cssi/SciSciGPT-SciSciNet huggingface dataset:
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms

Approximate probabilistic inference algorithms are central to many fields. Examples include sequential Monte Carlo inference in robotics, variational inference in machine learning, and Markov chain Monte Carlo inference in statistics. A key problem faced by practitioners is measuring the accuracy of an approximate inference algorithm on a specific data set. This paper introduces the auxiliary inference divergence estimator (AIDE), an algorithm for measuring the accuracy of approximate inference algorithms. AIDE is based on the observation that inference algorithms can be treated as probabilistic models and the random variables used within the inference algorithm can be viewed as auxiliary variables. This view leads to a new estimator for the symmetric KL divergence between the approximating distributions of two inference algorithms. The paper illustrates application of AIDE to algorithms for inference in regression, hidden Markov, and Dirichlet process mixture models. The experiments show that AIDE captures the qualitative behavior of a broad class of inference algorithms and can detect failure modes of inference algorithms that are missed by standard heuristics.
Periodic free resolutions from twisted matrix factorizations

Abstract The notion of a matrix factorization was introduced by Eisenbud in the commutative case in his study of bounded (periodic) free resolutions over complete intersections. Since then, matrix factorizations have appeared in a number of applications. In this work, we extend the notion of (homogeneous) matrix factorizations to regular normal elements of connected graded algebras over a field. Next, we relate the category of twisted matrix factorizations to an element over a ring and certain Zhang twists. We also show that in the setting of a quotient of a ring of finite global dimension by a normal regular element, every sufficiently high syzygy module is the cokernel of some twisted matrix factorization. Furthermore, we show that in the noetherian AS-regular setting, there is an equivalence of categories between the homotopy category of twisted matrix factorizations and the singularity category of the hypersurface, following work of Orlov.
A 6.3pJ/b 30Mbps −30dB SIR-tolerant broadband interference-robust human body communication transceiver using time domain signal-interference separation

Human Body Communication (HBC) provides a low power communication medium for energy constrained wearable/ implantable devices in and around the human body. This paper presents a broadband HBC transceiver implemented in 65nm CMOS that achieves 6.3pJ/b energy efficiency at 30Mbps with −30dB interference-tolerance. Capacitive termination at the receiver end is used to achieve a wideband HBC channel, and Time Domain Signal-Interference Separation (TD-SIS) using Integrating DDR (I-DDR) receiver allows a tolerance of −30 dB Signal to Interference Ratio (SIR) with a BER <10−3. The transceiver achieves 18X improvement in energy-efficiency compared to the State-of-the-Art HBC transceivers while being simultaneously broadband (carrier-less, low-energy) and interference-robust. Such order-of-magnitude improvement in energy-efficiency and private communication through the human body may enable applications like closed-loop neuromodulation, health-monitoring, secure authentication among many others.
Aspects of non-commutative function theory

We discuss non commutative functions, which naturally arise when dealing with functions of more than one matrix variable.
Single-source/sink network error correction is as hard as multiple-unicast

We study the problem of communicating over a single-source single-terminal network in the presence of an adversary that may jam a single link of the network. If any one of the edges can be jammed, the capacity of such networks is well understood and follows directly from the connection between the minimum cut and maximum flow in single-source singleterminal networks. In this work we consider networks in which some edges cannot be jammed, and show that determining the network communication capacity is at least as hard as solving the multiple-unicast network coding problem for the error-free case. The latter problem is a long standing open problem.
Secure and Privacy-Preserving Consensus

Consensus is fundamental for distributed systems since it underpins key functionalities of such systems ranging from distributed information fusion, decision making, to decentralized control. In order to reach an agreement, existing consensus algorithms require each agent to exchange explicit state information with its neighbors. This leads to the disclosure of private state information, which is undesirable in cases where privacy is of concern. In this paper, we propose a novel approach for undirected networks, which can enable secure and privacy-preserving average consensus in a decentralized architecture in the absence of an aggregator or third party. By leveraging partial homomorphic cryptography to embed secrecy in pairwise interaction dynamics, our approach can guarantee convergence to the consensus value (subject to a quantization error) in a deterministic manner without disclosing a node's state to its neighbors. We provide a new privacy definition for dynamical systems, and give a new framework to rigorously prove that a node's privacy can be protected as long as it has at least one legitimate neighbor, which follows the consensus protocol faithfully without attempts to infer other nodes’ states. In addition to enabling resilience to passive attackers aiming to steal state information, the approach also allows easy incorporation of defending mechanisms against active attackers who try to alter the content of exchanged messages. Furthermore, in contrast to existing noise-injection-based privacy-preserving mechanisms that have to reconfigure the entire network when the topology or number of nodes varies, our approach is applicable to dynamic environments with time-varying coupling topologies. This secure and privacy-preserving approach is also applicable to weighted average consensus as well as maximum/minimum consensus under a new update rule. Numerical simulations and comparison with existing approaches confirm the theoretical results. Experimental results on a Raspberry-Pi board based microcontroller network are also presented to verify the effectiveness and efficiency of the approach.
Manifolds with the fixed point property and their squares

Abstract The Cartesian squares (powers) of manifolds with the fixed point property (f.p.p.) are considered. Examples of manifolds with the f.p.p. are constructed whose symmetric squares fail to have the f.p.p.
Random attractors for the stochastic FitzHugh–Nagumo system on unbounded domains

Abstract The existence of a random attractor for the stochastic FitzHugh–Nagumo system defined on an unbounded domain is established. The pullback asymptotic compactness of the stochastic system is proved by uniform estimates on solutions for large space and time variables. These estimates are obtained by a cut-off technique.
On the service capacity region of accessing erasure coded content

Cloud storage systems generally add redundancy in storing content files such that K files are replicated or erasure coded and stored on N > K nodes. In addition to providing reliability against failures, the redundant copies can be used to serve a larger volume of content access requests. A request for one of the files can be either be sent to a systematic node, or one of the repair groups. In this paper, we seek to maximize the service capacity region, that is, the set of request arrival rates for the K files that can be supported by a coded storage system. We explore two aspects of this problem: 1) for a given erasure code, how to optimally split incoming requests between systematic nodes and repair groups, and 2) choosing an underlying erasure code that maximizes the achievable service capacity region. In particular, we consider MDS and Simplex codes. Our analysis demonstrates that erasure coding makes the system more robust to skews in file popularity than simply replicating a file at multiple servers, and that coding and replication together can make the capacity region larger than either alone.
On the Influence of the Seed Graph in the Preferential Attachment Model

We study the influence of the seed graph in the preferential attachment model, focusing on the case of trees. We first show that the seed has no effect from a weak local limit point of view. On the other hand, we conjecture that different seeds lead to different distributions of limiting trees from a total variation point of view. We take a first step in proving this conjecture by showing that seeds with different degree profiles lead to different limiting distributions for the (appropriately normalized) maximum degree, implying that such seeds lead to different (in total variation) limiting trees.
CSWA: Aggregation-Free Spatial-Temporal Community Sensing.

In this paper, we present a novel community sensing paradigm -- {C}ommunity {S}ensing {W}ithout {A}ggregation}. CSWA is designed to obtain the environment information (e.g., air pollution or temperature) in each subarea of the target area, without aggregating sensor and location data collected by community members. CSWA operates on top of a secured peer-to-peer network over the community members and proposes a novel \emph{Decentralized Spatial-Temporal Compressive Sensing} framework based on \emph{Parallelized Stochastic Gradient Descent}. Through learning the \emph{low-rank structure} via distributed optimization, CSWA approximates the value of the sensor data in each subarea (both covered and uncovered) for each sensing cycle using the sensor data locally stored in each member's mobile device. Simulation experiments based on real-world datasets demonstrate that CSWA exhibits low approximation error (i.e., less than $0.2 ^\circ$C in city-wide temperature sensing task and $10$ units of PM2.5 index in urban air pollution sensing) and performs comparably to (sometimes better than) state-of-the-art algorithms based on the data aggregation and centralized computation.
The Affine Wealth Model: An agent-based model of asset exchange that allows for negative-wealth agents and its empirical validation

Abstract We present a stochastic, agent-based, binary-transaction Asset-Exchange Model (AEM) for wealth distribution that allows for agents with negative wealth. This model retains certain features of prior AEMs such as redistribution and wealth-attained advantage, but it also allows for shifts as well as scalings of the agent density function. We derive the Fokker–Planck equation describing its time evolution and we describe its numerical solution, including a methodology for solving the inverse problem of finding the model parameters that best match empirical data. Using this methodology, we compare the steady-state solutions of the Fokker–Planck equation with data from, inter alia, the United States Survey of Consumer Finances over a time period of 27 years. In doing so, we demonstrate agreement with empirical data with an average error less than 0.16% over this time period. We present the model parameters for the US wealth distribution data as a function of time under the assumption that the distribution responds to their variation adiabatically. We argue that the time series of model parameters thus obtained provides a valuable new diagnostic tool for analyzing wealth inequality.
Chow quotients of toric varieties as moduli of stable log maps

Let $X$ be a projective normal toric variety and $T_0$ a rank one subtorus of the defining torus of $X$. We show that the normalization of the Chow quotient $X//T_0$, in the sense of Kapranov-Sturmfels-Zelevinsky, coarsely represents the moduli space of stable log maps to $X$ with discrete data given by $T_0\subset X$.
Blind Identification of Invertible Graph Filters with Multiple Sparse Inputs

This paper deals with the problem of blind identification of a graph filter and its sparse input signal, thus broadening the scope of classical blind deconvolution of temporal and spatial signals to irregular graph domains. While the observations are bilinear functions of the unknowns, a mild requirement on invertibility of the filter enables an efficient convex formulation, without relying on matrix lifting that can hinder applicability to large graphs. On top of scaling, it is argued that (non-cyclic) permutation ambiguities may arise with some particular graphs. Deterministic sufficient conditions under which the proposed convex relaxation can exactly recover the unknowns are stated, along with those guaranteeing identifiability under the Bernoulli-Gaussian model for the inputs. Numerical tests with synthetic and real-world networks illustrate the merits of the proposed algorithm, as well as the benefits of leveraging multiple signals to aid the (blind) localization of sources of diffusion.
Reconstruction of jointly sparse vectors via manifold optimization

Abstract In this paper, we consider the challenge of reconstructing jointly sparse vectors from linear measurements. Firstly, we show that by utilizing the rank of the output data matrix we can reduce the problem to a full column rank case. This result reveals a reduction in the computational complexity of the original problem and enables a simple implementation of joint sparse recovery algorithms for full-rank setting. Secondly, we propose a new method for joint sparse recovery in the form of a non-convex optimization problem on a non-compact Stiefel manifold. In our numerical experiments our method outperforms the commonly used l 2 , 1 minimization in the sense that much fewer measurements are required for accurate sparse reconstructions. We postulate this approach possesses the desirable rank aware property, that is, being able to take advantage of the rank of the unknown matrix to improve the recovery.
A Functional Integral Approaches to the Makeenko–Migdal Equations

Makeenko and Migdal (Phys Lett B 88(1):135–137, 1979) gave heuristic identities involving the expectation of the product of two Wilson loop functionals associated to splitting a single loop at a self-intersection point. Kazakov and Kostov (Nucl Phys B 176(1):199–215, 1980) reformulated the Makeenko–Migdal equations in the plane case into a form which made rigorous sense. Nevertheless, the first rigorous proof of these equations (and their generalizations) was not given until the fundamental paper of Levy (2017). Subsequently Driver, Kemp, and Hall Commun. Math. Phys. 351(2), 741–774 (2017) gave a simplified proof of Levy’s result and then with Driver, Gabriel, Kemp, and Hall Commun. Math. Phys. 352(3), 967–978 (2017) we showed that these simplified proofs extend to the Yang–Mills measure over arbitrary compact surfaces. All of the proofs to date are elementary but tricky exercises in finite dimensional integration by parts. The goal of this article is to give a rigorous functional integral proof of the Makeenko–Migdal equations guided by the original heuristic machinery invented by Makeenko and Migdal. Although this stochastic proof is technically more difficult, it is conceptually clearer and explains “why” the Makeenko–Migdal equations are true. It is hoped that this paper will also serve as an introduction to some of the problems involved in making sense of quantizing Yang–Mill’s fields.
The Conley Conjecture and Beyond

This is (mainly) a survey of recent results on the problem of the existence of infinitely many periodic orbits for Hamiltonian diffeomorphisms and Reeb flows. We focus on the Conley conjecture, proved for a broad class of closed symplectic manifolds, asserting that under some natural conditions on the manifold every Hamiltonian diffeomorphism has infinitely many (simple) periodic orbits. We discuss in detail the established cases of the conjecture and related results including an analog of the conjecture for Reeb flows, the cases where the conjecture is known to fail, the question of the generic existence of infinitely many periodic orbits, and local geometrical conditions that force the existence of infinitely many periodic orbits. We also show how a recently established variant of the Conley conjecture for Reeb flows can be applied to prove the existence of infinitely many periodic orbits of a low-energy charge in a non-vanishing magnetic field on a surface other than a sphere.
Randomized matrix-free trace and log-determinant estimators

We present randomized algorithms for estimating the trace and determinant of Hermitian positive semi-definite matrices. The algorithms are based on subspace iteration, and access the matrix only through matrix vector products. We analyse the error due to randomization, for starting guesses whose elements are Gaussian or Rademacher random variables. The analysis is cleanly separated into a structural (deterministic) part followed by a probabilistic part. Our absolute bounds for the expectation and concentration of the estimators are non-asymptotic and informative even for matrices of low dimension. For the trace estimators, we also present asymptotic bounds on the number of samples (columns of the starting guess) required to achieve a user-specified relative error. Numerical experiments illustrate the performance of the estimators and the tightness of the bounds on low-dimensional matrices, and on a challenging application in uncertainty quantification arising from Bayesian optimal experimental design.
Interpreting and Evaluating Neural Network Robustness.

Recently, adversarial deception becomes one of the most considerable threats to deep neural networks. However, compared to extensive research in new designs of various adversarial attacks and defenses, the neural networks' intrinsic robustness property is still lack of thorough investigation. This work aims to qualitatively interpret the adversarial attack and defense mechanism through loss visualization, and establish a quantitative metric to evaluate the neural network model's intrinsic robustness. The proposed robustness metric identifies the upper bound of a model's prediction divergence in the given domain and thus indicates whether the model can maintain a stable prediction. With extensive experiments, our metric demonstrates several advantages over conventional adversarial testing accuracy based robustness estimation: (1) it provides a uniformed evaluation to models with different structures and parameter scales; (2) it over-performs conventional accuracy based robustness estimation and provides a more reliable evaluation that is invariant to different test settings; (3) it can be fast generated without considerable testing cost.
Chronic subconvulsive activity during early postnatal life produces autistic behavior in the absence of neurotoxicity in the juvenile weanling period.

Abstract The diagnosis of autism spectrum disorder (ASD) varies from very mild to severe social and cognitive impairments. We hypothesized that epigenetic subconvulsive activity in early postnatal life may contribute to the development of autistic behavior in a sex-related manner. Low doses of kainic acid (KA) (25–100 μg) were administered to rat pups for 15 days beginning on postnatal (P) day 6 to chronically elevate neuronal activity. A battery of classical and novel behavioral tests was used, and sex differences were observed. Our novel open handling test revealed that ASD males nose poked more often and ASD females climbed and escaped more frequently with age. In the social interaction test, ASD males were less social than ASD females who were more anxious in handling and elevated plus maze (EPM) tasks. To evaluate group dynamics, sibling and non-sibling control and experimental animals explored 3 different shaped novel social environments. Control pups huddled quickly and more frequently in all environments whether they socialized with littermates or non-siblings compared to ASD groups. Non-sibling ASD pups were erratic and huddled in smaller groups. In the object recognition test, only ASD males spent less time with the novel object compared to control pups. Data suggest that chronic subconvulsive activity in early postnatal life leads to an ASD phenotype in the absence of cell death. Males were more susceptible to developing asocial behaviors and cognitive pathologies, whereas females were prone to higher levels of hyperactivity and anxiety, validating our postnatal ASD model apparent in the pre-juvenile period.
Load-Based On/Off Scheduling for Energy-Efficient Delay-Tolerant 5G Networks

Dense deployment of small cells is seen as one of the major approaches for addressing the traffic demands in next-generation 5G wireless networks. The energy efficiency, however, becomes a concern along with the deployment of massive amount of small cells. In this paper, we consider the energy-efficient small cell networks (SCNs) using smart on/off scheduling (OOS) strategies, where a certain fraction of small base stations (SBSs) are put into less energy-consuming sleeping states to save energy. To this end, we first represent the overall SCN traffic by a new load variable, and analyze its statistics rigorously using Gamma approximation. We then propose two novel OOS algorithms exploiting this load variable in centralized and distributed fashions. We show that proposed load-based OOS algorithms can lead to as high as 50% of energy savings without sacrificing the average SCN throughput. In addition, load-based strategies are shown to work well under high SCN traffic and delay-intolerant circumstances, and can be implemented efficiently using the load statistics. We also show that the performance of load-based algorithms gets maximized for certain length of sleeping periods, where assuming short sleep periods is as energy-inefficient as keeping SBSs in sleep states for very long.
State Institutions and Tax Capacity: An Empirical Investigation of Causality

Would better state institutions increase tax collection, or would higher tax collection help improve state institutions? In the absence of conclusive guidance from theory, this paper searches for an empirical answer to this question, using a panel dataset covering 110 non-resource-rich countries from 1996 to 2017. Employing a panel vector error correction model, the paper finds that tax capacity and state institutions cause and reinforce each other for a wide range of country groups. The bi-directional causality results suggest that developing tax capacity and building state institutions need to go hand in hand for best results, particularly in developing countries. Based on the impulse response analyses, the paper also finds that the causal effects in advanced economies are generally low in both directions, while in developing countries, both tax capacity and institutions shocks have larger positive impacts on institutions and tax capacity, respectively.
From Predictive to Prescriptive Analytics

We combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe optimal decisi...
An improved bound in Vizing's conjecture

A well-known conjecture of Vizing (Vycisl Sistemy 9:30–43, 1963) is that $$\gamma (G \square H) \ge \gamma (G)\gamma (H)$$ for any pair of graphs G, H, where $$\gamma $$ is the domination number and $$G \square H$$ is the Cartesian product of G and H. Suen and Tarr [7], improving a result of Clark and Suen [5], showed $$\gamma (G \square H) \ge \frac{1}{2}\gamma (G)\gamma (H) + \frac{1}{2}\min (\gamma (G),\gamma (H))$$. We further improve their result by showing $$\gamma (G \square H) \ge \frac{1}{2}\gamma (G)\gamma (H) + \frac{1}{2}\max (\gamma (G),\gamma (H))$$.
MIMO Beampattern and Waveform Design with Low Resolution DACs

Digital beamforming and waveform generation techniques in MIMO radar offer enormous advantages in terms of flexibility for signal design and performance compared to conventional radar systems based on analog implementations. To allow for such fully digital design with an efficient hardware complexity, we consider the use of low resolution digital-to-analog converters (DACs) while maintaining a separate radiofrequency chain per antenna. A sum of squared residuals (SSR) formulation for the beampattern and spectral shaping problem is solved based on the Generalized Approximate Message Passing (GAMP) algorithm. Numerical results demonstrate good performance in terms of spectral shaping as well as cross-correlation properties of the different probing waveforms even with just 2-bit resolution per antenna.
Information- and Health-care Seeking Behaviors in Patients With Irritable Bowel Syndrome

Irritable bowel syndrome (IBS) is a common and clinically heterogeneous gastrointestinal disorder that can be divided into 4 subtypes: IBS with constipation (IBS-C), IBS with diarrhea (IBS-D), IBS with mixed bowel habits, and unclassified IBS. IBS decreases quality of life1 and imposes a substantial economic burden on the healthcare system.2 To develop efficient approaches to address the individual needs of IBS patients while minimizing healthcare resource overutilization, it is important to identify the factors that drive patients to seek care, to clarify the burden associated with distinct IBS subtypes, and to be aware of the resources from which IBS patients seek health-related information. We aimed to compare healthcare and information seeking between individuals with IBS-C and IBS-D.
Accurate and Fast Retrieval for Complex Non-metric Data via Neighborhood Graphs

We demonstrate that a graph-based search algorithm—relying on the construction of an approximate neighborhood graph—can directly work with challenging non-metric and/or non-symmetric distances without resorting to metric-space mapping and/or distance symmetrization, which, in turn, lead to substantial performance degradation. Although the straightforward metrization and symmetrization is usually ineffective, we find that constructing an index using a modified, e.g., symmetrized, distance can improve performance. This observation paves a way to a new line of research of designing index-specific graph-construction distance functions.
Abelian subgroups, nilpotent subgroups, and the largest character degree of a finite group

Abstract Let H be an abelian subgroup of a finite group G and π the set of prime divisors of | H | . We prove that | H O π ( G ) / O π ( G ) | is bounded above by the largest character degree of G. A similar result is obtained when H is nilpotent.
Scaling laws and operation of wireless ad-hoc and sensor networks

We present a novel approach based on coherent beamforming for increasing throughput in a wireless ad-hoc network. These results follow Gupta and Kumar's 'optimal' throughput capacity results, which was shown to follow a scaling law of O(1//spl radic/n) bits/sec per node. The result therein is based on a communication model that is primarily characterized by transmit power resulting in omni-directional transmission. Our communication model is characterized by both phase and transmit power. Consequently, with cooperation among several neighboring nodes we can achieve directional transmission and reception by means of coherent beamforming. We construct a feasible routing scheme and show that a throughput capacity of O (W/n/sup 1/3/) bits/sec per node is achievable, where W is the link capacity and n nodes are uniformly and randomly distributed in a unit area. Although, these bounds still imply vanishing throughput capacity per node, the improved scaling law has implications for the problem of estimation of a spatially correlated random field to an arbitrary degree of accuracy in distributed sensor networks.
A Recent Systematic Increase in Vapor Pressure Deficit over Tropical South America

We show a recent increasing trend in Vapor Pressure Deficit (VPD) over tropical South America in dry months with values well beyond the range of trends due to natural variability of the climate system defined in both the undisturbed Preindustrial climate and the climate over 850–1850 perturbed with natural external forcing. This trend is systematic in the southeast Amazon but driven by episodic droughts (2005, 2010, 2015) in the northwest, with the highest recoded VPD since 1979 for the 2015 drought. The univariant detection analysis shows that the observed increase in VPD cannot be explained by greenhouse-gas-induced (GHG) radiative warming alone. The bivariate attribution analysis demonstrates that forcing by elevated GHG levels and biomass burning aerosols are attributed as key causes for the observed VPD increase. We further show that There is a negative trend in evaporative fraction in the southeast Amazon, where lack of atmospheric moisture, reduced precipitation together with higher incoming solar radiation (~7% decade−1 cloud-cover reduction) influences the partitioning of surface energy fluxes towards less evapotranspiration. The VPD increase combined with the decrease in evaporative fraction are the first indications of positive climate feedback mechanisms, which we show that will continue and intensify in the course of unfolding anthropogenic climate change.
ezBFT: Decentralizing Byzantine Fault-Tolerant State Machine Replication

We present ezBFT, a novel leaderless, distributed consensus protocol capable of tolerating byzantine faults. ezBFT's main goal is to minimize the client-side latency in WAN deployments. It achieves this by (i) having no designated primary replica, and instead, enabling every replica to order the requests that it receives from clients; (ii) using only three communication steps to order requests in the common case; and (iii) involving clients actively in the consensus process. In addition, ezBFT minimizes the potentially negative effect of a byzantine replica on the overall system performance. We developed ezBFT's formal specification in TLA+, show that it provides the classic properties of BFT protocols including consistency, stability, and liveness, and developed an implementation. Our experimental evaluation reveals that ezBFT improves client-side latency by as much as 40% over state-of-the-art byzantine fault-tolerant protocols including PBFT, FaB, and Zyzzyva.
Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides

Importance Deep learning–based methods, such as the sliding window approach for cropped-image classification and heuristic aggregation for whole-slide inference, for analyzing histological patterns in high-resolution microscopy images have shown promising results. These approaches, however, require a laborious annotation process and are fragmented. Objective To evaluate a novel deep learning method that uses tissue-level annotations for high-resolution histological image analysis for Barrett esophagus (BE) and esophageal adenocarcinoma detection. Design, Setting, and Participants This diagnostic study collected deidentified high-resolution histological images (N = 379) for training a new model composed of a convolutional neural network and a grid-based attention network. Histological images of patients who underwent endoscopic esophagus and gastroesophageal junction mucosal biopsy between January 1, 2016, and December 31, 2018, at Dartmouth-Hitchcock Medical Center (Lebanon, New Hampshire) were collected. Main Outcomes and Measures The model was evaluated on an independent testing set of 123 histological images with 4 classes: normal, BE-no-dysplasia, BE-with-dysplasia, and adenocarcinoma. Performance of this model was measured and compared with that of the current state-of-the-art sliding window approach using the following standard machine learning metrics: accuracy, recall, precision, and F1 score. Results Of the independent testing set of 123 histological images, 30 (24.4%) were in the BE-no-dysplasia class, 14 (11.4%) in the BE-with-dysplasia class, 21 (17.1%) in the adenocarcinoma class, and 58 (47.2%) in the normal class. Classification accuracies of the proposed model were 0.85 (95% CI, 0.81-0.90) for the BE-no-dysplasia class, 0.89 (95% CI, 0.84-0.92) for the BE-with-dysplasia class, and 0.88 (95% CI, 0.84-0.92) for the adenocarcinoma class. The proposed model achieved a mean accuracy of 0.83 (95% CI, 0.80-0.86) and marginally outperformed the sliding window approach on the same testing set. The F1 scores of the attention-based model were at least 8% higher for each class compared with the sliding window approach: 0.68 (95% CI, 0.61-0.75) vs 0.61 (95% CI, 0.53-0.68) for the normal class, 0.72 (95% CI, 0.63-0.80) vs 0.58 (95% CI, 0.45-0.69) for the BE-no-dysplasia class, 0.30 (95% CI, 0.11-0.48) vs 0.22 (95% CI, 0.11-0.33) for the BE-with-dysplasia class, and 0.67 (95% CI, 0.54-0.77) vs 0.58 (95% CI, 0.44-0.70) for the adenocarcinoma class. However, this outperformance was not statistically significant. Conclusions and Relevance Results of this study suggest that the proposed attention-based deep neural network framework for BE and esophageal adenocarcinoma detection is important because it is based solely on tissue-level annotations, unlike existing methods that are based on regions of interest. This new model is expected to open avenues for applying deep learning to digital pathology.
Separating distinct structures of multiple macromolecular assemblies from cryo-EM projections

Single particle analysis for structure determination in cryo-electron microscopy is traditionally applied to samples purified to near homogeneity as current reconstruction algorithms are not designed to handle heterogeneous mixtures of structures from many distinct macromolecular complexes. We extend on long established methods and demonstrate that relating two-dimensional projection images by their common lines in a graphical framework is sufficient for partitioning distinct protein and multiprotein complexes within the same data set. The feasibility of this approach is first demonstrated on a large set of synthetic reprojections from 35 unique macromolecular structures spanning a mass range of hundreds to thousands of kilodaltons. We then apply our algorithm on cryo-EM data collected from a mixture of five protein complexes and use existing methods to solve multiple three-dimensional structures ab initio. Incorporating methods to sort single particle cryo-EM data from extremely heterogeneous mixtures will alleviate the need for stringent purification and pave the way toward investigation of samples containing many unique structures.
Reception Capacity: Definitions, Game Theory and Hardness.

The capacity of wireless networks is a classic and important topic of study. Informally, the capacity of a network is simply the total amount of information which it can transfer. In the context of models of wireless radio networks, this has usually meant the total number of point-to-point messages which can be sent or received in one time step. This definition has seen intensive study in recent years, particularly with respect to more accurate models of radio networks such as the SINR model. This paper is motivated by an obvious fact: radio antennae are (at least traditionally) omnidirectional, and hence point-to-point connections are not necessarily the best definition of the true capacity of a wireless network. To fix this, we introduce a new definition of reception capacity as the maximum number of messages which can be received in one round, and show that this is related to a new optimization problem we call the Maximum Perfect Dominated Set (MaxPDS) problem. Using this relationship we give a tight lower bound for approximating this capacity which essentially matches a known upper bound. As our main result, we analyze this notion of capacity under game-theoretic constraints, giving tight bounds on the average quality achieved at any coarse correlated equilibrium (and thus at any Nash). This immediately gives bounds on the average behavior of the natural distributed algorithm in which every transmitter uses online learning algorithms to learn whether to transmit.
Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems

This manuscript contributes a general and practical framework for casting a Markov process model of a system at equilibrium as a structural causal model, and carrying out counterfactual inference. Markov processes mathematically describe the mechanisms in the system, and predict the system’s equilibrium behavior upon intervention, but do not support counterfactual inference. In contrast, structural causal models support counterfactual inference, but do not identify the mechanisms. This manuscript leverages the benefits of both approaches. We define the structural causal models in terms of the parameters and the equilibrium dynamics of the Markov process models, and counterfactual inference flows from these settings. The proposed approach alleviates the identifiability drawback of the structural causal models, in that the counterfactual inference is consistent with the counterfactual trajectories simulated from the Markov process model. We showcase the benefits of this framework in case studies of complex biomolecular systems with nonlinear dynamics. We illustrate that, in presence of Markov process model misspecification, counterfactual inference leverages prior data, and therefore estimates the outcome of an intervention more accurately than a direct simulation.
Complier Stochastic Direct Effects: Identification and Robust Estimation

Mediation analysis is critical to understanding the mechanisms underlying exposure-outcome relationships. In this article, we identify the instrumental variable-direct effect of the exposure on the...
Comment on `Boson-fermion model beyond the mean-field approximation'

In a recent paper, it has been suggested that there is no Cooper pairing in boson-fermion models of superconductivity. This result is based on an inconsistent approximation which violates an exact identity. Quite generally, the divergence of the fermion t-matrix (the Thouless criterion) is accompanied by the condensation of a boson mode.
Trading Prior to the Disclosure of Material Information: Evidence from Regulation Fair Disclosure Form 8‐Ks

Regulation Fair Disclosure (Reg FD) Form 8-K filings provide a venue where managers release information to the market as a whole that they designate as being material. Using this setting, we study trading patterns immediately prior to the public disclosure of material information. We offer three main results. First, using both intra-day and daily trading data, we find abnormal trading volume of 21 percent (13 percent) in the hour (day) prior to the public disclosure, respectively. Second, we find that this pre-disclosure abnormal trading volume is concentrated in firms that are smaller, have more growth opportunities, issue fewer voluntary disclosures, and have weaker external monitoring. Finally, we find that this pre-disclosure volume is concentrated in subsamples in which the information relates to a firm’s material contracts, a firm holds investor/analyst conferences, and there is insider trading activity in a firm’s shares. Our results do not concentrate in a small number of firms or industries, and do not appear to be explained by the form through which managers first release the material information (e.g., Form 8-K, press release, website posting, or social media). Our results are also robust to controlling for the firm’s other filings and peer filings that occur around the disclosure. Overall, the trading patterns we document may show that, inconsistent with the spirit of Reg FD, a subset of investors trade on information managers deem material prior to its broad, public release.
Linear Bandits with Feature Feedback

This paper explores a new form of the linear bandit problem in which the algorithm receives the usual stochastic rewards as well as stochastic feedback about which features are relevant to the rewards, the latter feedback being the novel aspect. The focus of this paper is the development of new theory and algorithms for linear bandits with feature feedback which can achieve regret over time horizon T that scales like k√T, without prior knowledge of which features are relevant nor the number k of relevant features. In comparison, the regret of traditional linear bandits is d√T, where d is the total number of (relevant and irrelevant) features, so the improvement can be dramatic if k ≪ d. The computational complexity of the algorithm is proportional to k rather than d, making it much more suitable for real-world applications compared to traditional linear bandits. We demonstrate the performance of the algorithm with synthetic and real human-labeled data.
Desk Organization: Effect of Multimodal Inputs on Spatial Relational Learning

For robots to operate in a three dimensional world and interact with humans, learning spatial relationships among objects in the surrounding is necessary. Reasoning about the state of the world requires inputs from many different sensory modalities including vision (V) and haptics (H). We examine the problem of desk organization: learning how humans spatially position different objects on a planar surface according to organizational “preference”. We model this problem by examining how humans position objects given multiple features received from vision and haptic modalities. However, organizational habits vary greatly between people both in structure and adherence. To deal with user organizational preferences, we add an additional modality, “utility” (U), which informs on a particular human’s perceived usefulness of a given object. Models were trained as generalized (over many different people) or tailored (per person). We use two types of models: random forests, which focus on precise multi-task classification, and Markov logic networks, which provide an easily interpretable insight into organizational habits. The models were applied to both synthetic data, which proved to be learnable when using fixed organizational constraints, and human-study data, on which the random forest achieved over 90% accuracy. Over all combinations of {H, U, V} modalities, UV and HUV were the most informative for organization. In a follow-up study, we gauged participants preference of desk organizations by a generalized random forest organization vs. by a random model. On average, participants rated the random forest models as 4.15 on a 5-point Likert scale compared to 1.84 for the random model.
tRNA-derived fragments and microRNAs in the maternal-fetal interface of a mouse maternal-immune-activation autism model

tRNA-derived small fragments (tRFs) and tRNA halves have emerging functions in different biological pathways, such as regulating gene expression, protein translation, retrotransposon activity, tran...
SalGaze: Personalizing Gaze Estimation using Visual Saliency

Traditional gaze estimation methods typically require explicit user calibration to achieve high accuracy. This process is cumbersome and recalibration is often required when there are changes in factors such as illumination and pose. To address this challenge, we introduce SalGaze, a framework that utilizes saliency information in the visual content to transparently adapt the gaze estimation algorithm to the user without explicit user calibration. We design an algorithm to transform a saliency map into a differentiable loss map that can be used for the optimization of CNN-based models. SalGaze is also able to greatly augment standard point calibration data with implicit video saliency calibration data using a unified framework. We show accuracy improvements over 24% using our technique on existing methods.
Optimal Distribution System Restoration with Microgrids and Distributed Generators

Increasing emphasis on reliability and resiliency call for advanced distribution system restoration (DSR). The integration of grid sensors, remote controls, and distributed generators (DG) brings about exciting opportunities in DSR. In this context, this work considers the task of single-step restoration of a single-phase power distribution system. Different from existing works, the devised restoration scheme achieves optimal formation of islands without heuristically pre-identifying reference buses. It further facilitates multiple DGs running within the same island, and establishes a coordination hierarchy in terms of their PV/PQ operation modes. Generators without black-start capability are guaranteed to remain connected to a black-start DG or a substation. The proposed scheme models remotely-controlled voltage regulators exactly, and integrates them in the restoration process. Numerical tests on a modified IEEE 37-bus feeder demonstrate that the proposed mixed-integer linear program (MILP) takes less than four seconds to handle random outages of 1–5 lines. The scalability of this novel MILP formulation can be attributed to the unique use of cycles and paths on the grid infrastructure graph; the McCormick linearization technique; and an approximate power flow model.
Enhancing the Spatio-Temporal Observability of Residential Loads

Enhancing the spatio-temporal observability of residential loads is crucial for achieving secure and efficient operations in distribution systems with increasing penetration of distributed energy resources (DERs). This paper presents a joint inference framework for residential loads by leveraging the real-time measurements from distribution-level sensors. Specifically, smart meter data is available for almost every load with unfortunately low temporal resolution, while distribution synchrophasor data is at very fast rates yet available at limited locations. By combining these two types of data with respective strengths, the problem is cast as a matrix recovery one with much less number of observations than unknowns. To improve the recovery performance, we introduce two regularization terms to promote a low rank plus sparse structure of the load matrix via a difference transformation. Accordingly, the recovery problem can be formulated as a convex optimization one which is efficiently solvable. Numerical tests using real residential load data demonstrate the effectiveness of our proposed approaches in identifying appliance activities and recovering the PV output profiles.
Numerical Software to Compute Newton polytopes and Tropical Membership

We present our implementation of an algorithm which functions as a numerical oracle for the Newton polytope of a hypersurface. Additionally, we propose an algorithm which functions as a tropical membership test for higher codimension varieties based on ideas from Hept and Theobald. This tropical membership algorithm relies on a numerical oracle and we analyze some of the convergence rates involved. Our implementation is written as a Macaulay2 package called NumericalNP.m2. To showcase this package, we investigate the Newton polytope of both a hypersurface coming from algebraic vision and the Luroth invariant.
Soliton signature in the phonon spectrum of twisted bilayer graphene

The phonon spectra of twisted bilayer graphene (tBLG) are analyzed for a series of 692 twisting angle values in the $[0,30{\degree}]$ range. The evolution of the phonon bandstructure as a function of twist angle is examined using a band unfolding scheme where the large number of phonon modes computed at the $\Gamma$ point for the large moir\'e tBLG supercells are unfolded onto the Brillouin Zone (BZ) of one of the two constituent layers. In addition to changes to the low-frequency breathing and shear modes, a series of well-defined side-bands around high-symmetry points of the extended BZ emerge due to the twist angle-dependent structural relaxation. The results are rationalized by introducing a nearly-free-phonon model that highlights the central role played by solitons in the description of the new phonon branches, which are particularly pronounced for structures with small twist angles, below a buckling angle $\theta_{\rm B}\sim {3.75}{\degree}$.