cssi/SciSciGPT-SciSciNet huggingface dataset:
Deep Leakage from Gradients

Passing gradient is a widely used scheme in modern multi-node learning system (e.g, distributed training, collaborative learning). In a long time, people used to believe that gradients are safe to share: i.e, the training set will not be leaked by gradient sharing. However, in this paper, we show that we can obtain the private training set from the publicly shared gradients. The leaking only takes few gradient steps to process and can obtain the original training set instead of look-alike alternatives. We name this leakage as \textit{deep leakage from gradient} and practically validate the effectiveness of our algorithm on both computer vision and natural language processing tasks. We empirically show that our attack is much stronger than previous approaches and thereby and raise people's awareness to rethink the gradients' safety. We also discuss some possible strategies to defend this deep leakage.
A Systematic Review of Sellar and Parasellar Brown Tumors: An Analysis of Clinical, Diagnostic, and Management Profiles.

Objective To systematically review and analyze clinical, diagnostic, and management trends in sellar and parasellar brown tumors reported in existing literature. Methods In this systematic review, PubMed, Ovid MEDLINE, Scopus, and Google Scholar databases were searched for reported cases of sellar/parasellar brown tumors. Relevant titles and abstracts were screened in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. Articles meeting inclusion criteria were subjected to data extraction, summarization, and analysis. A rare case of parasellar brown tumor was also presented. Results Eight reports (including the current report) were eligible for inclusion. Mean patient age was 42.75 years. Reported symptoms included visual disturbances (n = 6), headache (n = 5), fatigue (n = 3), nausea/vomiting (n = 2), chest pain (n = 1), neck pain (n = 1), and dysphagia (n = 1). In cases where computed tomography findings were provided (n = 6), lesions were noted to be expansile and lytic. Lesions were hyperintense on T2-weighted magnetic resonance imaging (66.7%) and demonstrated contrast enhancement (83.3%). Histology unanimously showed multinucleated giant cells in a fibrovascular connective tissue stroma. Dramatic symptom resolution was noted in all patients who underwent resection of the sellar/parasellar brown tumor (n = 4; 50%). Conclusions Sellar/parasellar brown tumors are a rare, tertiary manifestation of hyperparathyroidism and can be elusive to diagnose. Diagnosis requires a high index of clinical suspicion in addition to comprehensive biochemical testing, imaging, and histopathologic analysis. Surgical extirpation is favored in cases where the lesion is causing compressive symptoms, or if it is unresponsive to management of hyperparathyroidism.
A Composable Specification Language for Reinforcement Learning Tasks

Reinforcement learning is a promising approach for learning control policies for robot tasks. However, specifying complex tasks (e.g., with multiple objectives and safety constraints) can be challenging, since the user must design a reward function that encodes the entire task. Furthermore, the user often needs to manually shape the reward to ensure convergence of the learning algorithm. We propose a language for specifying complex control tasks, along with an algorithm that compiles specifications in our language into a reward function and automatically performs reward shaping. We implement our approach in a tool called SPECTRL, and show that it outperforms several state-of-the-art baselines.
Risky decision-making predicts dopamine release dynamics in nucleus accumbens shell

The risky decision-making task (RDT) measures risk-taking in a rat model by assessing preference between a small, safe reward and a large reward with increasing risk of punishment (mild foot shock). It is well-established that dopaminergic drugs modulate risk-taking; however, little is known about how differences in baseline phasic dopamine signaling drive individual differences in risk preference. Here, we used in vivo fixed potential amperometry in male Long-Evans rats to test if phasic nucleus accumbens shell (NACs) dopamine dynamics are associated with risk-taking. We observed a positive correlation between medial forebrain bundle-evoked dopamine release in the NACs and risky decision-making, suggesting that risk-taking is associated with elevated dopamine sensitivity. Moreover, "risk-taking" subjects were found to demonstrate greater phasic dopamine release than "risk-averse" subjects. Risky decision-making also predicted enhanced sensitivity to the dopamine reuptake inhibitor nomifensine, and elevated autoreceptor function. Importantly, this hyperdopaminergic phenotype was selective for risky decision-making, as delay discounting performance was not predictive of phasic dopamine release or dopamine supply. These data identify phasic NACs dopamine release as a possible therapeutic target for alleviating the excessive risk-taking observed across multiple forms of psychopathology.
The Spin and Orbital Contributions to Magnetic Dipole Transitions

In previous works, we examined the systematics of magnetic dipole transitions in a single j shell. We here extend the study to large space calculations. We consider the nuclei 44Ti, 46Ti and 48Cr. ...
Validating Paired-End Read Alignments in Sequence Graphs

Graph based non-linear reference structures such as variation graphs and colored de Bruijn graphs enable incorporation of full genomic diversity within a population. However, transitioning from a simple string-based reference to graphs requires addressing many computational challenges, one of which concerns accurately mapping sequencing read sets to graphs. Paired-end Illumina sequencing is a commonly used sequencing platform in genomics, where the paired-end distance constraints allow disambiguation of repeats. Many recent works have explored provably good index-based and alignment-based strategies for mapping individual reads to graphs. However, validating distance constraints efficiently over graphs is not trivial, and existing sequence to graph mappers rely on heuristics. We introduce a mathematical formulation of the problem, and provide a new algorithm to solve it exactly. We take advantage of the high sparsity of reference graphs, and use sparse matrix-matrix multiplications (SpGEMM) to build an index which can be queried efficiently by a mapping algorithm for validating the distance constraints. Effectiveness of the algorithm is demonstrated using real reference graphs, including a human MHC variation graph, and a pan-genome de-Bruijn graph built using genomes of 20 B. anthracis strains. While the one-time indexing time can vary from a few minutes to a few hours using our algorithm, answering a million distance queries takes less than a second.
Market Uncertainty and the Importance of Media Coverage at Earnings Announcements

Abstract We investigate whether increased investor demand for financial information arising from higher market uncertainty leads to greater media coverage of earnings announcements. We also investigate whether greater coverage during times of higher uncertainty further destabilizes financial markets because of greater attention-based trading or, alternatively, improves trading and pricing by lowering investor acquisition and interpretation costs. When uncertainty is higher, we find evidence of greater media coverage of earnings announcements and that the greater coverage leads to improvements in investor informedness, information asymmetry, and intraperiod price timeliness, and greater trade by both retail and institutional investors. In contrast to the media serving an expanded role in improving capital markets during more uncertain times, we fail to find that changes in firm-initiated disclosures lead to similar improvements and find that less frequent analyst forecast revisions exacerbate problems in capital markets during earnings announcements.
Circulating Lysophosphatidylcholines, Phosphatidylcholines, Ceramides, and Sphingomyelins and Ovarian Cancer Risk: A 23-Year Prospective Study.

Background Experimental evidence supports a role of lipid dysregulation in ovarian cancer progression. We estimated associations with ovarian cancer risk for circulating levels of four lipid groups, previously hypothesized to be associated with ovarian cancer, measured 3-23 years before diagnosis. Methods Analyses were conducted among cases (N = 252) and matched controls (N = 252) from the Nurses' Health Studies. We used logistic regression adjusting for risk factors to investigate associations of lysophosphatidylcholines (LPCs), phosphatidylcholines (PCs), ceramides (CERs), and sphingomyelins (SMs) with ovarian cancer risk overall and by histotype. A modified Bonferroni approach (0.05/4 = 0.0125, four lipid groups) and the permutation-based Westfall and Young approach were used to account for testing multiple correlated hypotheses. Odds ratios (ORs; 10th-90th percentile), and 95% confidence intervals of ovarian cancer risk were estimated. All statistical tests were two-sided. Results SM sum was statistically significantly associated with ovarian cancer risk (OR = 1.97, 95% CI = 1.16 to 3.32; P = .01/permutation-adjusted P = .20). C16:0 SM, C18:0 SM, and C16:0 CERs were suggestively associated with risk (OR = 1.95-2.10; P = .004-.01; permutation-adjusted P = .08-.21). SM sum, C16:0 SM, and C16:0 CER had stronger odds ratios among postmenopausal women (OR = 2.16-3.22). Odds ratios were similar for serous/poorly differentiated and endometrioid/clear cell tumors, although C18:1 LPC and LPC to PC ratio were suggestively inversely associated, whereas C18:0 SM was suggestively positively associated with risk of endometrioid/clear cell tumors. No individual metabolites were associated with risk when using the permutation-based approach. Conclusions Elevated levels of circulating SMs 3-23 years before diagnosis were associated with increased risk of ovarian cancer, regardless of histotype, with stronger associations among postmenopausal women. Further studies are required to validate and understand the role of lipid dysregulation in ovarian carcinogenesis.
Producing Hfq/Sm Proteins and sRNAs for Structural and Biophysical Studies of Ribonucleoprotein Assembly

Hfq is a bacterial RNA-binding protein that plays key roles in the post-transcriptional regulation of gene expression. Like other Sm proteins, Hfq assembles into toroidal discs that bind RNAs with varying affinities and degrees of sequence specificity. By simultaneously binding to a regulatory small RNA (sRNA) and an mRNA target, Hfq hexamers facilitate productive RNA∙∙∙RNA interactions; the generic nature of this chaperone-like functionality makes Hfq a hub in many sRNA-based regulatory networks. That Hfq is crucial in diverse cellular pathways-including stress response, quorum sensing, and biofilm formation-has motivated genetic and "RNAomic" studies of its function and physiology (in vivo), as well as biochemical and structural analyses of Hfq∙∙∙RNA interactions (in vitro). Indeed, crystallographic and biophysical studies first established Hfq as a member of the phylogenetically conserved Sm superfamily. Crystallography and other biophysical methodologies enable the RNA-binding properties of Hfq to be elucidated in atomic detail, but such approaches have stringent sample requirements, viz.: reconstituting and characterizing an Hfq·RNA complex requires ample quantities of well-behaved (sufficient purity, homogeneity) specimens of Hfq and RNA (sRNA, mRNA fragments, short oligoribonucleotides, or even single nucleotides). The production of such materials is covered in this chapter, with a particular focus on recombinant Hfq proteins for crystallization experiments.
Presolar grains in primitive ungrouped carbonaceous chondrite Northwest Africa 5958

We report a correlated NanoSIMS-transmission electron microscopy study of the ungrouped carbonaceous chondrite Northwest Africa (NWA) 5958. We identified 10 presolar SiC grains, 2 likely presolar graphite grains, and 20 presolar silicate and/or oxide grains in NWA 5958. We suggest a slight modification of the commonly used classification system for presolar oxides and silicates that better reflects the grains' likely stellar origins. The matrix-normalized presolar SiC abundance in NWA 5958 is $18_{-10}^{+15}$ ppm (2$\sigma$), similar to that seen in many classes of unmetamorphosed chondrites. In contrast, the matrix-normalized abundance of presolar O-rich phases (silicates and oxides) is $30.9_{-13.1}^{+17.8}$ ppm (2$\sigma$), much lower than seen in interplanetary dust particles and the least altered CR, CO and ungrouped C chondrites, but close to that reported for CM chondrites. NanoSIMS mapping also revealed an unusual $^{13}$C-enriched ($\delta ^{13}$C$\sim$ 100--200 permil) carbonaceous rim surrounding a 1.4 $\mu$m diameter phyllosilicate grain. TEM analysis of two presolar grains with a likely origin in asymptotic giant branch stars identified one as enstatite and one as Al-Mg spinel with minor Cr. The enstatite grain amorphized rapidly under the electron beam, suggesting partial hydration. TEM data of NWA 5958 matrix confirm that it has experienced aqueous alteration and support the suggestion of Jacquet et al. (2016) that this meteorite has affinities to CM2 chondrites.
Bank dependence in emerging countries: Cross-border information percolation in mutual fund equity investing

In contrast to the literature involving U.S. bank domestic lending, we find that mutual funds affiliated with lending banks reduce their equity investment and turnover in the non-U.S.-listed stock of their non-U.S. borrowers compared to non-lending banks or unaffiliated mutual funds. Reduced equity holdings increase loan spreads, preserving the lending bank’s cross-border information monopoly. Equity market holdings and turnover are reduced when banks lend to firms in emerging nations and when the geographic distance between the lender and the mutual fund manager is greatest. Thereby, long-range information percolation may benefit global institutions at the expense of individual subsidiaries.
In situ Electrochemical Studies of the Terrestrial Deep Subsurface Biosphere at the Sanford Underground Research Facility, South Dakota, USA

The terrestrial deep subsurface is host to significant and diverse microbial populations. However, these microbial populations remain poorly characterized, partially due to the inherent difficulty of sampling, in situ studies, and isolating of the in situ microbes. Motivated by the ability of microbes to gain energy from redox reactions at mineral interfaces, we here present in situ electrochemical colonization (ISEC) reactor as a method to directly study microbial electron transfer activity and to enable the capture and isolation of electrochemically active microbes. We installed a potentiostatically controlled ISEC reactor containing four working electrodes 1500 m below the surface at the Sanford Underground Research Facility. The working electrodes were poised at different redox potentials to mimic energy-yielding mineral reducing and oxidizing reactions predicted to occur at this site. We present a 16S rRNA analysis of the in situ electrode-associated microbial communities, revealing the dominance of novel bacterial lineages under cathodic conditions. We also demonstrate that the in situ electrodes can be further used for downstream electrochemical laboratory enrichment and isolation of novel strains. Using this workflow, we isolated Bacillus, Anaerospora, Comamonas, Cupriavidus, and Azonexus strains from the electrode-attached biomass. Finally, the extracellular electron transfer activity of Comamonas strain (isolated at -0.19 V vs. SHE and designated WE1-1D1) and Bacillus strain (isolated at +0.53 V vs. SHE and designated WE4-1A1-BC) from and to a poised electrode, respectively, were confirmed in electrochemical reactors. Our study highlights the utility of in situ electrodes and electrochemical enrichment workflows to shed light on microbial activity in the deep terrestrial subsurface.
Knowledge of uncertain worlds: programming with logical constraints

Programming with logic for sophisticated applications must deal with recursion and negation, which together have created significant challenges in logic, leading to many different, conflicting semantics of rules. This paper describes a unified language, DA logic, for design and analysis logic, based on the unifying founded semantics and constraint semantics, that support the power and ease of programming with different intended semantics. The key idea is to provide meta-constraints, support the use of uncertain information in the form of either undefined values or possible combinations of values, and promote the use of knowledge units that can be instantiated by any new predicates, including predicates with additional arguments.
Polarity reversals under sluicing

This paper presents novel English sluicing data that challenge even the most successful existing theories of the relationship between antecedent and elided content in sluicing constructions. The data supply robust evidence for a previously unobserved phenomenon in which the elided content and antecedent content in a sluicing construction contain opposite polarity. The data challenge current accounts of identity conditions on ellipsis by demonstrating that a greater mismatch between antecedent and elided content is possible than previously thought; specifically, the paper shows that the identity condition for sluicing must be sensitive to pragmatic — i.e. non-truth-conditional — content as well as to semantic content. This observation motivates a proposal in which sluicing is treated as a pragmatics-sensitive phenomenon licensed by local contextual entailment. EARLY ACCESS
Phase separation of YAP reorganizes genome topology for long-term YAP target gene expression

Yes-associated protein (YAP) is a transcriptional co-activator that regulates cell proliferation and survival by binding to a select set of enhancers for target gene activation. How YAP coordinates these transcriptional responses is unknown. Here, we demonstrate that YAP forms liquid-like condensates in the nucleus. Formed within seconds of hyperosmotic stress, YAP condensates compartmentalized the YAP transcription factor TEAD1 and other YAP-related co-activators, including TAZ, and subsequently induced the transcription of YAP-specific proliferation genes. Super-resolution imaging using assay for transposase-accessible chromatin with photoactivated localization microscopy revealed that the YAP nuclear condensates were areas enriched in accessible chromatin domains organized as super-enhancers. Initially devoid of RNA polymerase II, the accessible chromatin domains later acquired RNA polymerase II, transcribing RNA. The removal of the intrinsically-disordered YAP transcription activation domain prevented the formation of YAP condensates and diminished downstream YAP signalling. Thus, dynamic changes in genome organization and gene activation during YAP reprogramming is mediated by liquid-liquid phase separation.
Topographic specificity of alpha power during auditory spatial attention.

Abstract Visual and somatosensory spatial attention both induce parietal alpha (8–14 ​Hz) oscillations whose topographical distribution depends on the direction of spatial attentional focus. In the auditory domain, contrasts of parietal alpha power for leftward and rightward attention reveal qualitatively similar lateralization; however, it is not clear whether alpha lateralization changes monotonically with the direction of auditory attention as it does for visual spatial attention. In addition, most previous studies of alpha oscillation did not consider individual differences in alpha frequency, but simply analyzed power in a fixed spectral band. Here, we recorded electroencephalography in human subjects when they directed attention to one of five azimuthal locations. After a cue indicating the direction of an upcoming target sequence of spoken syllables (yet before the target began), alpha power changed in a task-specific manner. Individual peak alpha frequencies differed consistently between central electrodes and parieto-occipital electrodes, suggesting multiple neural generators of task-related alpha. Parieto-occipital alpha increased over the hemisphere ipsilateral to attentional focus compared to the contralateral hemisphere, and changed systematically as the direction of attention shifted from far left to far right. These results showing that parietal alpha lateralization changes smoothly with the direction of auditory attention as in visual spatial attention provide further support to the growing evidence that the frontoparietal attention network is supramodal.
International Students' Perceived Language Competence, Domestic Student Support, and Psychological Well-Being at a U.S. University

To increase knowledge of international students’ psychological well-being at U.S. universities, we examined the degree to which demographic factors, perceived language competence, and domestic student social support were associated with Ryff’s (1989a, 1989b) six aspects of psychological well-being. Participants (n = 216) were undergraduate and graduate students from one mid-sized private university in the Southeast. Analyses revealed differential psychological well-being scores based on demographics. Perceived language competence and domestic student social support were associated positively with multiple aspects of psychological well-being. The novel findings of this case study provide initial evidence of a potentially useful new approach toward international student adjustment and well-being. The authors provide initial recommendations for researchers, practitioners, and international students.
Conditional Parallel Coordinates

Parallel Coordinates [11],[12] are a popular data visualization technique for multivariate data. Dating back to as early as 1880 [8] PC are nearly as old as John Snow’s famous cholera outbreak map [18] of 1855, which is frequently regarded as a historic landmark for modern data visualization. Numerous extensions have been proposed to address integrity, scalability and readability. We make a new case to employ PC on conditional data, where additional dimensions are only unfolded if certain criteria are met in an observation. Compared to standard PC which operate on a flat set of dimensions the ontology of our input to Conditional Parallel Coordinates is of hierarchical nature. We therefore briefly review related work around hierarchical PC using aggregation or nesting techniques. Our contribution is a visualization to seamlessly adapt PC for conditional data under preservation of intuitive interaction patterns to select or highlight polylines. We conclude with intuitions on how to operate CPC on two data sets: an AutoML hyperparameter search log, and session results from a conversational agent.
EXTREMAL RAYS IN THE HERMITIAN EIGENVALUE PROBLEM FOR ARBITRARY TYPES

The Hermitian eigenvalue problem asks for the possible eigenvalues of a sum of Hermitian matrices given the eigenvalues of the summands. This is a problem about the Lie algebra of the maximal compact subgroup of G = SL(n). There is a polyhedral cone (the \eigencone") determining the possible answers to the problem. These eigencones can be defined for arbitrary semisimple groups G, and also control the (suitably stabilized) problem of existence of non-zero invariants in tensor products of irreducible representations of G. We give a description of the extremal rays of the eigencones for arbitrary semisimple groups G by first observing that extremal rays lie on regular facets, and then classifying extremal rays on an arbitrary regular face. Explicit formulas are given for some extremal rays, which have an explicit geometric meaning as cycle classes of interesting loci, on an arbitrary regular face. The remaining extremal rays on that face are understood by a geometric process we introduce, and explicate numerically, called induction from Levi subgroups. Several numerical examples are given. The main results, and methods, of this paper generalize [B3] which handled the case of G = SL(n).
Distribution of the C statistic with applications to the sample mean of Poisson data

The C statistic, also known as the Cash statistic, is often used in astronomy for the analysis of low-count Poisson data. The main advantage of this statistic, compared to the more commonly used χ2...
Order-Independent Structure Learning of Multivariate Regression Chain Graphs

This paper deals with multivariate regression chain graphs (MVR CGs), which were introduced by Cox and Wermuth in the nineties to represent linear causal models with correlated errors. We consider the PC-like algorithm for structure learning of MVR CGs, a constraint-based method proposed by Sonntag and Pena in 2012. We show that the PC-like algorithm is order-dependent, because the output can depend on the order in which the variables are given. This order-dependence is a minor issue in low-dimensional settings. However, it can be very pronounced in high-dimensional settings, where it can lead to highly variable results. We propose two modifications of the PC-like algorithm that remove part or all of this order-dependence. Simulations under a variety of settings demonstrate the competitive performance of our algorithms in comparison with the original PC-like algorithm in low-dimensional settings and improved performance in high-dimensional settings.
Multistage ice-damming of volcanic flows and fluvial systems in Northeast Syrtis Major

Abstract The northeast edge of the Syrtis Major volcanic complex records a diverse history of volcanism and climate of early Mars which has long been argued to be dominated by icy or wet conditions. This region contains a stratigraphic record that spans the period of phyllosilicate secondary mineralogy from moderate pH alteration of the Early Noachian crust, to the more acidic, sulfate-forming, Hesperian period dominated by the adjacent volcanic plains of Syrtis Major Planum. A paleo-fluvial basin and channel system etched in the Syrtis Major volcanics is identified and analyzed using multiple instruments onboard the Mars Reconnaissance Orbiter. These observations and analysis link the current landscape and the basin's existence at the base of the Syrtis Major lava flow to a paleo-glaciation within the Isidis Basin. This paleo-glaciation formed ice sheets kilometers thick and ranged over hundreds of kilometers laterally and is responsible for halting the Syrtis Major flows in the Northeast Syrtis area to form the observed steep cliffs and the topographically flat mesa. Following this large scale glaciation, channels fed primarily by precipitation were etched into the Hesperian volcanics before filling the basin and flowing out an area of higher topography than is currently available due to the existence of an episodic ice dam before terminating in a potential, highly eroded, fan. The dynamic relationship between the climate and landscape evolution has left an imprint on the local geologic record of this complex region of Mars, with testable observations that could be made with the Mars 2020 Rover in the form of a highly jointed surface formed from lava ice interactions on the cliff face of the volcanic mesa.
Brain energetics, mitochondria, and traumatic brain injury

We review current thinking about, and draw connections between, brain energetics and metabolism, and between mitochondria and traumatic brain injury. Energy is fundamental to proper brain function. Its creation in a useful form for neurons and glia, and consistently in response to the brain's high energy needs, is critical for physiological pathways. Dysfunction in the mechanisms of energy production is at the center of neurological and neuropsychiatric pathologies. We examine the connections between energetics and mitochondria - the organelle responsible for almost all the energy production in the cell - and how secondary pathologies in traumatic brain injury result from energetic dysfunction. This paper interweaves these topics, a necessity since they are closely coupled, and identifies where there exist a lack of understanding and of data. In addition to summarizing current thinking in these disciplines, our goal is to suggest a framework for the mathematical modeling of mechanisms and pathways based on optimal energetic decisions.
Development of a Steel Bridge Climbing Robot

Motivated by a high demand for automated inspection of civil infrastructure, this work presents an efficient design and development of a tank-like robot for structural health monitoring. Unlike most existing magnetic wheeled mobile robot designs, which may be suitable for climbing on flat steel surface, our proposed tank-like robot design uses reciprocating mechanism and roller-chains to make it capable of climbing on different structural shapes (e.g., cylinder, cube) with coated or non-coated steel surfaces. The developed robot is able to pass through the joints and transition from one surface to the other (e.g., from flat to curving surfaces). Taking into account several strict considerations (including tight dimension, efficient adhesion and climbing flexibility) to adapt with various shapes of steel structures, a prototype tank-like robot integrating multiple sensors (hall-effects, sonars, inertial measurement unit, Eddy current and cameras), has been developed. Rigorous analysis of robot kinematics, adhesion force, sliding failure and turn-over failure has been conducted to demonstrate the stability of the proposed design. Mechanical and magnetic force analysis together with sliding/turn-over failure investigation can serve as an useful framework for designing various steel climbing robots in the future. The robot is integrated with cameras and Eddy current sensor for visual and in-depth fatigue crack inspection of steel structures. Experimental results and field deployments confirm the adhesion, climbing, inspection capability of the developed robot.
Genetic Control of Radical Cross-linking in a Semisynthetic Hydrogel

Enhancing materials with the qualities of living systems, including sensing, computation, and adaptation, is an important challenge in designing next-generation technologies. Living materials addre...
Learning-Based Resource Optimization in Ultra Reliable Low Latency HetNets

In this paper, the problems of user offloading and resource optimization are jointly addressed to support ultrareliable and low latency communications (URLLC) in HetNets. In particular, a multi-tier network with a single macro base station (MBS) and multiple overlaid small cell base stations (SBSs) is considered that includes users with different latency and reliability constraints. Modeling the latency and reliability constraints of users with probabilistic guarantees, the joint problem of user offloading and resource allocation (JUR) in a URLLC setting is formulated as an optimization problem to minimize the cost of serving users for the MBS. In the considered scheme, SBSs bid to serve URLLC users under their coverage at a given price, and the MBS decides whether to serve each user locally or to offload it to one of the overlaid SBSs. Since the JUR optimization is NP-hard, we propose a low complexity learningbased heuristic method (LHM) which includes a support vector machine-based user association model and a convex resource optimization (CRO) algorithm. To further reduce the delay, we propose an alternating direction method of multipliers (ADMM)- based solution to the CRO problem. Simulation results show that using LHM, the MBS significantly decreases the spectrum access delay for users (by ↑ 93%) as compared to JUR, while also reducing its bandwidth and power costs in serving users (by ↑ 33%) as compared to directly serving users without offloading.
Convective dynamics and the response of precipitation extremes to warming in radiative-convective equilibrium

AbstractTropical precipitation extremes are expected to strengthen with warming, but quantitative estimates remain uncertain because of a poor understanding of changes in convective dynamics. This ...
Cayley modification for strongly stable path-integral and ring-polymer molecular dynamics

Path-integral-based molecular dynamics (MD) simulations are widely used for the calculation of numerically exact quantum Boltzmann properties and approximate dynamical quantities. A nearly universal feature of MD numerical integration schemes for equations of motion based on imaginary-time path integrals is the use of harmonic normal modes for the exact evolution of the free ring-polymer positions and momenta. In this work, we demonstrate that this standard practice creates numerical artifacts. In the context of conservative (i.e., microcanonical) equations of motion, it leads to numerical instability. In the context of thermostated (i.e., canonical) equations of motion, it leads to nonergodicity of the sampling. These pathologies are generally proven to arise at integration time steps that depend only on the system temperature and the number of ring-polymer beads, and they are numerically demonstrated for the cases of conventional ring-polymer MD (RPMD) and thermostated RPMD (TRPMD). Furthermore, it is demonstrated that these numerical artifacts are removed via replacement of the exact free ring-polymer evolution with a second-order approximation based on the Cayley transform. The Cayley modification introduced here can immediately be employed with almost every existing integration scheme for path-integral-based MD—including path-integral MD (PIMD), RPMD, TRPMD, and centroid MD—providing strong symplectic stability and ergodicity to the numerical integration, at no penalty in terms of computational cost, algorithmic complexity, or accuracy of the overall MD time step. Furthermore, it is shown that the improved numerical stability of the Cayley modification allows for the use of larger MD time steps. We suspect that the Cayley modification will therefore find useful application in many future path-integral-based MD simulations.
The Asymptotic Capacity of Private Search

The private search problem is introduced, where a dataset comprised of $L$ i.i.d. records is replicated across $N$ non-colluding servers, and a user wishes to search for all records that match a privately chosen value, without revealing any information about the chosen value to any individual server. Each record contains $P$ symbols, and each symbol takes values uniformly and independently from an alphabet of size $K$ . Considering the large number of records in modern datasets, it is assumed that $L$ is much larger than the alphabet size $K$ . The capacity of private search is the maximum number of bits of desired information that can be retrieved per bit of download. The asymptotic (large $K$ ) capacity of private search is shown to be $1-1/N$ , even when the scope of private search is further generalized to allow OR search, AND search, NOT search and sequence search. The results are based on the asymptotic behavior of a new converse bound for private information retrieval with arbitrarily dependent messages. The asymptotic behavior is also applicable to $T$ -colluding servers or $(N, T)$ -MDS coded servers.
Attack-resilient Estimation for Linear Discrete-time Stochastic Systems with Input and State Constraints

In this paper, an attack-resilient estimation algorithm is developed for linear discrete-time stochastic systems with inequality constraints on the actuator attacks and states. The proposed algorithm consists of optimal estimation and information aggregation. The optimal estimation provides minimum-variance unbiased (MVU) estimates, and then they are projected onto the constrained space in the information aggregation step. It is shown that the estimation errors and their covariances from the proposed algorithm are less than those from the unconstrained algorithm. Moreover, we proved that the state estimation errors of the proposed estimation algorithm are practically exponentially stable. A simulation on mobile robots demonstrates the effectiveness of the proposed algorithm compared to an existing algorithm.
COVID-19 Clinical Trials: A Primer for the Cardiovascular and Cardio-Oncology Communities.

The coronavirus disease-2019 (COVID-19) pandemic has resulted in a proliferation of clinical trials designed to slow the spread of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Many therapeutic agents that are being used to treat patients with COVID-19 are repurposed treatments for influenza, Ebola, or for malaria that were developed decades ago and are unlikely to be familiar to the cardiovascular and cardio-oncology communities. Here, the authors provide a foundation for cardiovascular and cardio-oncology physicians on the front line providing care to patients with COVID-19, so that they may better understand the emerging cardiovascular epidemiology and the biological rationale for the clinical trials that are ongoing for the treatment of patients with COVID-19.
RF current condensation in the presence of turbulent enhanced transport

Sharp temperature gradients in a magnetically confined plasma can lead to turbulent motion of the plasma. This turbulence in turn enhances the transport of heat across magnetic field lines. The enhanced transport impacts the temperature differential that can be sustained in magnetic islands between the island center and its periphery. It is shown here that, by limiting this temperature differential, this enhanced transport can have a profound influence on the extent to which the RF current condensation effect stabilizes the island growth. Interestingly, because the heat transport is no longer simply linear in the temperature gradient, the RF current condensation effect also exhibits entirely new hysteresis phenomena.
Via wearout detection with on-chip monitors

This project aims to detect the onset of chip failure due to via voiding through monitoring the delays of paths in a chip. The proposed method relates the probability of failure of individual vias to an increase in delay for monitors of the system using data for 65 nm technology. The delay increase, as a function of the failure distribution parameters, the path length, gate type, and process variation, has been investigated. An on-chip, ring oscillator-based wearout monitoring circuit is presented. The proposed scheme monitors the delay through a data path using a delay detection circuit (DDC).
A Survey of Protocols and Standards for Internet of Things

The rapid growth in technology and internet connected devices has enabled Internet of Things (IoT) to be one of the important fields in computing. Standards, technologies and platforms targeting IoT ecosystem are being developed at a very fast pace. IoT enables things to communicate and coordinate decisions for many different types of applications including healthcare, home automation, disaster recovery, and industry automation. It is expected to expand to even more applications in the future. This paper surveys several standards by IEEE, IETF and ITU that enable technologies enabling the rapid growth of IoT. These standards include communications, routing, network and session layer protocols that are being developed to meet IoT requirements. The discussion also includes management and security protocols in addition to the current challenges in IoT which gives insights into the current research to solve such challenges.
Δ9-Tetrahydrocannabinol (THC) impairs visual working memory performance: a randomized crossover trial.

With the increasing prevalence of legal cannabis use and availability, there is an urgent need to identify cognitive impairments related to its use. It is widely believed that cannabis, or its main psychoactive component Δ9-tetrahydrocannabinol (THC), impairs working memory, i.e., the ability to temporarily hold information in mind. However, our review of the literature yielded surprisingly little empirical support for an effect of THC or cannabis on working memory. We thus conducted a study with three main goals: (1) quantify the effect of THC on visual working memory in a well-powered sample, (2) test the potential role of cognitive effects (mind wandering and metacognition) in disrupting working memory, and (3) demonstrate how insufficient sample size and task duration reduce the likelihood of detecting a drug effect. We conducted two double-blind, randomized crossover experiments in which healthy adults (N = 23, 23) performed a reliable and validated visual working memory task (the “Discrete Whole Report task”, 90 trials) after administration of THC (7.5 and/or 15 mg oral) or placebo. We also assessed self-reported “mind wandering” (Exp 1) and metacognitive accuracy about ongoing task performance (Exp 2). THC impaired working memory performance (d = 0.65), increased mind wandering (Exp 1), and decreased metacognitive accuracy about task performance (Exp 2). Thus, our findings indicate that THC does impair visual working memory, and that this impairment may be related to both increased mind wandering and decreased monitoring of task performance. Finally, we used a down-sampling procedure to illustrate the effects of task length and sample size on power to detect the acute effect of THC on working memory.
Equilibrium Tuition, Applications, Admissions and Enrollment in the College Market

I develop and estimate a structural equilibrium model of the college market. Students, having heterogeneous abilities and preferences, make college application decisions, subject to uncertainty and application costs. Colleges, observing only noisy measures of student ability, choose tuition and admissions policies to compete for more able students. Tuition, applications, admissions and enrollment are joint outcomes from a subgame perfect Nash equilibrium. I estimate the structural parameters of the model using data from the National Longitudinal Survey of Youth 1997, via a three-step procedure to deal with potential multiple equilibria. In counterfactual experiments, I use the model …rst to examine the extent to which college enrollment can be increased by expanding the supply of colleges, and then to assess the importance of various measures of student ability.
Planning Multi-fingered Grasps as Probabilistic Inference in a Learned Deep Network

We propose a novel approach to multi-fingered grasp planning leveraging learned deep neural network models. We train a convolutional neural network to predict grasp success as a function of both visual information of an object and grasp configuration. We can then formulate grasp planning as inferring the grasp configuration which maximizes the probability of grasp success. We efficiently perform this inference using a gradient-ascent optimization inside the neural network using the backpropagation algorithm. Our work is the first to directly plan high quality multi-fingered grasps in configuration space using a deep neural network without the need of an external planner. We validate our inference method performing both multi-finger and two-finger grasps on real robots. Our experimental results show that our planning method outperforms existing planning methods for neural networks; while offering several other benefits including being data-efficient in learning and fast enough to be deployed in real robotic applications.
The Coronavirus Pandemic: Public Health and American Values.

The United States is known for valuing personal independence and individualism. With such societal values, would the American people respond to the coronavirus pandemic by complying with quarantines, social distancing, and other measures in which group interests are placed ahead of individual ones? With a relatively small number of exceptions, the American people, including health care providers, have demonstrated an extraordinary degree of community-oriented behavior. Unfortunately, a lack of public health preparedness and a series of blunders by elected officials have increased the peril for all Americans.
Separating subgroups of mapping class groups in homological representations

Let Γ be either the mapping class group of a closed surface of genus ≥ 2, or the automorphism group of a free group of rank ≥ 3. Given any homological representation ρ of Γ corresponding to a finit...
Complexity and ultrastructure of infectious extracellular vesicles from cells infected by non-enveloped virus

Enteroviruses support cell-to-cell viral transmission prior to their canonical lytic spread of virus. Poliovirus (PV), a prototype for human pathogenic positive-sense RNA enteroviruses, and picornaviruses in general, transport multiple virions en bloc via infectious extracellular vesicles, 100~1000 nm in diameter, secreted from host cells. Using biochemical and biophysical methods we identify multiple components in secreted microvesicles, including mature PV virions; positive-sense genomic and negative-sense replicative, template viral RNA; essential viral replication proteins; and cellular proteins. Using cryo-electron tomography, we visualize the near-native three-dimensional architecture of secreted infectious microvesicles containing both virions and a unique morphological component that we describe as a mat-like structure. While the composition of these mat-like structures is not yet known, based on our biochemical data they are expected to be comprised of unencapsidated RNA and proteins. In addition to infectious microvesicles, CD9-positive exosomes released from PV-infected cells are also infectious and transport virions. Thus, our data show that, prior to cell lysis, non-enveloped viruses are secreted within infectious vesicles that also transport viral unencapsidated RNAs, viral and host proteins. Understanding the structure and function of these infectious particles helps elucidate the mechanism by which extracellular vesicles contribute to the spread of non-enveloped virus infection.
Adapting Deep Visuomotor Representations with Weak Pairwise Constraints

Real-world robotics problems often occur in domains that differ significantly from the robot’s prior training environment. For many robotic control tasks, real world experience is expensive to obtain, but data is easy to collect in either an instrumented environment or in simulation. We propose a novel domain adaptation approach for robot perception that adapts visual representations learned on a large easy-to-obtain source dataset (e.g. synthetic images) to a target real-world domain, without requiring expensive manual data annotation of real world data before policy search. Supervised domain adaptation methods minimize cross-domain differences using pairs of aligned images that contain the same object or scene in both the source and target domains, thus learning a domain-invariant representation. However, they require manual alignment of such image pairs. Fully unsupervised adaptation methods rely on minimizing the discrepancy between the feature distributions across domains. We propose a novel, more powerful combination of both distribution and pairwise image alignment, and remove the requirement for expensive annotation by using weakly aligned pairs of images in the source and target domains. Focusing on adapting from simulation to real world data using a PR2 robot, we evaluate our approach on a manipulation task and show that by using weakly paired images, our method compensates for domain shift more effectively than previous techniques, enabling better robot performance in the real world.