cssi/SciSciGPT-SciSciNet huggingface dataset:
Combinatorial prediction of marker panels from single-cell transcriptomic data.

Single-cell transcriptomic studies are identifying novel cell populations with exciting functional roles in various in vivo contexts, but identification of succinct gene marker panels for such populations remains a challenge. In this work, we introduce COMET, a computational framework for the identification of candidate marker panels consisting of one or more genes for cell populations of interest identified with single-cell RNA-seq data. We show that COMET outperforms other methods for the identification of single-gene panels and enables, for the first time, prediction of multi-gene marker panels ranked by relevance. Staining by flow cytometry assay confirmed the accuracy of COMET's predictions in identifying marker panels for cellular subtypes, at both the single- and multi-gene levels, validating COMET's applicability and accuracy in predicting favorable marker panels from transcriptomic input. COMET is a general non-parametric statistical framework and can be used as-is on various high-throughput datasets in addition to single-cell RNA-sequencing data. COMET is available for use via a web interface (http://www.cometsc.com/) or a stand-alone software package (https://github.com/MSingerLab/COMETSC).
Vaccination strategies to control Ebola epidemics in the context of variable household inaccessibility levels.

Despite a very effective vaccine, active conflict and community distrust during the ongoing DRC Ebola epidemic are undermining control efforts, including a ring vaccination strategy that requires the prompt immunization of close contacts of infected individuals. However, in April 2019, it was reported 20% or more of close contacts cannot be reached or refuse vaccination, and it is predicted that the ring vaccination strategy would not be effective with such a high level of inaccessibility. The vaccination strategy is now incorporating a “third ring” community-level vaccination that targets members of communities even if they are not known contacts of Ebola cases. To assess the impact of vaccination strategies for controlling Ebola epidemics in the context of variable levels of community accessibility, we employed an individual-level stochastic transmission model that incorporates four sources of heterogeneity: a proportion of the population is inaccessible for contact tracing and vaccination due to lack of confidence in interventions or geographic inaccessibility, two levels of population mixing resembling household and community transmission, two types of vaccine doses with different time periods until immunity, and transmission rates that depend on spatial distance. Our results indicate that a ring vaccination strategy alone would not be effective for containing the epidemic in the context of significant delays to vaccinating contacts even for low levels of household inaccessibility and affirm the positive impact of a supplemental community vaccination strategy. Our key results are that as levels of inaccessibility increase, there is a qualitative change in the effectiveness of the vaccination strategy. For higher levels of vaccine access, the probability that the epidemic will end steadily increases over time, even if probabilities are lower than they would be otherwise with full community participation. For levels of vaccine access that are too low, however, the vaccination strategies are not expected to be successful in ending the epidemic even though they help lower incidence levels, which saves lives, and makes the epidemic easier to contain and reduces spread to other communities. This qualitative change occurs for both types of vaccination strategies: ring vaccination is effective for containing an outbreak until the levels of inaccessibility exceeds approximately 10% in the context of significant delays to vaccinating contacts, a combined ring and community vaccination strategy is effective until the levels of inaccessibility exceeds approximately 50%. More broadly, our results underscore the need to enhance community engagement to public health interventions in order to enhance the effectiveness of control interventions to ensure outbreak containment.
Is justice blind? Evidence from federal corruption convictions

Are federal prosecutors influenced by partisan political concerns? We examine that question by analyzing 40 years of federal corruption convictions at the state and federal district levels. Our key finding is that state-level federal corruption convictions fall by roughly 9% in years when a state’s governor belongs to the same party as the president who appointed local US Attorneys, a measure of state-federal political alignment. The result is robust to controls for the state political environment, election cycles, party tenure in the executive branch, public sector employment, federal aid to states, a state’s electoral importance, and the changes in Honest Services law, the statutory basis for many federal corruption cases. Our results are consistent with a significant level of partisan prosecutorial bias on the part of US Attorneys. In a placebo test, we find no evidence that state-federal political alignment affects the total number of federal criminal convictions. That finding provides support for the mechanism that we propose, namely the partisan exercise of prosecutorial discretion, rather than the partisan allocation of prosecutorial resources across federal districts.
International, Inaccessible, and Incomplete: A Texas Case Study of International Student Websites

As the number of international students attending U.S. higher education institutions has declined 2 consecutive years (Saul, 2018), several institutions are exploring ways to increase international student interest. In Texas, the sociopolitical climate has become increasingly anti-immigrant (Watkins, 2017), leading many international students to question whether study in the US is feasible and safe. As institutional websites have been found to be important sources of international student information (Huang & Bilal, 2017; Taylor & Bicak, 2018), this study examines international student information on all public, 4-year institution websites in Texas. Findings suggest international student materials are difficult to read, are rarely translated, and lack basic information such as cost and housing information. Implications for future research and practice are addressed.
SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective

Phishing and spear phishing are typical examples of masquerade attacks since trust is built up through impersonation for the attack to succeed. Given the prevalence of these attacks, considerable research has been conducted on these problems along multiple dimensions. We reexamine the existing research on phishing and spear phishing from the perspective of the unique needs of the security domain, which we call security challenges : real-time detection, active attacker, dataset quality and base-rate fallacy. We explain these challenges and then survey the existing phishing/spear phishing solutions in their light. This viewpoint consolidates the literature and illuminates several opportunities for improving existing solutions. We organize the existing literature based on detection techniques for different attack vectors (e.g., URLs, websites, emails) along with studies on user awareness. For detection techniques we examine properties of the dataset, feature extraction, detection algorithms used, and performance evaluation metrics. This work can help guide the development of more effective defenses for phishing, spear phishing and email masquerade attacks of the future, as well as provide a framework for a thorough evaluation and comparison.
"Don't Ask, Don't Tell": A Qualified Defense

Upon the 10th Anniversary of the so-called “Don’t Ask, Don’t Tell” policy, I was asked to reflect upon the policy by Hofstra University. This article presents my remarks at a symposium dedicagted to this topic. The article first reviews in detail the origins of the policy. It then explores the moral debate implicated by the policy, including the traditional arguments against homosexuality with special attention to the natural law, and latitudinarian arguments in favor of homosexuality or opposed to discrimination based on the basis of homosexuality. The article next surveys contemporary attitudes about homosexuality, focusing on military service by homosexuals. Finally, the article provides a qualified defense of the policy in light of competing moral claims and contemporary attitudes.
Nanoemulsion as an Effective Treatment against Human-Pathogenic Fungi

ABSTRACT Infections triggered by pathogenic fungi cause a serious threat to the public health care system. In particular, an increase of antifungal drug-resistant fungi has resulted in difficulty in treatment. A limited variety of antifungal drugs available to treat patients has left us in a situation where we need to develop new therapeutic approaches that are less prone to development of resistance by pathogenic fungi. In this study, we demonstrate the efficacy of the nanoemulsion NB-201, which utilizes the surfactant benzalkonium chloride, against human-pathogenic fungi. We found that NB-201 exhibited in vitro activity against Candidaalbicans, including both planktonic growth and biofilms. Furthermore, treatments with NB-201 significantly reduced the fungal burden at the infection site and presented an enhanced healing process after subcutaneous infections by multidrug-resistant C. albicans in a murine host system. NB-201 also exhibited in vitro growth inhibition activity against other fungal pathogens, including Cryptococcus spp., Aspergillus fumigatus, and Mucorales. Due to the nature of the activity of this nanoemulsion, there is a minimized chance of drug resistance developing, presenting a novel treatment to control fungal wound or skin infections. IMPORTANCE Advances in medicine have resulted in the discovery and implementation of treatments for human disease. While these recent advances have been beneficial, procedures such as solid-organ transplants and cancer treatments have left many patients in an immunocompromised state. Furthermore, the emergence of immunocompromising diseases such as HIV/AIDS or other immunosuppressive medical conditions have opened an opportunity for fungal infections to afflict patients globally. The development of drug resistance in human-pathogenic fungi and the limited array of antifungal drugs has left us in a scenario where we need to develop new therapeutic approaches to treat fungal infections that are less prone to the development of resistance by pathogenic fungi. The significance of our work lies in utilizing a novel nanoemulsion formulation to treat topical fungal infections while minimizing risks of drug resistance development.
A Test of the Calculability of a Three-Body Relativistic, Cluster Decomposable, Unitary, Covariant Scattering Theory

In this work a calculation of the cluster decomposable formalism for relativistic scattering as developed by Lindesay, Markevich, Noyes, and Pastrana (LMNP) is made for an ultra-light quantum model. After highlighting areas of the theory vital for calculation, a description is made of the process to go from the general theory to an eigen-integral equation for bound state problems, and calculability is demonstrated. An ultra-light quantum exchange model is then developed to examine calculability.
A Spline Interpolation–based Data Reconstruction Technique for Estimation of Strain Time Constant in Ultrasound Poroelastography:

Ultrasound poroelastography is a cost-effective and noninvasive imaging technique, which can be used to reconstruct mechanical parameters of tissues such as Young’s modulus, Poisson’s ratio, inters...
Multivariate Difference-Differential Dimension Polynomials

We present a method of characteristic sets with respect to several term orderings that allows one to prove the existence and determine invariants of multivariate difference–differential dimension polynomials associated with arbitrary partitions of the sets of basic derivations and translations. Our results essentially extend existing results on difference–differential dimension polynomials; we also determine invariants of such polynomials and show how these invariants can be applied to the equivalence problem for systems of algebraic partial difference–differential equations.
Velocity Analysis By Inversion

Abstract : In conventional inversion methods, imaging structure inside the earth requires reasonable background velocities. In this paper, velocity analysis and structural imaging are done at the same time. The medium is assumed to consist of constant-velocity layers separated by arbitrary, smooth interfaces. The objective of the inversion is to determine layer velocities and locations of the interfaces. The velocity analysis is based on the principles that the images will be distorted when erroneous velocities are used. In particular, the difference between the depths computed by inversions from different experiments can be a measure of the error in velocity. The formulas of sensitivity to velocity error are derived for some special cases. Some computer implementations for both synthetic data and experimental data are done.
Extending p-divisible Groups and Barsotti–Tate Deformation Ring in the Relative Case

Let $k$ be a perfect field of characteristic $p > 2$, and let $K$ be a finite totally ramified extension of $W(k)[\frac{1}{p}]$ of ramification degree $e$. We consider an unramified base ring $R_0$ over $W(k)$ satisfying certain conditions, and let $R = R_0\otimes_{W(k)}\mathcal{O}_K$. Examples of such $R$ include $R = \mathcal{O}_K[\![s_1, \ldots, s_d]\!]$ and $R = \mathcal{O}_K\langle t_1^{\pm 1}, \ldots, t_d^{\pm 1}\rangle$. We show that the generalization of Raynaud's theorem on extending $p$-divisible groups holds over the base ring $R$ when $e < p-1$, whereas it does not hold when $R = \mathcal{O}_K[\![s]\!]$ with $e \geq p$. As an application, we prove that if $R$ has Krull dimension $2$ and $e < p-1$, then the locus of Barsotti-Tate representations of $\mathrm{Gal}(\overline{R}[\frac{1}{p}]/R[\frac{1}{p}])$ cuts out a closed subscheme of the universal deformation scheme. If $R = \mathcal{O}_K[\![s]\!]$ with $e \geq p$, we prove that such a locus is not $p$-adically closed.
Night watchers and terrorists

Abstract A massive movement of Night Watchers or vigilantes’ patrols emerged among the most impoverished indigenous communities in the Andes at the end of the twentieth century to combat terrorism. Northern peasant patrols based their organization on democratic mechanisms while the southern patrols built a hierarchical structure. How does the variation of external threats shape the variation of governance structures and collective responses within extralegal groups? To organize the provision of security and defense against terrorism, these Night Watchers required mechanisms to control opportunistic behavior and prevent internal predation. This article develops an organizational theory of defense. The time horizon explains why the Night Watchers produced arrangements in vertical or horizontal forms. Peasant vigilantes depended on hierarchical mechanisms to enforce their agreements if and only if they confront a short time horizon and existential threat. Comparative analysis of the northern and southern Peruvian communities provides empirical support for the theory.
TCP in q-Lorentz Theories

The connection between spin and statistics implied by the continuous Lorentz group together with strong reflection (TCP) is shown to hold also for the q-Lorentz group.
iSeqQC: a tool for expression-based quality control in RNA sequencing

Quality Control in any high-throughput sequencing technology is a critical step, which if overlooked can compromise an experiment and the resulting conclusions. A number of methods exist to identify biases during sequencing or alignment, yet not many tools exist to interpret biases due to outliers. Hence, we developed iSeqQC, an expression-based QC tool that detects outliers either produced due to variable laboratory conditions or due to dissimilarity within a phenotypic group. iSeqQC implements various statistical approaches including unsupervised clustering, agglomerative hierarchical clustering and correlation coefficients to provide insight into outliers. It can be utilized through command-line (Github: https://github.com/gkumar09/iSeqQC) or web-interface (http://cancerwebpa.jefferson.edu/iSeqQC). A local shiny installation can also be obtained from github (https://github.com/gkumar09/iSeqQC). iSeqQC is a fast, light-weight, expression-based QC tool that detects outliers by implementing various statistical approaches.
Arithmetic combinatorics on Vinogradov systems

In this paper, we present a variant of the Balog-Szemeredi-Gowers theorem for the Vinogradov system. We then use our result to deduce a higher degree analogue of the sum-product phenomenon.
Machine Learning Meets Stochastic Geometry: Determinantal Subset Selection for Wireless Networks

In wireless networks, many problems can be formulated as subset selection problems where the goal is to select a subset from the ground set with the objective of maximizing some objective function. These problems are typically NP-hard and hence solved through carefully constructed heuristics, which are themselves mostly NP-complete and thus not easily applicable to large networks. On the other hand, subset selection problems occur in slightly different context in machine learning (ML) where the goal is to select a subset of high quality yet diverse items from a ground set. This balance in quality and diversity is often maintained in the ML problems by using determinantal point process (DPP), which endows distributions on the subsets such that the probability of selecting two similar items is negatively correlated. While DPPs have been explored more generally in stochastic geometry (SG) to model inter-point repulsion, they are particularly conducive for ML applications because the parameters of their distributions can be efficiently learnt from a training set. In this paper, we introduce a novel DPP-based learning (DPPL) framework for efficiently solving subset selection problems in wireless networks. The DPPL is intended to replace the traditional optimization algorithms for subset selection by learning the quality-diversity trade-off in the optimal subsets selected by an optimization routine. As a case study, we apply DPPL to the wireless link scheduling problem, where the goal is to determine the subset of simultaneously active links which maximizes the network-wide sum-rate. We demonstrate that the proposed DPPL approaches the optimal solution with significantly lower computational complexity than the popular optimization algorithms used for this problem in the literature.
The intergenerational transmission of mental and physical health in the United Kingdom

We estimate intergenerational health persistence in the United Kingdom using Quality Adjusted Life Years (QALY), a broad measure of health derived from the SF-12 Survey. We estimate that both the rank-rank slope and the intergenerational health association (IHA) are 0.21. We use components of the SF-12 to create mental and physical health indices and find that mental health is at least as persistent across generations as physical health. Importantly, parents' mental health is much more strongly associated with children's health than parents' physical health indicating that mental health might be a more important transmission channel. Finally, we construct an overall measure of welfare that combines income and health, and estimate a rank-rank association of 0.31. This is considerably lower than a comparable estimate of 0.43 for the US, suggesting greater mobility of overall welfare in the UK than the US.
A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures.

Background: Exposure mixtures frequently occur in data across many domains, particularly in the fields of environmental and nutritional epidemiology. Various strategies have arisen to answer questi...
On a Relaxation of Time-Varying Actuator Placement

We consider the time-varying actuator placement in continuous time, where the goal is to maximize the trace of the controllability Grammian. A natural relaxation of the problem is to allow the binary {0, 1} variable indicating whether an actuator is used at a given time to take on values in the closed interval [0, 1]. We show that all optimal solutions of both the original and the relaxed problems can be given via an explicit formula, and that, as long as the input matrix has no zero columns, the solutions sets of the original and relaxed problem coincide.
Low-temperature oxidation

Low-temperature oxidation is a reaction, occurring at or below room temperature, between a solid and a gas. It usually involves the combination of oxygen with metals, and it has the greatest commercial impact in the presence of moisture, as in corrosion. Cabrera and Mott put forward a theory of low-temperature oxidation, based on the assumption that cation migration occurs under the influence of a potential built up across the growing oxide film. Recent experimental results require that this theory be expanded to explain recent observations such as anion migration during oxide growth and the transition from the initial chemisorbed monolayer to a bulk, threedimensional oxide. The additional ideas put forward in the present paper may be summarized as follows. Low-temperature oxidation is controlled by the nature of the oxide; whether it is a network former or a modifier. A period of fast, linear oxidation is followed by a slow logarithmic reaction whose rate, in turn, can increase if the oxide film crystallizes to form grain boundaries. The initial fast oxidation is a continuation of the chemisorption process. Place exchange (anions and cations interchanging positions) occurs when the energy due to the image force of an oxygen ion is greater than the bond energy holding the ion in place. A stable film forms when this bond energy is greater than the image force energy. The oxygen ions formed on the oxide surface then set up a potential across the film. This potential provides the driving force for continued reaction. Oxide growth during this later stage is a slow, logarithmic process. A barrier to ion transport exists at the gas-oxide interface in the case of anion migration and at the metal-oxide interface in the case of cation migration. In both cases, the field built up across the oxide lowers the barrier sufficiently so that ion migration can occur. Network modifiers allow cation migration. The reaction rate is sensitive to crystallographic orientation of the metal, but not to oxygen pressure. A constant voltage is maintained across the film, so that the Cabrera-Mott theory explains the logarithmic kinetics. Network-forming oxides allow onion migration. The number of anions, and hence, the rate of reaction, is sensitive to oxygen pressure, but not crystallographic orientation of the metal substrate. Since the potential is a result of the mobile anions, the film tends to grow under constant field. The logarithmic kinetics then must be explained by an increasing activation energy for ion transport, as proposed by Eley and Wilkinson. The logarithmic growth rate can be increased by the presence of water vapor if the water introduces “dangling” bonds into an oxide network structure. Crystallization of the oxide film also increases its rate of growth and results in the formation of oxide islands.
Bases, Bullets and Ballots: the Effect of U.S. Military Aid on Political Conflict in Colombia

Does foreign military assistance strengthen or further weaken fragile states facing internal conflict? Aid may strengthen the state by bolstering its repressive capacity vis-a-vis armed nonstate actors or weaken it if resources are diverted to these very groups. We examine how US military aid affects political violence in Colombia. We exploit the allocation of US military aid to Colombian military bases and compare how aid affects municipalities with and without bases. We use an instrument based on worldwide increases in US military aid (excluding Latin America). We find that US military assistance leads to differential increases in attacks by paramilitaries but has no effect on guerilla attacks. Aid also results in more paramilitary (but not guerrilla) homicides during election years, particularly in politically competitive municipalities. The findings suggest that foreign military assistance may strengthen armed nonstate actors, undermining domestic political institutions.
Alignment-Independent Chip-to-Chip Communication for Sensor Applications Using Passive Capacitive Signaling

We propose a capacitive coupling based method for sensor data retrieval that can be easily integrated with miniature sensor nodes of sub-mm scale. To enable passive operation of the sensor chip, the data retrieval chip sends power to/receives signal from the sensor chip simultaneously. An alignment detection and pad reconfiguration mechanism is implemented to allow convenient read-out without precise positioning. A test chip was fabricated in 0.13 μm CMOS technology. The silicon measurement results demonstrate < 15 % difference in achievable data rate can be obtained when the sensor chip is randomly dropped on the data retrieval chip regardless of alignment.
External Debt and Growth

This paper assesses the non linear impact of external debt on growth using panel data for 93 developing countries. The estimates support a non-linear, hump- shaped relationship between debt and growth, especially when the debt burden is measured relative to GDP. For a country with average indebtedness, doubling the debt ratio reduces growth by a third to a half percentage point after controlling for endogeneity. Our findings also suggest that the average impact of debt becomes negative at about 160-170 percent of exports or 35-40 percent of GDP and the marginal impact of debt at about half of these values.
Information arrival, delay, and clustering in financial markets with dynamic freeriding

Abstract We study informational freeriding in a model where agents privately acquire information and then decide when to reveal it by taking an action. Examples of such freeriding are prevalent in financial markets, e.g., the timing of initial public offerings, analysts’ forecasts, and mutual funds’ investment decisions. The main results show that, in large populations, few agents provide significant information while the vast majority of agents freeride. We highlight the role of uncertainty and market size in shaping the dynamics of price discovery. Among other results, we find that heightened uncertainty over the underlying state enhances information production, yet weakens the precision and speed of information aggregation in the market.
Distinct activities of Scrib module proteins organize epithelial polarity.

A polarized architecture is central to both epithelial structure and function. In many cells, polarity involves mutual antagonism between the Par complex and the Scribble (Scrib) module. While molecular mechanisms underlying Par-mediated apical determination are well-understood, how Scrib module proteins specify the basolateral domain remains unknown. Here, we demonstrate dependent and independent activities of Scrib, Discs-large (Dlg), and Lethal giant larvae (Lgl) using the Drosophila follicle epithelium. Our data support a linear hierarchy for localization, but rule out previously proposed protein–protein interactions as essential for polarization. Cortical recruitment of Scrib does not require palmitoylation or polar phospholipid binding but instead an independent cortically stabilizing activity of Dlg. Scrib and Dlg do not directly antagonize atypical protein kinase C (aPKC), but may instead restrict aPKC localization by enabling the aPKC-inhibiting activity of Lgl. Importantly, while Scrib, Dlg, and Lgl are each required, all three together are not sufficient to antagonize the Par complex. Our data demonstrate previously unappreciated diversity of function within the Scrib module and begin to define the elusive molecular functions of Scrib and Dlg.
Optimal progressive capital income taxes in the infinite horizon model

Abstract This paper analyzes optimal progressive capital income taxation in an infinite horizon model where individuals differ only through their initial wealth. We consider progressive capital income tax schedules taking a simple two-bracket form with an exemption bracket at the bottom and a single marginal tax rate above a time varying exemption threshold. Individuals are taxed until their wealth is reduced down to the exemption threshold. The fraction of individuals subject to capital income taxation vanishes to zero in the long-run in analogy to the zero long-run capital tax result of Chamley and Judd with optimal linear taxes. However, in contrast to linear taxation, optimal nonlinear capital taxation can have a drastic impact on the long-run wealth distribution. When the intertemporal elasticity of substitution is not too large and the top tail of the initial wealth distribution is infinite and thick enough, the optimal exemption threshold converges to a finite limit. As a result, the optimal tax system drives all the large fortunes down a finite level and produces a truncated long-run wealth distribution.
Covid-19 Crisis Triage - Optimizing Health Outcomes and Disability Rights.

Covid-19 Crisis Triage Crisis triage protocols should seek to optimize health outcomes, but doing so requires explicit protection of core values, such as the equal moral worth of all people, includ...
Diverse Image Generation via Self-Conditioned GANs

We introduce a simple but effective unsupervised method for generating diverse images. We train a class-conditional GAN model without using manually annotated class labels. Instead, our model is conditional on labels automatically derived from clustering in the discriminator’s feature space. Our clustering step automatically discovers diverse modes, and explicitly requires the generator to cover them. Experiments on standard mode collapse benchmarks show that our method outperforms several competing methods when addressing mode collapse. Our method also performs well on large-scale datasets such as ImageNet and Places365, improving both diversity and standard metrics (e.g., Frechet Inception Distance), compared to previous methods.
A Computational Approach to Measuring Vote Elasticity and Competitiveness

The recent wave of attention to partisan gerrymandering has come with a push to refine or replace the laws that govern political redistricting around the country. A common element in several states...
Low-Dimensional Hyperbolic Knowledge Graph Embeddings

Knowledge graph (KG) embeddings learn low- dimensional representations of entities and relations to predict missing facts. KGs often exhibit hierarchical and logical patterns which must be preserved in the embedding space. For hierarchical data, hyperbolic embedding methods have shown promise for high-fidelity and parsimonious representations. However, existing hyperbolic embedding methods do not account for the rich logical patterns in KGs. In this work, we introduce a class of hyperbolic KG embedding models that simultaneously capture hierarchical and logical patterns. Our approach combines hyperbolic reflections and rotations with attention to model complex relational patterns. Experimental results on standard KG benchmarks show that our method improves over previous Euclidean- and hyperbolic-based efforts by up to 6.1% in mean reciprocal rank (MRR) in low dimensions. Furthermore, we observe that different geometric transformations capture different types of relations while attention- based transformations generalize to multiple relations. In high dimensions, our approach yields new state-of-the-art MRRs of 49.6% on WN18RR and 57.7% on YAGO3-10.
Learning a Weakly-Supervised Video Actor-Action Segmentation Model With a Wise Selection

We address weakly-supervised video actor-action segmentation (VAAS), which extends general video object segmentation (VOS) to additionally consider action labels of the actors. The most successful methods on VOS synthesize a pool of pseudo-annotations (PAs) and then refine them iteratively. However, they face challenges as to how to select from a massive amount of PAs high-quality ones, how to set an appropriate stop condition for weakly-supervised training, and how to initialize PAs pertaining to VAAS. To overcome these challenges, we propose a general Weakly-Supervised framework with a Wise Selection of training samples and model evaluation criterion (WS^2). Instead of blindly trusting quality-inconsistent PAs, WS^2 employs a learning-based selection to select effective PAs and a novel region integrity criterion as a stopping condition for weakly-supervised training. In addition, a 3D-Conv GCAM is devised to adapt to the VAAS task. Extensive experiments show that WS^2 achieves state-of-the-art performance on both weakly-supervised VOS and VAAS tasks and is on par with the best fully-supervised method on VAAS.
Asymmetric expansion preserves hyperbolic convexity

In an earlier paper we showed that the radial expansion of a hyperbolic convex set in the Poincare disk about any point inside it results in a hyperbolic convex set. In this work, we generalize this result by showing that the asymmetric expansion of a hyperbolic convex set about any point inside it also results in a hyperbolic convex set.
From Motions to Emotions: Can the Fundamental Emotions be Expressed in a Robot Swarm?

This paper explores the expressive capabilities of a swarm of miniature mobile robots within the context of inter-robot interactions and their mapping to the so-called fundamental emotions. In particular, we investigate how motion asnd shape descriptors that are psychologically associated with different emotions can be incorporated into different swarm behaviors for the purpose of artistic expositions. Based on these characterizations from social psychology, a set of swarm behaviors is created, where each behavior corresponds to a fundamental emotion. The effectiveness of these behaviors is evaluated in a survey in which the participants are asked to associate different swarm behaviors with the fundamental emotions. The results of the survey show that most of the research participants assigned to each video the emotion intended to be portrayed by design. These results confirm that abstract descriptors associated with the different fundamental emotions in social psychology provide useful motion characterizations that can be effectively transformed into expressive behaviors for a swarm of simple ground mobile robots.
Two Amino Acids Proximate to the Renin Cleavage Site of Human Angiotensinogen Do Not Affect Blood Pressure and Atherosclerosis in Mice-Brief Report.

Objective: Renin cleavage of angiotensinogen has species specificity. As the residues at positions 11 and 12 are different between human angiotensinogen and mouse angiotensinogen, we determined whe...
Longitudinal changes in EEG power, sleep cycles and behaviour in a tau model of neurodegeneration.

Disturbed sleep is associated with cognitive decline in neurodegenerative diseases such as Alzheimer’s disease (AD) and frontotemporal dementia (FTD). The progressive sequence of how neurodegeneration affects aspects of sleep architecture in conjunction with behavioural changes is not well understood. We investigated changes in sleep architecture, spectral power and circadian rhythmicity in the tet-off rTg4510 mouse overexpressing human P301L tau within the same subjects over time. Doxycycline-induced transgene-suppressed rTg4510 mice, tTa carriers and wild-type mice were used as comparators. Spectral power and sleep stages were measured from within the home cage environment using EEG electrodes. In addition, locomotor activity and performance during a T-maze task were measured. Spectral power in the delta and theta bands showed a time-dependent decrease in rTg4510 mice compared to all other groups. After the initial changes in spectral power, wake during the dark period increased whereas NREM and number of REM sleep bouts decreased in rTg4510 compared to wild-type mice. Home cage locomotor activity in the dark phase significantly increased in rTg4510 compared to wild-type mice by 40 weeks of age. Peak-to-peak circadian rhythm amplitude and performance in the T-maze was impaired throughout the experiment independent of time. At 46 weeks, rTG4510 mice had significant degeneration in the hippocampus and cortex whereas doxycycline-treated rTG4510 mice were protected. Pathology significantly correlated with sleep and EEG outcomes, in addition to locomotor and cognitive measures. We show that reduced EEG spectral power precedes reductions in sleep and home cage locomotor activity in a mouse model of tauopathy. The data shows increasing mutant tau changes sleep architecture, EEG properties, behaviour and cognition, which suggest tau-related effects on sleep architecture in patients with neurodegenerative diseases.
Spectral evolution of multiply impulsive solar bursts

Hard X-ray and microwave observations of multiply impulsive solar bursts, identified in the OSO 5 data, have been analyzed in a search for the physical basis of the multiplicity of spikes within these events. Spectra in both frequency ranges have been used to determine whether the source properties change from peak to peak within individual bursts. Two categories of microwave spectral behavior have been identified: those events during which the turnover frequency and spectral shape change significantly. These categories correspond to two classes of multiply impulsive bursts: those for which the emission can be characterized by a constant magnetic field and therefore a single source region, in which case the multiplicity may be due to modulation of the emission process; and those in which groups of component spikes appear to originate in regions of different magnetic-field strengths, corresponding to separate source regions which flare sequentially. Examples of the latter type of events are presented, with a detailed analysis of the discrete flaring regions and estimates of their spatial separations.
Interpreting holographic molecular binding assays with effective medium theory.

Holographic molecular binding assays use holographic video microscopy to directly detect molecules binding to the surfaces of micrometer-scale colloidal beads by monitoring associated changes in the beads’ light-scattering properties. Holograms of individual spheres are analyzed by fitting to a generative model based on the Lorenz-Mie theory of light scattering. Each fit yields an estimate of a probe bead’s diameter and refractive index with sufficient precision to watch a population of beads grow as molecules bind. Rather than modeling the molecular-scale coating, however, these fits use effective medium theory, treating the coated sphere as if it were homogeneous. This effective-sphere analysis is rapid and numerically robust and so is useful for practical implementations of label-free immunoassays. Here, we assess how measured effective-sphere properties reflect the actual properties of molecular-scale coatings by modeling coated spheres with the discrete-dipole approximation and analyzing their holograms with the effective-sphere model.
Same Point Composable and Nonmalleable Obfuscated Point Functions

A point obfuscator is an obfuscated program that indicates if a user enters a previously stored password. A digital locker is stronger: outputting a key if a user enters a previously stored password. The real-or-random transform allows one to build a digital locker from a composable point obfuscator (Canetti and Dakdouk, Eurocrypt 2008).
Toward free resolutions over scrolls

Let $R = k[x]/I$ where $I$ is the defining ideal of a rational normal $k$-scroll. We compute the Betti numbers of the ground field $\mathbb{k}$ as a module over $R$. For $k = 2$, we give the minimal free resolution of $\mathbb{k}$ over $R$.
Robust Deep Learning Ensemble Against Deception

Deep neural network (DNN) models are known to be vulnerable to maliciously crafted adversarial examples and to out-of-distribution inputs drawn sufficiently far away from the training data. How to protect a machine learning model against deception of both types of destructive inputs remains an open challenge. This article presents XEnsemble, a diversity ensemble verification methodology for enhancing the adversarial robustness of DNN models against deception caused by either adversarial examples or out-of-distribution inputs. XEnsemble by design has three unique capabilities. First, XEnsemble builds diverse input denoising verifiers by leveraging different data cleaning techniques. Second, XEnsemble develops a disagreement-diversity ensemble learning methodology for guarding the output of the prediction model against deception. Third, XEnsemble provides a suite of algorithms to combine input verification and output verification to protect the DNN prediction models from both adversarial examples and out of distribution inputs. Evaluated using 11 popular adversarial attacks and two representative out-of-distribution datasets, we show that XEnsemble achieves a high defense success rate against adversarial examples and a high detection success rate against out-of-distribution data inputs, and outperforms existing representative defense methods with respect to robustness and defensibility.
Can I Trust You? A User Study of Robot Mediation of a Support Group

Socially assistive robots have the potential to improve group dynamics when interacting with groups of people in social settings. This work contributes to the understanding of those dynamics through a user study of trust dynamics in the novel context of a robot mediated support group. For this study, a novel framework for robot mediation of a support group was developed and validated. To evaluate interpersonal trust in the multi-party setting, a dyadic trust scale was implemented and found to be uni-factorial, validating it as an appropriate measure of general trust. The results of this study demonstrate a significant increase in average interpersonal trust after the group interaction session, and qualitative post-session interview data report that participants found the interaction helpful and successfully supported and learned from one other. The results of the study validate that a robot-mediated support group can improve trust among strangers and allow them to share and receive support for their academic stress.
Question-driven summarization of answers to consumer health questions.

Automatic summarization of natural language is a widely studied area in computer science, one that is broadly applicable to anyone who needs to understand large quantities of information. In the medical domain, automatic summarization has the potential to make health information more accessible to people without medical expertise. However, to evaluate the quality of summaries generated by summarization algorithms, researchers first require gold standard, human generated summaries. Unfortunately there is no available data for the purpose of assessing summaries that help consumers of health information answer their questions. To address this issue, we present the MEDIQA-Answer Summarization dataset, the first dataset designed for question-driven, consumer-focused summarization. It contains 156 health questions asked by consumers, answers to these questions, and manually generated summaries of these answers. The dataset’s unique structure allows it to be used for at least eight different types of summarization evaluations. We also benchmark the performance of baseline and state-of-the-art deep learning approaches on the dataset, demonstrating how it can be used to evaluate automatically generated summaries. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12676655
Implementation and Characterization of a Two-Dimensional Printed Circuit Dynamic Metasurface Aperture for Computational Microwave Imaging

We present the design, fabrication, and experimental characterization of a 2-D, dynamically tuned, metasurface aperture, emphasizing its potential performance in computational imaging applications. The dynamic metasurface aperture (DMA) consists of an irregular, planar cavity that feeds a multitude of tunable metamaterial elements, all fabricated in a compact, multilayer printed circuit board process. The design considerations for the metamaterial element as a tunable radiator, the associated biasing circuitry, as well as cavity parameters are examined and discussed. A sensing matrix can be constructed from the measured transmit patterns, the singular value spectrum of which provides insight into the information capacity of the apertures. We investigate the singular value spectra of the sensing matrix over a variety of operating parameters, such as the number of metamaterial elements, number of masks (aka tuning states), and number of radiating elements. After optimizing over these key parameters, we demonstrate computational microwave imaging of simple test objects.
From Bipedal Walking to Quadrupedal Locomotion: Full-Body Dynamics Decomposition for Rapid Gait Generation

This paper systematically decomposes a quadrupedal robot into bipeds to rapidly generate walking gaits and then recomposes these gaits to obtain quadrupedal locomotion. We begin by decomposing the full-order, nonlinear and hybrid dynamics of a three-dimensional quadrupedal robot, including its continuous and discrete dynamics, into two bipedal systems that are subject to external forces. Using the hybrid zero dynamics (HZD) framework, gaits for these bipedal robots can be rapidly generated (on the order of seconds) along with corresponding controllers. The decomposition is achieved in such a way that the bipedal walking gaits and controllers can be composed to yield dynamic walking gaits for the original quadrupedal robot — the result is the rapid generation of dynamic quadruped gaits utilizing the full-order dynamics. This methodology is demonstrated through the rapid generation (3.96 seconds on average) of four stepping-in-place gaits and one diagonally symmetric ambling gait at 0.35 m/s on a quadrupedal robot — the Vision 60, with 36 state variables and 12 control inputs — both in simulation and through outdoor experiments. This suggested a new approach for fast quadrupedal trajectory planning using full-body dynamics, without the need for empirical model simplification, wherein methods from dynamic bipedal walking can be directly applied to quadrupeds.
Deep learning interfacial momentum closures in coarse-mesh CFD two-phase flow simulation using validation data

Abstract Multiphase flow phenomena have been widely observed in the industrial applications, yet it remains a challenging unsolved problem. Three-dimensional computational fluid dynamics (CFD) approaches resolve of the flow fields on finer spatial and temporal scales, which can complement dedicated experimental study. However, closures must be introduced to reflect the underlying physics in multiphase flow. Among them, the interfacial forces, including drag, lift, turbulent-dispersion and wall-lubrication forces, play an important role in bubble distribution and migration in liquid-vapor two-phase flows. Development of those closures traditionally rely on the experimental data and analytical derivation with simplified assumptions that usually cannot deliver a universal solution across a wide range of flow conditions. In this paper, a data-driven approach, named as feature-similarity measurement (FSM), is developed and applied to improve the simulation capability of two-phase flow with coarse-mesh CFD approach. Interfacial momentum transfer in adiabatic bubbly flow serves as the focus of the present study. Both a mature and a simplified set of interfacial closures are taken as the low-fidelity data. Validation data (including relevant experimental data and validated fine-mesh CFD simulations results) are adopted as high-fidelity data. Qualitative and quantitative analysis are performed in this paper. These reveal that FSM can substantially improve the prediction of the coarse-mesh CFD model, regardless of the choice of interfacial closures. It demonstrates that data-driven methods can aid the multiphase flow modeling by exploring the connections between local physical features and simulation errors.