cssi/SciSciGPT-SciSciNet huggingface dataset:
The Galactic Dependencies Treebanks: Getting More Data by Synthesizing New Languages

We release Galactic Dependencies 1.0—a large set of synthetic languages not found on Earth, but annotated in Universal Dependencies format. This new resource aims to provide training and development data for NLP methods that aim to adapt to unfamiliar languages. Each synthetic treebank is produced from a real treebank by stochastically permuting the dependents of nouns and/or verbs to match the word order of other real languages. We discuss the usefulness, realism, parsability, perplexity, and diversity of the synthetic languages. As a simple demonstration of the use of Galactic Dependencies, we consider single-source transfer, which attempts to parse a real target language using a parser trained on a “nearby” source language. We find that including synthetic source languages somewhat increases the diversity of the source pool, which significantly improves results for most target languages.
Providing wireless coverage to high-rise buildings using UAVs

Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider an Air-to-Ground path loss model, which assumes that the users are outdoor and they are located on a 2D plane. In this paper, we propose using a single UAV to provide wireless coverage for indoor users inside a high-rise building under disaster situations (such as earthquakes or floods), when cellular networks are down. First, we present a realistic Outdoor-Indoor path loss model and describe the tradeoff introduced by this model. Then, we study the problem of efficient UAV placement, where the objective is to minimize the total transmit power required to cover the entire high-rise building. The formulated problem is non-convex and is generally difficult to solve. To that end, we consider two cases of practical interest and provide the efficient solutions to the formulated problem under these cases. In the first case, we aim to find the minimum transmit power such that an indoor user with the maximum path loss can be covered. In the second case, we assume that the locations of indoor users are symmetric across the dimensions of each floor.
On k -connectivity and minimum vertex degree in random s -intersection graphs

Random s-intersection graphs have recently received much interest in a wide range of application areas. Broadly speaking, a random s-intersection graph is constructed by first assigning each vertex a set of items in some random manner, and then putting an undirected edge between all pairs of vertices that share at least s items (the graph is called a random intersection graph when s = 1). A special case of particular interest is a uniform random s-intersection graph, where each vertex independently selects the same number of items uniformly at random from a common item pool. Another important case is a binomial random s-intersection graph, where each item from a pool is independently assigned to each vertex with the same probability. Both models have found numerous applications thus far including cryptanalysis, and modeling recommender systems, secure sensor networks, online social networks, trust networks and small-world networks (uniform random s-intersection graphs), as well as clustering analysis, classification, and the design of integrated circuits (binomial random s-intersection graphs). In this paper, for binomial/uniform random s-intersection graphs, we present results related to k-connectivity and minimum vertex degree. Specifically, we derive the asymptotically exact probabilities and zero--one laws for the following three properties: (i) k-vertex-connectivity, (ii) k-edge-connectivity and (iii) the property of minimum vertex degree being at least k.
Stochastic electron heating in the laser and quasi-static electric and magnetic fields

The dynamics of relativistic electrons in the intense laser radiation and quasi-static electromagnetic fields both along and across the laser propagating direction are studied in the 3/2 dimensional (3/2D) Hamiltonian framework. It is shown that the unperturbed oscillations of the relativistic electron in these electric fields could exhibit a long tail of the amplitude of harmonics which makes an onset of stochastic electron motion be a primary candidate for electron heating. Chirikov-like mappings which describe the recurrence relations of electron energy and time passing through zero canonical momentum plane are derived, and then, the criteria for instability are obtained. It follows that for both transverse and longitudinal electric fields, there exist upper limits of the stochastic electron energy depending on the laser intensity and electric field strength. These maximum energies could be increased by a weak electric field. However, the maximum energy is reduced for the superluminal phase velocity in both cases. The impacts of the magnetic fields on the electron dynamics are different for these two cases and discussed qualitatively. These analytic results are confirmed by the numerical simulations of solving the 3/2D Hamiltonian equations directly.
Psychological and normative theories of causal power and the probabilities of causes

This paper (1) shows that the best supported current psychological theory (Cheng, 1997) of how human subjects judge the causal power or influence of variations in presence or absence of one feature on another, given data on their covariation, tacitly uses a Bayes network which is either a noisy or gate (for causes that promote the effect) or a noisy and gate (for causes that inhibit the effect); (2) generalizes Cheng's theory to arbitrary acyclic networks of noisy or and noisy and gates; (3) gives various sufficient conditions for the estimation of the parameters in such networks when there are independent, unobserved causes; (4) distinguishes direct causal influence of one feature on another (influence along a path with one edge) from total influence (influence along all paths from one variable to another) and gives sufficient conditions for estimating each when there are unobserved causes of the outcome variable; (5) describes the relation between Cheng models and a simplified version of the "Rubin" framework for representing causal relations.
Weighted estimates for one-sided martingale transforms

Let $ Tf =\sum_{ I} \varepsilon_I \langle f,h_{I^+}\rangle h_{I^-}$. Here, $ \lvert \varepsilon _I\rvert=1 $, and $ h_J$ is the Haar function defined on dyadic interval $ J$. We show that, for instance, \begin{equation*} \lVert T \rVert _{L ^{2} (w) \to L ^{2} (w)} \lesssim [w] _{A_2 ^{+}} . \end{equation*} Above, we use the one sided $ A_2$ characteristic for the weight $ w$. This is an instance of a one sided $A_2$ conjecture. Our proof of this fact is difficult, as the very quick known proofs of the $A_2$ theorem do not seem to apply in the one sided setting.
Adapting RNN Sequence Prediction Model to Multi-label Set Prediction

We present an adaptation of RNN sequence models to the problem of multi-label classification for text, where the target is a set of labels, not a sequence. Previous such RNN models define probabilities for sequences but not for sets; attempts to obtain a set probability are after-thoughts of the network design, including pre-specifying the label order, or relating the sequence probability to the set probability in ad hoc ways. Our formulation is derived from a principled notion of set probability, as the sum of probabilities of corresponding permutation sequences for the set. We provide a new training objective that maximizes this set probability, and a new prediction objective that finds the most probable set on a test document. These new objectives are theoretically appealing because they give the RNN model freedom to discover the best label order, which often is the natural one (but different among documents). We develop efficient procedures to tackle the computation difficulties involved in training and prediction. Experiments on benchmark datasets demonstrate that we outperform state-of-the-art methods for this task.
Existence and upper semicontinuity of attractors for stochastic equations with deterministic non-autonomous terms

We prove the existence and uniqueness of tempered random attractors for stochastic Reaction–Diffusion equations on unbounded domains with multiplicative noise and deterministic non-autonomous forcing. We establish the periodicity of the tempered attractors when the stochastic equations are forced by periodic functions. We further prove the upper semicontinuity of these attractors when the intensity of stochastic perturbations approaches zero.
Epitaxial graphene homogeneity and quantum Hall effect in millimeter-scale devices

Abstract Quantized magnetotransport is observed in 5.6 × 5.6 mm2 epitaxial graphene devices, grown using highly constrained sublimation on the Si-face of SiC(0001) at high temperature (1900 °C). The precise quantized Hall resistance of R x y = h 2 e 2 is maintained up to record level of critical current Ixx = 0.72 mA at T = 3.1 K and 9 T in a device where Raman microscopy reveals low and homogeneous strain. Adsorption-induced molecular doping in a second device reduced the carrier concentration close to the Dirac point (n ≈ 1010 cm−2), where mobility of 18760 cm2/V is measured over an area of 10 mm2. Atomic force, confocal optical, and Raman microscopies are used to characterize the large-scale devices, and reveal improved SiC terrace topography and the structure of the graphene layer. Our results show that the structural uniformity of epitaxial graphene produced by face-to-graphite processing contributes to millimeter-scale transport homogeneity, and will prove useful for scientific and commercial applications.
Suboptimality bounds for stochastic shortest path problems

We consider how to use the Bellman residual of the dynamic programming operator to compute suboptimality bounds for solutions to stochastic shortest path problems. Such bounds have been previously established only in the special case that "all policies are proper," in which case the dynamic programming operator is known to be a contraction, and have been shown to be easily computable only in the more limited special case of discounting. Under the condition that transition costs are positive, we show that suboptimality bounds can be easily computed even when not all policies are proper. In the general case when there are no restrictions on transition costs, the analysis is more complex. But we present preliminary results that show such bounds are possible.
Strong Homology, Derived Limits, and Set Theory

We consider the question of the additivity of strong homology. This entails isolating the set-theoretic content of the higher derived limits of an inverse system indexed by the functions from $\mathbb{N}$ to $\mathbb{N}$. We show that this system governs, at a certain level, the additivity of strong homology over sums of arbitrary cardinality. We show in addition that, under the Proper Forcing Axiom, strong homology is not additive, not even on closed subspaces of $\mathbb{R}^4$.
A cotangent fibre generates the Fukaya category

Abstract We prove that the algebra of chains on the based loop space recovers the derived (wrapped) Fukaya category of the cotangent bundle of a closed smooth oriented manifold. The main new idea is the proof that a cotangent fibre generates the Fukaya category using a version of the map from symplectic cohomology to the homology of the free loop space introduced by Cieliebak and Latschev.
A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels

Abstract A deep neural network was developed for the purpose of predicting thermal conductivity with a case study performed on neutron irradiated nuclear fuel. Traditional thermal conductivity modeling approaches rely on existing theoretical frameworks that describe known, relevant phenomena that govern the microstructural evolution processes during neutron irradiation (such as recrystallization, and pore size, distribution and morphology). Current empirical modeling approaches, however, do not represent all irradiation test data well. Here, we develop a machine learning approach to thermal conductivity modeling that does not require a priori knowledge of a specific material microstructure and system of interest. Our approach allows researchers to probe dependency of thermal conductivity on a variety of reactor operating and material conditions. The purpose of building such a model is to allow for improved predictive capabilities linking structure-property-processing-performance relationships in the system of interest (here, irradiated nuclear fuel), which could lead to improved experimental test planning and characterization. The uranium-molybdenum system is the fuel system studied in this work, and historic irradiation test data is leveraged for model development. Our model achieved a mean absolute percent error of approximately 4% for the validation data set (when a leave-one-out cross validation approach was applied). Results indicate our model generalizes well to never before seen data, and thus use of deep learning methods for material property predictions from limited, historic irradiation test data is a viable approach. This work is at the frontier of the evolving paradigm in materials science, where machine learning methods are being applied to material property predictions in lieu of limited experimental data fitted to low-dimensionality phenomenological models. The work presented here aims to demonstrate the potential and limitations of machine learning in the field of materials science and material property modeling.
A cohomology theory for colored tangles

We employ the sl(2) foam cohomology to define a cohomology theory for oriented framed tangles whose components are labelled by irreducible representations of U_q(sl(2)). We show that the corresponding colored invariants of tangles can be assembled into invariants of bigger tangles. For the case of knots and links, the corresponding theory is a categorification of the colored Jones polynomial, and provides a tool for efficient computations of the resulting colored invariant of knots and links. Our theory is defined over the Gaussian integers Z[i] (and more generally over Z[i][a,h], where a,h are formal parameters), and enhances the existing categorifications of the colored Jones polynomial.
PROMIS 4-item measures and numeric rating scales efficiently assess SPADE symptoms compared with legacy measures.

Abstract Objective The 5 SPADE (sleep, pain, anxiety, depression, and low energy/fatigue) symptoms are among the most prevalent and disabling symptoms in clinical practice. This study evaluates the minimally important difference (MID) of Patient-Reported Outcomes Measurement Information System (PROMIS) measures and their correspondence with other brief measures to assess SPADE symptoms. Study Design and Setting Three hundred primary care patients completed a 4-item PROMIS scale, a numeric rating scale (NRS), and a non-PROMIS legacy scale for each of the 5 SPADE symptoms. Optimal NRS cutpoints were examined, and cross-walk units for converting legacy measure scores to PROMIS scores were determined. PROMIS scores corresponding to standard deviation (SD) and standard error of measurement (SEM) changes in legacy scores were used to estimate MID. Results At an NRS ≥5, the mean PROMIS T-score exceeded 55 (the operational threshold for a clinically meaningful symptom) for each SPADE symptom. Correlations were high (0.70–0.86) between each PROMIS scale and its corresponding non-PROMIS legacy scale. Changes in non-PROMIS legacy scale scores of 0.35 SD and 1 SEM corresponded to mean PROMIS T-scores of 2.92 and 3.05 across the 5 SPADE symptoms, with changes in 0.2 and 0.5 SD corresponding to mean PROMIS T-scores of 1.67 and 4.16. Conclusion A 2-step screening process for SPADE symptoms might use single-item NRS scores, proceeding to PROMIS scales for NRS scores ≥5. A PROMIS T-score change of three points represents a reasonable MID estimate, with two to four points approximating lower and upper bounds.
Slopes for higher rank Artin–Schreier–Witt towers

Author(s): Ren, Rufei; Wan, Daqing; Xiao, Liang; Yu, Myungjun | Abstract: We fix a monic polynomial $\bar f(x) \in \mathbb{F}_q[x]$ over a finite field of characteristic $p$, and consider the $\mathbb{Z}_{p^{\ell}}$-Artin-Schreier-Witt tower defined by $\bar f(x)$; this is a tower of curves $\cdots \to C_m \to C_{m-1} \to \cdots \to C_0 =\mathbb{A}^1$, whose Galois group is canonically isomorphic to $\mathbb{Z}_{p^\ell}$, the degree $\ell$ unramified extension of $\mathbb{Z}_p$, which is abstractly isomorphic to $(\mathbb{Z}_p)^\ell$ as a topological group. We study the Newton slopes of zeta functions of this tower of curves. This reduces to the study of the Newton slopes of L-functions associated to characters of the Galois group of this tower. We prove that, when the conductor of the character is large enough, the Newton slopes of the L-function asymptotically form a finite union of arithmetic progressions. As a corollary, we prove the spectral halo property of the spectral variety associated to the $\mathbb{Z}_{p^{\ell}}$-Artin-Schreier-Witt tower. This extends the main result in [DWX] from rank one case $\ell=1$ to the higher rank case $\ell\geq 1$.
A Human-Computer Interface Design for Quantitative Measure of Regret Theory

Abstract Regret theory is a theory that describes human decision-making under risk. The key of obtaining a quantitative model of regret theory is to measure the preference in humans’ mind when they choose among a set of options. Unlike physical quantities, measuring psychological preference is not procedure invariant, i.e. the readings alter when the methods change. In this work, we alleviate this influence by choosing the procedure compatible with the way that an individual makes a choice. We believe the resulting model is closer to the nature of human decision-making. The preference elicitation process is decomposed into a series of short surveys to reduce cognitive workload and increase response accuracy. To make the questions natural and familiar to the subjects, we follow the insight that humans generate, quantify and communicate preference in natural language. The fuzzy sets theory is hence utilized to model responses from subjects. Based on these ideas, a graphical human-computer interface (HCI) is designed to articulate the information as well as to efficiently collect human responses. The design also accounts for human heuristics and biases, e.g. range effect and anchoring effect, to enhance its reliability. The overall performance of the survey is satisfactory because the measured model shows prediction accuracy equivalent to the subjects revisit performance.
A bordered Legendrian contact algebra

Sivek proves a "van Kampen" decomposition theorem for the combinatorial Legendrian contact algebra (also known as the Chekanov-Eliashberg algebra) of knots in standard contact $\R^3$ . We prove an analogous result for the holomorphic curve version of the Legendrian contact algebra of certain Legendrians submanifolds in standard contact $J^1(M).$ This includes all 1- and 2-dimensional Legendrians, and some higher dimensional ones. We present various applications including a Mayer-Vietoris sequence for linearized contact homology similar to Sivek's and a connect sum formula for the augmentation variety introduced by Ng. The main tool is the theory of gradient flow trees developed by Ekholm.
Dynamical Self-regulation in Self-propelled Particle Flows

We study a continuum model of overdamped self-propelled particles with aligning interactions in two dimensions. Combining analytical theory and computations, we map out the phase diagram for the parameter space covered by the model. We find that the system self-organizes into two robust structures in different regions of parameter space: solitary waves composed of ordered swarms moving through a low density disordered background, and stationary radially symmetric asters. The self-regulating nature of the flow yields phase separation, ubiquitous in this class of systems, and controls the formation of solitary waves. Self-propulsion and the associated active convection play a crucial role in aster formation.
Access-network association policies for media streaming in heterogeneous environments

We study the design of media streaming applications in the presence of multiple heterogeneous wireless access methods with different throughputs and costs. Our objective is to analytically characterize the trade-off between the usage cost and the Quality of user Experience (QoE), which is represented by the probability of interruption in media playback and the initial waiting time. We model each access network as a server that provides packets to the user according to a Poisson process with a certain rate and cost. Blocks are coded using random linear codes to alleviate the duplicate packet reception problem. Users must take decisions on how many packets to buffer before playout, and which networks to access during playout. We design, analyze and compare several control policies with a threshold structure. We formulate the problem of finding the optimal control policy as an MDP with a probabilistic constraint. We present the HJB equation for this problem by expanding the state space, and exploit it as a verification method for optimality of the proposed control law.
New classes of matrix decompositions

Abstract The idea of decomposing a matrix into a product of structured matrices such as triangular, orthogonal, diagonal matrices is a milestone of numerical computations. In this paper, we describe six new classes of matrix decompositions over complex number field, extending our work in [5] . We prove that every n × n complex matrix is a product of finitely many tridiagonal, skew symmetric (when n is even), companion and generalized Vandermonde matrices, respectively. We also prove that a generic complex n × n centrosymmetric matrix is a product of finitely many symmetric Toeplitz (resp. persymmetric Hankel) matrices. We determine an upper bound of the number of structured matrices needed to decompose a matrix for each case.
Control allocation for wide area coordinated damping

In this work, a modal-based sparse control allocation (CA) is proposed for coordinated and fault-tolerant wide-area damping controllers (WADCs). In our proposed method, the supervisory CA only communicates with necessary actuators to achieve the required damping performance and in case of actuator failures (e.g., due to loss of communication or scheduling), capabilities of the remaining actuators are fully used before the nominal performance is degraded. This method offers the advantages of modular design where WADC is initially designed to achieve satisfactory damping without the detailed knowledge of actuators. In the next step, CA is designed to manage actuator failures and limitations without the need to redesign the nominal WADC. The proposed approach is applied to a modified 286-bus Western Electricity Coordinating Council (WECC) system to verify the feasibility on a complex power system. Simulation results indicate the effectiveness of the proposed method in coordinating multiple actuators and building resiliency.
On Fast Decoding of High-Dimensional Signals from One-Bit Measurements.

In the problem of one-bit compressed sensing, the goal is to find a $\delta$-close estimation of a $k$-sparse vector $x \in \mathbb{R}^n$ given the signs of the entries of $y = \Phi x$, where $\Phi$ is called the measurement matrix. For the one-bit compressed sensing problem, previous work \cite{Plan-robust,support} achieved $\Theta (\delta^{-2} k \log(n/k))$ and $\tilde{ \Oh} ( \frac{1}{ \delta } k \log (n/k))$ measurements, respectively, but the decoding time was $\Omega ( n k \log (n / k ))$. \ In this paper, using tools and techniques developed in the context of two-stage group testing and streaming algorithms, we contribute towards the direction of very fast decoding time. We give a variety of schemes for the different versions of one-bit compressed sensing, such as the for-each and for-all version, support recovery; all these have $poly(k, \log n)$ decoding time, which is an exponential improvement over previous work, in terms of the dependence of $n$.
The differential structure of an orbifold

We prove that the underlying set of an orbifold equipped with the ring of smooth real-valued functions completely determines the orbifold atlas. Consequently, we obtain an essentially injective functor from orbifolds to differential spaces.
Breaking of de Sitter Symmetry

Author(s): Bander, M | Abstract: We show that an interacting spin-0 field on a de Sitter space background will break the underlying de Sitter symmetry. This is done first for a (1+1) de Sitter space where a boson-fermion correspondence permits us to solve certain interacting theories by transforming them into free ones of opposite statistics. A massless boson interacting by a sine-Gordon potential is shown to be equivalent to a free massive fermion with the mass depending on the de Sitter time thus breaking the symmetry explicitly. We then show that for larger dimensions and any boson potential, to one loop, an anomaly develops and the currents generating the de Sitter transformations are not conserved. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike Licence.
Multi-Path Low Delay Network Codes

The capability of mobile devices to use multiple interfaces to support a single session is becoming more prevalent. Prime examples include the desire to implement WiFi offloading and the introduction of 5G. Furthermore, an increasing fraction of Internet traffic is becoming delay sensitive. These two trends drive the need to investigate methods that enable communication over multiple parallel heterogeneous networks, while also ensuring that delay constraints are met. This paper approaches these challenges using a multi-path streaming code that uses forward error correction to reduce the in- order delivery delay of packets in networks with poor link quality and transient connectivity. A simple analysis is developed that provides a good approximation of the in-order delivery delay. Furthermore, numerical results help show that the delay penalty of communicating over multiple paths is insignificant when considering the potential throughput gains obtained through the fusion of multiple networks.
Iterations of the projection body operator and a remark on Petty's conjectured projection inequality

Abstract We prove that if a convex body has an absolutely continuous surface area measure, whose density is sufficiently close to a constant function, then the sequence { Π m K } of convex bodies converges to the ball with respect to the Banach–Mazur distance, as m → ∞ . Here, Π denotes the projection body operator. Our result allows us to show that the ellipsoid is a local solution to the conjectured inequality of Petty and to improve a related inequality of Lutwak.
PARAMETER ESTIMATIONS FOR SPDES WITH MULTIPLICATIVE FRACTIONAL NOISE

We study parameter estimation problem for diagonalizable stochastic partial differential equations driven by a multiplicative fractional noise with any Hurst parameter H ∈ (0, 1). Two classes of estimators are investigated: traditional maximum likelihood type estimators, and a new class called closed-form exact estimators. Finally the general results are applied to stochastic heat equation driven by a fractional Brownian motion.
Dependability in a Multi-tenant Multi-framework Deep Learning as-a-Service Platform

Deep learning (DL), a form of machine learning, is becoming increasingly popular in several application domains. As a result, cloud-based Deep Learning as a Service (DLaaS) platforms have become an essential infrastructure in many organizations. These systems accept, schedule, manage and execute DL training jobs at scale. This paper explores dependability in the context of a DLaaS platform used in IBM. We begin by explaining how DL training workloads are different, and what features ensure dependability in this context. We then describe the architecture, design and implementation of a cloud-based orchestration system for DL training. We show how this system has been architected with dependability in mind while also being horizontally scalable, elastic, flexible and efficient. We also present an initial empirical evaluation of the overheads introduced by our platform, and discuss tradeoffs between efficiency and dependability.
Improved non-malleable extractors, non-malleable codes and independent source extractors

In this paper we give improved constructions of several central objects in the literature of randomness extraction and tamper-resilient cryptography. Our main results are: (1) An explicit seeded non-malleable extractor with error e and seed length d=O(logn)+O(log(1/e)loglog(1/e)), that supports min-entropy k=Ω(d) and outputs Ω(k) bits. Combined with the protocol by Dodis and Wichs, this gives a two round privacy amplification protocol with optimal entropy loss in the presence of an active adversary, for all security parameters up to Ω(k/logk), where k is the min-entropy of the shared weak random source. Previously, the best known seeded non-malleable extractors require seed length and min-entropy O(logn)+log(1/e)2O√loglog(1/e), and only give two round privacy amplification protocols with optimal entropy loss for security parameter up to k/2O(√logk). (2) An explicit non-malleable two-source extractor for min entropy k ≥ (1 - Υ)n, some constant Υ>0, that outputs Ω(k) bits with error 2-Ω(n/logn). We further show that we can efficiently uniformly sample from the pre-image of any output of the extractor. Combined with the connection found by Cheraghchi and Guruswami this gives a non-malleable code in the two-split-state model with relative rate Ω(1/logn). This exponentially improves previous constructions, all of which only achieve rate n-Ω(1). (3) Combined with the techniques by Ben-Aroya et. al, our non-malleable extractors give a two-source extractor for min-entropy O(logn loglogn), which also implies a K-Ramsey graph on N vertices with K=(logN)O(logloglogN). Previously the best known two-source extractor by Ben-Aroya et. al requires min-entropy logn 2O(√logn), which gives a Ramsey graph with K=(logN)2O(√logloglogN). We further show a way to reduce the problem of constructing seeded non-malleable extractors to the problem of constructing non-malleable independent source extractors. Using the non-malleable 10-source extractor with optimal error by Chattopadhyay and Zuckerman, we give a 10-source extractor for min-entropy O(logn). Previously the best known extractor for such min-entropy by Cohen and Schulman requires O(loglogn) sources. Independent of our work, Cohen obtained similar results to (1) and the two-source extractor, except the dependence on e is log(1/e)poly loglog(1/e) and the two-source extractor requires min-entropy logn poly loglogn.
Dynamic Programming for Instance Annotation in Multi-Instance Multi-Label Learning

Labeling data for classification requires significant human effort. To reduce labeling cost, instead of labeling every instance, a group of instances (bag) is labeled by a single bag label. Computer algorithms are then used to infer the label for each instance in a bag, a process referred to as instance annotation. This task is challenging due to the ambiguity regarding the instance labels. We propose a discriminative probabilistic model for the instance annotation problem and introduce an expectation maximization framework for inference, based on the maximum likelihood approach. For many probabilistic approaches, brute-force computation of the instance label posterior probability given its bag label is exponential in the number of instances in the bag. Our contribution is a dynamic programming method for computing the posterior that is linear in the number of instances. We evaluate our method using both benchmark and real world data sets, in the domain of bird song, image annotation, and activity recognition. In many cases, the proposed framework outperforms, sometimes significantly, the current state-of-the-art MIML learning methods, both in instance label prediction and bag label prediction.
Assimilate or Differentiate? Contributors’ Choice of Subjects in User‐Generated Content

A key to the content diversity on user-generated content platforms is what subjects users choose to contribute on. This research investigates how two factors can shape contributors’ subject choice decisions, namely, the amount of existing content and content contributed by online friends, or “friend content.” Our experimental findings show that both the amount of existing content and friend content can shape a contributor’s subject choice decisions: ceteris paribus, contributors prefer subjects with less existing content and ones with friend content when the amount of existing content is the same. In addition, contributors’ preference for subjects with friend content weakens as the amount of existing content on other subjects decreases. Our findings hold important implications for research and practice in user-generated content platforms and beyond.
Overview of Multi-Layer Metal Insulation Development for Small Stirling Convertors at NASA GRC

The small Stirling convertor currently under development at the NASA Glenn Research Center (GRC) is designed to produce one watt of electrical power from eight watts of heat. Previous radioisotope power systems (RPS) made use of the General-Purpose Heat Source (GPHS) which produces 250 watts of heat but is unsuitable for a one-watt Stirling convertor. The only heat source available is the Light-Weight Radioisotope Heating Unit (LWRHU) which produces one watt of heat and is primarily used to provide heat to electronics and instrumentation to maintain their appropriate operating temperature. Unfortunately, the LWRHU has a heat flux of 272 W/m-squared compared to the GPHS heat flux of 6000 W/m-squared which greatly increases the demands on the insulation to ensure that enough of the heat produced is available to the convertor and not lost to the environment. An analysis was performed that showed that the insulation must have a thermal conductivity of 0.005 W/mÂ·K or better for the system to function. A multi-layer metal insulation package was designed and a prototype was fabricated and tested to investigate the feasibility of this design. The prototype did not meet the requirements; however, the improved thermal model generated using the test data will allow for a second iteration to be developed that has a much higher confidence and meets the performance requirements.
Reachable Space Characterization of Markov Decision Processes with Time Variability.

We propose a solution to a time-varying variant of Markov Decision Processes which can be used to address decision-theoretic planning problems for autonomous systems operating in unstructured outdoor environments. We explore the time variability property of the planning stochasticity and investigate the state reachability, based on which we then develop an efficient iterative method that offers a good trade-off between solution optimality and time complexity. The reachability space is constructed by analyzing the means and variances of states' reaching time in the future. We validate our algorithm through extensive simulations using ocean data, and the results show that our method achieves a great performance in terms of both solution quality and computing time.
Police and computer technology—The expectations and the results

In July, 1965, in the face of dramatic rises in reported crime and delinquency rates, the President’s Commission on Law Enforcement and the Administration of Justice (sometimes called the Crime Commission) was created. One area selected for special attention in the Commission’s final report was the potential contribution of science and technology in the generally labor-intensive field of law enforcement. Because criminal justice agencies must process enormous quantities of data, the use of computer technology—electronic computers and new techniques such as systems analysis, operations research and computer modeling—seemed particularly promising, and the use of computer technology by the police has expanded significantly since the mid-1960s.
Robinson-Foulds Reticulation Networks

Phylogenetic (hybridization) networks allow investigation of evolutionary species histories that involve complex phylogenetic events other than speciation, such as reassortment in virus evolution or introgressive hybridization in invertebrates and mammals. Reticulation networks can be inferred by solving the reticulation network problem, typically known as the hybridization network problem. Given a collection of phylogenetic input trees, this problem seeks a minimum reticulation network with the smallest number of reticulation vertices into which the input trees can be embedded exactly. Unfortunately, this problem is limited in practice, since minimum reticulation networks can be easily obfuscated by even small topological errors that typically occur in input trees inferred from biological data. We adapt the reticulation network problem to address erroneous input trees using the classic Robinson-Foulds distance. The RF embedding cost allows trees to be embedded into reticulation networks inexactly, but up to a measurable error. The adapted problem, called the Robinson-Foulds reticulation network (RF-Network) problem is, as we show and like many other problems applied in molecular biology, NP-hard. To address this, we employ local search strategies that have been successfully applied in other NP-hard phylogenetic problems. Our local search method benefits from recent theoretical advancements in this area. Further, we introduce in-practice effective algorithms for the computational challenges involved in our local search approach. Using simulations we experimentally validate the ability of our method, RF-Net, to reconstruct correct phylogenetic networks in the presence of error in input data. Finally, we demonstrate how RF-networks can help identify reassortment in influenza A viruses, and provide insight into the evolutionary history of these viruses. RF-Net was able to estimate a large and credible reassortment network with 164 taxa.
Correct-by-construction control synthesis for buck converters with event-triggered state measurement

In this paper, we illustrate a new correct-by-construction switching controller for a power converter with event-triggered measurements. The event-triggered measurement scheme is beneficial for high frequency power converters because it requires relatively low-speed sampling hardware and is immune to unmodeled switching transients. While providing guarantees on the closed-loop system behavior is crucial in this application, off-the-shelf abstraction-based techniques cannot be directly employed to synthesize a controller in this setting because controller cannot always get instantaneous access to the current state. As a result, the switching action has to be based on slightly out-of-date measurements. To tackle this challenge, we introduce the out-of-date measurement as an extra state variable and project out the inaccessible real state to construct a belief space abstraction. The properties preserved by this belief space abstraction are analyzed. Finally, an abstraction-based synthesis method is applied to this abstraction. We demonstrate the controller on a constant on-time buck voltage regulator plant with an event-triggered sampler. The simulation verifies the effectiveness of our controller.
Tree-CNN: A hierarchical Deep Convolutional Neural Network for incremental learning

Over the past decade, Deep Convolutional Neural Networks (DCNNs) have shown remarkable performance in most computer vision tasks. These tasks traditionally use a fixed dataset, and the model, once trained, is deployed as is. Adding new information to such a model presents a challenge due to complex training issues, such as "catastrophic forgetting", and sensitivity to hyper-parameter tuning. However, in this modern world, data is constantly evolving, and our deep learning models are required to adapt to these changes. In this paper, we propose an adaptive hierarchical network structure composed of DCNNs that can grow and learn as new data becomes available. The network grows in a tree-like fashion to accommodate new classes of data, while preserving the ability to distinguish the previously trained classes. The network organizes the incrementally available data into feature-driven super-classes and improves upon existing hierarchical CNN models by adding the capability of self-growth. The proposed hierarchical model, when compared against fine-tuning a deep network, achieves significant reduction of training effort, while maintaining competitive accuracy on CIFAR-10 and CIFAR-100.
Pulmonary embolism with clot in transit: An analysis of risk factors and outcomes.

Abstract Objectives Clot in transit (CIT) represents a rare and life-threatening manifestation of venous thromboembolism of which we have limited understanding. This study describes the risk factors, clinical characteristics, and outcomes associated with the development of CIT as well as death following CIT diagnosis. Methods We analyzed patients enrolled in our institutional Pulmonary Embolism Response Team (PERT) registry and compared 57 patients who had a CIT to 608 pulmonary embolism (PE) patients who did not have a CIT. We performed univariate and multivariate logistic regression to identify factors associated with CIT (vs PE without CIT) among patients who had an echocardiogram, as well as factors associated with 7-day death after CIT diagnosis. Results CIT was present in (57) 8.6% of patients who had an echocardiogram. Multivariate analysis showed heart failure (OR 2.8, 95% CI 1.2–6.5, P = 0.01), a pre-existing central venous catheter (OR 2.5, 95% CI 1.1–5.7, P = 0.03), and hypotension (OR 2.1, 95% CI 1.1–3.7, P = 0.02) to be independently associated with CIT. All-cause mortality by 7 days was higher in CIT patients (12.5% vs 5.1%, P = 0.02). CIT patients who died were more likely to have presented with hemodynamic collapse (57.1% vs 14.0%, P = 0.02), mental status change (100% vs 22.0%, P Conclusions The presence of heart failure, a central venous catheter, and hypotension should alert physicians to patients who may require an echocardiogram to diagnose CIT. The mortality of CIT is high, even relative to a population with severe PE.
Spectral partitioning identifies individual heterogeneity in the functional network topography of ventral and anterior medial prefrontal cortex.

Abstract Regions of human medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) are part of the default network (DN), and additionally are implicated in diverse cognitive functions ranging from autobiographical memory to subjective valuation. Our ability to interpret the apparent co-localization of task-related effects with DN-regions is constrained by a limited understanding of the individual-level heterogeneity in mPFC/PCC functional organization. Here we used cortical surface-based meta-analysis to identify a parcel in human PCC that was more strongly associated with the DN than with valuation effects. We then used resting-state fMRI data and a data-driven network analysis algorithm, spectral partitioning, to partition mPFC and PCC into “DN” and “non-DN” subdivisions in individual participants (n = 100 from the Human Connectome Project). The spectral partitioning algorithm identified individual-level cortical subdivisions that varied markedly across individuals, especially in mPFC, and were reliable across test/retest datasets. Our results point toward new strategies for assessing whether distinct cognitive functions engage common or distinct mPFC subregions at the individual level.
A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas.

BACKGROUND Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a highly accurate, MRI-based, voxelwise deep-learning IDH classification network using T2-weighted (T2w) MR images and compare its performance to a multicontrast network. METHODS Multiparametric brain MRI data and corresponding genomic information were obtained for 214 subjects (94 IDH-mutated, 120 IDH wild-type) from The Cancer Imaging Archive and The Cancer Genome Atlas. Two separate networks were developed, including a T2w image-only network (T2-net) and a multicontrast (T2w, fluid attenuated inversion recovery, and T1 postcontrast) network (TS-net) to perform IDH classification and simultaneous single label tumor segmentation. The networks were trained using 3D Dense-UNets. Three-fold cross-validation was performed to generalize the networks' performance. Receiver operating characteristic analysis was also performed. Dice scores were computed to determine tumor segmentation accuracy. RESULTS T2-net demonstrated a mean cross-validation accuracy of 97.14% ± 0.04 in predicting IDH mutation status, with a sensitivity of 0.97 ± 0.03, specificity of 0.98 ± 0.01, and an area under the curve (AUC) of 0.98 ± 0.01. TS-net achieved a mean cross-validation accuracy of 97.12% ± 0.09, with a sensitivity of 0.98 ± 0.02, specificity of 0.97 ± 0.001, and an AUC of 0.99 ± 0.01. The mean whole tumor segmentation Dice scores were 0.85 ± 0.009 for T2-net and 0.89 ± 0.006 for TS-net. CONCLUSION We demonstrate high IDH classification accuracy using only T2-weighted MR images. This represents an important milestone toward clinical translation.
Characterizing finite length local cohomology in terms of bounds on Koszul cohomology

Abstract Let ( R , m , κ ) be a local ring. We give a characterization of R-modules M whose local cohomology is finite length up to some index in terms of asymptotic vanishing of Koszul cohomology on parameter ideals up to the same index. In particular, we show that a quasi-unmixed module M is asymptotically Cohen-Macaulay if and only if M is Cohen-Macaulay on the punctured spectrum if and only if sup ⁡ { l ( H i ( f 1 , … , f d ; M ) ) | f 1 , … , f d = m , i d } ∞ for d = dim ⁡ ( M ) = dim ⁡ ( R ) .
Quark number fluctuations at high temperatures

We calculate the second, fourth and sixth order quark number fluctuations in the deconfined phase of 2+1 flavor QCD using lattices with temporal extent N{sub t} = 4,6,8 and 12. We consider light, strange and charm quarks. We use p4 action for valence quarks and gauge configurations generated with p4 action with physical value of the strange quark mass and light quark mass m{sub q} = 0.1 m{sub s} generated by the RBC-Bielefeld collaboration. We observe that for all quark masses the quark number fluctuations rapidly get close to the corresponding ideal gas limits. We compare our results to predictions of a quasi-particle model and resummed high temperature perturbative calculations. We also investigate correlations among different flavor channels.