cssi/SciSciGPT-SciSciNet huggingface dataset:
Is leukopenia and lymphopenia a characteristic feature of COVID-19 in children?

Objectives: To analyze whether leucopenia and lymphopenia a characteristic feature of children with COVID-19 and to find out its association with the disease severity. Methods: This was a descriptive cross-sectional study conducted at The Children’s Hospital Lahore from March 2020 to October 2020. All confirmed cases of COVID-19 infection and post-COVID MIS-C/Kawasaki Disease diagnosed on the basis of RT-PCR and Antibody test respectively were included. Complete blood and differential counts were performed on the day of admission. Results: Out of a total of 83 patients 60 (72%) were diagnosed as COVID-19 and 23 (28%) as post-COVID MIS-C/KD. The mean age of children was 7.0±4.3 years (95%CI: 6.07 - 8.75) with a male preponderance 51 (61%). Twenty (24%) children had an underlying comorbidity and 7 (8%) were surgical cases. Our case fatality rate was 5 (6%) and all children who died had an underlying comorbid condition. In both, COVID and MIS-C/KD the mean leukocyte count was (14.0 ± 12.5 vs 13.6 ± 6.9 x109/L), respectively (p=0.888). The mean lymphocyte count in children with COVID was (39.1 ± 21.4%). Patients with MIS-C/KD showed significantly higher levels of neutrophil count (76.5 ± 15.0%) as compared to children with COVID (52.0 ± 22.1%), absolute lymphocyte count was (5.02±4.81 vs 2.13±0.95 x109/L) in COVID and MIS-C respectively (p=<0.001). In 60 COVID-19 patients, the mean neutrophil lymphocyte ratio (NLR) in mild-moderate and severe-critical group was 2.00 and 5.08 respectively (p=0.009). Conclusion: The blood picture of COVID-19 in children does not show leukopenia. NLR was a prognostic factor to assess the severity in COVID-19 patients. The presence of an underlying comorbid conditions is significant a risk factor for poor outcome. doi: https://doi.org/10.12669/pjms.37.3.3848 How to cite this:Bari A, Ch A, Bano I, Saqlain N. Is leukopenia and lymphopenia a characteristic feature of COVID-19 in children?. Pak J Med Sci. 2021;37(3):869-873. doi: https://doi.org/10.12669/pjms.37.3.3848 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
OpenARC: Extensible OpenACC Compiler Framework for Directive-Based Accelerator Programming Study

Directive-based, accelerator programming models such as OpenACC have arisen as an alternative solution to program emerging Scalable Heterogeneous Computing (SHC) platforms. However, the increased complexity in the SHC systems incurs several challenges in terms of portability and productivity. This paper presents an open-sourced OpenACC compiler, called OpenARC, which serves as an extensible research framework to address those issues in the directive-based accelerator programming. This paper explains important design strategies and key compiler transformation techniques needed to implement the reference OpenACC compiler. Moreover, this paper demonstrates the efficacy of OpenARC as a research framework for directive-based programming study, by proposing and implementing OpenACC extensions in the OpenARC framework to 1) support hybrid programming of the unified memory and separate memory and 2) exploit architecture-specific features in an abstract manner. Porting thirteen standard OpenACC programs and three extended OpenACC programs to CUDA GPUs shows that OpenARC performs similarly to a commercial OpenACC compiler, while it serves as a high-level research framework.
Mechanosensory input shapes Drosophila motor behavior through Patterned Spontaneous Network Activity

Neural activity sculpts circuit wiring in many animals. In vertebrates, patterned spontaneous network activity (PaSNA) generates sensory maps and establishes local circuits. However, it remains unclear how PaSNA might shape neuronal circuits and behavior in invertebrates. Previous work in the developing Drosophila embryo discovered spontaneous muscle activity that did not require synaptic transmission, and hence was myogenic, preceding PaSNA. These studies, however, monitored muscle movement, not neural activity, and were therefore unable to observe how myogenic activity might relate to subsequent neural network engagement. Here we use calcium imaging to directly record neural activity and characterize the emergence of PaSNA. We demonstrate that the spatiotemporal properties of PaSNA are highly stereotyped across embryos, arguing for genetic programming. Consistent with previous observations, we observe neural activity well before it becomes patterned, initially emerging during the myogenic stage. Remarkably, inhibition of mechanosensory input results in excessive PaSNA, demonstrating that muscle movement serves as a brake. Finally, using an optogenetic strategy to selectively disrupt mechanosensory inputs during PaSNA, followed by quantitative modeling of larval behavior, we demonstrate that mechanosensory modulation during development is required for proper larval foraging. This work thus provides a foundation for using the Drosophila embryo to study the role of PaSNA in circuit formation, provides mechanistic insight into how PaSNA is entrained by motor activity, and demonstrates that spontaneous network activity is essential for locomotor behavior. These studies argue that sensory feedback during the earliest stages of circuit formation can sculpt locomotor behaviors through innate motor learning.
STREME: accurate and versatile sequence motif discovery

Motivation Sequence motif discovery algorithms can identify novel sequence patterns that perform biological functions in DNA, RNA and protein sequences-for example, the binding site motifs of DNA-and RNA-binding proteins. Results The STREME algorithm presented here advances the state-of-the-art in ab initio motif discovery in terms of both accuracy and versatility. Using in vivo DNA (ChIP-seq) and RNA (CLIP-seq) data, and validating motifs with reference motifs derived from in vitro data, we show that STREME is more accurate, sensitive and thorough than several widely used algorithms (DREME, HOMER, MEME, Peak-motifs) and two other representative algorithms (ProSampler and Weeder). STREME's capabilities include the ability to find motifs in datasets with hundreds of thousands of sequences, to find both short and long motifs (from 3 to 30 positions), to perform differential motif discovery in pairs of sequence datasets, and to find motifs in sequences over virtually any alphabet (DNA, RNA, protein and user-defined alphabets). Unlike most motif discovery algorithms, STREME reports a useful estimate of the statistical significance of each motif it discovers. STREME is easy to use individually via its web server or via the command line, and is completely integrated with the widely-used MEME Suite of sequence analysis tools. The name STREME stands for "Simple, Thorough, Rapid, Enriched Motif Elicitation". Availability The STREME web server and source code are provided freely for non-commercial use at http://meme-suite.org.
Assortment Optimization under the Decision Forest Model

The decision forest model is a recently proposed nonparametric choice model that is capable of representing any discrete choice model and in particular, can be used to represent non-rational customer behavior. In this paper, we study the problem of finding the assortment that maximizes expected revenue under the decision forest model. This problem is of practical importance because it allows a firm to tailor its product offerings to profitably exploit deviations from rational customer behavior, but at the same time is challenging due to the extremely general nature of the decision forest model. We approach this problem from a mixed-integer optimization perspective and propose three different formulations. We theoretically compare these formulations in strength, and analyze when these formulations are integral in the special case of a single tree. We propose a methodology for solving these problems at a large-scale based on Benders decomposition, and show that the Benders subproblem can be solved efficiently by primal-dual greedy algorithms when the master solution is fractional for two of our formulations, and in closed form when the master solution is binary for all of our formulations. Using synthetically generated instances, we demonstrate the practical tractability of our formulations and our Benders decomposition approach, and their edge over heuristic approaches.
Skills-based intervention to enhance collaborative decision-making: systematic adaptation and open trial protocol for veterans with psychosis.

Collaborative decision-making is an innovative decision-making approach that assigns equal power and responsibility to patients and providers. Most veterans with serious mental illnesses like schizophrenia want a greater role in treatment decisions, but there are no interventions targeted for this population. A skills-based intervention is promising because it is well-aligned with the recovery model, uses similar mechanisms as other evidence-based interventions in this population, and generalizes across decisional contexts while empowering veterans to decide when to initiate collaborative decision-making. Collaborative Decision Skills Training (CDST) was developed in a civilian serious mental illness sample and may fill this gap but needs to undergo a systematic adaptation process to ensure fit for veterans. In aim 1, the IM Adapt systematic process will be used to adapt CDST for veterans with serious mental illness. Veterans and Veteran’s Affairs (VA) staff will join an Adaptation Resource Team and complete qualitative interviews to identify how elements of CDST or service delivery may need to be adapted to optimize its effectiveness or viability for veterans and the VA context. During aim 2, an open trial will be conducted with veterans in a VA Psychosocial Rehabilitation and Recovery Center (PRRC) to assess additional adaptations, feasibility, and initial evidence of effectiveness. This study will be the first to evaluate a collaborative decision-making intervention among veterans with serious mental illness. It will also contribute to the field’s understanding of perceptions of collaborative decision-making among veterans with serious mental illness and VA clinicians, and result in a service delivery manual that may be used to understand adaptation needs generally in VA PRRCs. ClinicalTrials.gov Identifier: NCT04324944
The Extensive Margin of Exporting Products: A Firm-level Analysis ∗

We examine three-dimensional panel data for Brazilian and Chilean exporters, their products and destinations. The data show that (i) the distribution of the exporters’ number of goods (the exporter scope) is robust within destinations and approximately Pareto, (ii) exporter scope is positively associated with average sales per good within destinations but not across, and (iii) exports are concentrated in few top-selling goods by flrm. We present a heterogeneous-flrm model with product choice that implies these regularities and retains key predictions of previous trade models. At the country level, the model generates bilateral trade ∞ows consistent with gravity-equation evidence. Across flrms, the model explains regularities with convex product-entry costs that increase more than proportionally in scope on the distribution side. Within flrms, variable product-entry costs,
Classifying chronic pain using multidimensional pain-agnostic symptom assessments and clustering analysis.

Chronic pain conditions present in various forms, yet all feature symptomatic impairments in physical, mental, and social domains. Rather than assessing symptoms as manifestations of illness, we us...
Robust hypothesis testing and distribution estimation in Hellinger distance.

We propose a simple robust hypothesis test that has the same sample complexity as that of the optimal Neyman-Pearson test up to constants, but robust to distribution perturbations under Hellinger distance. We discuss the applicability of such a robust test for estimating distributions in Hellinger distance. We empirically demonstrate the power of the test on canonical distributions.
Sociological rational choice theory

Although rational choice theory has made considerable advances in other social sciences, its progress in sociology has been limited. Some sociologists' reservations about rational choice arise from a misunderstanding of the theory. The first part of this essay therefore introduces rational choice as a general theoretical perspective or family of theories, which explains social outcomes by constructing models of individual action and social context. Thin models of individual action are mute about actors' motivations, while thick models specify them ex ante. Other sociologists' reservations, however, stem from doubts about the empirical adequacy of rational choice explanations. To this end, the bulk of the essay reviews a sample of recent studies that provide empirical support for particular rational choice explanations in a broad spectrum of substantive areas in sociology. Particular attention is paid to studies on the family, gender and religion, for these subareas often are considered least amenable to understanding in terms of rational choice logic
DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set

Diffuse Large B-Cell Lymphoma (DLBCL) is the most common non-Hodgkin lymphoma. Though histologically DLBCL shows varying morphologies, no morphologic features have been consistently demonstrated to correlate with prognosis. We present a morphologic analysis of histology sections from 209 DLBCL cases with associated clinical and cytogenetic data. Duplicate tissue core sections were arranged in tissue microarrays (TMAs), and replicate sections were stained with H&E and immunohistochemical stains for CD10, BCL6, MUM1, BCL2, and MYC. The TMAs are accompanied by pathologist-annotated regions-of-interest (ROIs) that identify areas of tissue representative of DLBCL. We used a deep learning model to segment all tumor nuclei in the ROIs, and computed several geometric features for each segmented nucleus. We fit a Cox proportional hazards model to demonstrate the utility of these geometric features in predicting survival outcome, and found that it achieved a C-index (95% CI) of 0.635 (0.574,0.691). Our finding suggests that geometric features computed from tumor nuclei are of prognostic importance, and should be validated in prospective studies.
Factors That Influence Career Choice among Different Populations of Neuroscience Trainees.

Specific groups have historically been, and continue to be, underrepresented in the biomedical research workforce, especially academia. Career choice is a multi-factorial process that evolves over time; among all trainees, expressed interest in faculty research careers decreases over time in graduate school, but that trend is amplified in women and members of historically underrepresented (UR) racial and ethnic groups (Fuhrmann et al., 2011; Gibbs et al, 2014; Golde & Dore, 2004; Roach & Sauermann, 2017; Sauermann & Roach, 2012). This work was designed to investigate how career interest changes over time among recent neuroscience PhD graduates, and whether differences in career interests are associated with social identity, experiences in graduate school and postdoctoral training, and personal characteristics. We report results from a survey of 1,479 PhD neuroscientists (including 16% underrepresented and 54% women scientists). We saw repeated evidence that individual preferences about careers in general, and academic careers specifically, predict current career interest. These findings were mediated by social identity and experiences in graduate school and postdoctoral training. Our findings highlight the important influence of the advisor in shaping a trainee9s career path, and the ways in which academic culture is perceived as unwelcoming or incongruent with the values or priorities of certain groups. They suggest several areas for positive growth, ways to change how we think about the impact of mentorship, and policy and programmatic interventions that extend beyond trying to change or "fix" the individual and instead recognize the systemic structures that influence career choices. SIGNIFICANCE STATEMENT Specific groups have historically been, and continue to be, underrepresented in the biomedical research workforce. Career choice is a multi-factorial process that evolves over time; among all trainees, expressed interest in faculty research careers decreases over time, but that trend is amplified in women and members of historically underrepresented (UR) racial and ethnic groups. A survey of 1,479 PhD neuroscientists revealed evidence that preferences about careers in general, and academic careers specifically, affect career interest and are mediated by social identity and experiences in graduate and postdoctoral training. Our findings suggest areas for positive growth, ways to re-think the impact of mentorship, and interventions that extend beyond "fixing" the individual and instead recognize the systemic structures that influence career choices.
Aircraft agility maneuvers

A new dynamic model for aircraft motions is presented. This model can be viewed as intermediate between a point-mass model, in which the body attitude angles are control-like, and a rigid-body model, in which the body-attitude angles evolve according to Newton's Laws. Specifically, consideration is given to the case of symmetric flight, and a model is constructed in which the body roll-rate and the body pitch-rate are the controls. In terms of this body-rate model a minimum-time heading change maneuver is formulated. When the bounds on the body-rates are large the results are similar to the point-mass model in that the model can very quickly change the applied forces and produce an acceleration to turn the vehicle. With finite bounds on these rates, the forces change in a smooth way. This leads to a measurable effect of agility.
Thyroid hormone regulates abrupt skin morphogenesis during zebrafish postembryonic development.

Abstract Thyroid hormone is a key regulator of post-embryonic vertebrate development. Skin is a biomedically important thyroid hormone target organ, but the cellular and molecular mechanisms underlying skin pathologies associated with thyroid dysfunction remain obscure. The transparent skin of zebrafish is an accessible model system for studying vertebrate skin development. During post-embryonic development of the zebrafish, scales emerge in the skin from a hexagonally patterned array of dermal papillae, like other vertebrate skin appendages such as feathers and hair follicles. We show here that thyroid hormone regulates the rate of post-embryonic dermal development through interaction with nuclear hormone receptors. This couples skin development with body growth to generate a well ordered array of correctly proportioned scales. This work extends our knowledge of thyroid hormone actions on skin by providing in-vivo evidence that thyroid hormone regulates multiple aspects of dermal development.
Ventral stress fibers induce plasma membrane deformation in human fibroblasts

We observed fluorescent structures in myofibroblasts labeled with cytosolic fluorophores. Confocal and electron microscopy revealed these structures to be ridges in the plasma membrane caused by ve...
Surface fatigue and failure characteristics of hot-forged powder metal AISI 4620, AISI 4640, and machined AISI 4340 steel spur gears

Spur gear surface fatigue endurance tests were conducted to investigate hot forged powder metal AISI 4620 and 4640 steel for use as a gear material, to determine endurance characteristics and to compare the results with machined AISI 4340 and 9310 steel gear materials. The as-forged and unground AISI 4620 gear exhibited a 10 percent fatigue life that was approximately one-fourth of that for AISI 9310 and less than one-half that for the AISI 4340 gears. The forged and finish ground AISI 4620 gears exhibited a 10 percent life, approximately 70 percent that of AISI 9310 and slightly better than that of AISI 4340. The AISI 4640 hot forged gears had less fracture toughness and slightly less fatigue life than the AISI 4620 test gears.
The Sounds of Silence: A Framework, Theory, and Empirical Evidence of Audit Team Voice

Lower-level auditors are likely to encounter client information that may reflect important audit issues. The audit team cannot address these issues unless they are communicated upward. However, research indicates that lower-level auditors sometimes withhold issues, threatening audit effectiveness. We use a multi-method grounded theory approach to expand our understanding of the factors associated with auditors’ decision to speak up about potential audit issues. We use an experiential questionnaire to draw out participants’ real-life experiences with the decision to speak up or remain silent in the field (i.e., the “voice” decision). We summarize this work in a framework of audit voice determinants and a theoretical model of audit voice. We then use the determinants framework and the developed theory to conduct an experiment as an exemplar for how our work can be useful in generating future research.
Session logical relations for noninterference

Information flow control type systems statically restrict the propagation of sensitive data to ensure end-to-end confidentiality. The property to be shown is noninterference, asserting that an attacker cannot infer any secrets from made observations. Session types delimit the kinds of observations that can be made along a communication channel by imposing a protocol of message exchange. These protocols govern the exchange along a single channel and leave unconstrained the propagation along adjacent channels. This paper contributes an information flow control type system for linear session types. The type system stands in close correspondence with intuitionistic linear logic. Intuitionistic linear logic typing ensures that process configurations form a tree such that client processes are parent nodes and provider processes child nodes. To control the propagation of secret messages, the type system is enriched with secrecy levels and arranges these levels to be aligned with the configuration tree. Two levels are associated with every process: the maximal secrecy denoting the process' security clearance and the running secrecy denoting the highest level of secret information obtained so far. The computational semantics naturally stratifies process configurations such that higher-secrecy processes are parents of lower-secrecy ones, an invariant enforced by typing. Noninterference is stated in terms of a logical relation that is indexed by the secrecy-level-enriched session types. The logical relation contributes a novel development of logical relations for session typed languages as it considers open configurations, allowing for a more nuanced equivalence statement.
Reingestion characteristics and inlet flow distortion of V/STOL lift-engine fighter configurations.

Exhaust gas reingestion and inlet flow distortion in V/STOL lift engines, discussing static and wind tunnel testing using fighter configuration models
Materials representation and transfer learning for multi-property prediction

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements as well as the relationships among multiple properties to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates: (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 three-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data are available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with transfer learning [H-CLMP(T)] wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well suited for multi-target regression across the physical sciences.
A reaction network approach to the convergence to equilibrium of quantum Boltzmann equations for Bose gases

When the temperature of a trapped Bose gas is below the Bose-Einstein transition temperature and above absolute zero, the gas is composed of two distinct components: the Bose-Einstein condensate and the cloud of thermal excitations. The dynamics of the excitations can be described by quantum Boltzmann models. We establish a connection between quantum Boltzmann models and chemical reaction networks. We prove that the discrete differential equations for these quantum Boltzmann models converge to an equilibrium point. Moreover, this point is unique for all initial conditions that satisfy the same conservation laws. In the proof, we then employ a toric dynamical system approach, similar to the one used to prove the global attractor conjecture, to study the convergence to equilibrium of quantum kinetic equations.
Physical watermarking for replay attack detection in continuous-time systems

Abstract Physical watermarking is a well established technique for replay attack detection in cyber-physical systems (CPSs). Most of the watermarking methods proposed in the literature are designed for discrete-time systems. In general real physical systems evolve in continuous time. In this paper, we analyze the effect of watermarking on sampled-data continuous-time systems controlled via a Zero-Order Hold. We investigate the effect of sampling on detection performance and we provide a procedure to find a suitable sampling period that ensures detectability and acceptable control performance. Simulations on a quadrotor system are used to illustrate the effectiveness of the theoretical results.
Ductile fracture theories for pressurised pipes and containers

Abstract The probable mechanisms of fracture which may be encountered in various components may generally be classified in two main groups. In the first the fracture is of ‘plane strain’ type which may occur in components (e.g. thick walled pressure vessels and other heavy section components) where prior to and during a possible fracture propagation the material is not expected to undergo large scale plastic deformations. In this case the underlying fracture theory is rather well-understood, and a criterion based on KIC usually provides a highly reliable tool to deal with the problem. The second type of fracture failure falls into the general category of ‘plane stress’ or ‘high energy’ fracture. In a great variety of tubings and containers, due to relatively small wall thickness, large defect size, high material toughness, and high temperature, prior to and during a possible rupture process, around the defect region the material would be expected to undergo large scale plastic deformations. In this case the standard theories of fracture based on the concept of plane strain fracture toughness are not applicable. This type of fracture which is generally accompanied by large inelastic deformations is (somewhat loosely) termed the plane stress fracture for which currently there does not seem to be a universally accepted criterion. In some components an additional complicating factor arises where one is dealing essentially with a shell of given curvature rather than a flat plate. The theories which are currently in use in practice to analyse plane stress type of fracture are those which are based on the concepts of critical crack opening stretch, K R - characterisation , J- integral , and the recently proposed plastic instability. In this paper the application of the fracture criteria based on these concepts to the fracture of shells will be discussed and the concept of plastic instability will be developed in some detail. Since there is no widely accepted standard criterion to deal with this type of fracture, one of the aims of the paper will be to provide an up-to-date critical appraisal of the current theories.
A Non-cooperative Game-based Distributed Beam Scheduling Framework for 5G Millimeter-Wave Cellular Networks

This paper studies the problem of distributed beam scheduling for 5G millimeter-Wave (mm-Wave) cellular networks where base stations (BSs) belonging to different operators share the same spectrum without centralized coordination among them. Our goal is to design efficient distributed scheduling algorithms to maximize the network utility, which is a function of the achieved throughput by the user equipment (UEs), subject to the average and instantaneous power consumption constraints of the BSs. We propose a Media Access Control (MAC) and a power allocation/adaptation mechanism utilizing the Lyapunov stochastic optimization framework and non-cooperative games. In particular, we first decompose the original utility maximization problem into two sub-optimization problems for each time frame, which are a convex optimization problem and a non-convex optimization problem, respectively. By formulating the distributed scheduling problem as a non-cooperative game where each BS is a player attempting to optimize its own utility, we provide a distributed solution to the non-convex sub-optimization problem via finding the Nash Equilibrium (NE) of the game whose weights are determined optimally by the Lyapunov optimization framework. Finally, we conduct simulation under various network settings to show the effectiveness of the proposed game-based beam scheduling algorithm in comparison to that of several reference schemes.
BIoTA: Control-Aware Attack Analytics for Building Internet of Things

Modern building control systems adopt demand control heating, ventilation, and cooling (HVAC) for increased energy efficiency. The integration of the Internet of Things (IoT) in the building control system can determine real-time demand, which has made the buildings smarter, reliable, and efficient. As occupants in a building are the main source of continuous heat and CO 2 generation, estimating the accurate number of people in real-time using building IoT (BIoT) system facilities is essential for optimal energy consumption and occupants’ comfort. However, the incorporation of less secured IoT sensor nodes and open communication network in the building control system eventually increases the number of vulnerable points to be compromised. Exploiting these vulnerabilities, attackers can manipulate the controller with false sensor measurements and disrupt the system’s consistency. The attackers with the knowledge of overall system topology and control logics can launch attacks without alarming the system. This paper proposes a building internet of things analyzer (BIoTA) framework1 that assesses the smart building HVAC control system’s security using formal attack modeling. We evaluate the proposed attack analyzer’s effectiveness on the commercial occupancy dataset (COD) and the KTH live-in lab dataset. To the best of our knowledge, this is the first research attempt to formally model a BIoT-based HVAC control system and perform an attack analysis.
Maker Breaker on digraphs

We study two biassed Maker-Breaker games played on the complete digraph $\vec{K}_n$. In the strong connectivity game, Maker wants to build a strongly connected subgraph. We determine the asymptotic optimal bias for this game viz. $\frac{n}{\log n}$. In the Hamiltonian game, Maker wants to build a Hamiltonian subgraph. We determine the asymptotic optimal bias for this game up to a constant factor.
Volumetric alteration of olfactory bulb and immune-related molecular changes in olfactory epithelium in first episode psychosis patients

Background: Olfactory dysfunction has been reproducibly reported in patients with psychosis, including first episode psychosis (FEP) patients. Consistently, structural abnormalities in the olfactory bulb (OB), a key region of the peripheral olfactory system, have also been reported in psychotic disorders. Meanwhile, air pollution and viral infections in the upper respiratory tract, including those of SARS-CoV-2, are reportedly risk factors for brain dysfunction and mental disorders. These risk factors can disturb the olfactory epithelium (OE) that is located adjacent to the OB and connected via direct neuronal projections. Nevertheless, it is unknown how a disturbance of the OE possibly affects the OB in the pathophysiological context of psychotic disorders. Methods: We examined the volume of the OB in FEP patients and healthy controls from 3 Tesla magnetic resonance imaging and molecular expression profiles of olfactory neuronal cells (ONCs) enriched from freshly biopsied OE. Results: We observed a reduction of the OB volume in FEP patients compared with healthy controls. We also observed a significant alteration in gene expression profiles in the ONCs from FEP patients, supporting the pathological changes in the OE. Among such molecular changes, immune-related molecules and pathways were underscored in association with the OB volume changes in FEP patients. Conclusions: Our data support the OB and OE pathologies in FEP patients. Immune-related molecular changes in the OE can biologically link adverse factors in the nasal cavity, such as air pollution and viral infection, with the OB structural change, both of which have been reported for psychotic disorders.
Compression molding of anisotropic NdFeB bonded magnets in a polycarbonate matrix

Abstract Anisotropic bonded Nd2Fe14B (NdFeB) magnets in a polycarbonate (PC) binder matrix are fabricated using a compression molding process. The weight fractions (w.f.) of NdFeB in PC on the batch mixer are 20, 50, 75, 85 and 95% compared to the twin screw extruder with 20, 50 and 75% respectively. The density of the 95% batch mixed magnets fabricated was 5.34 g/cm3 and the magnetic properties are, intrinsic coercivity Hci = 942.99 kA/m, remanence Br = 0.86 T, and energy product (BH)max = 120.96 kJ/m3. The measured tensile properties are in the range of 27-59 MPa, comparable to that of polyamide (PA), polyphenylene sulfide (PPS) bonded magnets and demonstrating potential for bonded magnet applications. Scanning electron microscopy showed that the onset of failure occurs in the magnetic particle- matrix interface. This study demonstrates that compression additive molding technique can be used to fabricate high performance NdFeB polycarbonate composite magnets with improved mechanical properties.
Fast Security Evaluation for Operation of Water Distribution Systems Against Extreme Conditions.

This paper defines a security injection region (SIR) to guarantee reliable operation of water distribution systems (WDS) under extreme conditions. The model of WDSs is highly nonlinear and nonconvex. Understanding the accurate SIRs of WDSs involves the analysis of nonlinear constraints, which is computationally expensive. To reduce the computational burden, this paper first investigates the convexity of the SIR of WDSs under certain conditions. Then, an algorithm based on a monotone inner polytope sequence is proposed to effectively and accurately determine these SIRs. The proposed algorithm estimates a sequence of inner polytopes that converge to the whole convex region. Each polytope adds a new area to the SIR. The algorithm is validated on two different WDSs, and the conclusion is drawn. The computational study shows this method is applicable and fast for both systems.
The flash phase of solar flares - Satellite observations of electrons

Satellite observations of solar electrons bearing on flare particle acceleration and the generation of radio and X-ray emission are reviewed. The observations support a two-stage acceleration process for electrons, one stage commonly occurring at the flare flash phase and accelerating electrons up to about 100 keV, and a second stage occurring only in large proton flares and accelerating electrons up to relativistic energies. The location of the acceleration region appears to be no lower than the lower corona. The accelerated non-relativistic electrons generate type III radio burst emission as they escape from the sun. Direct spacecraft observations of the type III emission generated near 1 AU and the energetic electrons, provide quantitative information on the characteristics of the electrons exciting type III emission, the production of plasma waves, and the conversion from plasma waves to electromagnetic radiation.
Kemeny Consensus Complexity

The computational study of election problems generally focuses on questions related to the winner or set of winners of an election. But social preference functions such as Kemeny rule output a full ranking of the candidates (a consensus). We study the complexity of consensus-related questions, with a particular focus on Kemeny and its qualitative version Slater. The simplest of these questions is the problem of determining whether a ranking is a consensus, and we show that this problem is coNP-complete. We also study the natural question of the complexity of manipulative actions that have a specific consensus as a goal. Though determining whether a ranking is a Kemeny consensus is hard, the optimal action for manipulators is to simply vote their desired consensus. We provide evidence that this simplicity is caused by the combination of election system (Kemeny), manipulative action (manipulation), and manipulative goal (consensus). In the process we provide the first completeness results at the second level of the polynomial hierarchy for electoral manipulation and for optimal solution recognition.
Sleep Deprivation Exacerbates Seizures and Diminishes GABAergic Tonic Inhibition.

Patients with epilepsy report that sleep deprivation is a common trigger for breakthrough seizures. The basic mechanism of this phenomenon is unknown. In the Kv1.1-/- mouse model of epilepsy, daily sleep deprivation indeed exacerbated seizures though these effects were lost after the third day. Sleep deprivation also accelerated mortality in ~ 52% of Kv1.1-/- mice, not observed in controls. Voltage-clamp experiments on the day after recovery from sleep deprivation showed reductions in GABAergic tonic inhibition in dentate granule cells in epileptic Kv1.1-/- mice. Our results suggest that sleep deprivation is detrimental to seizures and survival, possibly due to reductions in GABAergic tonic inhibition. ANN NEUROL 2021;90:840-844.
Resilient and Consistent Multirobot Cooperative Localization With Covariance Intersection

Cooperative localization is fundamental to autonomous multirobot systems, but most algorithms couple interrobot communication with observation, making these algorithms susceptible to failures in both communication and observation steps. To enhance the resilience of multirobot cooperative localization algorithms in a distributed system, we use covariance intersection to formalize a localization algorithm with an explicit communication update and ensure estimation consistency at the same time. We investigate the covariance boundedness criterion of our algorithm with respect to communication and observation graphs, demonstrating provable localization performance under even sparse communications topologies. We substantiate the resilience of our algorithm as well as the boundedness analysis through experiments on simulated and benchmark physical data against varying communications connectivity and failure metrics. Especially when interrobot communication is entirely blocked or partially unavailable, we demonstrate that our method is less affected and maintains desired performance compared to existing cooperative localization algorithms.
Topological constructions of tensor fields on moduli spaces

Abstract We show how topology of a space may lead to tensor fields on (the smooth part of) moduli spaces of the fundamental group.
Regular saturated graphs and sum-free sets

Abstract In a recent paper, Gerbner, Patkos, Tuza and Vizer studied regular F-saturated graphs. One of the essential questions is given F, for which n does a regular n-vertex F-saturated graph exist. They proved that for all sufficiently large n, there is a regular K 3 -saturated graph with n vertices. We extend this result to both K 4 and K 5 and prove some partial results for larger complete graphs. Using a variation of sum-free sets from additive combinatorics, we prove that for all k ≥ 2 , there is a regular C 2 k + 1 -saturated with n vertices for infinitely many n. Studying the sum-free sets that give rise to C 2 k + 1 -saturated graphs is an interesting problem on its own and we state an open problem in this direction.
Isoform cell-type specificity in the mouse primary motor cortex.

Full-length SMART-seq1 single-cell RNA sequencing can be used to measure gene expression at isoform resolution, making possible the identification of specific isoform markers for different cell types. Used in conjunction with spatial RNA capture and gene-tagging methods, this enables the inference of spatially resolved isoform expression for different cell types. Here, in a comprehensive analysis of 6,160 mouse primary motor cortex cells assayed with SMART-seq, 280,327 cells assayed with MERFISH2 and 94,162 cells assayed with 10x Genomics sequencing3, we find examples of isoform specificity in cell types—including isoform shifts between cell types that are masked in gene-level analysis—as well as examples of transcriptional regulation. Additionally, we show that isoform specificity helps to refine cell types, and that a multi-platform analysis of single-cell transcriptomic data leveraging multiple measurements provides a comprehensive atlas of transcription in the mouse primary motor cortex that improves on the possibilities offered by any single technology.
Fluorescence detection of a trapped ion with a monolithically integrated single-photon-counting avalanche diode

We report on the demonstration of fluorescence detection from a trapped ion using single-photon avalanche photodiodes (SPADs) monolithically integrated with a microfabricated surface ion trap. The SPADs are located below the trapping positions of the ions and designed to detect 370 nm photons emitted from single 174Yb+ and 171Yb+ ions. We achieve an ion/no-ion detection fidelity for 174Yb+ of 0.99 with an average detection window of 7.7(1) ms. We report a dark count rate as low as 1.2 kcps for room temperature operation. The fidelity is limited by laser scatter, dark counts, and heating that prevents holding the ion directly above the SPAD. We measure count rates from each of the contributing sources and fluorescence as a function of ion position. We use the ion as a calibrated light source along with measurements of the active detector area to estimate a SPAD quantum efficiency of 24 ± 1%.
The Mechanosensitive Ion Channel MSL10 Modulates Susceptibility to Pseudomonas syringae in Arabidopsis thaliana.

Plants sense and respond to molecular signals associated with the presence of pathogens and their virulence factors. Mechanical signals generated during pathogenic invasion may also be important, but their contributions have rarely been studied. Here we investigate the potential role of a mechanosensitive ion channel, MscS-Like (MSL)10, in defense against the bacterial pathogen Pseudomonas syringae in Arabidopsis thaliana. We previously showed that overexpression of MSL10-GFP, phospho-mimetic versions of MSL10, and the gain-of-function allele msl10-3G all produce dwarfing, spontaneous cell death, and the hyperaccumulation of reactive oxygen species. These phenotypes are shared by many autoimmune mutants and are frequently suppressed by growth at high temperature in those lines. Here, we found that the same was true for all three MSL10 hypermorphs. In addition, we show that the SGT1/RAR1/HSP90 co-chaperone complex was required for dwarfing and ectopic cell death, PAD4 and SID2 were partially required, and the immune regulators EDS1 and NDR1 were dispensable. All MSL10 hypermorphs exhibited reduced susceptibility to infection by P. syringae strain Pto DC3000, Pto DC3000 expressing the avirulence genes avrRpt2 or avrRpm1, but not Pto DC3000 hrpL, and showed an accelerated induction of PR1 expression compared to wild-type plants. Null msl10-1 mutants were delayed in PR1 induction and displayed modest susceptibility to infection by COR-deficient Pst. Finally, stomatal closure was reduced in msl10-1 loss-of-function mutants in response to Pst COR-. These data show that MSL10 modulates pathogen responses and begin to address the possibility that mechanical signals are exploited by the plant for pathogen perception.
Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs

We address the issue of safety in reinforcement learning. We pose the problem in an episodic framework of a constrained Markov decision process. Existing results have shown that it is possible to achieve a reward regret of $\tilde{\mathcal{O}}(\sqrt{K})$ while allowing an $\tilde{\mathcal{O}}(\sqrt{K})$ constraint violation in $K$ episodes. A critical question that arises is whether it is possible to keep the constraint violation even smaller. We show that when a strictly safe policy is known, then one can confine the system to zero constraint violation with arbitrarily high probability while keeping the reward regret of order $\tilde{\mathcal{O}}(\sqrt{K})$. The algorithm which does so employs the principle of optimistic pessimism in the face of uncertainty to achieve safe exploration. When no strictly safe policy is known, though one is known to exist, then it is possible to restrict the system to bounded constraint violation with arbitrarily high probability. This is shown to be realized by a primal-dual algorithm with an optimistic primal estimate and a pessimistic dual update.
Single-dataset Experts for Multi-dataset Question Answering

Many datasets have been created for training reading comprehension models, and a natural question is whether we can combine them to build models that (1) perform better on all of the training datasets and (2) generalize and transfer better to new datasets. Prior work has addressed this goal by training one network simultaneously on multiple datasets, which works well on average but is prone to over- or under-fitting different sub- distributions and might transfer worse compared to source models with more overlap with the target dataset. Our approach is to model multi-dataset question answering with an ensemble of single-dataset experts, by training a collection of lightweight, dataset-specific adapter modules (Houlsby et al., 2019) that share an underlying Transformer model. We find that these Multi-Adapter Dataset Experts (MADE) outperform all our baselines in terms of in-distribution accuracy, and simple methods based on parameter-averaging lead to better zero-shot generalization and few-shot transfer performance, offering a strong and versatile starting point for building new reading comprehension systems.
Ethical AI for Social Good

The concept of AI for Social Good(AI4SG) is gaining momentum in both information societies and the AI community. Through all the advancement of AI-based solutions, it can solve societal issues effectively. To date, however, there is only a rudimentary grasp of what constitutes AI socially beneficial in principle, what constitutes AI4SG in reality, and what are the policies and regulations needed to ensure it. This paper fills the vacuum by addressing the ethical aspects that are critical for future AI4SG efforts. Some of these characteristics are new to AI, while others have greater importance due to its usage.