cssi/SciSciGPT-SciSciNet huggingface dataset:
Algebraic Symmetry and Self-Duality of an Open ASEP

We consider the asymmetric simple exclusion process (ASEP) with open boundary condition at the left boundary, where particles exit at rate γ and enter at rate α = γτ2, and where τ is the asymmetry parameter in the bulk. At the right boundary, particles neither enter nor exit. By mapping the generator to the Hamiltonian of an XXZ quantum spin chain with reflection matrices, and using previously known results, we show algebraic symmetry and self–duality for the model.
Caught in the Cross-Fire: The Psychological and Emotional Impact of the Individuals with Disabilities Education Act (IDEA) upon Teachers of Children with Disabilities, A Therapeutic Jurisprudence Analysis

The shortage of special education teachers in the United States, and the adverse consequences flowing from factors related to this condition provide a unique opportunity for scholars to study these issues through interdisciplinary research. Educational scholars have typically focused their research on educational practice and institutional policy. Although this scholarship frequently acknowledges the statutory and regulatory foundations of the IDEA, the literature does not generally adopt a legal framework for research purposes. This is not a criticism of educational scholars. It is merely an observation that opportunities exist to study special education teacher issues in a broader context. This Article argues for such an approach, and thus, seeks to analyze the psychological and emotional impact of Special Education Law upon special education teachers through the lens of Therapeutic Jurisprudence. Therapeutic Jurisprudence is one of the vectors of a comprehensive law movement that began during the last few decades as a means to assess ways by which law and its processes could better serve the needs of society. Examples of other vectors of this movement include: collaborative law, creative problem solving, holistic justice, preventive law, problem solving courts, procedural justice, restorative justice, and transformative mediation. The list is not exhaustive, and the vectors are not exclusive. Indeed there have been occasions where the interests among vectors have overlapped creating synergies and opportunities for collaboration found useful to both. An example of this paradigm is the successful collaboration between Preventive Law and Therapeutic Jurisprudence.
What Hedge Funds Can Teach Corporate America: A Roadmap for Achieving Institutional Investor Oversight

Hedge funds and other private equity funds are aggressive monitors of corporate America. Their investment strategies are designed to squeeze agency costs and other inefficiencies out of under performing companies. Mutual funds and public pension funds, by contrast, have remained relentlessly passive despite their many resources. Rather than seek to improve the performance of their portfolio companies, they generally prefer to exit any investments that turn sour. Why the difference? In this Article, I compare the business environments and regulatory regimes affecting different types of institutional investors. I conclude that the primary reason that most institutional investors do not better discipline corporate wrongdoing is that their individual fund managers have little incentive to do so. Were they permitted to adopt the incentive compensation structure of a hedge fund, however, mutual funds and public pension funds would compete to provide the oversight necessary to make corporate managers more accountable. The result would be a deeper market for good corporate governance.
DeepNAG: Deep Non-Adversarial Gesture Generation

Synthetic data generation to improve classification performance (data augmentation) is a well-studied problem. Recently, generative adversarial networks (GAN) have shown superior image data augmentation performance, but their suitability in gesture synthesis has received inadequate attention. Further, GANs prohibitively require simultaneous generator and discriminator network training. We tackle both issues in this work. We first discuss a novel, device-agnostic GAN model for gesture synthesis called DeepGAN. Thereafter, we formulate DeepNAG by introducing a new differentiable loss function based on dynamic time warping and the average Hausdorff distance, which allows us to train DeepGAN’s generator without requiring a discriminator. Through evaluations, we compare the utility of DeepGAN and DeepNAG against two alternative techniques for training five recognizers using data augmentation over six datasets. We further investigate the perceived quality of synthesized samples via an Amazon Mechanical Turk user study based on the HYPE∞ benchmark. We find that DeepNAG outperforms DeepGAN in accuracy, training time (up to 17 × faster), and realism, thereby opening the door to a new line of research in generator network design and training for gesture synthesis. Our source code is available at https://www.deepnag.com.
Comments on magnetic black holes

We discuss aspects of magnetically charged black holes in the Standard Model. For a range of charges, we argue that the electroweak symmetry is restored in the near horizon region. The extent of this phase can be macroscopic. If Q is the integer magnetic charge, the fermions lead to order Q massless two dimensional fermions moving along the magnetic field lines. These greatly enhance Hawking radiation effects.
Capital controls and international trade: An industry financial vulnerability perspective

Abstract Capital control policies have consequences on economic growth and international trade. Using data on 99 countries from 1995–2014, we find evidence that the effect of capital controls on trade vary across industries that have differing levels of external financing and asset tangibility. For exporter countries that tighten capital controls, industries that rely more heavily on external financing experience a larger decline in exports, while industries that possess more tangible assets experience a smaller decline in exports. For importer countries, tighter capital controls imply a decrease in trade, and this effect is uniform across all industries. The pattern with respect to external financing persists after accounting for availability of domestic credit and the differences in industry shares, and are predominantly found in countries with low levels of financial development. On the other hand, the varying effect related to asset tangibility is mostly absorbed by domestic credit market.
Effect of flexural stress on fabrication and optical properties of a composite photonic material

We spin coated thin films of poly(methyl) methacrylate (PMMA) polymer doped with 2-[7-(1,3-dihydro-1,3,3-trimethyl-2H-indol-2-ylidene)-1,3,5-heptatrienyl]-1,3,3-trimethyl-3H-indolium iodide (HITC) laser dye onto microscope cover slides under applied flexural stress and found the patterns of scattering (milkiness) to correlate with the spatial variation of the pressure pattern. We further deposited thin gold films on top of the HITC:PMMA films and measured highly unusual transmission spectra of Au collected from strongly deformed (stressed) local areas of the sample. These spectra, which strongly deferred from those of plain Au films deposited onto unstressed glass, could not be described by simple, e.g., Maxwell Garnett, effective media models, calling for a thorough theoretical study.
A Solver-Aided Language for Test Input Generation

Developing a small but useful set of inputs for tests is challenging. We show that a domain-specific language backed by a constraint solver can help the programmer with this process. The solver can generate a set of test inputs and guarantee that each input is \emph{different} from other inputs in a way that is useful for testing. This paper presents Iorek: a tool that empowers the programmer with the ability to express to any SMT solver what it means for inputs to be different. The core of Iorek is a rich language for constraining the set of inputs, which includes a novel bounded enumeration mechanism that makes it easy to define and encode a flexible notion of difference over a recursive structure. We demonstrate the flexibility of this mechanism for generating strings. We use Iorek to test real services and find that it is effective at finding bugs. We also build Iorek into a random testing tool and show that it increases coverage.
Tracing Topic Transitions with Temporal Graph Clusters

Twitter serves as a data source for many Natural Language Processing (NLP) tasks. It can be challenging to identify topics on Twitter due to continuous updating data stream. In this paper, we present an unsupervised graph based framework to identify the evolution of sub-topics within two weeks of real-world Twitter data. We first employ a Markov Clustering Algorithm (MCL) with a node removal method to identify optimal graph clusters from temporal Graph-of-Words (GoW). Subsequently, we model the clustering transitions between the temporal graphs to identify the topic evolution. Finally, the transition flows generated from both computational approach and human annotations are compared to ensure the validity of our framework.
Low-Complexity, Real-Time Joint Neural Echo Control and Speech Enhancement Based On Percepnet

Speech enhancement algorithms based on deep learning have greatly surpassed their traditional counterparts and are now being considered for the task of removing acoustic echo from hands-free communication systems. This is a challenging problem due to both real-world constraints like loudspeaker non-linearities, and to limited compute capabilities in some communication systems. In this work, we propose a system combining a traditional acoustic echo canceller, and a low-complexity joint residual echo and noise suppressor based on a hybrid signal processing/deep neural network (DSP/DNN) approach. We show that the proposed system outperforms both traditional and other neural approaches, while requiring only 5.5% CPU for real-time operation. We further show that the system can scale to even lower complexity levels.
The geometric average size of Selmer groups over function fields

We show, in the large q limit, that the average size of n-Selmer groups of elliptic curves of bounded height over 𝔽q(t) is the sum of the divisors of n. As a corollary, again in the large q limit, we deduce that 100% of elliptic curves of bounded height over 𝔽q(t) have rank 0 or 1.
Vulnerability detection is just the beginning

Vulnerability detection plays a key role in secure software development [1]--[4]. There are many different vulnerability detection tools and techniques to choose from, and insufficient information on which vulnerability detection techniques to use and when. The goal of this research is to assist managers and other decision-makers on software projects in making informed choices about the use of different software vulnerability detection techniques through empirical analysis of the efficiency and effectiveness of each technique. We will examine the relationships between the vulnerability detection technique used to find a vulnerability, the type of vulnerability found, the exploitability of the vulnerability, and the effort needed to fix a vulnerability on two projects where we ensure all vulnerabilities found have been fixed. We will then examine how these relationships are seen in Open Source Software more broadly where practitioners may use different vulnerability detection techniques, or may not fix all vulnerabilities found due to resource constraints.
The Development and Validation of the Social Enterprise of Science Index (SESI): An Instrument to Measure Grasp of the Social-Institutional Aspects of Science

Significant gaps remain between public opinion and the scientific consensus on many issues. We present the results of three studies (N = 722 in total) for the development and testing of a novel ins...
Adversarial Attacks and Defenses: An Interpretation Perspective

Despite the recent advances in a wide spectrum of applications, machine learning models, especially deep neural networks, have been shown to be vulnerable to adversarial attacks. Attackers add carefully-crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions. Techniques to protect models against adversarial input are called adversarial defense methods. Although many approaches have been proposed to study adversarial attacks and defenses in different scenarios, an intriguing and crucial challenge remains that how to really understand model vulnerability? Inspired by the saying that "if you know yourself and your enemy, you need not fear the battles", we may tackle the challenge above after interpreting machine learning models to open the black-boxes. The goal of model interpretation, or interpretable machine learning, is to extract human-understandable terms for the working mechanism of models. Recently, some approaches start incorporating interpretation into the exploration of adversarial attacks and defenses. Meanwhile, we also observe that many existing methods of adversarial attacks and defenses, although not explicitly claimed, can be understood from the perspective of interpretation. In this paper, we review recent work on adversarial attacks and defenses, particularly from the perspective of machine learning interpretation. We categorize interpretation into two types, feature-level interpretation, and model-level interpretation. For each type of interpretation, we elaborate on how it could be used for adversarial attacks and defenses. We then briefly illustrate additional correlations between interpretation and adversaries. Finally, we discuss the challenges and future directions for tackling adversary issues with interpretation.
Zonotopes whose cellular strings are all coherent

Abstract A cellular string of a polytope is a sequence of faces stacked on top of each other in a given direction. The poset of cellular strings, ordered by refinement, is known to be homotopy equivalent to a sphere. The subposet of coherent cellular strings is the face lattice of the fiber polytope, hence is homeomorphic to a sphere. In some special cases, every cellular string is coherent. Such polytopes are said to be all-coherent. We give a complete classification of zonotopes with the all-coherence property in terms of their oriented matroid structure. Although the face lattice of the fiber polytope in this case is not an oriented matroid invariant, we prove that the all-coherence property is invariant.
Quenching Timescales of Dwarf Satellites around Milky Way-mass Hosts

Observations of the low-mass satellites in the Local Group have shown high fractions of gas-poor, quiescent galaxies relative to isolated dwarfs, implying that the host halo environment plays an important role in the quenching of dwarf galaxies. In this work, we present measurements of the quenched fractions and quenching timescales of dwarf satellite galaxies in the DC Justice League suite of 4 high-resolution cosmological zoom-in simulations of Milky Way-mass halos. We show that these simulations accurately reproduce the satellite luminosity functions of observed nearby galaxies, as well as the variation in satellite quenched fractions from $M_* \sim 10^5$ solar masses to $10^{10}$ solar masses. We then trace the histories of satellite galaxies back to $z \sim 15$, and find that many satellites with $M_* \sim 10^{6-8}$ solar masses quench within 2 Gyr of infall into the host halo, while others in the same mass range remain star-forming for as long as 5 Gyr. We show that this scatter can be explained by the satellite's gas mass and the ram pressure it feels at infall. Finally, we identify a characteristic stellar mass scale of $10^8$ solar masses above which infalling satellites are largely resistant to rapid environmental quenching.
Graph streaming lower bounds for parameter estimation and property testing via a streaming XOR lemma

We study space-pass tradeoffs in graph streaming algorithms for parameter estimation and property testing problems such as estimating the size of maximum matchings and maximum cuts, weight of minimum spanning trees, or testing if a graph is connected or cycle-free versus being far from these properties. We develop a new lower bound technique that proves that for many problems of interest, including all the above, obtaining a (1+є)-approximation requires either nΩ(1) space or Ω(1/є) passes, even on highly restricted families of graphs such as bounded-degree planar graphs. For multiple of these problems, this bound matches those of existing algorithms and is thus (asymptotically) optimal. Our results considerably strengthen prior lower bounds even for arbitrary graphs: starting from the influential work of [Verbin, Yu; SODA 2011], there has been a plethora of lower bounds for single-pass algorithms for these problems; however, the only multi-pass lower bounds proven very recently in [Assadi, Kol, Saxena, Yu; FOCS 2020] rules out sublinear-space algorithms with exponentially smaller o(log(1/є)) passes for these problems. One key ingredient of our proofs is a simple streaming XOR Lemma, a generic hardness amplification result, that we prove: informally speaking, if a p-pass s-space streaming algorithm can only solve a decision problem with advantage δ > 0 over random guessing, then it cannot solve XOR of l independent copies of the problem with advantage much better than δl. This result can be of independent interest and useful for other streaming lower bounds as well.
Column Partition Based Distributed Algorithms for Coupled Convex Sparse Optimization: Dual and Exact Regularization Approaches

This paper develops column partition based distributed schemes for a class of convex sparse optimization problems, e.g., basis pursuit (BP), LASSO, basis pursuit denosing (BPDN), and their extensions, e.g., fused LASSO. We are particularly interested in the cases where the number of (scalar) decision variables is much larger than the number of (scalar) measurements, and each agent has limited memory or computing capacity such that it only knows a small number of columns of a measurement matrix. The problems in consideration are densely coupled and cannot be formulated as separable convex programs. To overcome this difficulty, we consider their dual problems which are separable or locally coupled. Once a dual solution is attained, it is shown that a primal solution can be found from the dual of corresponding regularized BP-like problems under suitable exact regularization conditions. A wide range of existing distributed schemes can be exploited to solve the obtained dual problems. This yields two-stage column partition based distributed schemes for LASSO-like and BPDN-like problems; the overall convergence of these schemes is established. Numerical results illustrate the performance of the proposed two-stage distributed schemes.
Understanding Hard Negatives in Noise Contrastive Estimation

The choice of negative examples is important in noise contrastive estimation. Recent works find that hard negatives—highest-scoring incorrect examples under the model—are effective in practice, but they are used without a formal justification. We develop analytical tools to understand the role of hard negatives. Specifically, we view the contrastive loss as a biased estimator of the gradient of the cross-entropy loss, and show both theoretically and empirically that setting the negative distribution to be the model distribution results in bias reduction. We also derive a general form of the score function that unifies various architectures used in text retrieval. By combining hard negatives with appropriate score functions, we obtain strong results on the challenging task of zero-shot entity linking.
Conservative and Semismooth Derivatives are Equivalent for Semialgebraic Maps

Subgradient and Newton algorithms for nonsmooth optimization require generalized derivatives to satisfy subtle approximation properties: conservativity for the former and semismoothness for the latter. Though these two properties originate in entirely different contexts, we show that in the semi-algebraic setting they are equivalent. Both properties for a generalized derivative simply require it to coincide with the standard directional derivative on the tangent spaces of some partition of the domain into smooth manifolds. An appealing byproduct is a new short proof that semi-algebraic maps are semismooth relative to the Clarke Jacobian.
Asymptotic Functions of Entire Functions

If f is an entire function and a is a complex number, a is said to be an asymptotic value of f if there exists a path $$\gamma $$ from 0 to infinity such that $$f(z) - a$$ tends to 0 as z tends to infinity along $$\gamma $$ . The Denjoy–Carleman–Ahlfors Theorem asserts that if f has n distinct asymptotic values, then the rate of growth of f is at least order n/2, mean type. A long-standing problem asks whether this conclusion holds for entire functions having n distinct asymptotic (entire) functions, each of growth at most order 1/2, minimal type. In this paper conditions on the function f and associated asymptotic paths are obtained that are sufficient to guarantee that f satisfies the conclusion of the Denjoy–Carleman–Ahlfors Theorem. In addition, for each positive integer n, an example is given of an entire function of order n having n distinct, prescribed asymptotic functions, each of order less than 1/2.
SARS-CoV-2 detection and genomic sequencing from hospital surface samples collected at UC Davis.

Author(s): Coil, David A; Albertson, Timothy; Banerjee, Shefali; Brennan, Greg; Campbell, AJ; Cohen, Stuart H; Dandekar, Satya; Diaz-Munoz, Samuel L; Eisen, Jonathan A; Goldstein, Tracey; Jose, Ivy R; Juarez, Maya; Robinson, Brandt A; Rothenburg, Stefan; Sandrock, Christian; Stoian, Ana MM; Tompkins, Daniel G; Tremeau-Bravard, Alexandre; Haczku, Angela | Abstract: RationaleThere is little doubt that aerosols play a major role in the transmission of SARS-CoV-2. The significance of the presence and infectivity of this virus on environmental surfaces, especially in a hospital setting, remains less clear.ObjectivesWe aimed to analyze surface swabs for SARS-CoV-2 RNA and infectivity, and to determine their suitability for sequence analysis.MethodsSamples were collected during two waves of COVID-19 at the University of California, Davis Medical Center, in COVID-19 patient serving and staff congregation areas. qRT-PCR positive samples were investigated in Vero cell cultures for cytopathic effects and phylogenetically assessed by whole genome sequencing.Measurements and main resultsImproved cleaning and patient management practices between April and August 2020 were associated with a substantial reduction of SARS-CoV-2 qRT-PCR positivity (from 11% to 2%) in hospital surface samples. Even though we recovered near-complete genome sequences in some, none of the positive samples (11 of 224 total) caused cytopathic effects in cultured cells suggesting this nucleic acid was either not associated with intact virions, or they were present in insufficient numbers for infectivity. Phylogenetic analysis suggested that the SARS-CoV-2 genomes of the positive samples were derived from hospitalized patients. Genomic sequences isolated from qRT-PCR negative samples indicate a superior sensitivity of viral detection by sequencing.ConclusionsThis study confirms the low likelihood that SARS-CoV-2 contamination on hospital surfaces contains infectious virus, disputing the importance of fomites in COVID-19 transmission. Ours is the first report on recovering near-complete SARS-CoV-2 genome sequences directly from environmental surface swabs.
Particle radial distribution function and relative velocity measurement in turbulence at small particle-pair separations

Particle collisions in turbulent flow are critical to particle agglomeration and droplet coalescence. The collision kernel can be evaluated by radial distribution function (RDF) and radial relative velocity (RV) between particles at small separations can be attributed to particle–particle interactions including hydrodynamic interactions, which are not well-understood. Uncertainty analysis substantiates the observed trends. This first-ever simultaneous RDF and RV measurement at small separations provides a clear glimpse into the clustering and relative velocities of particles in turbulence near-contact.
NASA Common Research Model: A History and Future Plans

The NASA Common Research Model (CRM) has enabled many formal and informal international cooperative activities and has enabled aeronautical researchers and engineers in industry, government, and academia to work together across organizational and international borders sharing results on relevant problems for the benefit of all. The NASA Common Research Model (CRM) was conceived in 2007 and its aerodynamic design completed in 2008 responding to needs broadly expressed both within the US and international aeronautics communities for modern/industry-relevant and open/public geometries coupled with advanced experimental data for applied computational fluid dynamic validation studies. This paper provides a brief history of the development of the CRM, along with a summary of wind tunnel model data that has been obtained over the past 10 years. This paper presents data obtained from the NASA Langley National Transonic Facility, the Ames 11-ft Transonic Wind Tunnel and the European Transonic Windtunnel. Sample comparisons are given between the three wind tunnels for lift, drag and pitching moment. Several CRM-derivatives that have been developed are also summarized in this paper.
A Path to Transformation: Asking “The Woman Question” in International Law

This article proposes the "woman question," a method that reveals the implications of legal rules on women, as a potentially widely shared method in the study of international law. This question revolves around the impact of the law on women and is designed to expose those elements in the law that disadvantage women or submerge their perspectives in ways that might appear neutral. However, this central concept has been diluted over the years and has receded to the background of feminist thought. This paper brings it back to the foreground and develops the concept to make it an effective methodology for feminist scholars from a wide range of streams of feminist thought. The article shows that the central feminist question in international law is in fact the woman question, probing: What is the impact of the law and its institutions on women? I argue that the question of gender impact is central to our understanding of the international system and can be widely shared. It allows us to look beyond abstract domestic and global concepts and to find the actual impact of rules on women within States. As I note in the article, "while this question seeks to highlight and address the continuing injustice that women experience, it also allows scholars to see beyond the gender binary in ways that take into consideration a spectrum of genders and the impact of the law on people of all genders. It proposes clarity and promises a feminist sensitivity to any analysis of international law. Above all, it solves a rather perplexing dilemma of a choice between the ramified feminist approaches." Based on this method, I develop a unique analytical model that tackles the distinctive ways in which the international legal system perpetuates women’s inequality. The model is predicated on the evolving global idea of transformative equality asking us to reimagine the rules by which our society operates. It urges scholars to undertake a transformative reconstruction endeavor and is intended to encourage transformative processes that confront entrenched social and legal gendered structures within the international legal system. It requires a complicated intellectual effort to reimagine the future as means to move towards a gender-just global system. Hopefully, reimagining the future will be the most empowering, fulfilling, and transformative result of this work.
Towards Immersive Virtual Reality Simulations of Bionic Vision

Bionic vision is a rapidly advancing field aimed at developing visual neuroprostheses (‘bionic eyes’) to restore useful vision to people who are blind. However, a major outstanding challenge is predicting what people ‘see’ when they use their devices. The limited field of view of current devices necessitates head movements to scan the scene, which is difficult to simulate on a computer screen. In addition, many computational models of bionic vision lack biological realism. To address these challenges, we propose to embed biologically realistic models of simulated prosthetic vision (SPV) in immersive virtual reality (VR) so that sighted subjects can act as ‘virtual patients’ in real-world tasks.
SARS-CoV-2 Subgenomic RNA Kinetics in Longitudinal Clinical Samples

Background Given the persistence of viral RNA in clinically recovered coronavirus disease 2019 (COVID-19) patients, subgenomic RNAs (sgRNAs) have been reported as potential molecular viability markers for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, few data are available on their longitudinal kinetics, compared with genomic RNA (gRNA), in clinical samples. Methods We analyzed 536 samples from 205 patients with COVID-19 from placebo-controlled, outpatient trials of peginterferon Lambda-1a (Lambda; n = 177) and favipiravir (n = 359). Nasal swabs were collected at 3 time points in the Lambda (days 1, 4, and 6) and favipiravir (days 1, 5, and 10) trials. N-gene gRNA and sgRNA were quantified by quantitative reverse transcription polymerase chain reaction. To investigate the decay kinetics in vitro, we measured gRNA and sgRNA in A549ACE2+ cells infected with SARS-CoV-2, following treatment with remdesivir or dimethylsulfoxide control. Results At 6 days in the Lambda trial and 10 days in the favipiravir trial, sgRNA remained detectable in 51.6% (32/62) and 49.5% (51/106) of the samples, respectively. Cycle threshold (Ct) values for gRNA and sgRNA were highly linearly correlated (marginal R 2 = 0.83), and the rate of increase did not differ significantly in the Lambda trial (1.36 cycles/d vs 1.36 cycles/d; P = .97) or the favipiravir trial (1.03 cycles/d vs 0.94 cycles/d; P = .26). From samples collected 15-21 days after symptom onset, sgRNA was detectable in 48.1% (40/83) of participants. In SARS-CoV-2-infected A549ACE2+ cells treated with remdesivir, the rate of Ct increase did not differ between gRNA and sgRNA. Conclusions In clinical samples and in vitro, sgRNA was highly correlated with gRNA and did not demonstrate different decay patterns to support its application as a viability marker.
LQR with Tracking: A Zeroth-order Approach and Its Global Convergence

There has been substantial recent progress on the theoretical understanding of model-free approaches to Linear Quadratic Regulator (LQR) problems. Much attention has been devoted to the special case when the goal is to drive the state close to a zero target. In this work, we consider the general case where the target is allowed to be arbitrary, which we refer to as the LQR tracking problem. We study the optimization landscape of this problem, and show that similar to the zero-target LQR problem, the LQR tracking problem also satisfies gradient dominance and local smoothness properties. This allows us to develop a zeroth-order policy gradient algorithm that achieves global convergence. We support our arguments with numerical simulations on a linear system.
Cytokine filter application in COVID-19 patients; island of hope for crash and burn patients or future solution for all septic acute respiratory distress syndrome (ARDS) patients.

Cytokine Filter Application in COVID-19 Patients; Island of Hope for Crash and Burn Patients or Future Solution for All Septic Acute Respiratory Distress Syndrome (ARDS) PatientsAli Ghodsizad MD, PhD, FACC, FETCS, FACSThe COVID-19 pandemic crisis certainly has challenged the scientific community as well as entire world. While incidence numbers have decreased following expedited vaccination and precautions, still some patients present with COVID 19 related pneumonia and ARDS requiring Veno-Venous Extracorporeal Membrane Oxygenation (VV ECMO) support to survive.In COVID-19 patients a cytokine release syndrome concomitant with ARDS can lead to overwhelming clinical scenario. Geraci and colleagues report on their single center feasibility study looking at application of the CytosorbTM hemadsorption device which was used as a parallel circuit within the VV ECMO circuit.The authors give evidence for safety and feasibility of the CytosorbTM hemadsorption device use in 10 patients with COVID-19 related ARDS in combination with VV ECMO. They show a reduction of inflammatory markers and cytokines following hemadsorption treatment. The cytokine storm can cause a critical clinical picture of septic shock. Only under high vasopressor and inotropic support end organ perfusion can be maintained. The required invasive pressure ventilation with high PEEP and peak pressure can decrease the intrathoracic venous return further and contributes more to the shock physiology (1). We have to look at inspiring results from current single center experience carefully understanding the evolving nature of COVID-19 related ARDS. Other groups have used plasmapheresis and CVVH modifications in COVID-19 cases. Dominik et al have shown a significant benefit only using hemadsorption comparing to other used protocols (2).We have shown successful application of somatic stem cells in COVID-19 patients on VV ECMO at our center. We could observe a reduction of inflammatory markers following somatic stem cell application (3). COVID 19 ARDS patients who required VV ECMO support, underwent a Pulmonary Artery (PA)-catheter placement and allogenic human stem cell injection into the PA using the PA-catheter as part of our expanded access protocol (3,4).Brouwer and colleagues, another group working with hemadsorption, have actually shown reduced survival in patients undergoing hemadsorption therapy (5). Geraci and colleagues describe their overall VV ECMO survival for COVID 19 related respiratory failure to be > 90%. Others including our center have experienced a much lower survival in that patient population. So patient selection clearly is a key point. The results presented by Geraci and colleagues have to be taken as a pioneering step, which can help in ARDS and septic clinical scenarios with different pathology in future.
A Two-stage Multi-modal Affect Analysis Framework for Children with Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a developmental disorder that influences the communication and social behavior of a person in a way that those in the spectrum have difficulty in perceiving other people's facial expressions, as well as presenting and communicating emotions and affect via their own faces and bodies. Some efforts have been made to predict and improve children with ASD's affect states in play therapy, a common method to improve children's social skills via play and games. However, many previous works only used pre-trained models on benchmark emotion datasets and failed to consider the distinction in emotion between typically developing children and children with autism. In this paper, we present an open-source two-stage multi-modal approach leveraging acoustic and visual cues to predict three main affect states of children with ASD's affect states (positive, negative, and neutral) in real-world play therapy scenarios, and achieved an overall accuracy of 72:40%. This work presents a novel way to combine human expertise and machine intelligence for ASD affect recognition by proposing a two-stage schema.
Obfuscation in mutual funds

Abstract Mutual funds hold 32% of the U.S. equity market and comprise 58% of retirement savings, yet retail investors consistently make poor choices when selecting funds. Theory suggests poor choices are partially due to fund managers creating unnecessarily complex disclosures and fee structures to keep investors uninformed and obfuscate poor performance. An empirical challenge in investigating this “strategic obfuscation” theory is isolating manipulated complexity from complexity arising from inherent differences across funds. We examine obfuscation among S&P 500 index funds, which have largely the same regulations, risks, and gross returns but charge widely different fees. Using bespoke measures of complexity designed for mutual funds, we find evidence consistent with funds attempting to obfuscate high fees. This study improves our understanding of why investors make poor mutual fund choices and how price dispersion persists among homogeneous index funds. We also discuss insights for mutual fund regulation and academic literature on corporate disclosures.
Nickel-catalyzed reductive coupling of unactivated alkyl bromides and aliphatic aldehydes

A mild, convenient coupling of aliphatic aldehydes and unactivated alkyl bromides has been developed. The catalytic system features the use of a common Ni(II) precatalyst and a readily available bioxazoline ligand and affords silyl-protected secondary alcohols. The reaction is operationally simple, utilizing Mn as a stoichiometric reductant, and tolerates a wide range of functional groups. The use of 1,5-hexadiene as an additive is an important reaction parameter that provides significant benefits in yield optimizations. Initial mechanistic experiments support a mechanism featuring an alpha-silyloxy Ni species that undergoes formal oxidative addition to the alkyl bromide via a reductive cross-coupling pathway.
Deep Learning for Mortgage Risk

This paper analyzes multi-period mortgage risk at loan and pool levels using an unprecedented dataset of over 120 million prime and subprime mortgages originated across the United States between 1995 and 2014, which includes the individual characteristics of each loan, monthly updates on loan performance over the life of a loan, and a number of time-varying economic variables at the zip code level. We develop, estimate, and test dynamic machine learning models for mortgage prepayment, delinquency, and foreclosure which capture loan-to-loan correlation due to geographic proximity and exposure to common risk factors. The basic building block is a deep neural network which addresses the nonlinear relationship between the explanatory variables and loan performance. Our likelihood estimators, which are based on 3.5 billion borrower-month observations, indicate that mortgage risk is strongly influenced by local economic factors such as zip-code level foreclosure rates. The out-of-sample predictive performance of our deep learning model is a significant improvement over linear models such as logistic regression. Model parameters are estimated using GPU parallel computing due to the computational challenges associated with the large amount of data. The deep learning model's superior accuracy compared to linear models directly translates into improved performance for investors. Portfolios constructed with the deep learning model have lower prepayment and delinquency rates than portfolios chosen with a logistic regression.
Recounting Special Lagrangian Cycles in Twistor Families of K3 Surfaces. Or: How I Learned to Stop Worrying and Count BPS States

We consider asymptotics of certain BPS state counts in M-theory compactified on a K3 surface. Our investigation is parallel to (and was inspired by) recent work in the mathematics literature by Filip, who studied the asymptotic count of special Lagrangian fibrations of a marked K3 surface, with fibers of volume at most $V_*$, in a generic twistor family of K3 surfaces. We provide an alternate proof of Filip's results by adapting tools that Douglas and collaborators have used to count flux vacua and attractor black holes. We similarly relate BPS state counts in 4d ${\cal N}=2$ supersymmetric gauge theories to certain counting problems in billiard dynamics and provide a simple proof of an old result in this field.
Rigidification of dendroidal infinity-operads

We give an explicit description of the rigidification of an $\infty$-operad as a simplicial operad. This description is based on the notion of dendroidal necklace, extending work of Dugger and Spivak from the categorical context to the operadic context, although with a different framework, which relates constructions involving necklaces to a standard factorization of maps in the category of trees.
Police Officers’ Best Friend?: An Exploratory Analysis of the Effect of Service Dogs on Perceived Organizational Support in Policing

This study explored the effectiveness of a novel technique for police departments to support their officers and promote wellness: the use of service dogs. We evaluated officer perceptions in two mi...
Unsupervised Domain Adaptation for Small Bowel Segmentation Using Disentangled Representation

We present a novel unsupervised domain adaptation method for small bowel segmentation based on feature disentanglement. To make the domain adaptation more controllable, we disentangle intensity and non-intensity features within a unique two-stream auto-encoding architecture, and selectively adapt the non-intensity features that are believed to be more transferable across domains. The segmentation prediction is performed by aggregating the disentangled features. We evaluated our method using intravenous contrast-enhanced abdominal CT scans with and without oral contrast, which are used as source and target domains, respectively. The proposed method showed clear improvements in terms of three different metrics compared to other domain adaptation methods that are without the feature disentanglement. The method brings small bowel segmentation closer to clinical application.
Phase-variable expression of pdcB, a phosphodiesterase, influences sporulation in Clostridioides difficile.

Clostridioides difficile is the causative agent of antibiotic-associated diarrhea and is the leading cause of nosocomial infection in developed countries. An increasing number of C. difficile infections are attributed to epidemic strains that produce more toxins and spores. C. difficile spores are the major factor for the transmission and persistence of the organism. Previous studies have identified global regulators that influence sporulation in C. difficile. This study discovers that PdcB, a phosphodiesterase, enhances sporulation in C. difficile strain UK1. Through genetic and biochemical assays, we show that phase-variable expression of pdcB results in hypo- and hyper-sporulation phenotypes. In the "ON" orientation, the identified promotor is in the right orientation to drive the expression of pdcB. Production of the PdcB phosphodiesterase reduces the intracellular cyclic-di-GMP (c-di-GMP) concentration, resulting in a hyper-sporulation phenotype. Loss of PdcB due to the pdcB promoter being in the OFF orientation or mutation of pdcB results in increased c-di-GMP levels and a hypo-sporulation phenotype. Additionally, we demonstrate that CodY binds to the upstream region of pdcB. DNA inversion reorients the CodY binding site so that in the OFF orientation, CodY binds a site that is upstream of the pdcB promoter and can further repress gene expression.
Model-Free Reinforcement Learning for Stochastic Games with Linear Temporal Logic Objectives

We study synthesis of control strategies from linear temporal logic (LTL) objectives in unknown environments. We model this problem as a turn-based zero-sum stochastic game between the controller and the environment, where the transition probabilities and the model topology are fully unknown. The winning condition for the controller in this game is the satisfaction of the given LTL specification, which can be captured by the acceptance condition of a deterministic Rabin automaton (DRA) directly derived from the LTL specification. We introduce a model-free reinforcement learning (RL) methodology to find a strategy that maximizes the probability of satisfying a given LTL specification when the Rabin condition of the derived DRA has a single accepting pair. We then generalize this approach to any LTL formulas, for which the Rabin accepting condition may have more than one pairs, providing a lower bound on the satisfaction probability. Finally, we show applicability of our RL method on two planning case studies.
Models and Predictive Control for Nonplanar Vehicle Navigation

We present a simplified model of a vehicle driving on a nonplanar road. A parametric surface is used to describe the nonplanar road which can describe any combination of curvature, bank and slope. We show that the proposed modeling approach generalizes planar vehicle models that reference a centerline, such as the Frenet model. We use the proposed approach for vehicle path planning and following using model predictive control. We also model and control vehicle contact with the road surface. We demonstrate that the proposed controller improves speed and lane following on complex roads compared to planar vehicle controllers, and mitigates loss of control on complex road surfaces including off-camber turns.
TopoShot: uncovering Ethereum's network topology leveraging replacement transactions

Ethereum relies on a peer-to-peer overlay network to propagate information. The knowledge of Ethereum network topology holds the key to understanding Ethereum's security, availability, and user anonymity. However, an Ethereum network's topology is stored in individual nodes' internal routing tables, measuring which poses challenges and remains an open research problem in the existing literature. This paper presents TopoShot, a new method uniquely repurposing Ethereum's transaction replacement/eviction policies for topology measurement. TopoShot can be configured to support Geth, Parity and other major Ethereum clients. As validated on local nodes, TopoShot achieves 100% measurement precision and high recall (88% ~ 97%). To efficiently measure the large Ethereum networks in the wild, we propose a non-trivial schedule to run pair-wise measurements in parallel. To enable ethical measurement on Ethereum mainnet, we propose workload-adaptive configurations of TopoShot to minimize the service interruption to target nodes/network. We systematically measure a variety of Ethereum networks and obtain new knowledge including the full-network topology in major testnets (Ropsten, Rinkeby and Goerli) and critical sub-network topology in the mainnet. The results on testnets show interesting graph-theoretic properties, such as all testnets exhibit graph modularity significantly lower than random graphs, implying resilience to network partitions. The mainnet results show biased neighbor selection strategies adopted by critical Ethereum services such as mining pools and transaction relays, implying a degree of centralization in real Ethereum networks.