cssi/SciSciGPT-SciSciNet huggingface dataset:
OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning

Machine learning (ML) is increasingly being used to make decisions in our society. ML models, however, can be unfair to certain demographic groups (e.g., African Americans or females) according to various fairness metrics. Existing techniques for producing fair ML models either are limited to the type of fairness constraints they can handle (e.g., preprocessing) or require nontrivial modifications to downstream ML training algorithms (e.g., in-processing). We propose a declarative system OmniFair for supporting group fairness in ML. OmniFair features a declarative interface for users to specify desired group fairness constraints and supports all commonly used group fairness notions, including statistical parity, equalized odds, and predictive parity. OmniFair is also model-agnostic in the sense that it does not require modifications to a chosen ML algorithm. OmniFair also supports enforcing multiple user declared fairness constraints simultaneously while most previous techniques cannot. The algorithms in OmniFair maximize model accuracy while meeting the specified fairness constraints, and their efficiency is optimized based on the theoretically provable monotonicity property regarding the trade-off between accuracy and fairness that is unique to our system. We conduct experiments on commonly used datasets that exhibit bias against minority groups in the fairness literature. We show that OmniFair is more versatile than existing algorithmic fairness approaches in terms of both supported fairness constraints and downstream ML models. OmniFair reduces the accuracy loss by up to 94.8% compared with the second best method. OmniFair also achieves similar running time to preprocessing methods, and is up to 270x faster than in-processing methods.
Human-like informative conversations: Better acknowledgements using conditional mutual information

This work aims to build a dialogue agent that can weave new factual content into conversations as naturally as humans. We draw insights from linguistic principles of conversational analysis and annotate human-human conversations from the Switchboard Dialog Act Corpus to examine humans strategies for acknowledgement, transition, detail selection and presentation. When current chatbots (explicitly provided with new factual content) introduce facts into a conversation, their generated responses do not acknowledge the prior turns. This is because models trained with two contexts - new factual content and conversational history - generate responses that are non-specific w.r.t. one of the contexts, typically the conversational history. We show that specificity w.r.t. conversational history is better captured by pointwise conditional mutual information (pcmi_h) than by the established use of pointwise mutual information (pmi). Our proposed method, Fused-PCMI, trades off pmi for pcmi_h and is preferred by humans for overall quality over the Max-PMI baseline 60% of the time. Human evaluators also judge responses with higher pcmi_h better at acknowledgement 74% of the time. The results demonstrate that systems mimicking human conversational traits (in this case acknowledgement) improve overall quality and more broadly illustrate the utility of linguistic principles in improving dialogue agents.
Combinatorial Bernoulli factories: matchings, flows, and other polytopes

A Bernoulli factory is an algorithmic procedure for exact sampling of certain random variables having only Bernoulli access to their parameters. Bernoulli access to a parameter p ∈ [0,1] means the algorithm does not know p, but has sample access to independent draws of a Bernoulli random variable with mean equal to p. In this paper, we study the problem of Bernoulli factories for polytopes: given Bernoulli access to a vector x∈ P for a given polytope P⊂ [0,1]n, output a randomized vertex such that the expected value of the i-th coordinate is exactly equal to xi. For example, for the special case of the perfect matching polytope, one is given Bernoulli access to the entries of a doubly stochastic matrix [xij] and asked to sample a matching such that the probability of each edge (i,j) be present in the matching is exactly equal to xij. We show that a polytope P admits a Bernoulli factory if and and only if P is the intersection of [0,1]n with an affine subspace. Our construction is based on an algebraic formulation of the problem, involving identifying a family of Bernstein polynomials (one per vertex) that satisfy a certain algebraic identity on P. The main technical tool behind our construction is a connection between these polynomials and the geometry of zonotope tilings. We apply these results to construct an explicit factory for the perfect matching polytope. The resulting factory is deeply connected to the combinatorial enumeration of arborescences and may be of independent interest. For the k-uniform matroid polytope, we recover a sampling procedure known in statistics as Sampford sampling.
Assessment of Computational Fluid Dynamics for Supersonic Shock Containing Jets

This paper describes a combined experimental and computational investigation of supersonic jets operating off design. Axisymmetric steady-state computational fluid dynamics simulations were performed with a Reynolds-averaged Navier―Stokes solver using a two-equation turbulence model. These results were compared directly with experimental results for two differen t axisymmetric nozzles operating at over- and underexpanded conditions. Pitot and static pressure probes were used to measure the Mach number and local static pressure at various downstream locations. A comparison of both experimental and numerically predicted schlieren visualizations were performed for a qualitative assessment of the accuracy of the flowfield predictions. Quantitative comparisons of pitot and static pressures were made between the experiments and simulations. This provides an assessment of the quality of the numerical simulations as well as an indication of the errors associated with any interference between the pressure probes and the flow in the experiments.
Helicopter Rotor Periodic Differential Pressures and Structural Response Measured in Transient and Steady-State Maneuvers

Helicopter rotor blade differential pressure and structural load characteristics in transient and steady state maneuvers
H-Transformer-1D: Fast One-Dimensional Hierarchical Attention for Sequences

We describe an efficient hierarchical method to compute attention in the Transformer architecture. The proposed attention mechanism exploits a matrix structure similar to the Hierarchical Matrix (H-Matrix) developed by the numerical analysis community, and has linear run time and memory complexity. We perform extensive experiments to show that the inductive bias embodied by our hierarchical attention is effective in capturing the hierarchical structure in the sequences typical for natural language and vision tasks. Our method is superior to alternative sub-quadratic proposals by over +6 points on average on the Long Range Arena benchmark. It also sets a new SOTA test perplexity on One-Billion Word dataset with 5x fewer model parameters than that of the previous-best Transformer-based models.
LipSync3D: Data-Efficient Learning of Personalized 3D Talking Faces from Video using Pose and Lighting Normalization

In this paper, we present a video-based learning framework for animating personalized 3D talking faces from audio. We introduce two training-time data normalizations that significantly improve data sample efficiency. First, we isolate and represent faces in a normalized space that decouples 3D geometry, head pose, and texture. This decomposes the prediction problem into regressions over the 3D face shape and the corresponding 2D texture atlas. Second, we leverage facial symmetry and approximate albedo constancy of skin to isolate and remove spatio-temporal lighting variations. Together, these normalizations allow simple networks to generate high fidelity lip-sync videos under novel ambient illumination while training with just a single speaker-specific video. Further, to stabilize temporal dynamics, we introduce an auto-regressive approach that conditions the model on its previous visual state. Human ratings and objective metrics demonstrate that our method outperforms contemporary state-of-the-art audio-driven video reenactment benchmarks in terms of realism, lip-sync and visual quality scores. We illustrate several applications enabled by our framework.
OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport

A normalizing flow is an invertible mapping between an arbitrary probability distribution and a standard normal distribution; it can be used for density estimation and statistical inference. Computing the flow follows the change of variables formula and thus requires invertibility of the mapping and an efficient way to compute the determinant of its Jacobian. To satisfy these requirements, normalizing flows typically consist of carefully chosen components. Continuous normalizing flows (CNFs) are mappings obtained by solving a neural ordinary differential equation (ODE). The neural ODE's dynamics can be chosen almost arbitrarily while ensuring invertibility. Moreover, the log-determinant of the flow's Jacobian can be obtained by integrating the trace of the dynamics' Jacobian along the flow. Our proposed OT-Flow approach tackles two critical computational challenges that limit a more widespread use of CNFs. First, OT-Flow leverages optimal transport (OT) theory to regularize the CNF and enforce straight trajectories that are easier to integrate. Second, OT-Flow features exact trace computation with time complexity equal to trace estimators used in existing CNFs. On five high-dimensional density estimation and generative modeling tasks, OT-Flow performs competitively to state-of-the-art CNFs while on average requiring one-fourth of the number of weights with an 8x speedup in training time and 24x speedup in inference.
Graphical Convergence of Subgradients in Nonconvex Optimization and Learning

We investigate the stochastic optimization problem of minimizing population risk, where the loss defining the risk is assumed to be weakly convex. Compositions of Lipschitz convex functions with sm...
A Low-Cost Attack against the hCaptcha System

CAPTCHAs are a defense mechanism to prevent malicious bot programs from abusing websites on the Internet. hCaptcha is a relatively new but emerging image CAPTCHA service. This paper presents an automated system that can break hCaptcha challenges with a high success rate. We evaluate our system against 270 hCaptcha challenges from live websites and demonstrate that it can solve them with 95.93% accuracy while taking only 18.76 seconds on average to crack a challenge. We run our attack from a docker instance with only 2GB memory (RAM), 3 CPUs, and no GPU devices, demonstrating that it requires minimal resources to launch a successful large-scale attack against the hCaptcha system.
A Review of Machine Learning Classification Using Quantum Annealing for Real-World Applications

Optimizing the training of a machine learning pipeline helps in reducing training costs and improving model performance. One such optimizing strategy is quantum annealing, which is an emerging computing paradigm that has shown potential in optimizing the training of a machine learning model. The implementation of a physical quantum annealer has been realized by D-wave systems and is available to the research community for experiments. Recent experimental results on a variety of machine learning applications using quantum annealing have shown interesting results where the performance of classical machine learning techniques is limited by limited training data and high dimensional features. This article explores the application of D-wave’s quantum annealer for optimizing machine learning pipelines for real-world classification problems. We review the application domains on which a physical quantum annealer has been used to train machine learning classifiers. We discuss and analyze the experiments performed on the D-Wave quantum annealer for applications such as image recognition, remote sensing imagery, computational biology, and particle physics. We discuss the possible advantages and the problems for which quantum annealing is likely to be advantageous over classical computation.
Decentralized Matching in a Probabilistic Environment

We consider a model for repeated stochastic matching where compatibility is probabilistic, is realized the first time agents are matched, and persists in the future. Such a model has applications in the gig economy, kidney exchange, and mentorship matching. We ask whether adecentralized matching process can approximate the optimal online algorithm. In particular, we consider a decentralizedstable matching process where agents match with the most compatible partner who does not prefer matching with someone else, and known compatible pairs continue matching in all future rounds. We demonstrate that the above process provides a 0.316-approximation to the optimal online algorithm for matching on general graphs. We also provide a 1/7-approximation for many-to-one bipartite matching, a 1/11-approximation for capacitated matching on general graphs, and a 1/2k-approximation for forming teams of up to k agents. Our results rely on a novel coupling argument that decomposes the successful edges of the optimal online algorithm in terms of their round-by-round comparison with stable matching.
The Effects of Annealing After Equal Channel Angular Extrusion (ECAE) on Mechanical and Magnetic Properties of 49Fe-49Co-2V Alloy

Equal channel angular extrusion (ECAE) of 49Fe-49Co-2V, also known as Hiperco® 50A or Permendur-2V, greatly improves the strength and ductility of this alloy, while sacrificing soft magnetic performance. ECAE Hiperco specimens were subjected to post-ECAE annealing in order to improve soft magnetic properties. The microstructure, mechanical properties, and magnetic performance are summarized in this study. Annealing begins above 650 °C and a steep decline in yield strength is observed for heat treatments between 700 °C and 840 °C due to grain growth and the Hall–Petch effect, although some strength benefit is still observed in fully annealed ECAE material compared to conventionally processed bar. Soft magnetic properties were assessed through B–H hysteresis curves from which coercivity (Hc) values were extracted. Hc decreases rapidly with annealing above 650 °C as well, i.e., improved soft magnetic behavior. The observed trend is attributed to annealing and grain growth in this temperature regime, which facilitates magnetic domain wall movement. The coercivity vs grain size results generally follow the trend predicted in the literature. The magnetic behavior of annealed ECAE material compares favorably to conventional bar, possibly due to mild crystallographic texturing which enhances properties in the post-ECAE annealed material. Overall, this study highlights a definitive tradeoff between mechanical and magnetic properties brought about by post-ECAE annealing and grain growth.
Vision-Aided Dynamic Blockage Prediction for 6G Wireless Communication Networks

Unlocking the full potential of millimeter-wave and sub-terahertz wireless communication networks hinges on realizing unprecedented low-latency and high-reliability requirements. The challenge in meeting those requirements lies partly in the sensitivity of signals in the millimeter-wave, and sub-terahertz frequency ranges to blockages. One promising way to tackle that challenge is to help a wireless network develop a sense of its surrounding using machine learning. This paper attempts to do that by utilizing deep learning and computer vision. It proposes a novel solution that proactively predicts dynamic link blockages. More specifically, it develops a deep neural network architecture that learns from observed sequences of RGB images and beam-forming vectors how to predict possible future link blockages. The proposed architecture is evaluated on a publicly available dataset that represents a synthetic dynamic communication scenario with multiple moving users and blockages. It scores a link-blockage prediction accuracy in the neighborhood of 86%, a performance that is unlikely to be matched without utilizing visual data.
Ricci Flow and Quantum Theory

In [9, 13, 14] we indicated some relations between Weyl geometry and the quantum potential, between conformal general relativity (GR) and Dirac-Weyl theory, and between Ricci flow and the quantum potential. We would now like to develop this a little further. First we consider simple Ricci flow as in [35, 49]. Thus from [35] we take the Perelman entropy functional as (1A) F(g; f) = R M (jrf j2 + R) exp( f)dV (restricted to f such that R M exp( f)dV = 1) and a Nash (or differential) entropy via (1B) N(u) = R M u log(u)dV where u = exp( f) (M is a compact Riemannian manifold without boundary). One writes dV = p det(g) Q dxi and shows that if g ! g + sh (g; h 2 M = Riem(M)) then (1C) @s det(g)js=0 = ghij det(g) = (Trgh) det(g). This comes from a matrix formula of the following form (1D) @s det(A+ B)js=0 = (A 1 : B) det(A) where A 1 : B = abji = abij for symmetricB (aij comes fromA 1). If one has Ricci flow (1E) @sg= 2Ric (i.e. @sgij = 2Rij) then, considering h 2Ric, one arrives at (1F) @sdV = = RdV whereR=gRij (more general Ricci flow involves (1G) @tgik = 2(Rik +rirk )). We use now t and s interchangeably and suppose @tg= 2Ric with u= exp( f) satisfying u= 0 where = @t +R. Then R M exp( f)dV = 1 is preserved since (1H) @t R M udV = = R M (@su Ru)dV = R M udV = 0 and, after some integration by parts,
Ear wound healing in MRL/MpJ mice is associated with gut microbiome composition and is transferable to non-healer mice via microbiome transplantation.

Objective Adult elastic cartilage has limited repair capacity. MRL/MpJ (MRL) mice, by contrast, are capable of spontaneously healing ear punctures. This study was undertaken to characterize microbiome differences between healer and non-healer mice and to evaluate whether this healing phenotype can be transferred via gut microbiome transplantation. Methods We orally transplanted C57BL/6J (B6) mice with MRL/MpJ cecal contents at weaning and as adults (n = 57) and measured ear hole closure 4 weeks after a 2.0mm punch and compared to vehicle-transplanted MRL and B6 (n = 25) and B6-transplanted MRL (n = 20) mice. Sex effects, timing of transplant relative to earpunch, and transgenerational heritability were evaluated. In a subset (n = 58), cecal microbiomes were profiled by 16S sequencing and compared to ear hole closure. Microbial metagenomes were imputed using PICRUSt. Results Transplantation of B6 mice with MRL microbiota, either in weanlings or adults, improved ear hole closure. B6-vehicle mice healed ear hole punches poorly (0.25±0.03mm, mm ear hole healing 4 weeks after a 2mm ear hole punch [2.0mm-final ear hole size], mean±SEM), whereas MRL-vehicle mice healed well (1.4±0.1mm). MRL-transplanted B6 mice healed roughly three times as well as B6-vehicle mice, and half as well as MRL-vehicle mice (0.74±0.05mm, P = 6.9E-10 vs. B6-vehicle, P = 5.2E-12 vs. MRL-vehicle). Transplantation of MRL mice with B6 cecal material did not reduce MRL healing (B6-transplanted MRL 1.3±0.1 vs. MRL-vehicle 1.4±0.1, p = 0.36). Transplantation prior to ear punch was associated with the greatest ear hole closure. Offspring of transplanted mice healed significantly better than non-transplanted control mice (offspring:0.63±0.03mm, mean±SEM vs. B6-vehicle control:0.25±0.03mm, n = 39 offspring, P = 4.6E-11). Several microbiome clades were correlated with healing, including Firmicutes (R = 0.84, P = 8.0E-7), Lactobacillales (R = 0.65, P = 1.1E-3), and Verrucomicrobia (R = -0.80, P = 9.2E-6). Females of all groups tended to heal better than males (B6-vehicle P = 0.059, MRL-transplanted B6 P = 0.096, offspring of MRL-transplanted B6 P = 0.0038, B6-transplanted MRL P = 1.6E-6, MRL-vehicle P = 0.0031). Many clades characteristic of female mouse cecal microbiota vs. males were the same as clades characteristic of MRL and MRL-transplanted B6 mice vs. B6 controls, including including increases in Clostridia and reductions in Verrucomicrobia in female mice. Conclusion In this study, we found an association between the microbiome and tissue regeneration in MRL mice and demonstrate that this trait can be transferred to non-healer mice via microbiome transplantation. We identified several microbiome clades associated with healing.
Variable autoinhibition among deafness-associated variants of Diaphanous 1 (DIAPH1)

One of the earliest mapped human deafness genes, DIAPH1, encodes the formin DIAPH1. To date, at least three distinct mutations in the C-terminal domains and two additional mutations in the N-terminal region are associated with autosomal dominant hearing loss. The underlying molecular mechanisms are not known, and the role of formins in the inner ear is not well understood. In this study, we use biochemical assays to test the hypotheses that autoinhibition and/or actin assembly activities are disrupted by DFNA1 mutations. Our results indicate that C-terminal mutant forms of DIAPH1 are functional in vitro and promote actin filament assembly. Similarly, N-terminal mutants are well-folded and have quaternary structures and thermal stabilities similar to those of the wild-type (WT) protein. The strength of the autoinhibitory interactions varies widely among mutants, with the ttaa, A265S, and I530S mutations having an affinity similar to that of WT and the 1213x and Δag mutations completely abolishing autoinhibition. These data indicate that, in some cases, hearing loss may be linked to weakened inhibition of actin assembly.
Scaled Small-Gain Approach to Robust Control of LPV Systems with Uncertain Varying Delay

Time-delay linear parameter-varying (LPV) systems with varying uncertainty in the delay are subject to performance degradation and instability. In this line, we investigate the stability of such systems invoking an input-output stability approach. By considering explicit bounds on the delay rate and time-varying delay uncertainty, the scaled small-gain theorem is adopted to form an interconnected time-delay LPV system for the uncertain dynamics. For such an interconnected time-delay LPV system subject to external disturbances, a Lyapunov-Krasovskii functional (LKF) is proposed whose derivative is augmented with terms resulted from the descriptor method. Then, stability analysis, along with control synthesis conditions, characterized via a prescribed induced $\mathcal{L}_{2}$ -norm of the closed-loop system, are derived in a convex linear matrix inequality (LMI) setting. As a benchmark problem, we examine the automated mean arterial blood pressure (MAP) control in a hypotensive patient where the MAP response dynamics to drug infusion is characterized in a time-delay LPV representation. Finally, closed-loop simulation results are provided to assess the proposed methodology's performance.
Interactive 3D Modeling from Multiple Images Using Scene Regularities

We present some recent progress in designing and implementing two interactive image-based 3D modeling systems. The first system constructs 3D models from a collection of panoramic image mosaics. A panoramic mosaic consists of a set of images taken around the same viewpoint, and a camera matrix associated with each input image. The user first interactively specifies features such as points, lines, and planes. Our system recovers the camera pose for each mosaic from known line directions and reference points. It then constructs the 3D model using all available geometrical constraints. The second system extracts structure from stereo by representing the scene as a collection of approximately planar layers. The user first interactively segments the images into corresponding planar regions. Our system recovers a composite mosaic for each layer, estimates the plane equation for the layer, and optionally recovers the camera locations as well as out-of-plane displacements. By taking advantage of known scene regularities, our interactive systems avoid difficult feature correspondence problems that occur in traditional automatic modeling systems. They also shift the interactive high-level structural model specification stage to precede (or intermix with) the 3D geometry recovery. They are thus able to extract accurate wire frame and texture-mapped 3D models from multiple image sequences.
A role for reward valuation in the serotonergic modulation of impulsivity

Impulsive behavior is a deleterious component of a number of mental health disorders but has few targeted pharmacotherapies. One contributing factor to the difficulty in understanding the neural substrates of disordered impulsivity is the diverse presentations of impulsive behavior. Defining the behavioral and cognitive processes which contribute to different subtypes of impulsivity is important for understanding the neural underpinnings of dysregulated impulsive behavior. Using a mouse model for disordered impulsivity, our goal was to identify behavioral and cognitive processes that are associated with increased impulsivity. Specifically, we were interested in the facets of impulsivity modulated by serotonin signaling. We used mice lacking the serotonin 1B receptor (5-HT1BR) and measured different types of impulsivity as well as goal-directed responding, extinction, habitual-like behavior, cue reactivity, and reward reactivity. Mice lacking expression of 5-HT1BR had increased levels of impulsive action, goal-directed responding, and motivation, with no differences seen in rate of extinction, development of habitual behavior, delay discounting, or effort-based discounting. Interestingly, mice lacking 5-HT1BR expression also showed an overall increase in the choice of higher value rewards, increased hedonic responses to sweet rewards, and responded more for cues that predict reward. We developed a novel paradigm to demonstrate that increasing anticipated reward value could directly increase impulsive action. Furthermore, we found that 5-HT1BR KO-induced impulsivity could be ameliorated by decreasing the reward value relative to controls, suggesting that the increased 5-HT1BR-associated impulsive action may be a result of increased reward valuation. Taken together, these data show that the effects of serotonin on impulsive action are mediated through the modulation of hedonic value, which may alter the reward representations that motivate action. Overall, this data supports a role for reward value as an important substrate in impulsive action which may drive clinically relevant increases in impulsivity.
Expression of Sonic Hedgehog and pathway components in the embryonic mouse head: anatomical relationships between regulators of positive and negative feedback.

Objective The Hedgehog pathway is a fundamental signaling pathway in organogenesis. The expression patterns of the ligand Sonic Hedgehog (Shh) and key pathway components have been studied in many tissues but direct spatial comparisons across tissues with different cell compositions and structural organization are not common and could reveal tissue-specific differences in pathway dynamics. Results We directly compared the expression characteristics of Shh, and four genes with functional roles in signaling and whose expression levels serve as readouts of pathway activity in multiple tissues of the embryonic mouse head at embryonic day 15.5 by serial in situ hybridization. The four readout genes were the positive feedback regulator Gli1, and three negative feedback regulators, Patched1, Patched2, and Hedgehog Interacting Protein. While the relative abundance of Gli1 was similar across tissues, the relative expression levels and spatial distribution of Shh and the negative feedback regulators differed, suggesting that feedback regulation of hedgehog signaling is context dependent. This comparative analysis offers insight into how consistent pathway activity could be achieved in tissues with different morphologies and characteristics of ligand expression.
No Watchman in the Night: How Presidential and Congressional Responses to the Coronavirus Pandemic Undermine the Hamiltonian–Madisonian Model of Accountability

The federal government’s response to the coronavirus pandemic raises questions about accountability for a president who fails to competently perform central duties of the office. This paper conside...
Harnessing fluctuations in thermodynamic computing via time-reversal symmetries

We experimentally demonstrate that highly structured distributions of work emerge during even the simple task of erasing a single bit. These are signatures of a refined suite of time-reversal symmetries in distinct functional classes of microscopic trajectories. As a consequence, we introduce a broad family of conditional fluctuation theorems that the component work distributions must satisfy. Since they identify entropy production, the component work distributions encode the frequency of various mechanisms of both success and failure during computing, as well giving improved estimates of the total irreversibly dissipated heat. This new diagnostic tool provides strong evidence that thermodynamic computing at the nanoscale can be constructively harnessed. We experimentally verify this functional decomposition and the new class of fluctuation theorems by measuring transitions between flux states in a superconducting circuit.
Cancer genes generated by rare chromosomal rearrangements rather than activation of oncogenes.

In order to understand cancer, it is necessary to identify cancer genes. The search for such genes and for mechanisms that generate such genes must take into consideration that at the cellular level cancer is a very rare event. The kind of cellular transformation that leads to cancer in vivo occurs only in about one out of 2 × 1017 mitoses in humans and animals. The basis for this estimate is that most animal and human cancers are derived from single transformed cells and are hence monoclonal [1–5], that humans and corresponding animals represent about 1016 mitoses (assuming 1014 cells that go through an average 102 mitoses), and that about one person in five dies from tumors [6].
Routing Algorithm in Networks on the Globe

Packet switching of data in networks is done by either the distance-vector or the link-state routing protocols. These protocols use the Bellman-Ford and the Dijkstra's algorithms re- spectively for the least cost path from a source base station to a destination station. For inter-network transmission, the path-vector routing protocol is in use. With progress of time, the network topologies are becoming huge in size, requiring large demand on book keeping of routing tables and transmission of the data packets dynamically to several other stations of the network by broadcast, increasing the load on the network. Here, assuming the router stations to be terrestrially located with links along the ground, a large network is assumed to lie on a spherical surface, and so the shortest geodesic path from source to destination becomes a great circular arc. For fast transmission, the cost of a link to a node is multicast to its neighboring nodes only for selection of the path lying as close as possible to the geodesic line between the source and the destination. As the arrival and dispatch of data packets at a nodal station occurs randomly, the cost of a link is estimated in this paper by the waiting time of a queueing process. This process at a router station is thus modeled by the Markovian M=M=c model, where c is the number of servers at the router station. If other commercial xed charge is involved for the use of a link, then that can be included in the total cost of a link. Finally, a method of search of a mobile destination is also presented using sphericity of the network. Algorithms for the near geodesic path, costs of links as waiting times and destination search in mobile environment are clearly presented.
Tangential Loewner hulls

Through the Loewner equation, real-valued driving functions generate sets called Loewner hulls. We analyze driving functions that approach 0 at least as fast as \(a (T-t)^r\) as \(t \to T\), where \(r \in (0, 1/2)\), and show that the corresponding Loewner hulls have tangential behavior at time \(T\). We also prove a result about trace existence and apply it to show that the Loewner hulls driven by \(a(T-t)^r\) for \(r \in (0,1/2)\) have a tangential trace curve.
Scalable Monte Carlo inference and rescaled local asymptotic normality

In this paper, we generalize the property of local asymptotic normality (LAN) to an enlarged neighborhood, under the name of rescaled local asymptotic normality (RLAN). We obtain sufficient conditions for a regular parametric model to satisfy RLAN. We show that RLAN supports the construction of a statistically efficient estimator which maximizes a cubic approximation to the log-likelihood on this enlarged neighborhood. In the context of Monte Carlo inference, we find that this maximum cubic likelihood estimator can maintain its statistical efficiency in the presence of asymptotically increasing Monte Carlo error in likelihood evaluation.
Pocformer: A Lightweight Transformer Architecture For Detection Of Covid-19 Using Point Of Care Ultrasound

The rapid and seemingly endless expansion of COVID-19 can be traced back to the inefficiency and shortage of testing kits that offer accurate results in a timely manner. An emerging popular technique, which adopts improvements made in mobile ultrasound technology, allows for healthcare professionals to conduct rapid screenings on a large scale. We present an image-based solution that aims at automating the testing process which allows for rapid mass testing to be conducted with or without a trained medical professional that can be applied to rural environments and third world countries. Our contributions towards rapid large-scale testing include a novel deep learning architecture capable of analyzing ultrasound data that can run in real-time and significantly improve the current state-of-the-art detection accuracies using image-based COVID-19 detection.
Estimating Perinatal Critical Windows to Environmental Mixtures via Structured Bayesian Regression Tree Pairs

BACKGROUND AND AIM: Maternal exposure to environmental chemicals during pregnancy can alter birth and children’s health outcomes. Research seeks to identify critical windows, time periods when expo...
The False Promise of the Converse-1983 Action

The federal government is out of control. At least that’s what many states will tell you. Not only is the federal government passing patently unconstitutional legislation, but its street-level officers are ignoring citizens’ constitutional rights. How can states stop this federal juggernaut? Many are advocating a “repeal amendment,” whereby two-thirds of the states could vote to repeal federal legislation. But the repeal amendment will only address unconstitutional legislation, not unconstitutional actions. States can’t repeal a stop-and-frisk that occurred last Thursday. States might, however, enact a so-called “converse-1983” action. The idea for converse-1983 laws has been around for some time but until now has escaped academic treatment. A converse-1983 action would operate similarly to the popular § 1983 action in that it would provide a cause of action for damages where federal constitutional rights have been violated. Unlike § 1983, however, a converse-1983 would be enacted by a state (rather than Congress) and provide a cause of action against a federal officer (rather than a state officer). By enacting converse-1983 laws, states could thus punish the federal government when its officers disregard the Constitution. The problem with converse-1983 laws, however, is that they just won’t work. In this Article, I explain that converse-1983 laws will always be subject to limitations imposed by Congress or the federal courts. It can hardly be said that converse-1983 laws are a valuable opportunity for states to rein in the federal government if those laws can only be enforced at the pleasure of the federal government. In making this argument, I take the reader on a tour of a variety of topics in the field of constitutional enforcement, including officer immunity, federal common law, the nature of Bivens actions, the constitutional right to a remedy, and Founding-era practices through which states imposed their views on the federal government. Together, these discussions make clear that the converse-1983 action, which has been often cited but rarely questioned, is a cause of action without any legal value.
Real-time oblivious erasure correction with linear time decoding and constant feedback

We continue the study of rateless codes for transmission of information across channels whose rate of erasure is unknown. In such a code, an infinite stream of encoding symbols can be generated from the message and sent across the erasure channel, and the decoder can decode the message after it has successfully collected a certain number of encoding symbols. A rateless erasure code is real-time oblivious if rather than collecting encoding symbols as they are received, the receiver either immediately decodes or discards each symbol it receives. Efficient real-time oblivious erasure correction uses a feedback channel in order to maximize the probability that a received encoding symbol is decoded rather than discarded. We construct codes which are real-time oblivious, but require fewer feedback messages and have faster decoding compared to previous work in [1]. Specifically, for a message of length $k^{\prime}$ , we improve the expected complexity of the feedback channel from $O(\sqrt{k^{\prime}})$ to $O(1)$ , and the expected decoding complexity from $O(k^{\prime}\log(k^{\prime}))$ to $O(k^{\prime})$ . Our method involves using an appropriate block erasure code to first encode the $k^{\prime}$ message symbols, and then using a truncated version of the real-time oblivious erasure correction of [1] to transmit the encoded message to the receiver, which then uses the decoding algorithm for the outer code to recover the message.
An efficient 146-line 3D sensitivity analysis code of stress-based topology optimization written in MATLAB

This paper presents an efficient and compact MATLAB code for three-dimensional stress-based sensitivity analysis. The 146 lines code includes the finite element analysis and p-norm stress sensitivity analysis based on the adjoint method. The 3D sensitivity analysis for p-norm global stress measure is derived and explained in detail accompanied by corresponding MATLAB code. The correctness of the analytical sensitivity is verified by comparison with finite difference approximation. The nonlinear optimization solver is chosen as the Method of moving asymptotes (MMA). Three typical volume-constrained stress minimization problems are presented to verify the effectiveness of sensitivity analysis code. The MATLAB code presented in this paper can be extended to resolve different stress related 3D topology optimization problems. The complete program for sensitivity analysis is given in the Appendix and is intended for educational purposes. MATLAB code is additionally provided in electronic supplementary material for a simple cantilever beam optimization.
Generalized Gelfand–Yaglom formula for a discretized quantum mechanic system

The Gelfand–Yaglom formula relates the regularized determinant of a differential operator to the solution of an initial value problem. Here, we develop a generalized Gelfand–Yaglom formula for a Hamiltonian system with Lagrangian boundary conditions in the discrete and continuous settings. Later, we analyze the convergence of the discretized Hamilton–Jacobi operator and propose a lattice regularization for the determinant.
Toward a Physics Based Model of Hypervelocity Dust Impacts

There has been important understanding of the process by which a hypersonic dust impact makes an electrical signal on a spacecraft sensor, leading to a fuller understanding of the physics. Zaslavsky (2015) showed that the most important signal comes from the charging of the spacecraft, less from charging of an antenna. The present work is an extension of the work of Zaslavsky. An analytical treatment of the physics of a hypersonic dust impact and the mechanism for generating an electrical signal in a sensor, an antenna, is presented. The treatment is compared with observations from STEREO and Parker Solar Probe. A full treatment of this process by simulations seems beyond present computer capabilities, but some parts of the treatment can must depend on simulations but other features can be better understood through analytical treatment. Evidence for a somewhat larger contribution from the antenna part of the signal than in previous publications is presented. Importance of electrostatic forces in forming the exiting plasma cloud is emphasized. Electrostatic forces lead to a rapid expansion of the escaping cloud, so that it expands more rapidly than escapes, and frequently surrounds one or more antennas. This accounts for the ability of dipole antennas to detect dust impacts. Some progress toward an understanding occasional negative charging of an antenna is presented, together with direct evidence of such charging. Use of laboratory measurements of charge to estimate size of spacecraft impacts are shown to be not reliable without further calibration work.
Time's arrow and self-locating probability

One of the most difficult problems in the foundations of physics is what gives rise to the arrow of time. Since the fundamental dynamical laws of physics are (essentially) symmetric in time, the explanation for time's arrow must come from elsewhere. A promising explanation introduces a special cosmological initial condition, now called the Past Hypothesis: the universe started in a low-entropy state. Unfortunately, in a universe where there are many copies of us (in the distant ''past'' or the distant ''future''), the Past Hypothesis is not enough; we also need to postulate self-locating (de se) probabilities. However, I show that we can similarly use self-locating probabilities to strengthen its rival--the Fluctuation Hypothesis, leading to in-principle empirical underdetermination and radical epistemological skepticism. The underdetermination is robust in the sense that it is not resolved by the usual appeal to 'empirical coherence' or 'simplicity.' That is a serious problem for the vision of providing a completely scientific explanation of time's arrow.
A state space modeling approach to real-time phase estimation

Brain rhythms have been proposed to facilitate brain function, with an especially important role attributed to the phase of low-frequency rhythms. Understanding the role of phase in neural function requires interventions that perturb neural activity at a target phase, necessitating estimation of phase in real-time. Current methods for real-time phase estimation rely on bandpass filtering, which assumes narrowband signals and couples the signal and noise in the phase estimate, adding noise to the phase and impairing detections of relationships between phase and behavior. To address this, we propose a state space phase estimator for real-time tracking of phase. By tracking the analytic signal as a latent state, this framework avoids the requirement of bandpass filtering, separately models the signal and the noise, accounts for rhythmic confounds, and provides credible intervals for the phase estimate. We demonstrate in simulations that the state space phase estimator outperforms current state-of-the-art real-time methods in the contexts of common confounds such as broadband rhythms, phase resets, and co-occurring rhythms. Finally, we show applications of this approach to in vivo data. The method is available as a ready-to-use plug-in for the Open Ephys acquisition system, making it widely available for use in experiments.
Political structures and the topology of simplicial complexes

Abstract We use the topology of simplicial complexes to model political structures following Abdou and Keiding (2019). Simplicial complexes are a natural tool to encode interactions in political structures since a simplex can be used to represent a subset of compatible agents. We translate the wedge, cone, and suspension operations into the language of political structures and show how these constructions correspond to merging structures and introducing mediators. We introduce the notions of the viability of an agent and the stability of a political system and examine their interplay with the simplicial complex topology, casting their interactions in category-theoretic language whenever possible. We also introduce a refinement of the model by assigning weights to simplices corresponding to the number of issues the agents agree on. In addition, homology of simplicial complexes is used to detect non-viabilities, certain cycles of incompatible agents, and the (non)presence of mediators. Finally, we extend some results from Abdou and Keiding (2019), bringing viability and stability into the language of friendly delegations and using homology to examine the existence of R -compromises and D -compromises.
Smoothed Analysis of the Condition Number Under Low-Rank Perturbations.

Let M be an arbitrary n by n matrix of rank n-k. We study the condition number of M plus a low-rank perturbation UV^T where U, V are n by k random Gaussian matrices. Under some necessary assumptions, it is shown that M+UV^T is unlikely to have a large condition number. The main advantages of this kind of perturbation over the well-studied dense Gaussian perturbation, where every entry is independently perturbed, is the O(nk) cost to store U,V and the O(nk) increase in time complexity for performing the matrix-vector multiplication (M+UV^T)x. This improves the Ω(n²) space and time complexity increase required by a dense perturbation, which is especially burdensome if M is originally sparse. Our results also extend to the case where U and V have rank larger than k and to symmetric and complex settings. We also give an application to linear systems solving and perform some numerical experiments. Lastly, barriers in applying low-rank noise to other problems studied in the smoothed analysis framework are discussed.
Non-Invertible Global Symmetries and Completeness of the Spectrum

It is widely believed that consistent theories of quantum gravity satisfy two basic kinematic constraints: they are free from any global symmetry, and they contain a complete spectrum of gauge charges. For compact, abelian gauge groups, completeness follows from the absence of a 1-form global symmetry. However, this correspondence breaks down for more general gauge groups, where the breaking of the 1-form symmetry is insufficient to guarantee a complete spectrum. We show that the correspondence may be restored by broadening our notion of symmetry to include non-invertible topological operators, and prove that their absence is sufficient to guarantee a complete spectrum for any compact, possibly disconnected gauge group. In addition, we prove an analogous statement regarding the completeness of twist vortices: codimension-2 objects defined by a discrete holonomy around their worldvolume, such as cosmic strings in four dimensions. We discuss how this correspondence is modified in various, more general contexts, including non-compact gauge groups, Higgsing of gauge theories, and the addition of Chern-Simons terms. Finally, we discuss the implications of our results for the Swampland program, as well as the phenomenological implications of the existence of twist strings.