cssi/SciSciGPT-SciSciNet huggingface dataset:
Learning a Faceted Customer Segmentation for Discovering new Business Opportunities at Intel

For sales and marketing organizations within large enterprises, identifying and understanding new markets, customers and partners is a key challenge. Intel’s Sales and Marketing Group (SMG) faces similar challenges while growing in new markets and domains and evolving its existing business. In today’s complex technological and commercial landscape, there is need for intelligent automation supporting a fine-grained understanding of businesses in order to help SMG sift through millions of companies across many geographies and languages and identify relevant directions. We present a system developed in our company that mines millions of public business web pages, and extracts a faceted customer representation. We focus on two key customer aspects that are essential for finding relevant opportunities: industry segments (ranging from broad verticals such as healthcare, to more specific fields such as “video analytics”) and functional roles (e.g., “manufacturer” or “retail”). To address the challenge of labeled data collection, we enrich our data with external information gleaned from Wikipedia, and develop a semi-supervised multi-label, multi-lingual deep learning model that parses customer website texts and classifies them into their respective facets. Our system scans and indexes companies as part of a large-scale knowledge graph that currently holds tens of millions of connected entities with thousands being fetched, enriched and connected to the graph by the hour in real time, and also supports knowledge and insight discovery. In experiments conducted in our company, we are able to significantly boost the performance of sales personnel in the task of discovering new customers and commercial partnership opportunities.
Positive semigroups and generalized Frobenius numbers over totally real number fields

The Frobenius problem and its many generalizations have been extensively studied in several areas of mathematics. We study semigroups of totally positive algebraic integers in totally real number fields, defining analogues of the Frobenius numbers in this context. We use a geometric framework recently introduced by Aliev, De Loera and Louveaux to produce upper bounds on these Frobenius numbers in terms of a certain height function. We discuss some properties of this function, relating it to absolute Weil height and obtaining a lower bound in the spirit of Lehmer’s conjecture for algebraic vectors satisfying some special conditions. We also use a result of Borosh and Treybig to obtain bounds on the size of representations and number of elements of bounded height in such positive semigroups of totally real algebraic integers.
Cohomology for Anyone

Crystallography has proven a rich source of ideas over several centuries. Among the many ways of looking at space groups, N. David Mermin has pioneered the Fourier-space approach. Recently, we have supplemented this approach with methods borrowed from algebraic topology. We now show what topology, which studies global properties of manifolds, has to do with crystallography. No mathematics is assumed beyond what the typical physics or crystallography student will have seen of group theory; in particular, the reader need not have any prior exposure to topology or to cohomology of groups.
Adversarial Model Predictive Control via Second-Order Cone Programming

We study the problem of designing attacks to safety-critical systems in which the adversary seeks to maximize the overall system cost within a model predictive control framework. Although in general this problem is NP-hard, we characterize a family of problems that can be solved in polynomial time via a second-order cone programming relaxation. In particular, we show that positive systems fall under this family. We provide examples demonstrating the design of optimal attacks on an autonomous vehicle and a microgrid.
Transposable elements in individual genotypes of Drosophila simulans.

Transposable elements are abundant, dynamic components of the genome that affect organismal phenotypes and fitness. In Drosophila melanogaster, they have increased in abundance as the species spread out of Africa, and different populations differ in their transposable element content. However, very little is currently known about how transposable elements differ between individual genotypes, and how that relates to the population dynamics of transposable elements overall. The sister species of D. melanogaster, D. simulans, has also recently become cosmopolitan, and panels of inbred genotypes exist from cosmopolitan and African flies. Therefore, we can determine whether the differences in colonizing populations are repeated in D. simulans, what the dynamics of transposable elements are in individual genotypes, and how that compares to wild flies. After estimating copy number in cosmopolitan and African D. simulans, I find that transposable element load is higher in flies from cosmopolitan populations. In addition, transposable element load varies considerably between populations, between genotypes, but not overall between wild and inbred lines. Certain genotypes either contain active transposable elements or are more permissive of transposition and accumulate copies of particular transposable elements. Overall, it is important to quantify genotype-specific transposable element dynamics as well as population averages to understand the dynamics of transposable element accumulation over time.
The problems you're having may not be the problems you think you're having: results from a latency study of Windows NT

This paper is intended to catalyze discussions on two intertwined systems topics. First, it presents early results from a latency study of Windows NT that identifies some specific causes of long thread scheduling latencies, many of which delay the dispatching of runnable threads for tens of milliseconds. Reasons for these delays, including technical, methodological, and economic are presented and possible solutions are discussed. Secondly, and equally importantly, it is intended to serve as a cautionary tale against believing one's own intuition about the causes of poor system performance. We went into this study believing we understood a number of the causes of these delays, with our beliefs informed more by conventional wisdom and hunches than data. In nearly all cases the reasons we discovered via instrumentation and measurement surprised us. In fact, some directly contradicted "facts" we thought we "knew".
Robonomics: The Study of Robot-Human Peer-to-Peer Financial Transactions and Agreements

Can or should a robot ever engage in a financial transaction with a human? If so, how? How about an enforceable agreement? Blockchain technology has enabled the development of cryptocurrencies, smart contracts, and unlocked a plethora of other disruptive technologies. But, beyond its use case in cryptocurrencies, and in network coordination, blockchain technology may have serious sociotechnical implications in the future co-existence of robots and humans. Motivated by the recent explosion of interest around blockchains, and our extensive work on open-source blockchain technology and its integration into robotics - this paper addresses these questions and provides insights into how blockchains and other decentralized technologies can impact our interactions with robots and enable the social integration of robots into human society.
Pairs of Theories Satisfying a Mordell-Lang Condition

This paper proposes a new setup for studying pairs of structures. This new framework includes many of the previously studied classes of pairs, such as dense pairs of o-minimal structures, lovely pairs, fields with Mann groups, and $H$-structures, but also includes new ones, such as pairs consisting of a real closed field and a pseudo real closed subfield, and pairs of vector spaces with different fields of scalars. We use the larger generality of this framework to answer three concrete open questions raised in earlier work on this subject.
Assortment Optimization Under the Paired Combinatorial Logit Model

When retailers decide which assortment of products to offer, they can make use of a choice model that describes how customers choose and substitute among the products. The key is to use a choice mo...
Covariance structures for multidimensional data

Certain covariance constraint sets useful in a wide class of signal processing problems are described. The constraint sets define structure in the covariance matrices of naturally multidimensional data organized into column-vector form. A notation characterizing these constraint sets is established. The structures involve a hierarchy of sub-blocks within the matrix, and include block-circulant and block-Toeplitz matrices and their respective generalizations. An example describing the structured covariance for wideband data from a uniform linear array of unstructured subarrays is included.
The hybrid dimensional representation of permeability tensor: A reinterpretation of the discrete fracture model and its extension on nonconforming meshes

Abstract The discrete fracture model (DFM) has been widely used in the simulation of fluid flow in fractured porous media. Traditional DFM uses the so-called hybrid-dimensional approach to treat fractures explicitly as low-dimensional entries (e.g. line entries in 2D media and face entries in 3D media) on the interfaces of matrix cells and then couple the matrix and fracture flow systems together based on the principle of superposition with the fracture thickness used as the dimensional homogeneity factor. Because of this methodology, DFM is considered to be limited on conforming meshes and thus may raise difficulties in generating high quality unstructured meshes due to the complexity of fracture's geometrical morphology. In this paper, we clarify that the DFM actually can be extended to non-conforming meshes without any essential changes. To show it clearly, we provide another perspective for DFM based on hybrid-dimensional representation of permeability tensor to describe fractures as one-dimensional line Dirac delta functions contained in permeability tensor. A finite element DFM scheme for single-phase flow on non-conforming meshes is then derived by applying Galerkin finite element method to it. Analytical analysis and numerical experiments show that our DFM automatically degenerates to the classical finite element DFM when the mesh is conforming with fractures. Moreover, the accuracy and efficiency of the model on non-conforming meshes are demonstrated by testing several benchmark problems. This model is also applicable to curved fracture with variable thickness.
Chapter 2 – Modeling and Theory

In a biosensor-based assay, the molecule to be detected (analyte) is present in solution and the appropriate receptor is immobilized on a solid surface. The interaction between the analyte and the receptor on the solid biosensor surface is detected either by a change in the refractive index (in surface plasmon resonance (SPR) instruments) or by changes in the fluorometric intensity, ultraviolet light intensity, etc. The SPR biosensor protocol analyzes the binding (and dissociation where applicable) kinetic curves using classical saturation models involving analyte-receptor binding using 1:1, 1:2, etc. ratios, generally under diffusion-free conditions and assuming that the receptors are homogeneously distributed over the sensor surface. Though a careful analysis and experimental protocol may eliminate or minimize the influence of diffusional limitations; realistically speaking, it is more appropriate to include a heterogeneous distribution on the sensing surface. Heterogeneity on the sensing surface and in the biosensor systems itself may be due to other reasons, such as nonspecific binding, inherent irregularities on the sensing surface, mixture of receptors on the surface, and mixture of analytes in solution which includes the analyte of interest.
CHAPTER 13 – Computer Programming Languages

This chapter describes a few computer programming languages. The only language a computer understands is its machine language—the binary commands telling it exactly what to do. The standard programming languages in common use convert logical constructs and instructions that make sense to people into a series of commands a computer can understand and carry out. A set of commands used to perform some desired function is considered a program. The terms high-level and low-level are often associated with computer languages. A low-level language is very close to machine language. The most common instance of this is an assembly language that does a one-to-one conversion of simple mnemonic commands into machine language commands, or object code. A high-level language is more abstract than a low-level language. Besides high-level versus low-level, there are two other distinctions between different types of computer languages: compiled versus interpreted. In a compiled language, the program under development is translated into machine language through a separate, independent series of steps.
Lambda calculus characterizations of poly-time

We consider typed λ-calculi with pairing over the algebra W of words over {0,1}, with a destructor and discriminator function. We show that the poly-time functions are precisely the functions (1) λ-representable using simple types, with abstract input (represented by Church-like terms) and concrete output (represented by algebra terms); (2) λ-representable using simple types, with abstract input and output, but with the input and output representations differing slightly; (3) λ-representable using polymorphic typing with type quantification ranging over multiplicative types only; (4) λ-representable using simple and list types (akin to ML style) with abstract input and output; and (5) λ-representable over the algebra of flat lists (in place of W), using simple types, with abstract input and output.
What Caused the Bank Capital Build-up of the 1990s?

Large U.S. banks dramatically increased their capitalization during the 1990s, to the highest levels in more than 50 years. We document this buildup of capital and evaluate several potential motivations. Our results support the hypothesis that regulatory innovations in the early 1990s weakened conjectural government guarantees and enhanced the bank counterparties’ incentive to monitor and price default risk. We find no evidence that a bank holding company’s market capitalization increases with its asset volatility prior to 1994. Thereafter, the data display a strong cross-sectional relation between capitalization and asset risk. Our estimates indicate that most of the bank capital buildup over the sample period can be explained by greater bank risk exposures and the market's increased demand that large banks’ default risk be priced.
Disconnection-mediated twin embryo growth in Mg

Abstract While deformation twinning in hexagonal close-packed metals has been widely studied due to its substantial impact on mechanical properties, an understanding of the detailed atomic processes associated with twin embryo growth is still lacking. Conducting molecular dynamics simulations on Mg, we show that the propagation of twinning disconnections emitted by basal-prismatic interfaces controls the twin boundary motion and is the rate-limiting mechanism during the initial growth of the twin embryo. The time needed for disconnection propagation is related to the distance between the twin tips, with widely spaced twin tips requiring more time for a unit twin boundary migration event to be completed. Thus, a phenomenological model, which unifies the two processes of disconnection and twin tip propagation, is proposed here to provide a quantitative analysis of twin embryo growth. The model fits the simulation data well, with two key parameters (twin tip velocity and twinning disconnection velocity) being extracted. In addition, a linear relationship between the ratio of twinning disconnection velocity to twin tip velocity and the applied shear stress is observed. Using an example of twin growth in a nanoscale single crystal from the recent literature, we find that our molecular dynamics simulations and analytical model are in good agreement with experimental data.
Cross-Region Transfers in a Monetary Union : Evidence from the US and Some Implications

US federal transfers to individuals are large, countercyclical, vary geographically, and are often credited for helping stabilize regional economies. This paper estimates the short-run effects of these transfers using plausibly exogenous regional variation in temporary stimulus packages and earlier permanent Social Security increases. States that received larger transfers tended to grow faster contemporaneously, with a multiplier of around 1.5 for permanent transfers and 1/3 for temporary transfers. Results are broadly consistent with an open-economy New Keynesian model. At business-cycle frequencies, cross-region transfer multipliers are not large, suggesting only modest gains in regional stabilization from US federal automatic stabilizers.
The Role of Context in Educational RCT Findings: A Call to Redefine “Evidence-Based Practice”:

In this commentary, we complement other constructive critiques of educational randomized control trials (RCTs) by calling attention to the commonly ignored role of context in causal mechanisms unde...
Strategic usefulness of ignorance: evidence from income smoothing via retained interest of securitized loans

This paper examines the notion of strategic ignorance in an earnings management context. Managers possess less information about loans subjected to securitization and auditors are aware of this shortcoming. Consistent with managers exploiting their own ignorance strategically and using loan loss provisions (LLP) of retained seller’s interest of securitized loans (SIL) more for earnings management than LLP of other loans, we find the use of LLP for income smoothing to be greater for banks that hold SIL. Moreover, the propensity to smooth income via LLP is increasing in the ratio of SIL to total loans. We also find that SIL is particularly useful for income smoothing in the fourth quarter, when greater auditor scrutiny makes it more difficult to manage earnings via LLP of non-securitized loans.
MEASUREMENTS OF THE DYNAMICS OF THE HIGH-LATITUDE THERMOSPHERE.

The study of the dynamics and thermodynamics of the earth's upper atmosphere has made significant progress over the past few years owing to the availability of new global-scale data sets from the Dynamics Explorer satellites. The thermospheric wind and temperature fields at high altitude have been observed to depend strongly on forcing processes of magnetospheric origin. A key momentum source is due to the drag effect of ions convecting in response to electric fields mapped down on the ionosphere from magnetospheric boundary regions. Likewise, an important heat source derives from Joule or frictional dissipation due to ion/neutral difference velocities governed by magnetospheric forcing. In this paper the progress made over the last 2-3 years initiated by the new satellite measurements is discussed, and published data on ion and neutral motions are reviewed in the context of the energy and momentum coupling between the magnetosphere and the ionosphere/neutral upper atmosphere.
Studying Ransomware Attacks Using Web Search Logs

Cyber attacks are increasingly becoming prevalent and causing significant damage to individuals, businesses and even countries. In particular, ransomware attacks have grown significantly over the last decade. We do the first study on mining insights about ransomware attacks by analyzing query logs from Bing web search engine. We first extract ransomware related queries and then build a machine learning model to identify queries where users are seeking support for ransomware attacks. We show that user search behavior and characteristics are correlated with ransomware attacks. We also analyse trends in the temporal and geographical space and validate our findings against publicly available information. Lastly, we do a case study on 'Nemty', a popular ransomware, to show that it is possible to derive accurate insights about cyber attacks by query log analysis.
Faster energy maximization for faster maximum flow

In this paper we provide an algorithm which given any m-edge n-vertex directed graph with integer capacities at most U computes a maximum s-t flow for any vertices s and t in m 11/8+o(1) U 1/4 time with high probability. This running time improves upon the previous best of O(m 10/7 U 1/7) (Mądry 2016), O(m √n logU) (Lee Sidford 2014), and O(mn) (Orlin 2013) when the graph is not too dense or has large capacities. We achieve this result by leveraging recent advances in solving undirected flow problems on graphs. We show that in the maximum flow framework of (Mądry 2016) the problem of optimizing the amount of perturbation of the central path needed to maximize energy and thereby reduce congestion can be efficiently reduced to a smoothed l2-l p flow optimization problem, which can be solved approximately via recent work (Kyng, Peng, Sachdeva, Wang 2019). Leveraging this new primitive, we provide a new interior point method for maximum flow with faster convergence and simpler analysis that no longer needs global potential functions involving energy as in previous methods (Mądry 2013, Mądry 2016).
Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction

Better machine understanding of pedestrian behaviors enables faster progress in modeling interactions between agents such as autonomous vehicles and humans. Pedestrian trajectories are not only influenced by the pedestrian itself but also by interaction with surrounding objects. Previous methods modeled these interactions by using a variety of aggregation methods that integrate different learned pedestrians states. We propose the Social Spatio-Temporal Graph Convolutional Neural Network (Social-STGCNN), which substitutes the need of aggregation methods by modeling the interactions as a graph. Our results show an improvement over the state of art by 20% on the Final Displacement Error (FDE) and an improvement on the Average Displacement Error (ADE) with 8.5 times less parameters and up to 48 times faster inference speed than previously reported methods. In addition, our model is data efficient, and exceeds previous state of the art on the ADE metric with only 20% of the training data. We propose a kernel function to embed the social interactions between pedestrians within the adjacency matrix. Through qualitative analysis, we show that our model inherited social behaviors that can be expected between pedestrians trajectories. Code is available at https://github.com/abduallahmohamed/Social-STGCNN.
Thermal decoherence and laser cooling of Kerr microresonator solitons

Thermal noise is ubiquitous in microscopic systems and high-precision measurements. The control of thermal noise would reveal quantum regimes1 and enable fundamental physics searches2. Recently, nonlinearity in microresonators has enabled laser devices such as Kerr microresonator soliton frequency combs3. Soliton microcombs explore nonlinear dynamics and enable optical synthesizers4, optical clockwork5 and data communications systems6. Here, we explore how thermal noise leads to the fundamental decoherence of microcombs. We show that a particle-like soliton, which is an ensemble of comb modes, is closely coupled to the thermal fluctuations of its silicon-chip-based resonator. The microcomb modal linewidth is thus thermally broadened, and we characterize these thermal-noise correlations through a soliton effective temperature. Moreover, we demonstrate that passive laser cooling reduces soliton thermal decoherence to far below the ambient-temperature limit. We implement laser cooling by photothermal forcing, and we observe cooling of the frequency comb modes to 84 K. Our work illuminates inherent connections between nonlinear photonics and microscopic fluctuations. Observations of decoherence from thermodynamic noise in microresonator soliton frequency combs and laser cooling that reduces soliton thermal decoherence to far below the ambient-temperature limit are described, linking nonlinear photonics and microscopic fluctuations.
Parasite community of Patagonotothen cornucola (Pisces: Nototheniidae) from intertidal rocky pools from Strait of Magellan

Notothens are abundant fish in the Austral Ocean, therefore they stablish many interactions with other species, and thus they have become important components in the ecosystem. However, the relationships between these fish and their parasites are poorly understood. The parasite community of the intertidal notothen Patagonotothen cornucola has been analyzed in this study considering two sampling sites at the Strait of Magellan and the sex of the fish. The fish samples were collected in January 2017 at Rinconada Bulnes (RB), and San Isidro (SI). A total of 98 fish individuals, with total body length between 4.2 and 13.4 cm, were obtained. Of these fish, 15 parasite species were recorded. Four species were the most prevalent or abundant, three digeneans (Caudotestis kerguelensis, Whitegonimus ozoufae and Genolinea cf. bowersi) and one acantocephalan (Corynosoma beaglense). The similarity of parasite composition based on the abundance of parasites showed significant differences among sites because the fish from SI had only five parasite species and two of them with higher abundances than RB. There were no differences in parasite composition between male and female fish. Therefore, the sampling site was important for composition and abundance of parasites, indicating ecological and environmental differences between sites that affect parasite transmission. This is the first study on the parasite community of a species of Patagonotothen in the Patagonia area, showing a wide diversity of parasite species in this little fish.
The black hole weak gravity conjecture with multiple charges

We study the effect of higher-derivative corrections on asymptotically flat, four-dimensional, dyonic black holes in low-energy models of gravity coupled to N U(1) gauge fields. For large extremal black holes, the leading $$ \mathcal{O} $$ (1/Q2) correction to the extremality bound is calculated from the most general low-energy effective action containing operators with up to four derivatives. Motivated by the multi-charge generalization of the Weak Gravity Conjecture, we analyze the necessary kinematic conditions for an asymptotically large extremal black hole to decay into a multi-particle state of extremal black holes. In the large black hole regime, we show that the convex hull condition degenerates to the requirement that a certain quartic form constructed from the Wilson coefficients of the four- derivative effective operators, is everywhere positive. Using on-shell unitarity methods, we show that higher-derivative operators are renormalized at one-loop only if they generate local, on-shell matrix elements that are invariant tensors of the electromagnetic duality group U(N). The one-loop logarithmic running of the four-derivative Wilson coefficients is calculated and shown to imply the positivity of the extremality form at some finite value of Q2. This result generalizes an argument recently given by Charles [1], and shows that under the given assumptions the multi-charge Weak Gravity Conjecture is not a Swampland criterion.
Deep Octree-Based CNNs With Output-Guided Skip Connections for 3D Shape and Scene Completion

Acquiring complete and clean 3D shape and scene data is challenging due to geometric occlusion and insufficient views during 3D capturing. We present a simple yet effective deep learning approach for completing the input noisy and incomplete shapes or scenes. Our network is built upon the octree-based CNNs (O-CNN) with U-Net like structures, which enjoys high computational and memory efficiency and supports to construct a very deep network structure for 3D CNNs. A novel output-guided skip-connection is introduced to the network structure for better preserving the input geometry and learning geometry prior from data effectively. We show that with these simple adaptions - output-guided skip-connection and deeper O-CNN (up to 70 layers), our network achieves state-of-the-art results in 3D shape completion and semantic scene computation.
Positional Isomers of a Non-Nucleoside Substrate Differentially Affect Myosin Function

Molecular motors have evolved to transduce chemical energy from ATP into mechanical work to drive essential cellular processes, from muscle contraction to vesicular transport. Dysfunction of these motors is a root cause of many pathologies necessitating the need for intrinsic control over molecular motor function. Herein, we demonstrate that positional isomerism can be used as a simple and powerful tool to control the molecular motor of muscle, myosin. Using three isomers of a synthetic non-nucleoside triphosphate, we demonstrate that myosin's force- and motion-generating capacity can be dramatically altered at both the ensemble and single-molecule levels. By correlating our experimental results with computation, we show that each isomer exerts intrinsic control by affecting distinct steps in myosin's mechanochemical cycle. Our studies demonstrate that subtle variations in the structure of an abiotic energy source can be used to control the force and motility of myosin without altering myosin's structure.
Optimal Mechanism Design for Single-Minded Agents

We consider optimal (revenue maximizing) mechanism design in the interdimensional setting, where one dimension is the 'value' of the buyer, and the other is a 'type' that captures some auxiliary information. A prototypical example of this is the FedEx Problem, for which Fiat et al. [2016] characterize the optimal mechanism for a single agent. Another example of this is when the type encodes the buyer's budget [DW17]. The question we address is how far can such characterizations goIn particular, we consider the setting of single-minded agents. A seller has heterogenous items. A buyer has a valuation vfor a specific subset of items S, and obtains value vif and only if he gets all the items in S(and potentially some others too). We show the following results. Deterministic mechanisms (i.e. posted prices) are optimal for distributions that satisfy the "declining marginal revenue" (DMR) property. In this case we give an explicit construction of the optimal mechanism. Without the DMR assumption, the result depends on the structure of the minimal directed acyclic graph (DAG) representing the partial order among types. When the DAG has out-degree at most 1, we characterize the optimal mechanism ala FedEx; this can be thought of as a generalization of the FedEx characterization since FedEx corresponds to a DAG that is a line. Surprisingly, without the DMR assumption andwhen the DAG has at least one node with an out-degree of at least 2, then we show that there is no hope of such a characterization. The minimal such example happens on a DAG with 3 types. We show that in this case the menu complexity is unboundedin that for any M, there exist distributions over (v,S) pairs such that the menu complexity of the optimal mechanism is at least M. For the case of 3 types, we also show that for all distributions there exists an optimal mechanism of finitemenu complexity. This is in contrast to the case where you have 2 heterogenous items with additive utilities for which the menu complexity could be uncountably infinite [DDT15, MV07]. In addition, we prove that optimal mechanisms for Multi-Unit Pricing (without a DMR assumption) can have unbounded menu complexity as well, and we further propose an extension where the menu complexity of optimal mechanisms can be countably infinite, but not uncountably infinite. Taken together, these results establish that optimal mechanisms in interdimensional settings are both surprisingly richer than single-dimensional settings, yet also vastly more structured than multi-dimensional settings.
Categorical Bernstein operators and the Boson-Fermion correspondence

We prove a conjecture of Cautis and Sussan providing a categorification of the Boson-Fermion correspondence as formulated by Frenkel and Kac. We lift the Bernstein operators to infinite chain complexes in Khovanov’s Heisenberg category $${\mathcal {H}}$$ and from them construct categorical analogues of the Kac-Frenkel fermionic vertex operators. These fermionic functors are then shown to satisfy categorical Clifford algebra relations, solving a conjecture of Cautis and Sussan. We also prove another conjecture of Cautis and Sussan demonstrating that the categorical Fock space representation of $${\mathcal {H}}$$ is a direct summand of the regular representation by showing that certain infinite chain complexes are categorical Fock space idempotents. In the process, we enhance the graphical calculus of $${\mathcal {H}}$$ by lifting various Littlewood-Richardson branching isomorphisms to the Karoubian envelope of $${\mathcal {H}}$$ .
ECONOMIC POLICY UNCERTAINTY AND BOARD MONITORING: EVIDENCE FROM CEO TURNOVERS

We examine whether economic policy uncertainty (EPU) affects a board's chief executive officer (CEO) replacement decision. We find that high EPU reduces the likelihood of forced CEO turnover. Our results support the idea that performance assessment may be more difficult when uncertainty is high. We provide evidence that succession planning may be important to firms in reducing the effects of EPU, as firms with an identifiable heir apparent are not influenced by high EPU. Likewise, voluntary CEO turnovers are not affected by EPU. Overall, our results provide evidence that boards make personnel decisions in response to external macroeconomic pressures.
Asynchronous and Parallel Distributed Pose Graph Optimization

We present Asynchronous Stochastic Parallel Pose Graph Optimization (ASAPP), the first asynchronous algorithm for distributed pose graph optimization (PGO) in multi-robot simultaneous localization and mapping. By enabling robots to optimize their local trajectory estimates without synchronization, ASAPP offers resiliency against communication delays and alleviates the need to wait for stragglers in the network. Furthermore, ASAPP can be applied on the rank-restricted relaxations of PGO, a crucial class of non-convex Riemannian optimization problems that underlies recent breakthroughs on globally optimal PGO. Under bounded delay, we establish the global first-order convergence of ASAPP using a sufficiently small stepsize. The derived stepsize depends on the worst-case delay and inherent problem sparsity, and furthermore matches known result for synchronous algorithms when there is no delay. Numerical evaluations on simulated and real-world datasets demonstrate favorable performance compared to state-of-the-art synchronous approach, and show ASAPP's resilience against a wide range of delays in practice.
Transfer Learning for HVAC System Fault Detection

Faults in HVAC systems degrade thermal comfort and energy efficiency in buildings and have received significant attention from the research community, with data driven methods gaining in popularity. Yet the lack of labeled data, such as normal versus faulty operational status, has slowed the application of machine learning to HVAC systems. In addition, for any particular building, there may be an insufficient number of observed faults over a reasonable amount of time for training. To overcome these challenges, we present a transfer method-ology for a novel Bayesian classifier designed to distinguish between normal operations and faulty operations. The key is to train this classifier on a building with a large amount of sensor and fault data (for example, via simulation or standard test data) then transfer the classifier to a new building using a small amount of normal operations data from the new building. We demonstrate a proof-of-concept for transferring a classifier between architecturally similar buildings in different climates and show few samples are required to maintain classification precision and recall.
Improving Network Robustness through Edge Augmentation While Preserving Strong Structural Controllability.

In this paper, we consider a network of agents with Laplacian dynamics, and study the problem of improving network robustness by adding a maximum number of edges within the network while preserving a lower bound on its strong structural controllability (SSC) at the same time. Edge augmentation increases network's robustness to noise and structural changes, however, it could also deteriorate network controllability. Thus, by exploiting relationship between network controllability and distances between nodes in graphs, we formulate an edge augmentation problem with a constraint to preserve distances between certain node pairs, which in turn guarantees that a lower bound on SSC is maintained even after adding edges. In this direction, first we choose a node pair and maximally add edges while maintaining the distance between selected nodes. We show that an optimal solution belongs to a certain class of graphs called clique chains. Then, we present an algorithm to add edges while preserving distances between a certain collection of nodes. Further, we present a randomized algorithm that guarantees a desired approximation ratio with high probability to solve the edge augmentation problem. Finally, we evaluate our results on various networks.
1-Shell totally symmetric plane partitions (TSPPs) modulo powers of 5

Let s(n) be the number of 1-shell totally symmetric plane partitions (TSPPs) of n. In this paper, an infinite family of congruences modulo powers of 5 for s(n) will be deduced through an elementary approach. Namely, $$\begin{aligned} s\left( 2\cdot 5^{2\alpha -1}n+5^{2\alpha -1}\right) \equiv 0 \pmod {5^{\alpha }}. \end{aligned}$$
ARDS in an ex-premature infant with bronchopulmonary dysplasia and COVID-19.

SARS-CoV-2 infection has caused a pandemic which has resulted in more severe disease in adults than children. We report an ex-premature infant with bronchopulmonary dysplasia who developed COVID-19 associated severe acute respiratory distress syndrome causing bradycardic cardiac arrest and seizures.
On well-posedness of the Muskat problem with surface tension

Abstract We consider the Muskat problem with surface tension for one fluid or two fluids, with or without viscosity jump, with infinite depth or Lipschitz rigid boundaries, and in arbitrary dimension d of the interface. The problem is nonlocal, quasilinear, and to leading order, is scaling invariant in the Sobolev space H s c ( R d ) with s c = 1 + d 2 . We prove local well-posedness for large data in all subcritical Sobolev spaces H s ( R d ) , s > s c , allowing for initial interfaces whose curvatures are unbounded and, furthermore when d = 1 , not locally square integrable. To the best of our knowledge, this is the first large-data well-posedness result that covers all subcritical Sobolev spaces for the Muskat problem with surface tension. We reformulate the problem in terms of the Dirichlet-Neumann operator and use a paradifferential approach to reduce the problem to an explicit parabolic equation, which is of independent interest.
Lyapunov exponents and entanglement entropy transition on the noncommutative hyperbolic plane

We study quantum dynamics on noncommutative spaces of negative curvature, focusing on the hyperbolic plane with spatial noncommutativity in the presence of a constant magnetic field. We show that the synergy of noncommutativity and the magnetic field tames the exponential divergence of operator growth caused by the negative curvature of the hyperbolic space. Their combined effect results in a first-order transition at a critical value of the magnetic field in which strong quantum effects subdue the exponential divergence for {\it all} energies, in stark contrast to the commutative case, where for high enough energies operator growth always diverge exponentially. This transition manifests in the entanglement entropy between the `left' and `right' Hilbert spaces of spatial degrees of freedom. In particular, the entanglement entropy in the lowest Landau level vanishes beyond the critical point. We further present a non-linear solvable bosonic model that realizes the underlying algebraic structure of the noncommutative hyperbolic plane with a magnetic field.
A strong law of large numbers for simultaneously testing parameters of Lancaster bivariate distributions

Abstract We prove a strong law of large numbers for simultaneously testing parameters of a large number of dependent, Lancaster bivariate random variables with infinite supports, and discuss its implications.
Finite-Blocklength and Error-Exponent Analyses for LDPC Codes in Point-to-Point and Multiple Access Communication

This paper applies error-exponent and dispersionstyle analyses to derive finite-blocklength achievability bounds for low-density parity-check (LDPC) codes over the point-to-point channel (PPC) and multiple access channel (MAC). The errorexponent analysis applies Gallager’s error exponent to bound achievable symmetrical and asymmetrical rates in the MAC. The dispersion-style analysis begins with a generalization of the random coding union (RCU) bound from random code ensembles with i.i.d. codewords to random code ensembles in which codewords may be statistically dependent; this generalization is useful since the codewords of random linear codes such as LDPC codes are dependent. Application of the RCU bound yields finiteblocklength error bounds and asymptotic achievability results for both i.i.d. random codes and LDPC codes. For discrete, memoryless channels, these results show that LDPC codes achieve first- and second-order performance that is optimal for the PPC and identical to the best prior results for the MAC.
Parent–Teacher Disagreement on Ratings of Behavior Problems in Children with ASD: Associations with Parental School Involvement Over Time

ASD symptomology and behavioral problems pose challenges for children with ASD in school. Disagreement between parents and teachers in ratings of children's behavior problems may provide clinically relevant information. We examined parent-teacher disagreement on ratings of behavior problems among children with ASD during the fall and spring of the school year. When child, teacher, and class characteristics were considered simultaneously, only ASD symptom severity predicted informant disagreement on internalizing and externalizing behavior problems. We also examined associations between informant disagreement and parent school involvement. Cross-lagged panel analyses revealed that higher informant disagreement on children's behavior problems in the fall predicted lower parent school involvement in the spring, suggesting that greater informant agreement may foster parental school involvement over time.
DEEP-URL: A Model-Aware Approach to Blind Deconvolution Based on Deep Unfolded Richardson-Lucy Network

The lack of interpretability in current deep learning models causes serious concerns as they are extensively used for various life-critical applications. Hence, it is of paramount importance to develop interpretable deep learning models. In this paper, we consider the problem of blind deconvolution and propose a novel model-aware deep architecture that allows for the recovery of both the blur kernel and the sharp image from the blurred image. In particular, we propose the Deep Unfolded Richardson-Lucy (Deep-URL) framework – an interpretable deep-learning architecture that can be seen as an amalgamation of classical estimation technique and deep neural network, and consequently leads to improved performance. Our numerical investigations demonstrate significant improvement compared to state-of-the-art algorithms.
Multi‐level Electro‐thermal Switching of Optical Phase‐Change Materials Using Graphene

Reconfigurable photonic systems featuring minimal power consumption are crucial for integrated optical devices in real-world technology. Current active devices available in foundries, however, use volatile methods to modulate light, requiring a constant supply of power and significant form factors. Essential aspects to overcoming these issues are the development of nonvolatile optical reconfiguration techniques which are compatible with on-chip integration with different photonic platforms and do not disrupt their optical performances. In this paper, a solution is demonstrated using an optoelectronic framework for nonvolatile tunable photonics that employs undoped-graphene microheaters to thermally and reversibly switch the optical phase-change material Ge$_2$Sb$_2$Se$_4$Te$_1$ (GSST). An in-situ Raman spectroscopy method is utilized to demonstrate, in real-time, reversible switching between four different levels of crystallinity. Moreover, a 3D computational model is developed to precisely interpret the switching characteristics, and to quantify the impact of current saturation on power dissipation, thermal diffusion, and switching speed. This model is used to inform the design of nonvolatile active photonic devices; namely, broadband Si$_3$N$_4$ integrated photonic circuits with small form-factor modulators and reconfigurable metasurfaces displaying 2$\pi$ phase coverage through neural-network-designed GSST meta-atoms. This framework will enable scalable, low-loss nonvolatile applications across a diverse range of photonics platforms.
Whole MILC: Generalizing learned dynamics across tasks, datasets, and populations

Behavioral changes are the earliest signs of a mental disorder, but arguably, the dynamics of brain function gets affected even earlier. Subsequently, spatio-temporal structure of disorder-specific dynamics is crucial for early diagnosis and understanding the disorder mechanism. A common way of learning discriminatory features relies on training a classifier and evaluating feature importance. Classical classifiers, based on handcrafted features are quite powerful, but suffer the curse of dimensionality when applied to large input dimensions of spatio-temporal data. Deep learning algorithms could handle the problem and a model introspection could highlight discriminatory spatio-temporal regions but need way more samples to train. In this paper we present a novel self supervised training schema which reinforces whole sequence mutual information local to context (whole MILC). We pre-train the whole MILC model on unlabeled and unrelated healthy control data. We test our model on three different disorders (i) Schizophrenia (ii) Autism and (iii) Alzheimers and four different studies. Our algorithm outperforms existing self-supervised pre-training methods and provides competitive classification results to classical machine learning algorithms. Importantly, whole MILC enables attribution of subject diagnosis to specific spatio-temporal regions in the fMRI signal.
On Reinforcement Learning for Turn-based Zero-sum Markov Games

We consider the problem of finding Nash equilibrium for two-player turn-based zero-sum games. Inspired by the AlphaGo Zero (AGZ) algorithm, we develop a Reinforcement Learning based approach. Specifically, we propose Explore-Improve-Supervise (EIS) method that combines "exploration", "policy improvement" and "supervised learning" to find the value function and policy associated with Nash equilibrium. We identify sufficient conditions for convergence and correctness for such an approach. For a concrete instance of EIS where random policy is used for "exploration", Monte-Carlo Tree Search is used for "policy improvement" and Nearest Neighbors is used for "supervised learning", we establish that this method finds an $\varepsilon$-approximate value function of Nash equilibrium in $\widetildeO(\varepsilon^-(d+4))$ steps when the underlying state-space of the game is continuous and d-dimensional. This is nearly optimal as we establish a lower bound of $\widetildeOmega (\varepsilon^-(d+2) )$ for any policy.