using microcomputed tomography and histomorphometry analyses. Findings: Orthopedic surgery induced microglial activation in the hippocampus and cortex without overtly affecting microglial process dynamics. Surgically-treated mice exhibited impaired object place and identity discrimination, indicating cognitive dysfunction. URMC-099 prophylaxis prevented surgery-induced microgliosis and memory impairment. Analysis of fracture callouses showed no effect of URMC-099 on fracture healing. Interpretation: These findings show that prophylactic URMC-099 treatment is sufficient to prevent surgery-induced microgliosis and cognitive impairment without interfering with the fracture healing process. Funding Statement: Design, synthesis and validation of URMC-099 was supported by NIH grants P01MH64570 and RO1 MH104147 (HAG). The study was supported by the NIH RO1 AG057525 (NT). Declaration of Interests: URMC-099 is owned by URMC (HA Gelbard. lead inventor: patent nos. US 8,846,909 B2; 8,877,772; and 9,181,247 and associated international patents). HAG has no commercial disclosures regarding URMC-099 at present. Ethics Approval Statement: All animal procedures were carried out under protocols approved by Duke University Medical Center and University of Rochester Medical Center, Institutional Animal Care and Use Committee under the National Research Council Guide for the Care and Use of Laboratory Animals, 8th edition. Both Duke University and University of Rochester Medical Center are AAALAC accredited institutions.
Excess Risk of COVID-19 to University Populations Resulting from In-Person Sporting Events.

Background: One of the consequences of COVID-19 has been the cancelation of collegiate sporting events. We explore the impact of sports on COVID-19 transmission on a college campus. Methods: Using a compartmental model representing the university, we model the impact of influxes of 10,000 visitors attending events and ancillary activities (dining out, visiting family, shopping, etc.) on 20,000 students. We vary the extent visitors interact with the campus, the number of infectious visitors, and the extent to which the campus has controlled COVID-19 absent events. We also conduct a global sensitivity analysis. Results: Events caused an increase in the number of cases ranging from a 25% increase when the campus already had an uncontrolled COVID-19 outbreak and visitors had a low prevalence of COVID-19 and mixed lightly with the campus community to an 822% increase where the campus had controlled their COVID-19 outbreak and visitors had both a high prevalence of COVID-19 and mixed heavily with the campus community. The model was insensitive to parameter uncertainty, save for the duration a symptomatic individual was infectious. Conclusion: Sporting events represent a threat to the health of the campus community. This is the case even in circumstances where COVID-19 seems controlled both on-campus and among the general population.
Talker discontinuity disrupts attention to speech: Evidence from EEG and pupillometry.

Abstract Speech is processed less efficiently from discontinuous, mixed talkers than one consistent talker, but little is known about the neural mechanisms for processing talker variability. Here, we measured psychophysiological responses to talker variability using electroencephalography (EEG) and pupillometry while listeners performed a delayed recall of digit span task. Listeners heard and recalled seven-digit sequences with both talker (single- vs. mixed-talker digits) and temporal (0- vs. 500-ms inter-digit intervals) discontinuities. Talker discontinuity reduced serial recall accuracy. Both talker and temporal discontinuities elicited P3a-like neural evoked response, while rapid processing of mixed-talkers’ speech led to increased phasic pupil dilation. Furthermore, mixed-talkers’ speech produced less alpha oscillatory power during working memory maintenance, but not during speech encoding. Overall, these results are consistent with an auditory attention and streaming framework in which talker discontinuity leads to involuntary, stimulus-driven attentional reorientation to novel speech sources, resulting in the processing interference classically associated with talker variability.
The experiences and support needs of siblings of people with mucopolysaccharidosis.

The mucopolysaccharidoses (MPS) are a group of rare genetic disorders characterized by progressive multisystem disease. We sought to identify the perceptions and support needs of siblings, who often have lifelong relationships and assume important roles for their brothers and sisters with MPS. We designed an online survey to ask siblings about their experiences through a series of Likert statements and open-ended questions. A mixed methods approach was used to analyze the results. We analyzed eligible responses from 97 participants, ages 18.1-61.2 years, who have brothers and sisters with MPS I, II, III, IV, and VI. Participants reported serving as caregivers for their siblings with MPS, at all ages. While over 62% of siblings often felt sad because they have a brother or sister with MPS, over 90% of siblings reported that they like their brothers and sisters and expressed feelings of pride. Siblings wanted information about MPS, guidance for caregiving activities, genetic counseling, and opportunities to connect with other siblings. Families and professionals should acknowledge the unique experiences and needs of siblings, include siblings in medical conversations and care plans when appropriate, and connect siblings to resources for informational and emotional support.
Smoothed quantile regression with large-scale inference

Abstract Quantile regression is a powerful tool for learning the relationship between a response variable and a multivariate predictor while exploring heterogeneous effects. This paper focuses on statistical inference for quantile regression in the “increasing dimension” regime. We provide a comprehensive analysis of a convolution smoothed approach that achieves adequate approximation to computation and inference for quantile regression. This method, which we refer to as conquer, turns the non-differentiable check function into a twice-differentiable, convex and locally strongly convex surrogate, which admits fast and scalable gradient-based algorithms to perform optimization, and multiplier bootstrap for statistical inference. Theoretically, we establish explicit non-asymptotic bounds on estimation and Bahadur–Kiefer linearization errors, from which we show that the asymptotic normality of the conquer estimator holds under a weaker requirement on dimensionality than needed for conventional quantile regression. The validity of multiplier bootstrap is also provided. Numerical studies confirm conquer as a practical and reliable approach to large-scale inference for quantile regression. Software implementing the methodology is available in the R package conquer .
Black hole in Nielsen-Olesen vortex

In this article, we calculate the classical vortex solution of a spontaneously broken gauge theory interacting with gravity in (2+1)-dimension. We also calculate the conditions for the formation of a (2+1)-dimensional black hole due to magnetic vortex (a Nielsen-Olesen vortex). The semiclassical Hawking temperature for this black hole is calculated, where we see that the temperature of a BTZ black hole increases or decreases without changing the size of the horizon if we insert the magnetic vortex fields in the black hole. Finally, the first law of black hole thermodynamics is described for this particular solution, which shows that the additional work terms from the scalar and gauge fields compensate the change in the temperature relative to its usual value for the BTZ solution.
Gradient Ascent Algorithm for Enhancing Secrecy Rate in Wireless Communications for Smart Grid

The emerging Internet of Things (IoT) and bidirectional communications in smart grid are expected to improve smart grid capabilities and electricity management. Because of massive number of IoT devices in smart grid, size of the data to be transmitted increases, that demands a high data rate to meet the real-time smart grid communication requirement. Sub-6 GHz, millimeter-wave (mmWave) technologies, and massive multiple-input multiple-output (MIMO) technologies can meet high data rate demands. However, IoT enabled smart grid is still subject to various security challenges such as eavesdropping, where attackers attempt to overhear the transmitted signals and the jamming attack, where the attacker perturbs the received signals at the receiver. In this paper, our goal is to investigate jamming and eavesdropping attacks while improving secrecy capacity for smart grid communications. Specifically, we propose to employ a hybrid beamforming design for wireless communication in smart energy grid. In previous works, the secrecy capacity is increased by randomly augmenting the source power or setting the system combiners. Unlike state-of-the-art, we design and evaluate the Gradient Ascent algorithm to search for the best combiners/waveform that maximizes the secrecy capacity in smart grid communications. We also study two different optimization scenarios by considering both fixed and variable transmit power. Numerical results are used for performance evaluation and supporting our formal analysis.
Sketching and Sequence Alignment: A Rate-Distortion Perspective

Pairwise alignment of DNA sequencing data is a ubiquitous task in bioinformatics and typically represents a heavy computational burden. A standard approach to speed up this task is to compute “sketches” of the DNA reads (typically via hashing-based techniques) that allow the efficient computation of pairwise alignment scores. We propose a rate-distortion framework to study the problem of computing sketches that achieve the optimal tradeoff between sketch size and alignment estimation distortion. We consider the simple setting of i.i.d. error-free sources of length $n$ and introduce a new sketching algorithm called “locational hashing.” While standard approaches in the literature based on min-hashes require $B=(1/D)\cdot O(\log n)$ bits to achieve a distortion $D$ , our proposed approach only requires $B=\log^{2}(1/D)\cdot O(1)$ bits. This can lead to significant computational savings in pairwise alignment estimation.
Different effects of lockdown on public health and economy of countries: Results from first wave of the COVID-19 pandemic

This paper analyzes how longer and shorter period of national lockdown during the first wave of COVID-19 can affect public health and economy of nations. Results show that a) countries with shorter period of lockdown (about 15 days) have a variation of confirmed cases/population (%) higher than countries with longer period of lockdown (more than one month); b) countries with shorter period of lockdown have average fatality rate lower than countries with longer period of lockdown, whereas variation of fatality rate suggests a higher reduction in countries with longer period of lockdown. However, the study reveals that the impact of longer period of national lockdown, as policy response of governments against COVID-19, pandemic seems to generate rather ambiguous effects on public health, however, this longer period of lockdown has a higher negative impact on economic growth of countries in terms of contraction of gross domestic product growth. Lessons learned can be important to design effective public responses for future waves of the COVID-19 and future epidemics similar to the COVID-19. Keywords. Lockdown, COVID-19, Crisis management, Public policy, Healthcare, Economic Growth, Contraction, Environmental impact, Air pollution. JEL. O15, O18, O24, R13, R23, R59, H29, H71, H73, J42, J61, J62, J68, F22, F42.
Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing

Machine learning algorithms deployed on edge devices must meet certain resource constraints and efficiency requirements. Random Vector Functional Link (RVFL) networks are favored for such applications due to their simple design and training efficiency. We propose a modified RVFL network that avoids computationally expensive matrix operations during training, thus expanding the network's range of potential applications. Our modification replaces the least-squares classifier with the Generalized Learning Vector Quantization (GLVQ) classifier, which only employs simple vector and distance calculations. The GLVQ classifier can also be considered an improvement upon certain classification algorithms popularly used in the area of Hyperdimensional Computing. The proposed approach achieved state-of-the-art accuracy on a collection of datasets from the UCI Machine Learning Repository-higher than previously proposed RVFL networks. We further demonstrate that our approach still achieves high accuracy while severely limited in training iterations (using on average only 21% of the least-squares classifier computational costs).
Quasi-normal mode theory of the scattering matrix, enforcing fundamental constraints for truncated expansions

We develop a quasi-normal mode theory (QNMT) to calculate a system's scattering $S$ matrix, simultaneously satisfying both energy conservation and reciprocity even for a small truncated set of resonances. It is a practical reduced-order (few-parameter) model based on the resonant frequencies and constant mode-to-port coupling coefficients, easily computed from an eigensolver without the need for QNM normalization. Furthermore, we show how low-$Q$ modes can be separated into an effective slowly varying background response $C$, giving an additional approximate formula for $S$, which is useful to describe general Fano-resonant phenomena. We demonstrate our formulation for both normal and fixed-angle oblique plane-wave incidence on various electromagnetic metasurfaces.
Foundations of Human Rights: The Unfinished Business

Reviewing the discussions on the foundations of human rights that took place in the U.N.'s first Human Rights Commission to draft the 1948 Universal Declaration of Human Rights, the author points out that the Commissioners, due to the situation at that time, had few discussions on the foundations of human rights and left the problem of foundations for another day. She rejects the idea that the Universal Declaration is western believing that all effective cultures in the world had a creative hand in the shaping of the document. The article then answers the question that how there can be universal rights in diverse cultures. It continues to explain the derby to deconstruct the Declaration and in the end concludes that the challenge of the Declaration is its incoherence and the different meanings and interpretations of human “dignity”. s but also seriously damage the hard gained doctor-patient trust and the historical honor of their profession. Within the law and justice system in practice in the Islamic Republic of Iran, fee splitting in considered unacceptable and illegal and fee splitters may be pursued by law. The same is true in different societies and most medical councils and accepted ethical codes and guidelines. It is clearly stated in the fifth paragraph of the medical affidavit vowed by all Iranian medical students and doctors that fee splitting is considered amoral and even illegal and that healthcare and medical practices are not to be used as a means of gathering wealth for oneself. All Islamic religious leaders have also questioned the lawfulness of money and wealth earned by fee splitting. The act of fee splitting is also recognized as unethical and unlawful in the guidelines and codes affected by universal medicine societies.
Data-driven acceleration of thermal radiation transfer calculations with the dynamic mode decomposition and a sequential singular value decomposition

Abstract We present a method for accelerating discrete ordinates radiative transfer calculations for radiative transfer. Our method works with nonlinear positivity fixes, in contrast to most acceleration schemes. The method is based on the dynamic mode decomposition (DMD) and using a sequence of rank-one updates to compute the singular value decomposition needed for DMD. Using a sequential method allows us to automatically determine the number of solution vectors to include in the DMD acceleration. We present results for slab geometry discrete ordinates calculations with the standard temperature linearization. Compared with positive source iteration, our results demonstrate that our acceleration method reduces the number of transport sweeps required to solve the problem by a factor of about 3 on a standard diffusive Marshak wave problem, a factor of several thousand on a cooling problem where the effective scattering ratio approaches unity, and a factor of 20 improvement in a realistic, multimaterial radiating shock problem.
Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding

This paper presents a new Vision Transformer (ViT) architecture Multi-Scale Vision Longformer, which significantly enhances the ViT of \cite{dosovitskiy2020image} for encoding high-resolution images using two techniques. The first is the multi-scale model structure, which provides image encodings at multiple scales with manageable computational cost. The second is the attention mechanism of vision Longformer, which is a variant of Longformer \cite{beltagy2020longformer}, originally developed for natural language processing, and achieves a linear complexity w.r.t. the number of input tokens. A comprehensive empirical study shows that the new ViT significantly outperforms several strong baselines, including the existing ViT models and their ResNet counterparts, and the Pyramid Vision Transformer from a concurrent work \cite{wang2021pyramid}, on a range of vision tasks, including image classification, object detection, and segmentation. The models and source code are released at \url{this https URL}.
Skeletal muscle mitochondrial respiration in a model of age-related osteoarthritis is impaired after dietary rapamycin

Abstract A decline in skeletal muscle mitochondrial function is associated with the loss of skeletal muscle size and function during knee osteoarthritis (OA). We have recently reported that 12-weeks of dietary rapamycin (Rap, 14 ppm), with or without metformin (Met, 1000 ppm), increased plasma glucose and OA severity in male Dunkin Hartley (DH) guinea pigs, a model of naturally occurring, age-related OA. The purpose of the current study was to determine if increased OA severity after dietary Rap and Rap+Met was accompanied by impaired skeletal muscle mitochondrial function. Mitochondrial respiration and hydrogen peroxide (H2O2) emissions were evaluated in permeabilized muscle fibers via high-resolution respirometry and fluorometry using either a saturating bolus or titration of ADP. Rap and Rap+Met decreased complex I (CI)-linked respiration and tended to increase ADP sensitivity, consistent with previous findings in patients with end-stage OA. The decrease in CI-linked respiration was accompanied with lower CI protein abundance. Rap and Rap+Met did not change mitochondrial H2O2 emissions. There were no differences between mitochondrial function in Rap versus Rap+Met suggesting that Rap was likely driving the change in mitochondrial function. This is the first inquiry into how lifespan extending treatments Rap and Rap+Met can influence skeletal muscle mitochondria in a model of age-related OA. Collectively, our data suggest that Rap with or without Met inhibits CI-linked capacity and increases ADP sensitivity in DH guinea pigs that have greater OA severity.