cssi/SciSciGPT-SciSciNet huggingface dataset:
Dual-Frequency Microwave-Enhanced Infrared Thermography

In this paper, we propose to extend the Microwave Enhanced Infrared Thermography (MEIT) method for landmine detection using two excitation microwave frequencies. We present the results of a detailed modeling effort. The extended method has the potential for detecting buried objects under rough surfaces at depths greater than with the original single frequency concept. Our result shows that the two-frequency technique minimizes the clutter introduced by an irregular surface, and can lead to better detection of buried objects. A 2-D computational model of this method has been developed to simulate real-world landmine detection. Receiver Operating Characteristic (ROC) curves were generated to evaluate the performance of this new method. Finally we demonstrate improvements achieved in detection depth under rough surfaces, compared to the single-frequency MEIT technology. The theoretical results obtained so far are presently being used to plan laboratory and field experiments for validation of our assumptions.
Chasing convex bodies with linear competitive ratio

We study the problem of chasing convex bodies online: given a sequence of convex bodies Kt Rd the algorithm must respond with points xt e Kt in an online fashion (i.e., xt is chosen before Kt+1 is revealed). The objective is to minimize the total distance between successive points in this sequence. Recently, Bubeck et al. (STOC 2019) gave a 2O(d)-competitive algorithm for this problem. We give an algorithm that is [MATH HERE]-competitive for any sequence of length T.
Fairness and utilization in allocating resources with uncertain demand

Resource allocation problems are a fundamental domain in which to evaluate the fairness properties of algorithms. The trade-offs between fairness and utilization have a long history in this domain. A recent line of work has considered fairness questions for resource allocation when the demands for the resource are distributed across multiple groups and drawn from probability distributions. In such cases, a natural fairness requirement is that individuals from different groups should have (approximately) equal probabilities of receiving the resource. A largely open question in this area has been to bound the gap between the maximum possible utilization of the resource and the maximum possible utilization subject to this fairness condition. Here, we obtain some of the first provable upper bounds on this gap. We obtain an upper bound for arbitrary distributions, as well as much stronger upper bounds for specific families of distributions that are typically used to model levels of demand. In particular, we find --- somewhat surprisingly --- that there are natural families of distributions (including Exponential and Weibull) for which the gap is non-existent: it is possible to simultaneously achieve maximum utilization and the given notion of fairness. Finally, we show that for power-law distributions, there is a non-trivial gap between the solutions, but this gap can be bounded by a constant factor independent of the parameters of the distribution.
Genetic Algorithm Design of MOF-based Gas Sensor Arrays for CO2-in-Air Sensing

Gas sensor arrays, also known as electronic noses, leverage a diverse set of materials to identify the components of complex gas mixtures. Metal-organic frameworks (MOFs) have emerged as promising materials for electronic noses due to their high-surface areas and chemical as well as structural tunability. Using our recently reported genetic algorithm design approach, we examined a set of 50 MOFs and searched through over 1.125 × 1015 unique array combinations to identify optimal arrays for the detection of CO2 in air. We found that despite individual MOFs having lower selectivity for O2 or N2 relative to CO2, intelligently selecting the right combinations of MOFs enables accurate prediction of the concentrations of all components in the mixture (i.e., CO2, O2, N2). We also analyzed the physical properties of the elements in the arrays to develop an intuition for improving array design. Notably, we found that an array whose MOFs have diversity in their volumetric surface areas has improved sensing. Consistent with this observation, we found that the best arrays consistently had greater structural diversity (e.g., pore sizes, void fractions, and surface areas) than the worst arrays.
Benefits of Coding on Age of Information in Broadcast Networks

Age of Information (AoI) is studied in two-user broad-cast networks with feedback, and lower and upper bounds are derived on the expected weighted sum AoI of the users. In particular, a class of simple coding actions is considered and within this class, randomized and deterministic policies are devised. Explicit conditions are found for symmetric dependent channels under which coded randomized policies strictly outperform the corresponding uncoded policies. Similar behaviour is shown numerically for deterministic policies.
Learning to See before Learning to Act: Visual Pre-training for Manipulation

Does having visual priors (e.g. the ability to detect objects) facilitate learning to perform vision-based manipulation (e.g. picking up objects)? We study this problem under the framework of transfer learning, where the model is first trained on a passive vision task (i.e., the data distribution does not depend on the agent’s decisions), then adapted to perform an active manipulation task (i.e., the data distribution does depend on the agent’s decisions). We find that pre-training on vision tasks significantly improves generalization and sample efficiency for learning to manipulate objects. However, realizing these gains requires careful selection of which parts of the model to transfer. Our key insight is that outputs of standard vision models highly correlate with affordance maps commonly used in manipulation. Therefore, we explore directly transferring model parameters from vision networks to affordance prediction networks, and show that this can result in successful zero-shot adaptation, where a robot can pick up certain objects with zero robotic experience. With just a small amount of robotic experience, we can further fine-tune the affordance model to achieve better results. With just 10 minutes of suction experience or 1 hour of grasping experience, our method achieves ∼ 80% success rate at picking up novel objects.
Mining News Events from Comparable News Corpora: A Multi-Attribute Proximity Network Modeling Approach

We present ProxiModel, a novel event mining framework for extracting high-quality structured event knowledge from large, redundant, and noisy news data sources. The proposed model differentiates itself from other approaches by modeling both the event correlation within each individual document as well as across the corpus. To facilitate this, we introduce the concept of a proximity-network, a novel space-efficient data structure to facilitate scalable event mining. This proximity network captures the corpus-level co-occurence statistics for candidate event descriptors, event attributes, as well as their connections. We probabilistically model the proximity network as a generative process with sparsity-inducing regularization. This allows us to efficiently and effectively extract high-quality and interpretable news events. Experiments on three different news corpora demonstrate that the proposed method is effective and robust at generating high-quality event descriptors and attributes. We briefly detail many interesting applications from our proposed framework such as news summarization, event tracking and multi-dimensional analysis on news. Finally, we explore a case study on visualizing the events for a Japan Tsunami news corpus and demonstrate ProxiModel’s ability to automatically summarize emerging news events.
Immunotoxin SS1P is rapidly removed by proximal tubule cells of kidney, whose damage contributes to albumin loss in urine

Recombinant immunotoxins (RITs) are chimeric proteins composed of an Fv and a protein toxin being developed for cancer treatment. The Fv brings the toxin to the cancer cell, but most of the RITs do not reach the tumor and are removed by other organs. To identify cells responsible for RIT removal, and the pathway by which RITs reach these cells, we studied SS1P, a 63-kDa RIT that targets mesothelin-expressing tumors and has a short serum half-life. The major organs that remove RIT were identified by live mouse imaging of RIT labeled with FNIR-Z-759. Cells responsible for SS1P removal were identified by immunohistochemistry and intravital two-photon microscopy of kidneys of rats. The primary organ of SS1P removal is kidney followed by liver. In the kidney, SS1P passes through the glomerulus, is taken up by proximal tubular cells, and transferred to lysosomes. In the liver, macrophages are involved in removal. The short half-life of SS1P is due to its very rapid filtration by the kidney followed by degradation in proximal tubular cells of the kidney. In mice treated with SS1P, proximal tubular cells are damaged and albumin in the urine is increased. SS1P uptake by kidney is reduced by coadministration of l-lysine. Our data suggests that l-lysine administration to humans might prevent SS1P-mediated kidney damage, reduce albumin loss in urine, and alleviate capillary leak syndrome.
QCOR: A Language Extension Specification for the Heterogeneous Quantum-Classical Model of Computation

Quantum computing (QC) is an emerging computational paradigm that leverages the laws of quantum mechanics to perform elementary logic operations. Existing programming models for QC were designed with fault-tolerant hardware in mind, envisioning stand-alone applications. However, the susceptibility of near-term quantum computers to noise limits their stand-alone utility. To better leverage limited computational strengths of noisy quantum devices, hybrid algorithms have been suggested whereby quantum computers are used in tandem with their classical counterparts in a heterogeneous fashion. This modus operandi calls out for a programming model and a high-level programming language that natively and seamlessly supports heterogeneous quantum-classical hardware architectures in a single-source-code paradigm. Motivated by the lack of such a model, we introduce a language extension specification, called QCOR, which enables single-source quantum-classical programming. Programs written using the QCOR library–based language extensions can be compiled to produce functional hybrid binary executables. After defining QCOR’s programming model, memory model, and execution model, we discuss how QCOR enables variational, iterative, and feed-forward QC. QCOR approaches quantum-classical computation in a hardware-agnostic heterogeneous fashion and strives to build on best practices of high-performance computing. The high level of abstraction in the language extension is intended to accelerate the adoption of QC by researchers familiar with classical high-performance computing.
A kinesin Klp10A mediates cell cycle-dependent shuttling of Piwi between nucleus and nuage

The piRNA pathway protects germline genomes from selfish genetic elements (e.g. transposons) through their transcript cleavage in the cytoplasm and/or their transcriptional silencing in the nucleus. Here, we describe a mechanism by which the nuclear and cytoplasmic arms of the piRNA pathway are linked. We find that during mitosis of Drosophila spermatogonia, nuclear Piwi interacts with nuage, the compartment that mediates the cytoplasmic arm of the piRNA pathway. At the end of mitosis, Piwi leaves nuage to return to the nucleus. Dissociation of Piwi from nuage occurs at the depolymerizing microtubules of the central spindle, mediated by a microtubule-depolymerizing kinesin, Klp10A. Depletion of klp10A delays the return of Piwi to the nucleus and affects piRNA production, suggesting the role of nuclear-cytoplasmic communication in piRNA biogenesis. We propose that cell cycle-dependent communication between the nuclear and cytoplasmic arms of the piRNA pathway may play a previously unappreciated role in piRNA regulation.
A Novel Optimal Modulation Strategy for Modular Multilevel Converter Based HVDC Systems

Unlike conventional converters, modular multilevel converter (MMC) has a higher switching frequency – which has direct implication on important parameters like converter loss and reliability – mainly due to increased number of switching components. However, conventional switching techniques, where submodule sorting is just based on capacitor voltage balancing, are not able to achieve switching frequency reduction objective. A novel modulation algorithm for modular multilevel converters (MMCs) is proposed in this paper to reduce the switching frequency of MMC operation by defining a constrained multi-objective optimization model. The optimized switching algorithm incorporates all control objectives required for the proper operation of MMC and adds new constraints to limit the number of submodule switching events at each time step. Variation of severity of the constraints leads to a desired level of controllability in MMC switching algorithm to trade-off between capacitor voltage regulation and switching frequency reduction. Finally, performance of the proposed algorithm is validated against a seven-level back-to-back MMC-HVDC system under various operating conditions.
K-FIT: An accelerated kinetic parameterization algorithm using steady-state fluxomic data

Kinetic models predict the metabolic flows by directly linking metabolite concentrations and enzyme levels to reaction fluxes. Robust parameterization of organism-level kinetic models that faithfully reproduce the effect of different genetic or environmental perturbations remains an open challenge due to the intractability of existing algorithms. This paper introduces Kinetics-based Fluxomics Integration Tool (K-FIT), a robust kinetic parameterization workflow that leverages a novel decomposition approach to identify steady-state fluxes in response to genetic perturbations followed by a gradient-based update of kinetic parameters until predictions simultaneously agree with the fluxomic data in all perturbed metabolic networks. The applicability of K-FIT to large-scale models is demonstrated by parameterizing an expanded kinetic model for E. coli (307 reactions and 258 metabolites) using fluxomic data from six mutants. The achieved thousand-fold speed-up afforded by K-FIT over meta-heuristic approaches is transformational enabling follow-up robustness of inference analyses and optimal design of experiments to inform metabolic engineering strategies.
Improved Approximate Degree Bounds for k-Distinctness.

An open problem that is widely regarded as one of the most important in quantum query complexity is to resolve the quantum query complexity of the k-distinctness function on inputs of size N. While the case of k=2 (also called Element Distinctness) is well-understood, there is a polynomial gap between the known upper and lower bounds for all constants k>2. Specifically, the best known upper bound is O (N^{(3/4)-1/(2^{k+2}-4)}) (Belovs, FOCS 2012), while the best known lower bound for k≥ 2 is Ω(N^{2/3} + N^{(3/4)-1/(2k)}) (Aaronson and Shi, J. ACM 2004; Bun, Kothari, and Thaler, STOC 2018). For any constant k ≥ 4, we improve the lower bound to Ω(N^{(3/4)-1/(4k)}). This yields, for example, the first proof that 4-distinctness is strictly harder than Element Distinctness. Our lower bound applies more generally to approximate degree. As a secondary result, we give a simple construction of an approximating polynomial of degree O(N^{3/4}) that applies whenever k ≤ polylog(N).
Whence to Learn? Transferring Knowledge in Configurable Systems using BEETLE

As software systems grow in complexity and the space of possible configurations increases exponentially, finding the near-optimal configuration of a software system becomes challenging. Recent approaches address this challenge by learning performance models based on a sample set of configurations. However, collecting enough sample configurations can be very expensive since each such sample requires configuring, compiling, and executing the entire system using a complex test suite. When learning on new data is too expensive, it is possible to use Transfer Learning to “transfer” old lessons to the new context. Traditional transfer learning has a number of challenges, specifically, (a) learning from excessive data takes excessive time, and (b) the performance of the models built via transfer can deteriorate as a result of learning from a poor source. To resolve these problems, we propose a novel transfer learning framework called BEETLE, which is a “bellwether”-based transfer learner that focuses on identifying and learning from the most relevant source from amongst the old data. This paper evaluates BEETLE with 57 different software configuration problems based on five software systems (a video encoder, an SAT solver, a SQL database, a high-performance C-compiler, and a streaming data analytics tool). In each of these cases, BEETLE found configurations that are as good as or better than those found by other state-of-the-art transfer learners while requiring only a fraction 1/7th of the measurements needed by those other methods. Based on these results, we say that BEETLE is a new high-water mark in optimally configuring software.
Adversarial Anomaly Detection for Marked Spatio-Temporal Streaming Data

Spatio-temporal event data are becoming increasingly commonplace in a wide variety of applications, such as electronic transaction records, social network data, and crime incident reports. How to efficiently detect anomalies in these dynamic systems using these streaming event dataƒ This work proposes a novel anomaly detection framework for such event data combining the Long Short-Term Memory (LSTM) and marked spatio-temporal point processes. The detection procedure can be computed in an online and distributed fashion via feeding the streaming data through an LSTM and a neural network-based discriminator. This work studies the false-alarm-rate and detection delay using theory and simulation and shows that it can achieve weak signal detection by aggregating local statistics over time and networks. Finally, we demonstrate the good performance using real-world data sets.
Calculating Comparable Statistics from Incomparable Surveys, with an Application to Poverty in India

Applied economists are often interested in studying trends in economic indicators such as inequality or poverty; however, comparisons over time can be made impossible by changes in data collection methodology. We describe an easily implemented procedure, based on inverse probability weighting, that allows recovery of comparability of estimated parameters identified implicitly by a moment condition. The validity of the procedure requires the existence of a set of auxiliary variables whose reports are not affected by the different survey design and whose relationship with the main variable of interest is stable over time. We analyze the asymptotic properties of the estimator when data belong to a stratified and clustered survey. The main empirical motivation of the article is provided by a recent controversy regarding the extent of poverty reduction in India in the 1990s. Due to changes in the expenditure questionnaire adopted for data collection in the 1999–2000 round of the Indian National Sample Survey, ...
Chapter 6 – Orthogonality

In Chapters 6, we restrict our attention to RnRn and present some additional structures and properties related to the dot product. In Section 6.1, we examine special bases for RnRn whose vectors are mutually orthogonal. In Section 6.2, we introduce orthogonal complements of subspaces of RnRn. Finally, in Section 6.3, we use orthogonality to diagonalize any symmetric matrix.
Chapter 18 – Cross-Border Mergers and Acquisitions: Analysis and Valuation

This chapter addresses common motives for international expansion, as well as the advantages and disadvantages of a variety of international market entry strategies. However, the focus of this chapter is on M&A (mergers and acquisitions) as a market entry or expansion mode. This chapter also addresses the challenges of M&A deal structures, financing, valuation, and execution in both developed and emerging countries. Finally, this chapter summarizes empirical studies investigating the actual benefits to both target and acquiring company shareholders of international diversification.
Bayesian Learning in Social Networks

We study the (perfect Bayesian) equilibrium of a sequential learning model over a general social network. Each individual receives a signal about the underlying state of the world, observes the past actions of a stochastically generated neighbourhood of individuals, and chooses one of two possible actions. The stochastic process generating the neighbourhoods defines the network topology. We characterize pure strategy equilibria for arbitrary stochastic and deterministic social networks and characterize the conditions under which there will be asymptotic learning—convergence (in probability) to the right action as the social network becomes large. We show that when private beliefs are unbounded (meaning that the implied likelihood ratios are unbounded), there will be asymptotic learning as long as there is some minimal amount of “expansion in observations”. We also characterize conditions under which there will be asymptotic learning when private beliefs are bounded.
On the User's Point of View

In this discussion, I shall try to identify those general criteria for interactive on-line systems which seem most important for the experimental solution of mathematical problems. To illustrate some of these, I shall refer to Professor Glen Culler's on-line system [1, 3], which has been in operation at the University of California at Santa Barbara since 1966. While it is far from an ideal system, I believe that it still ranks first in terms of the number of real problems (of at least moderate difficulty) which have actually been solved, by a variety of users, with the aid of this system or one of its earlier versions. Its strengths and weaknesses have, therefore, some general relevance to a discussion of on-line systems for experimental applied mathematics.
Managing Bank Liquidity Risk: How Deposit-Loan Synergies Vary with Market Conditions

Liquidity risk in banking has been attributed to transactions deposits and their potential to spark runs or panics. We show instead that transactions deposits help banks hedge liquidity risk from unused loan commitments. Bank stock-return volatility increases with unused commitments, but only for banks with low levels of transactions deposits. This deposit-lending hedge becomes more powerful during periods of tight liquidity, when nervous investors move funds into their banks. Our results reverse the standard notion of liquidity risk at banks, where runs from depositors had been seen as the cause of trouble.
Determining the switching criterion for time-optimal control

Publisher Summary This chapter presents the method for determining the switching criterion that is similar to the method of backing out from the origin, which has been used in special case. When G contains more than one point solutions, they are backed out from boundary points of G restricting solutions so that they satisfy the transversality condition at the boundary of G. The maximum principle of Pontryagin and a transversality condition are used to obtain extremal solutions for the problem P. Points on the optimal solution curves in phase–space where the control function changes sign can be determined. These points together with the boundary of G form a switching surface. This provides the desired switching criterion for time-optimal control.
Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling

Abstract Accurate prediction of building’s response subjected to earthquakes makes possible to evaluate building performance. To this end, we leverage the recent advances in deep learning and develop a physics-guided convolutional neural network (PhyCNN) for data-driven structural seismic response modeling. The concept is to train a deep PhyCNN model based on limited seismic input–output datasets (e.g., from simulation or sensing) and physics constraints, and thus establish a surrogate model for structural response prediction. Available physics (e.g., the law of dynamics) can provide constraints to the network outputs, alleviate overfitting issues, reduce the need of big training datasets, and thus improve the robustness of the trained model for more reliable prediction. The surrogate model is then utilized for fragility analysis given certain limit state criteria. In addition, an unsupervised learning algorithm based on K-means clustering is also proposed to partition the datasets to training, validation and prediction categories, so as to maximize the use of limited datasets. The performance of PhyCNN is demonstrated through both numerical and experimental examples. Convincing results illustrate that PhyCNN is capable of accurately predicting building’s seismic response in a data-driven fashion without the need of a physics-based analytical/numerical model. The PhyCNN paradigm also outperforms non-physics-guided neural networks.
187 MHz Subthreshold-Supply Charge-Recovery FIR

This paper presents a finite impulse response (FIR) filter chip that relies on a charge-recovery logic family to achieve multi-MHz clock frequencies with subthreshold DC supply levels. Fabricated in a 0.13 μm CMOS process with V th,nmos = 0.40 V, the FIR operates with a two-phase power-clock in the 5 MHz-187 MHz range and with DC supplies in the 0.16 V-0.36 V range. Using a single DC supply, the chip achieves its most energy-efficient operating point when resonating at 20 MHz with a 0.27 V supply. Recovering 89% of the energy supplied to its 57 pF per-phase load, it consumes 15.57 pJ per cycle and yields 17.37 nW/Tap/MHz/InBit/ CoeffBit. Using two subthreshold DC supplies at 20 MHz, energy per cycle can be further reduced by 17.1 %, yielding 14.40 nW/Tap/ MHz/InBit/CoeffBit.
Integrating citizen science data with expert surveys increases accuracy and spatial extent of species distribution models

Information on species9 habitat associations and distributions, across a wide range of spatial and temporal scales, are a fundamental source of ecological knowledge. However, collecting biological information at relevant scales if often cost prohibitive, although it is essential for framing the broader context of more focused research and conservation efforts. Citizen-science data has been signaled as an increasingly important source of biological information needed to fill in data gaps needed to make more comprehensive and robust inferences on species distributions. However, there are perceived trade-offs of combining highly structured, scientific survey data with largely unstructured, citizen-science data. As a result, the focus of most methodological advances to combine these sources of information has been on treating these sources as independent. The degree to which each source of information is allowed to directly inform a common underlying process (e.g. species distribution) depends on the perceived quality of the data. In this paper, we explore these trade-offs by applying a simplified approach of filtering citizen-science data to resemble structured survey data, and analyze both sources of data under a common framework. To accomplish this, we explored ways of integrating high-resolution survey data on shorebirds in the northern Central Valley of California with observations in eBird for the entire region that were filtered to improve their quality. The integration of survey data with the filtered citizen-science data in eBird resulted in improved inference and predictive ability, and increased the extent and accuracy of inferences on shorebirds for the Central Valley. The structured surveys were found to improve the overall accuracy of ecological inference based only on citizen-science data, by increasing the representation of data collected from high quality habitats for shorebirds (e.g. rice fields). The practical approach we have shown for data integration can be also be used to improve the efficiency of designing biological surveys in the context of larger, citizen-science monitoring efforts, ultimately reducing the financial and time expenditures typically required of monitoring programs and focused research. The simple processing and filtering method we present can be used to integrate other types of citizen-science data (e.g. camera traps) with more localized efforts (e.g. research projects), ultimately improving our ecological knowledge on the distribution and habitat associations of species of conservation concern worldwide.
Chapter 6 – Creating Remote Access and Site-to-Site VPNs with ISA Firewalls

Publisher Summary This chapter discusses the ISA 2006 firewall's virtual private networking (VPN) remote access server and VPN gateway features. It is observed that when a VPN remote-access client establishes a connection with the VPN server, the VPN client acts like a machine that is directly connected to the corporate network. This virtual link to the corporate network enables the remote VPN user to access almost every resource on the corporate network limited only by the access controls configured on the servers and workstations. User mapping is a feature that allows you to map virtual private network (VPN) clients connecting to ISA Server using an authentication method that is not based on Windows authentication to the Windows Active Directory namespace. One of the major improvements that the ISA 2004/2006 firewall has over ISA Server 2000 is that it can be configured to use IPSec tunnel mode for site-to-site VPN connections. The ISA firewall can authenticate VPN users with Remote Access Dial-In User Service (RADIUS). The RADIUS Protocol allows the ISA 2006 firewall to forward user credentials of a RADIUS server on the Internal network. It is suggested that one can significantly enhance the security of your ISA firewall's VPN remote access client connections by using Extensible Authentication Protocol user certificate authentication.
Sylvain Tesson : La Consolation de la forêt

French travel writer Sylvain Tesson relayed his six months stay alone in a hut by lake Baikal in Dans les forets de Siberie (2011). It is his response to the “call of the wild,” an ode to nature an...
Radar Cross-Section

Radar cross section (RCS) is a measure of the magnitude of the scattered electromagnetic wave from a body relative to the magnitude of the wave incident on the body. To many people RCS is synonymous with stealth. Stealth technology has become an integral part of all military systems since it was first employed in the 1970s. However, RCS is just one aspect of stealth, the design philosophy that seeks to make a platform (such as an aircraft, ship, or ground vehicle) undetectable to a wide range of sensors. Keywords: stealth technology; radar; electromagnetic wave; radar cross section; low-observable platform
Knowledge transfers from federally supported R&D

The purpose of this paper is to identify covariates with publication activity, a form of knowledge transfer, from SBIR publicly funded research. The paper offers an argument about the policy relevance of studying knowledge transfers from publicly funded research that occurs in private sector firms. Relevant explanatory variables are the length of the funded research project, university involvement in the project, the firm’s history of SBIR funding, and the academic background of firms’ founders.
On Distributed Estimation in Hierarchical Power Constrained Wireless Sensor Networks

We consider distributed estimation of a random source in a hierarchical power constrained wireless sensor network. Sensors within each cluster send their measurements to a cluster head (CH). CHs optimally fuse the received signals and transmit to the fusion center (FC) over orthogonal fading channels. To enable channel estimation at the FC, CHs send pilots, prior to data transmission. We derive the mean square error (MSE) corresponding to the linear minimum mean square error (LMMSE) estimator of the source at the FC, and obtain the Bayesian Cramer-Rao bound (CRB). Our goal is to find (i) the optimal training power, (ii) the optimal power that sensors in a cluster spend to transmit their amplified measurements to their CH, and (iii) the optimal weight vector employed by each CH for its linear signal fusion, such that the MSE is minimized, subject to a network power constraint. To untangle the performance gain that optimizing each set of these variables provide, we also analyze three special cases of the original problem, where in each special case, only two sets of variables are optimized across clusters. We define three factors that allow us to quantify the effectiveness of each power allocation scheme in achieving an MSE-power tradeoff that is close to that of the Bayesian CRB. Combining the information gained from the factors and Bayesian CRB with our computational complexity analysis provides the system designer with quantitative complexity-versus-MSE improvement tradeoffs offered by different power allocation schemes.
Time-Inconsistent Risk-Sensitive Equilibrium for Countable-Stated Markov Decision Processes

This paper is devoted to solving a time-inconsistent risk-sensitive control problem with parameter $$\varepsilon $$ and its limit case ( $$\varepsilon \rightarrow 0^+$$ ) for countable-stated Markov decision processes (MDPs for short). Since the cost functional is time-inconsistent, it is impossible to find a global optimal strategy for both cases. Instead, for each case, we will prove the existence of time-inconstant equilibrium strategies which verify some step-optimality. Moreover, we prove the convergence of the so-called $$\varepsilon $$ -risk-sensitive equilibria and the corresponding value functions as $$\varepsilon \rightarrow 0^+$$ .
Deep Learning of Protein Structural Classes: Any Evidence for an ‘Urfold’?

Recent computational advances in the accurate prediction of protein three-dimensional (3D) structures from amino acid sequences now present a unique opportunity to decipher the interrelationships between proteins. This task entails—but is not equivalent to—a problem of 3D structure comparison and classification. Historically, protein domain classification has been a largely manual and subjective activity, relying upon various heuristics. Databases such as CATH represent significant steps towards a more systematic (and automatable) approach, yet there still remains much room for the development of more scalable and quantitative classification methods, grounded in machine learning. We suspect that re-examining these relationships via a Deep Learning (DL) approach may entail a large-scale restructuring of classification schemes, improved with respect to the interpretability of distant relationships between proteins. Here, we describe our training of DL models on protein domain structures (and their associated physicochemical properties) in order to evaluate classification properties at CATH’s “homologous superfamily” (SF) level. To achieve this, we have devised and applied an extension of image-classification methods and image segmentation techniques, utilizing a convolutional autoencoder model architecture. Our DL architecture allows models to learn structural features that, in a sense, ‘define’ different homologous SFs. We evaluate and quantify pairwise ‘distances’ between SFs by building one model per SF and comparing the loss functions of the models. Hierarchical clustering on these distance matrices provides a new view of protein interrelationships—a view that extends beyond simple structural/geometric similarity, and towards the realm of structure/function properties.
Distinct signals in medial and lateral VTA dopamine neurons modulate fear extinction at different times

Dopamine (DA) neurons are to encode reward prediction error (RPE), in addition to other signals, such as salience. While RPE is known to support learning, the role of salience in learning remains less clear. To address this, we recorded and manipulated VTA DA neurons in mice during fear extinction. We applied deep learning to classify mouse freezing behavior, eliminating the need for human scoring. Our fiber photometry recordings showed DA neurons in medial and lateral VTA have distinct activity profiles during fear extinction: medial VTA activity more closely reflected RPE, while lateral VTA activity more closely reflected a salience-like signal. Optogenetic inhibition of DA neurons in either region slowed fear extinction, with the relevant time period for inhibition differing across regions. Our results indicate salience-like signals can have similar downstream consequences to RPE-like signals, although with different temporal dependencies.
Data preprocessing to mitigate bias: A maximum entropy based approach

Data containing human or social attributes may over- or under-represent groups with respect to salient social attributes such as gender or race, which can lead to biases in downstream applications. This paper presents an algorithmic framework that can be used as a data preprocessing method towards mitigating such bias. Unlike prior work, it can efficiently learn distributions over large domains, controllably adjust the representation rates of protected groups and achieve target fairness metrics such as statistical parity, yet remains close to the empirical distribution induced by the given dataset. Our approach leverages the principle of maximum entropy - amongst all distributions satisfying a given set of constraints, we should choose the one closest in KL-divergence to a given prior. While maximum entropy distributions can succinctly encode distributions over large domains, they can be difficult to compute. Our main contribution is an instantiation of this framework for our set of constraints and priors, which encode our bias mitigation goals, and that runs in time polynomial in the dimension of the data. Empirically, we observe that samples from the learned distribution have desired representation rates and statistical rates, and when used for training a classifier incurs only a slight loss in accuracy while maintaining fairness properties.
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation

Motivated by economic applications such as recommender systems, we study the behavior of stochastic bandits algorithms under \emph{strategic behavior} conducted by rational actors, i.e., the arms. Each arm is a \emph{self-interested} strategic player who can modify its own reward whenever pulled, subject to a cross-period budget constraint, in order to maximize its own expected number of times of being pulled. We analyze the robustness of three popular bandit algorithms: UCB, $\varepsilon$-Greedy, and Thompson Sampling. We prove that all three algorithms achieve a regret upper bound $\mathcal{O}(\max \{ B, K\ln T\})$ where $B$ is the total budget across arms, $K$ is the total number of arms and $T$ is length of the time horizon. This regret guarantee holds under \emph{arbitrary adaptive} manipulation strategy of arms. Our second set of main results shows that this regret bound is \emph{tight} -- in fact for UCB it is tight even when we restrict the arms' manipulation strategies to form a \emph{Nash equilibrium}. The lower bound makes use of a simple manipulation strategy, the same for all three algorithms, yielding a bound of $\Omega(\max \{ B, K\ln T\})$. Our results illustrate the robustness of classic bandits algorithms against strategic manipulations as long as $B=o(T)$.
Viral RNA is a target for Wolbachia-mediated pathogen blocking

The ability of the endosymbiont Wolbachia pipientis to restrict RNA viruses is presently being leveraged to curb global transmission of arbovirus-induced diseases. Past studies have shown that virus replication is limited early in arthropod cells colonized by the bacterium, although it is unclear if this phenomenon is replicated in mosquito cells that first encounter viruses obtained through a vertebrate blood meal. Furthermore, these cellular events neither explain how Wolbachia limits dissemination of viruses between mosquito tissues, nor how it prevents transmission of infectious viruses from mosquitoes to vertebrate host. In this study, we try to address these issues using an array of mosquito cell culture models, with an additional goal being to identify a common viral target for pathogen blocking. Our results establish the viral RNA as a cellular target for Wolbachia-mediated inhibition, with the incoming viral RNA experiencing rapid turnover following internalization in cells. This early block in replication in mosquito cells initially infected by the virus thus consequently reduces the production of progeny viruses from these same cells. However, this is not the only contributor to pathogen blocking. We show that the presence of Wolbachia reduces the per-particle infectivity of progeny viruses on naive mosquito and vertebrate cells, consequently limiting virus dissemination and transmission, respectively. Importantly, we demonstrate that this aspect of pathogen blocking is independent of any particular Wolbachia-host association and affects viruses belonging to Togaviridae and Flaviviridae families of RNA viruses. Finally, consistent with the idea of the viral RNA as a target, we find that the encapsidated virion RNA is less infectious for viruses produced from Wolbachia-colonized cells. Collectively, our findings present a common mechanism of pathogen blocking in mosquitoes that establish a link between virus inhibition in the cell to virus dissemination and transmission.
Exploring the Connection Between Binary and Spiking Neural Networks

On-chip edge intelligence has necessitated the exploration of algorithmic techniques to reduce the compute requirements of current machine learning frameworks. This work aims to bridge the recent algorithmic progress in training Binary Neural Networks and Spiking Neural Networks - both of which are driven by the same motivation and yet synergies between the two have not been fully explored. We show that training Spiking Neural Networks in the extreme quantization regime results in near full precision accuracies on large-scale datasets like CIFAR-$100$ and ImageNet. An important implication of this work is that Binary Spiking Neural Networks can be enabled by ``In-Memory" hardware accelerators catered for Binary Neural Networks without suffering any accuracy degradation due to binarization. We utilize standard training techniques for non-spiking networks to generate our spiking networks by conversion process and also perform an extensive empirical analysis and explore simple design-time and run-time optimization techniques for reducing inference latency of spiking networks (both for binary and full-precision models) by an order of magnitude over prior work. Our implementation source code and trained models are available at \href{https://github.com/NeuroCompLab-psu/SNN-Conversion}{(Link)}.
On Generalization Bounds of a Family of Recurrent Neural Networks

Recurrent Neural Networks (RNNs) have been widely applied to sequential data analysis. Due to their complicated modeling structures, however, the theory behind is still largely missing. To connect theory and practice, we study the generalization properties of vanilla RNNs as well as their variants, including Minimal Gated Unit (MGU), Long Short Term Memory (LSTM), and Convolutional (Conv) RNNs. Specifically, our theory is established under the PAC-Learning framework. The generalization bound is presented in terms of the spectral norms of the weight matrices and the total number of parameters. We also establish refined generalization bounds with additional norm assumptions, and draw a comparison among these bounds. We remark: (1) Our generalization bound for vanilla RNNs is significantly tighter than the best of existing results; (2) We are not aware of any other generalization bounds for MGU, LSTM, and Conv RNNs in the exiting literature; (3) We demonstrate the advantages of these variants in generalization.
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning

We consider the problem of off-policy evaluation for reinforcement learning, where the goal is to estimate the expected reward of a target policy $\pi$ using offline data collected by running a logging policy $\mu$. Standard importance-sampling based approaches for this problem suffer from a variance that scales exponentially with time horizon $H$, which motivates a splurge of recent interest in alternatives that break the "Curse of Horizon" (Liu et al. 2018, Xie et al. 2019). In particular, it was shown that a marginalized importance sampling (MIS) approach can be used to achieve an estimation error of order $O(H^3/ n)$ in mean square error (MSE) under an episodic Markov Decision Process model with finite states and potentially infinite actions. The MSE bound however is still a factor of $H$ away from a Cramer-Rao lower bound of order $\Omega(H^2/n)$. In this paper, we prove that with a simple modification to the MIS estimator, we can asymptotically attain the Cramer-Rao lower bound, provided that the action space is finite. We also provide a general method for constructing MIS estimators with high-probability error bounds.
Effective reinforcement learning through evolutionary surrogate-assisted prescription

There is now significant historical data available on decision making in organizations, consisting of the decision problem, what decisions were made, and how desirable the outcomes were. Using this data, it is possible to learn a surrogate model, and with that model, evolve a decision strategy that optimizes the outcomes. This paper introduces a general such approach, called Evolutionary Surrogate-Assisted Prescription, or ESP. The surrogate is, for example, a random forest or a neural network trained with gradient descent, and the strategy is a neural network that is evolved to maximize the predictions of the surrogate model. ESP is further extended in this paper to sequential decision-making tasks, which makes it possible to evaluate the framework in reinforcement learning (RL) benchmarks. Because the majority of evaluations are done on the surrogate, ESP is more sample efficient, has lower variance, and lower regret than standard RL approaches. Surprisingly, its solutions are also better because both the surrogate and the strategy network regularize the decision making behavior. ESP thus forms a promising foundation to decision optimization in real-world problems.
The Resolution of Keller’s Conjecture

We consider three graphs, \(G_{7,3}\), \(G_{7,4}\), and \(G_{7,6}\), related to Keller’s conjecture in dimension 7. The conjecture is false for this dimension if and only if at least one of the graphs contains a clique of size \(2^7 = 128\). We present an automated method to solve this conjecture by encoding the existence of such a clique as a propositional formula. We apply satisfiability solving combined with symmetry-breaking techniques to determine that no such clique exists. This result implies that every unit cube tiling of \(\mathbb {R}^7\) contains a facesharing pair of cubes. Since a faceshare-free unit cube tiling of \(\mathbb {R}^8\) exists (which we also verify), this completely resolves Keller’s conjecture.