cssi/SciSciGPT-SciSciNet huggingface dataset:
Controlling network ensembles

The field of optimal control typically requires the assumption of perfect knowledge of the system one desires to control, which is an unrealistic assumption for biological systems, or networks, typically affected by high levels of uncertainty. Here, we investigate the minimum energy control of network ensembles, which may take one of a number of possible realizations. We ensure the controller derived can perform the desired control with a tunable amount of accuracy and we study how the control energy and the overall control cost scale with the number of possible realizations. Our focus is in characterizing the solution of the optimal control problem in the limit in which the systems are drawn from a continuous distribution, and in particular, how to properly pose the weighting terms in the objective function. We verify the theory in three examples of interest: a unidirectional chain network with uncertain edge weights and self-loop weights, a network where each edge weight is drawn from a given distribution, and the Jacobian of the dynamics corresponding to the cell signaling network of autophagy in the presence of uncertain parameters.
Interactive visualization of unstructured grids using hierarchical 3D textures

We present a system for interactively rendering large, unstructured grids. Our approach is to voxelize the grid into a 3D voxel octree, and then to render the data using hierarchical, 3D texture mapping. This approach leverages the current 3D texture mapping PC hardware for the problem of unstructured grid rendering. We specialize the 3D texture octree to the task of rendering unstructured grids through a novel pad and stencil algorithm, which distinguishes between data and non-data voxels. Both the voxelization and rendering processes efficiently manage large, out-of-core datasets. The system manages cache usage in main memory and texture memory, as well as bandwidths among disk, main memory, and texture memory. It also manages rendering load to achieve interactivity at all times. It maximizes a quality metric for a desired level of interactivity. It has been applied to a number of large data and produces high quality images at interactive, user-selectable frame rates using standard PC hardware.
Communications Standards for Unmanned Aircraft Systems: The 3GPP Perspective and Research Drivers

An unmanned aircraft system (UAS) consists of an unmanned aerial vehicle (UAV) and its controller, which use radios to communicate. While the remote controller (RC) is traditionally operated by a person who is maintaining visual line of sight with the UAV it controls, the trend is moving toward long-range control and autonomous operation. To enable this, reliable and widely available wireless connectivity is needed because it is the only way to remotely control a UAV or take control of an autonomous UAV flight. This article surveys the ongoing Third Generation Partnership Project standardization activities for enabling networked UASs. In particular, we present the requirements, envisaged architecture, and services to be offered to/by UAVs and RCs, which will communicate with one another, with the UAS traffic management (UTM), and with other users through cellular networks. Critical research directions relate to security and spectrum coexistence, among others. We identify major R&D platforms that will drive the standardization of cellular communications networks and applications.
Distortion-Controlled Training for end-to-end Reverberant Speech Separation with Auxiliary Autoencoding Loss

The performance of speech enhancement and separation systems in anechoic environments has been significantly advanced with the recent progress in end-to-end neural network architectures. However, the performance of such systems in reverberant environments is yet to be explored. A core problem in reverberant speech separation is about the training and evaluation metrics. Standard time-domain metrics may introduce unexpected distortions during training and fail to properly evaluate the separation performance due to the presence of the reverberations. In this paper, we first introduce the "equal-valued contour" problem in reverberant separation where multiple outputs can lead to the same performance measured by the common metrics. We then investigate how "better" outputs with lower target-specific distortions can be selected by auxiliary autoencoding training (A2T). A2T assumes that the separation is done by a linear operation on the mixture signal, and it adds an loss term on the autoencoding of the direct-path target signals to ensure that the distortion introduced on the direct-path signals is controlled during separation. Evaluations on separation signal quality and speech recognition accuracy show that A2T is able to control the distortion on the direct-path signals and improve the recognition accuracy.
Unboundedness of the number of rational points on curves over function fields

We construct sequences of smooth nonisotrivial curves of every genus at least two, defined over a rational function field of positive characteristic, such that the (finite) number of rational points of the curves in the sequence cannot be uniformly bounded. The question of whether there is a uniform bound for the number of rational points on curves of fixed genus greater than one over a fixed number field has been considered by several authors. In particular, Caporaso et al. [CHM97] showed that this would follow from the Bombieri–Lang conjecture that the set of rational points on a variety of general type over a number field is not Zariski dense. Abramovich and the third author [AV96] extended this result to get other uniform boundedness consequences of the Bombieri– Lang conjecture and gave some counterexamples for function fields. These counterexamples are singular curves that “change genus”. They behave like positive genus curves (and, in particular, have finitely many rational points), but are parametrizable over an inseparable extension of the ground field. In [AV96] it is shown that, for this class of equation, uniform boundedness does not hold. Specifically, one gets a one-parameter family of equations which, for suitable choice of the parameter, have a finite but arbitrarily large number of solutions. However, a negative answer to the original uniform boundedness question for smooth curves of genus at least two remained open in the function field case. In this paper we provide counterexamples to this uniform boundedness, extending constructions of the first two authors [Con09, Ulm09] for elliptic curves. Theorem. Let p > 3 be a prime number and let r be an odd number coprime to p. The number of rational points over Fp(t) of the curve Xa with equation y2 = x(xr + 1)(xr + ar) is unbounded as a varies in Fp(t) \ Fp. Proof. Let d = pn + 1 and a = td. If m divides n and n/m is odd, then d′ = pm + 1 divides d. Setting e = d/d′, we have the rational point (x, y) = (te, te(r+1)/2(tre + 1)d /2) on Xa. Thus if we take n to be odd with many factors, we have many points. Received April 9, 2012. 2010 Mathematics Subject Classification. 11G30.
Slaves for Rent: Sexual Harassment in Housing as Involuntary Servitude

Recognizing the various shortcomings of the FHA when applied in the context of post-acquisition harassment in general, and sexual harassment in particular, this Article explores an alternative vehicle for victims of such abuse: the Thirteenth Amendment. Ratified in 1865, the Thirteenth Amendment provided a formal legal end to African chattel slavery across the United States. But the Thirteenth Amendment has legal importance beyond the abolition of slavery. The Amendment was and remains both a powerful liberating force and a guarantor of fundamental rights for all Americans. In particular, the text of the Thirteenth Amendment extends its reach beyond slavery to explicitly prohibit all forms of involuntary servitude, regardless of the victim's race, gender, or socio-economic background. The true breadth of the Amendment makes it an important tool in the eradication of modern forms of involuntary servitude-including the abusive and sexually coercive relationships that can be created between landlords and their poor, marginalized victims.Section II begins the analysis by defining what constitutes sexual harassment in the housing context and attempting to quantify the problem using measurements based on empirical studies, governmental and housing advocacy data, and investigative journalism. Section III briefly outlines the traditional vehicle for bringing claims of residential sexual harassment in federal court, the FHA, as well as its potential shortcomings and limitations in this context- including the argument gaining traction with some federal courts that the FHA has no application whatsoever to claims of post-acquisition harassment. Against that backdrop, Section IV explores the possible applicability of the Thirteenth Amendment to claims of sexual harassment in housing. In particular, Section IV calls for a broad but textually accurate reading of the Thirteenth Amendment to trigger the full extent of the Amendment's protections. Through the lens of the Thirteenth Amendment, sexual harassment of low-income, vulnerable women can reasonably be viewed as involuntary servitude.
Theoretical Constraints Imposed by Gradient Detection and Dispersal on Microbial Size in Astrobiological Environments.

The capacity to sense gradients efficiently and acquire information about the ambient environment confers many advantages such as facilitating movement toward nutrient sources or away from toxic ch...
Avoiding Communication in Logistic Regression

Stochastic gradient descent (SGD) is one of the most widely used optimization methods for solving various machine learning problems. SGD solves an optimization problem by iteratively sampling a few data points from the input data, computing gradients for the selected data points, and updating the solution. However, in a parallel setting, SGD requires interprocess communication at every iteration. We introduce a new communication-avoiding technique for solving the logistic regression problem using SGD. This technique re-organizes the SGD computations into a form that communicates every $s$ iterations instead of every iteration, where $s$ is a tuning parameter. We prove theoretical flops, bandwidth, and latency upper bounds for SGD and its new communication-avoiding variant. Furthermore, we show experimental results that illustrate that the new Communication-Avoiding SGD (CA-SGD) method can achieve speedups of up to 4.97× on a high-performance Infiniband cluster without altering the convergence behavior or accuracy.
Megakaryocyte progenitor cell function is enhanced upon aging despite the functional decline of aged hematopoietic stem cells

Age-related morbidity is associated with a decline in hematopoietic stem cell (HSC) function, but the mechanisms of HSC aging remain unclear. We performed heterochronic HSC transplants followed by quantitative analysis of cell reconstitution. Although young HSCs outperformed old HSCs in young recipients, young HSCs unexpectedly failed to outcompete the old HSCs of aged recipients. Interestingly, despite substantial enrichment of megakaryocyte progenitors (MkPs) in old mice in situ and reported platelet (Plt) priming with age, transplanted old HSCs were deficient in reconstitution of all lineages, including MkPs and Plts. We therefore performed functional analysis of young and old MkPs. Surprisingly, old MkPs displayed unmistakably greater regenerative capacity compared with young MkPs. Transcriptome analysis revealed putative molecular regulators of old MkP expansion. Collectively, these data demonstrated that aging affects HSCs and megakaryopoiesis in fundamentally different ways: whereas old HSCs functionally decline, MkPs gain expansion capacity upon aging.
Ternary Nitride Materials: Fundamentals and Emerging Device Applications

Interest in inorganic ternary nitride materials has grown rapidly over the past few decades, as their diverse chemistries and structures make them appealing for a variety of applications. Due to sy...
A persistent adjoint method with dynamic time-scaling and an application to mass action kinetics

In this article, we consider an optimization problem where the objective function is evaluated at the fixed-point of a contraction mapping parameterized by a control variable, and optimization takes place over this control variable. Since the derivative of the fixed-point with respect to the parameter can usually not be evaluated exactly, an adjoint dynamical system can be used to estimate gradients. Using this estimation procedure, the optimization algorithm alternates between derivative estimation and an approximate gradient descent step. We analyze a variant of this approach involving dynamic time-scaling, where after each parameter update the adjoint system is iterated until a convergence threshold is passed. We prove that, under certain conditions, the algorithm can find approximate stationary points of the objective function. We demonstrate the approach in the settings of an inverse problem in chemical kinetics, and learning in attractor networks.
Approximate Spielman-Teng Theorems for the Least Singular Value of Random Combinatorial Matrices

An approximate Spielman-Teng theorem for the least singular value sn(Mn) of a random n × n square matrix Mn is a statement of the following form: there exist constants C, c > 0 such that for all η > 0, Pr(sn(Mn) ≤ η) ≲ nCη + exp(−nc). The goal of this paper is to develop a simple and novel framework for proving such results for discrete random matrices. As an application, we prove an approximate Spielman-Teng theorem for {0, 1}-valued matrices, each of whose rows is an independent vector with exactly n/2 zero components. This improves on previous work of Nguyen and Vu, and is the first such result in a ‘truly combinatorial’ setting.
Unsupervised Learning for Asynchronous Resource Allocation In Ad-Hoc Wireless Networks

We consider optimal resource allocation problems under asynchronous wireless network setting. Without explicit model knowledge, we design an unsupervised learning method based on Aggregation Graph Neural Networks (Agg-GNNs). Depending on the localized aggregated information structure on each network node, the method can be learned globally and asynchronously while implemented locally. We capture the asynchrony by modeling the activation pattern as a characteristic of each node and train a policy-based power allocation method. We also propose a permutation invariance property which indicates the transferability of the trained Agg-GNN. We finally verify our strategy by numerical simulations compared with baseline methods.
EBMs Trained with Maximum Likelihood are Generator Models Trained with a Self-adverserial Loss.

Maximum likelihood estimation is widely used in training Energy-based models (EBMs). Training requires samples from an unnormalized distribution, which is usually intractable, and in practice, these are obtained by MCMC algorithms such as Langevin dynamics. However, since MCMC in high-dimensional space converges extremely slowly, the current understanding of maximum likelihood training, which assumes approximate samples from the model can be drawn, is problematic. In this paper, we try to understand this training procedure by replacing Langevin dynamics with deterministic solutions of the associated gradient descent ODE. Doing so allows us to study the density induced by the dynamics (if the dynamics are invertible), and connect with GANs by treating the dynamics as generator models, the initial values as latent variables and the loss as optimizing a critic defined by the very same energy that determines the generator through its gradient. Hence the term - self-adversarial loss. We show that reintroducing the noise in the dynamics does not lead to a qualitative change in the behavior, and merely reduces the quality of the generator. We thus show that EBM training is effectively a self-adversarial procedure rather than maximum likelihood estimation.
UVTOMO-GAN: An Adversarial Learning Based Approach For Unknown View X-Ray Tomographic Reconstruction

Tomographic reconstruction recovers an unknown image given its projections from different angles. State-of-the-art methods addressing this problem assume the angles associated with the projections are known a-priori. Given this knowledge, the reconstruction process is straightforward as it can be formulated as a convex problem. Here, we tackle a more challenging setting: 1) the projection angles are unknown, 2) they are drawn from an unknown probability distribution. In this set-up our goal is to recover the image and the projection angle distribution using an unsupervised adversarial learning approach. For this purpose, we formulate the problem as a distribution matching between the real projection lines and the generated ones from the estimated image and projection distribution. This is then solved by reaching the equilibrium in a min-max game between a generator and a discriminator. Our novel contribution is to recover the unknown projection distribution and the image simultaneously using adversarial learning. To accommodate this, we use Gumbel-softmax approximation of samples from categorical distribution to approximate the generator’s loss as a function of the unknown image and the projection distribution. Our approach can be generalized to different inverse problems. Our simulation results reveal the ability of our method in successfully recovering the image and the projection distribution in various settings.
Towards Activity-Centric Access Control for Smart Collaborative Ecosystems

The ubiquitous presence of smart devices along with advancements in connectivity coupled with the elastic capabilities of cloud and edge systems have nurtured and revolutionized smart ecosystems. Intelligent, integrated cyber-physical systems offer increased productivity, safety, efficiency, speed and support for data driven applications beyond imagination just a decade ago. Since several connected devices work together as a coordinated unit to ensure efficiency and automation, the individual operations they perform are often reliant on each other. Therefore, it is important to control what functions or activities different devices can perform at a particular moment of time, and how they are related to each other. It is also important to consider additional factors such as conditions, obligation or mutability of activities, which are critical in deciding whether or not a device can perform a requested activity. In this paper, we take an initial step to propose and discuss the concept of Activity-Centric Access Control (ACAC) for smart and connected ecosystem. We discuss the notion of activity with respect to the collaborative and distributed yet integrated systems and identify the different entities involved along with the important factors to make an activity control decision. We outline a preliminary approach for defining activity control expressions which can be applied to different smart objects in the system. The main goal of this paper is to present the vision and need for the activity-centric approach for access control in connected smart systems, and foster discussion on the identified future research agenda.
Inapproximability of unique games in fixed-point logic with counting

We study the extent to which it is possible to approximate the optimal value of a Unique Games instance in Fixed-Point Logic with Counting (FPC). We prove two new FPC- inexpressibility results for Unique Games: the existence of a $\left( {\frac{1}{2},\frac{1}{3} + \delta } \right)$-inapproximability gap, and inapproximability to within any constant factor. Previous recent work has established similar FPC-inapproximability results for a small handful of other problems. Our construction builds upon some of these ideas, but contains a novel technique. While most FPC-inexpressibility results are based on variants of the CFI-construction, ours is significantly different.
Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features

Knowledge-grounded dialogue systems are intended to convey information that is based on evidence provided in a given source text. We discuss the challenges of training a generative neural dialogue model for such systems that is controlled to stay faithful to the evidence. Existing datasets contain a mix of conversational responses that are faithful to selected evidence as well as more subjective or chit-chat style responses. We propose different evaluation measures to disentangle these different styles of responses by quantifying the informativeness and objectivity. At training time, additional inputs based on these evaluation measures are given to the dialogue model. At generation time, these additional inputs act as stylistic controls that encourage the model to generate responses that are faithful to the provided evidence. We also investigate the usage of additional controls at decoding time using resampling techniques. In addition to automatic metrics, we perform a human evaluation study where raters judge the output of these controlled generation models to be generally more objective and faithful to the evidence compared to baseline dialogue systems.
Discovery of a pre-mRNA structural scaffold as a contributor to the mammalian splicing code

The specific recognition of splice signals at or near exon-intron junctions is not explained by their weak conservation and instead is postulated to require a multitude of features embedded in the pre-mRNA strand. We explored the possibility of 3D structural scaffold of AdML-a model pre-mRNA substrate-guiding early spliceosomal components to the splice signal sequences. We find that mutations in the non-cognate splice signal sequences impede recruitment of early spliceosomal components due to disruption of the global structure of the pre-mRNA. We further find that the pre-mRNA segments potentially interacting with the early spliceosomal component U1 snRNP are distributed across the intron, that there is a spatial proximity of 5' and 3' splice sites within the pre-mRNA scaffold, and that an interplay exists between the structural scaffold and splicing regulatory elements in recruiting early spliceosomal components. These results suggest that early spliceosomal components can recognize a 3D structural scaffold beyond the short splice signal sequences, and that in our model pre-mRNA, this scaffold is formed across the intron involving the major splice signals. This provides a conceptual basis to analyze the contribution of recognizable 3D structural scaffolds to the splicing code across the mammalian transcriptome.
Extending rationally connected fibrations from ample subvarieties

Using deformation theory of rational curves, we prove a conjecture of Sommese on the extendability of morphisms from ample subvarieties when the morphism is a smooth (or mildly singular) fibration with rationally connected fibers. We apply this result in the context of Fano fibrations and prove a classification theorem for projective bundle and quadric fibration structures on ample subvarieties.
Computing higher symplectic capacities I

We present recursive formulas which compute the recently defined "higher symplectic capacities" for all convex toric domains. In the special case of four-dimensional ellipsoids, we apply homological perturbation theory to the associated filtered L-infinity algebras and prove that the resulting structure coefficients count punctured pseudoholomorphic curves in cobordisms between ellipsoids. As sample applications, we produce new previously inaccessible obstructions for stabilized embeddings of ellipsoids and polydisks, and we give new counts of curves with tangency constraints.
The effect of continuum elimination in identifying circumstellar dust around Mira

Asymptotic Giant Branch (AGB) stars are major contributors of cosmic dust to the universe. Typically, dust around AGB stars is investigated via radiative transfer (RT) modeling, or via simple deconstruction of observed spectra. However, methodologies applied vary. Using archival spectroscopic, photometric, and temporal data for the archetypal dusty star, Mira, we identify its circumstellar silicate dust grains. This is achieved by matching the positions and widths of observed spectral features with laboratory data. To do this comparison properly, it is necessary to account for the continuum emission. Here we investigate various ways in which a continuum is eliminated from observational spectra and how it affects the interpretation of spectral features. We find that while the precise continuum shapes and temperatures do not have a critical impact on the positions and shapes of dust spectral features, it is important to eliminate continua in a specific way. It is important to understand what contributes to the spectrum in order to remove the continuum in a way that allows comparison with laboratory spectra of candidate dust species. Our methodologies are applicable to optically thin systems, like that of Mira. Higher optical depths will require RT modeling, which cannot include many different potential astrominerals because there is a lack of complex refractive indices. Finally, we found that the classic silicate feature exhibited by Mira is not consistent with a real amorphous silicate alone but may be best explained with a small alumina contribution to match the observed FWHM of the 10 micron feature.
Force production mechanisms of a tangential jet on bodies at high alpha

This work explores the mechanisms by which tangential slot blowing creates forces on a body at a high angle of attack. The study is conducted numerically by solving the three-dimensional, compressible-flow Navier-Stokes equations. A tangent-ogive cylinder configuration is used with the blowing slot located both on the nose and on the cylindrical part of the body. The angle of attack used is 30 deg., the Mach no. is 0.2 and the Reynolds no., based on diameter, is 52,000. Several conclusions were made concerning the physical mechanisms by which the jet interacts with the ambient flowfield to produce a side force: (1) A centrifugal force component is created at the wall due to the momentum of the jet being forced to follow the curvature of the surface. (2) A large amount of vorticity is added to the flowfield by the jet. In the region of the slot, the vorticity has the effect of inducing circulation around the body. Downstream of the slot, the vorticity alters the strength of the nose vortices. (3) The position of the nose vortices can be altered to the jet changing the location of separation. And (4), the jet has the ability to excite unstable behavior producing a global change in the character of the flow.
Robust Reinforcement Learning Under Minimax Regret for Green Security

Green security domains feature defenders who plan patrols in the face of uncertainty about the adversarial behavior of poachers, illegal loggers, and illegal fishers. Importantly, the deterrence effect of patrols on adversaries' future behavior makes patrol planning a sequential decision-making problem. Therefore, we focus on robust sequential patrol planning for green security following the minimax regret criterion, which has not been considered in the literature. We formulate the problem as a game between the defender and nature who controls the parameter values of the adversarial behavior and design an algorithm MIRROR to find a robust policy. MIRROR uses two reinforcement learning-based oracles and solves a restricted game considering limited defender strategies and parameter values. We evaluate MIRROR on real-world poaching data.
Measuring the quantum efficiency of X-ray hybrid CMOS detectors

Next-generation X-ray observatories, such as the Lynx X-ray Observatory Mission Concept, will require detectors with high quantum efficiency (QE) across the soft X-ray band to observe the faint objects that drive their mission science cases. Hybrid CMOS Detectors (HCDs), a form of active-pixel sensor, are promising candidates for use on these missions because of their faster read-out, lower power consumption, and greater radiation hardness than detectors used in the current generation of X-ray telescopes. In this work, we present QE measurements of a Teledyne H2RG HCD. These measurements were performed using a gas-flow proportional counter as a reference detector to measure the absolute flux incident on the HCD. We find an effective QE of 95:0 ± 1:1% at the Mn ∝/Kβ lines (at 5.9 and 6.5 keV), 98:5 ± 1:8% at the Al Ka line (1.5 keV), and 85:0 ± 2:8% at the O K∝ line (0.52 keV).
Kinome-Wide Activity Classification of Small Molecules by Deep Learning

Deep learning is a machine learning technique to model high-level abstractions in data by utilizing a graph composed of multiple processing layers that experience various linear and non-linear transformations. This technique has been shown to perform well for applications in drug discovery, utilizing structural features of small molecules to predict activity. Here we report a large-scale study to predict the activity of small molecules across the human Kinome. Kinases represent a major family of drug targets and small molecule kinase inhibitors are an important class of anti-cancer agents and have demonstrated impressive clinical efficacy in several different diseases.However, resistance is often observed mediated by adaptive Kinome reprogramming or subpopulation diversity. Therefore, polypharmacology and combination therapies offer potential therapeutic strategies for patients with resistant disease. Their development would benefit from more comprehensive and dense knowledge of small-molecule inhibition across the human Kinome. Because such data is not publicly available, we evaluated multiple machine learning methods to predict small molecule inhibition of 342 kinases across the human Kinome using over 650K aggregated bioactivity annotations for over 300K small molecules curated from ChEMBL and the Kinase Knowledge Base (KKB). Our results demonstrated that multi-task deep neural networks outperform classical single-task methods, offering potential towards predicting activity profiles and filling gaps in the available data.
LABNet: Local graph aggregation network with class balanced loss for vehicle re-identification

Abstract Vehicle re-identification is an important computer vision task where the objective is to identify a specific vehicle among a set of vehicles seen at various viewpoints. Recent methods based on deep learning utilize a global average pooling layer after the backbone feature extractor, however, this ignores any spatial reasoning on the feature map. In this paper, we propose local graph aggregation on the backbone feature map, to learn associations of local information and hence improve feature learning as well as reduce the effects of partial occlusion and background clutter. Our local graph aggregation network considers spatial regions of the feature map as nodes and builds a local neighborhood graph that performs local feature aggregation before the global average pooling layer. We further utilize a batch normalization layer to improve the system effectiveness. Additionally, we introduce a class balanced loss to compensate for the imbalance in the sample distributions found in the most widely used vehicle re-identification datasets. Finally, we evaluate our method in three popular benchmarks and show that our approach outperforms many state-of-the-art methods.
Thalamocortical Mechanisms Regulating the Relationship between Transient Beta Events and Human Tactile Perception.

Transient neocortical events with high spectral power in the 15-29 Hz beta band are among the most reliable predictors of sensory perception. Prestimulus beta event rates in primary somatosensory cortex correlate with sensory suppression, most effectively 100-300 ms before stimulus onset. However, the neural mechanisms underlying this perceptual association are unknown. We combined human magnetoencephalography (MEG) measurements with biophysical neural modeling to test potential cellular and circuit mechanisms that underlie observed correlations between prestimulus beta events and tactile detection. Extending prior studies, we found that simulated bursts from higher-order, nonlemniscal thalamus were sufficient to drive beta event generation and to recruit slow supragranular inhibition acting on a 300 ms timescale to suppress sensory information. Further analysis showed that the same beta-generating mechanism can lead to facilitated perception for a brief period when beta events occur simultaneously with tactile stimulation before inhibition is recruited. These findings were supported by close agreement between model-derived predictions and empirical MEG data. The postevent suppressive mechanism explains an array of studies that associate beta with decreased processing, whereas the during-event facilitatory mechanism may demand a reinterpretation of the role of beta events in the context of coincident timing.
Capacity Bounds and User Identification Costs in Rayleigh-Fading Many-Access Channel

Many-access channel (MnAC) model allows the number of users in the system and the number of active users to scale as a function of the blocklength and as such is suited for dynamic communication systems with massive number of users such as the Internet of Things. Existing MnAC models assume a priori knowledge of channel gains which is impractical since acquiring Channel State Information (CSI) for massive number of users can overwhelm the available radio resources. This paper incorporates Rayleigh fading effects to the MnAC model and derives an upper bound on the symmetric message-length capacity of the Rayleigh-fading Gaussian MnAC. Furthermore, a lower bound on the minimum number of channel uses for discovering the active users is established. In addition, the performance of Noisy-Combinatorial Orthogonal Matching Pursuit (N-COMP) based group testing (GT) is studied as a practical strategy for active device discovery. Simulations show that, for a given SNR, as the number of users increase, the required number of channel uses for N-COMP GT scales approximately the same way as the lower bound on minimum user identification cost. Moreover, in the low SNR regime, for sufficiently large population sizes, the number of channel uses required by N-COMP GT was observed to be within a factor of two of the lower bound when the expected number of active users scales sub-linearly with the total population size.
Enhanced Cas12a multi-gene regulation using a CRISPR array separator

The type V-A Cas12a protein can process its CRISPR array, a feature useful for multiplexed gene editing and regulation. However, CRISPR arrays often exhibit unpredictable performance due to interference between multiple guide RNA (gRNAs). Here, we report that Cas12a array performance is hypersensitive to the GC content of gRNA spacers, as high-GC spacers can impair activity of the downstream gRNA. We analyze naturally occurring CRISPR arrays and observe that natural repeats always contain an AT-rich fragment that separates gRNAs, which we term a CRISPR separator. Inspired by this observation, we design short, AT-rich synthetic separators (synSeparators) that successfully remove the disruptive effects between gRNAs. We further demonstrate enhanced simultaneous activation of seven endogenous genes in human cells using an array containing the synSeparator. These results elucidate a previously underexplored feature of natural CRISPR arrays and demonstrate how nature-inspired engineering solutions can improve multi-gene control in mammalian cells.
Estimating housing rent depreciation for inflation adjustments

Abstract U.S. inflation measures, such as the Consumer Price Index, are adjusted for an aging-bias based on estimates of the average rent depreciation. This study analyzes the characteristics of rent depreciation using novel, market-based data on rental contracts in Las Vegas, NV. We find that the estimated annual depreciation rate for new properties is 0.9% for single-family residences and 1.5% for condominiums. The higher depreciation rate for condominiums is due to higher functional obsolescence instead of physical deterioration. Rent depreciation rates are lower for older and smaller structures and vary significantly across neighborhoods. Our results suggest that local inflation rates are biased downward where new and large units increased since the last update to the official rent depreciation estimates but upward where the housing stock became older. From an asset pricing perspective, failing to account for initially high depreciation results in an overvaluation of new properties and an undervaluation of old properties.
Constrained percolation in two dimensions

We prove absence of infinite clusters and contours in a class of critical constrained percolation models on the square lattice. The percolation configuration is assumed to satisfy certain hard local constraints, but only weak symmetry and ergodicity conditions are imposed on its law. The proofs use new combinatorial techniques exploiting planar duality. Applications include absence of infinite clusters of diagonal edges for critical dimer models on the square-octagon lattice, as well as absence of infinite contours and infinite clusters for critical XOR Ising models on the square grid. We also prove that there exists at most one infinite contour for high-temperature XOR Ising models, and no infinite contour for low-temperature XOR Ising model.
Does Analyst Coverage Affect Workplace Safety

We find firms’ work-related injury rates are negatively associated with the level of analyst coverage. This result is also robust at the establishment level at which we find local analysts have a m...
Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows

This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differences, we propose a hierarchical Transformer whose representation is computed with shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size. These qualities of Swin Transformer make it compatible with a broad range of vision tasks, including image classification (86.4 top-1 accuracy on ImageNet-1K) and dense prediction tasks such as object detection (58.7 box AP and 51.1 mask AP on COCO test-dev) and semantic segmentation (53.5 mIoU on ADE20K val). Its performance surpasses the previous state-of-the-art by a large margin of +2.7 box AP and +2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the potential of Transformer-based models as vision backbones. The code and models will be made publicly available at~\url{this https URL}.
Gauge independent quantum gravitational corrections to Maxwell’s equation

We consider quantum gravitational corrections to Maxwell’s equations on flat space background. Although the vacuum polarization is highly gauge dependent, we explicitly show that this gauge dependence is canceled by contributions from the source which disturbs the effective field and the observer who measures it. Our final result is a gauge independent, real and causal effective field equation that can be used in the same way as the classical Maxwell equation.
Credit cards and the floating rate channel of monetary policy

I quantify the impact of Federal Funds Rate (FFR) movements on consumers’ welfare via the floating, or variable, rate on their credit cards. I first newly document that 96% of card rates adjust to ...
Chain conformations and phase separation in polymer solutions with varying solvent quality

Molecular dynamics simulations are used to investigate the conformations of a single polymer chain, represented by the Kremer-Grest bead-spring model, in a solution with a Lennard-Jones liquid as the solvent when the interaction strength between the polymer and solvent is varied. Results show that when the polymer-solvent interaction is unfavorable, the chain collapses as one would expect in a poor solvent. For more attractive polymer-solvent interactions, the solvent quality improves and the chain is increasingly solvated and exhibits ideal and then swollen conformations. However, as the polymer-solvent interaction strength is increased further to be more than about twice of the strength of the polymer-polymer and solvent-solvent interactions, the chain exhibits an unexpected collapsing behavior. Correspondingly, for strong polymer-solvent attractions, phase separation is observed in the solutions of multiple chains. These results indicate that the solvent becomes effectively poor again with very attractive polymer-solvent interactions. Nonetheless, the mechanism of chain collapsing and phase separation in this limit differs from the case with a poor solvent rendered by unfavorable polymer-solvent interactions. In the latter, the solvent is excluded from the domain of the collapsed chains while in the former, the solvent is still present in the pervaded volume of a collapsed chain or in the polymer-rich domain that phase separates from the pure solvent. In the limit of strong polymer-solvent attractions, the solvent behaves as a glue to stick monomers together, causing a single chain to collapse and multiple chains to aggregate and phase separate.
A relation between higher-rank PT stable objects and quotients of coherent sheaves

On a smooth projective threefold, we construct an essentially surjective functor F from a category of two-term complexes to a category of quotients of coherent sheaves and describe the fibers of this functor. Under a coprime assumption on rank and degree, the domain of F coincides with the category of higher-rank PT-stable objects, which appears on one side of Toda’s higher-rank DT/PT correspondence formula. The codomain of F is the category of objects that appears on one side of a correspondence formula by Gholampour and Kool, between the generating series of topological Euler characteristics of two types of quot schemes.
Monopsonistic labor markets and international trade

Abstract We study the impact of trade on wage inequality and welfare when the labor market is monopsonistic. Firm heterogeneity in productivity along with workers’ idiosyncratic preferences for different firms generate between-firm wage inequality for workers with identical skills. The model features a novel welfare channel of workers’ “love of firm variety.” Trade liberalization provides additional welfare gains through the firm-variety channel when monopsony power is high but detracts from welfare gains when monopsony power is low.
Diffraction as scattering under the Born approximation

Light diffraction at an aperture is a basic problem that has generated a tremendous amount of interest in optics. Some of the most significant diffraction results are the Fresnel-Kirchhoff and Rayleigh-Sommerfeld formulas. These theories are based on solving the wave equation using Green’s theorem and result in slightly different expressions depending on the particular boundary conditions employed. In this paper, we show that the diffraction by a thin screen, which includes apertures, gratings, transparencies etc, can be treated more generally as a particular case of scattering. Furthermore, applying the first order Born approximation to 2D objects, we obtain a general diffraction formula, without angular approximations. Finally, our result, which contains no obliquity factor, is consistent with the 3D theory of scattering. We discuss several common approximations and place our results in the context of existing theories.
ON-OFF Privacy in the Presence of Correlation

We formulate and study the problem of ON-OFF privacy. ON-OFF privacy algorithms enable a user to continuously switch his privacy between ON and OFF. An obvious example is the incognito mode in internet browsers. But beyond internet browsing, ON-OFF privacy can be a desired feature in most online applications. The challenge is that the statistical correlation over time of a user’s online behavior can lead to leakage of information. We consider the setting in which a user is interested in retrieving the latest message generated by one of $N$ sources. The user’s privacy status can change between ON and OFF over time. When privacy is ON the user wants to hide his request. Moreover, since the user’s requests depend on personal attributes such as age, gender, and political views, they are typically correlated over time. As a consequence, the user cannot simply ignore privacy when privacy is OFF. We model the correlation between user’s requests by an $N$ state Markov chain. The goal is to design query schemes with optimal download rate, that preserve privacy in an ON-OFF privacy setting. In this paper, we present inner and outer bounds on the achievable download rate for $N$ sources. We also devise an efficient algorithm to construct an ON-OFF privacy scheme achieving the inner bound and prove its optimality in the case $N=2$ sources. For $N > 2$ , finding tighter outer bounds and efficient constructions of ON-OFF privacy schemes that would achieve them remains an open question.
Identity signaling, identity reception and the evolution of social recognition in a Neotropical frog

Animals recognize familiar individuals to perform a variety of important social behaviors. Social recognition is often mediated by communication between signalers who produce signals that contain identity information and receivers who categorize these signals based on previous experience. We tested two hypotheses about adaptations in signalers and receivers that enable the evolution of social recognition using two species of closely related territorial poison frogs. Male golden rocket frogs (Anomaloglossus beebei) recognize the advertisement calls of conspecific territory neighbors and display a "dear enemy effect" by responding less aggressively to neighbors than strangers, while male Kai rocket frogs (A. kaiei) do not. Our results did not support the identity signaling hypothesis: both species produced advertisement calls that contain similar amounts of identity information. Our results did support the identity reception hypothesis: both species exhibited habituation of aggression to playbacks simulating the arrival of a new neighbor, but only golden rocket frogs showed renewed aggression when they subsequently heard calls from a different male. These results suggest that an ancestral mechanism of plasticity in aggression common among frogs has been modified through natural selection to be specific to calls of individual males in golden rocket frogs, enabling a social recognition system. This article is protected by copyright. All rights reserved.