cssi/SciSciGPT-SciSciNet huggingface dataset:
Truthful multi-parameter auctions with online supply: an impossible combination

We study a basic auction design problem with online supply. There are two unit-demand bidders and two types of items. The first item type will arrive first for sure, and the second item type may or may not arrive. The auctioneer has to decide the allocation of an item immediately after each item arrives, but is allowed to compute payments after knowing how many items arrived. For this problem we show that there is no deterministic truthful and individually rational mechanism that, even with unbounded computational resources, gets any finite approximation factor to the optimal social welfare.
A Stochastic Differential Equation Framework for Guiding Online User Activities in Closed Loop

Recently, there is a surge of interest in using point processes to model continuous-time user activities. This framework has resulted in novel models and improved performance in diverse applications. However, most previous works focus on the "open loop" setting where learned models are used for predictive tasks. Typically, we are interested in the "closed loop" setting where a policy needs to be learned to incorporate user feedbacks and guide user activities to desirable states. Although point processes have good predictive performance, it is not clear how to use them for the challenging closed loop activity guiding task. In this paper, we propose a framework to reformulate point processes into stochastic differential equations, which allows us to extend methods from stochastic optimal control to address the activity guiding problem. We also design an efficient algorithm, and show that our method guides user activities to desired states more effectively than the state of the art.
Central Limit Theorem for the Free Energy of the Random Field Ising Model

A central limit theorem is proved for the free energy of the random field Ising model with all plus or all minus boundary condition, at any temperature (including zero temperature) and any dimension. This solves a problem posed by Wehr and Aizenman (J Stat Phys 60:287–306, 1990). The proof uses a variant of Stein’s method.
A note on a two-weight estimate for the dyadic square function

We show that the two-weight estimate for the dyadic square function proved by Lacey--Li in [2] is sharp.
Arithmetic diophantine approximation for continued fractions-like maps on the interval

We establish arithmetical properties and provide essential bounds for bi-sequences of approximation coefficients associated with the natural extension of maps, leading to continued fraction-like expansions. These maps are realized as the fractional part of M$\operatorname{\ddot{o}}$bius transformations which carry the end points of the unit interval to zero and infinity, extending the classical regular and backwards continued fractions expansions.
REPLICA CONDENSATION AND TREE DECAY

We give an intuitive method — using local, cyclic replica symmetry — to isolate exponential tree decay in truncated (connected) correlations. We give an expansion and use the symmetry to show that all terms vanish, except those displaying replica condensation. The condensation property ensures exponential tree decay. We illustrate our method in a low-temperature Ising system, but expect that one can use a similar method in other random field and quantum field problems. While considering the illustration, we prove an elementary upper bound on the entropy of random lattice surfaces.
Floating potential of emitting surfaces in plasmas with respect to the space potential

The potential difference between a floating emitting surface and the plasma surrounding it has been described by several sheath models, including the space-charge-limited sheath, the electron sheath with high emission current, and the inverse sheath produced by charge-exchange ion trapping. Our measurements reveal that each of these models has its own regime of validity. We determine the potential of an emissive filament relative to the plasma potential, emphasizing variations in emitted current density and neutral particle density. The potential of a filament in a diffuse plasma is first shown to vanish, consistent with the electron sheath model and increasing electron emission. In a denser plasma with ample neutral pressure, the floating filament potential is positive, as predicted by a derived ion trapping condition. Lastly, the filament floated negatively in a third plasma, where flowing ions and electrons and nonnegligible electric fields may have disrupted ion trapping. Depending on the regime chose...
Numerical methods for multiscale inverse problems

We consider the inverse problem of determining the highly oscillatory coefficient $a^\epsilon$ in partial differential equations of the form $-\nabla\cdot (a^\epsilon\nabla u^\epsilon)+bu^\epsilon = f$ from given measurements of the solutions. Here, $\epsilon$ indicates the smallest characteristic wavelength in the problem ($0 0$, and exploration seismology, $b < 0$.
Derived equivalences of K3 surfaces and twined elliptic genera

We use the unique canonically twisted module over a certain distinguished super vertex operator algebra—the moonshine module for Conway’s group—to attach a weak Jacobi form of weight zero and index one to any symplectic derived equivalence of a projective complex K3 surface that fixes a stability condition in the distinguished space identified by Bridgeland. According to work of Huybrechts, following Gaberdiel–Hohenegger–Volpato, any such derived equivalence determines a conjugacy class in Conway’s group, the automorphism group of the Leech lattice. Conway’s group acts naturally on the module we consider. In physics, the data of a projective complex K3 surface together with a suitable stability condition determines a supersymmetric non-linear sigma model, and supersymmetry-preserving automorphisms of such an object may be used to define twinings of the K3 elliptic genus. Our construction recovers the K3 sigma model twining genera precisely in all available examples. In particular, the identity symmetry recovers the usual K3 elliptic genus, and this signals a connection to Mathieu moonshine. A generalization of our construction recovers a number of Jacobi forms arising in umbral moonshine. We demonstrate a concrete connection to supersymmetric non-linear K3 sigma models by establishing an isomorphism between the twisted module we consider and the vector space underlying a particular sigma model attached to a certain distinguished K3 surface.
Polynomial-Time Probabilistic Reasoning with Partial Observations via Implicit Learning in Probability Logics.

Standard approaches to probabilistic reasoning require that one possesses an explicit model of the distribution in question. But, the empirical learning of models of probability distributions from partial observations is a problem for which efficient algorithms are generally not known. In this work we consider the use of bounded-degree fragments of the “sum-of-squares” logic as a probability logic. Prior work has shown that we can decide refutability for such fragments in polynomial-time. We propose to use such fragments to decide queries about whether a given probability distribution satisfies a given system of constraints and bounds on expected values. We show that in answering such queries, such constraints and bounds can be implicitly learned from partial observations in polynomial-time as well. It is known that this logic is capable of deriving many bounds that are useful in probabilistic analysis. We show here that it furthermore captures key polynomial-time fragments of resolution. Thus, these fragments are also quite expressive.
Censored quantile instrumental-variable estimation with Stata:

Many applications involve a censored dependent variable, an endogenous independent variable, or both. Chernozhukov, Fernandez-Val, and Kowalski (2015, Journal of Econometrics 186: 201–221) introduc...
TRACES OF INTERTWINERS FOR QUANTUM AFFINE ALGEBRAS AND DIFFERENCE EQUATIONS (AFTER ETINGOF–SCHIFFMANN–VARCHENKO)

We modify and give complete proofs for the results of Etingof–Schiffmann–Varchenko in [ESV02] on traces of intertwiners of untwisted quantum affine algebras in the opposite coproduct and the standard grading. More precisely, we show that certain normalized generalized traces $$ {F}^{V_1,\dots, {V}_n}\left({z}_1,\dots, {z}_n;\uplambda, \omega, \mu, k\right) $$ for $$ {U}_q\left(\widehat{\mathfrak{g}}\right) $$ solve four commuting systems of q-difference equations: the Macdonald–Ruijsenaars, dual Macdonald–Ruijsenaars, q-KZB, and dual q-KZB equations. In addition, we show a symmetry property for these renormalized trace functions. Our modifications are motivated by their appearance in the recent work [Sun16].
Minimal group determinants and the Lind–Lehmer problem for dihedral groups

We find the minimal non-trivial integer variable group determinant for any dihedral group of order less than $3.79\times 10^{47}$. We think of this as the Lind-Lehmer problem for the dihedral group. We give a complete description of the determinants for some dihedral groups including $D_{2p}$ and $D_{4p}$.
Neighbor discovery for wireless networks via compressed sensing

Abstract This paper studies the problem of neighbor discovery in wireless networks, namely, each node wishes to discover and identify the network interface addresses (NIAs) of those nodes within a single hop. A novel paradigm, called compressed neighbor discovery is proposed, which enables all nodes to simultaneously discover their respective neighborhoods with a single frame of transmission, which is typically of a few thousand symbol epochs. The key technique is to assign each node a unique on–off signature and let all nodes simultaneously transmit their signatures. Despite that the radios are half-duplex, each node observes a superposition of its neighbors’ signatures (partially) through its own off-slots. To identify its neighbors out of a large network address space, each node solves a compressed sensing (or sparse recovery) problem. Two practical schemes are studied. The first employs random on–off signatures, and each node discovers its neighbors using a noncoherent detection algorithm based on group testing. The second scheme uses on–off signatures based on a deterministic second-order Reed–Muller code, and applies a chirp decoding algorithm. The second scheme needs much lower signal-to-noise ratio (SNR) to achieve the same error performance. The complexity of the chirp decoding algorithm is sub-linear, so that it is in principle scalable to networks with billions of nodes with 48-bit IEEE 802.11 MAC addresses. The compressed neighbor discovery schemes are much more efficient than conventional random-access discovery, where nodes have to retransmit over many frames with random delays to be successfully discovered.
A comparison of policies on the participation of storage in U.S. frequency regulation markets

Because energy storage systems have better ramping characteristics than traditional generators, their participation in frequency regulation should facilitate the balancing of load and generation. However, they cannot sustain their output indefinitely. System operators have therefore implemented new frequency regulation policies to take advantage of the fast ramps that energy storage systems can deliver while alleviating the problems associated with their limited energy capacity. This paper contrasts several U.S. policies that directly affect the participation of energy storage systems in frequency regulation and compares the revenues that the owners of such systems might achieve under each policy.
Algebraic K -theory with coefficients of cyclic quotient singularities

Abstract In this note, by combining the work of Amiot–Iyama–Reiten and Thanhoffer de Volcsey–Van den Bergh on Cohen–Macaulay modules with the previous work of the author on orbit categories, we compute the algebraic K-theory with coefficients of cyclic quotient singularities.
The Uncertainty Bellman Equation and Exploration.

We consider the exploration/exploitation problem in reinforcement learning. For exploitation, it is well known that the Bellman equation connects the value at any time-step to the expected value at subsequent time-steps. In this paper we consider a similar \textit{uncertainty} Bellman equation (UBE), which connects the uncertainty at any time-step to the expected uncertainties at subsequent time-steps, thereby extending the potential exploratory benefit of a policy beyond individual time-steps. We prove that the unique fixed point of the UBE yields an upper bound on the variance of the posterior distribution of the Q-values induced by any policy. This bound can be much tighter than traditional count-based bonuses that compound standard deviation rather than variance. Importantly, and unlike several existing approaches to optimism, this method scales naturally to large systems with complex generalization. Substituting our UBE-exploration strategy for $\epsilon$-greedy improves DQN performance on 51 out of 57 games in the Atari suite.
Exponential sums and Riesz energies

Abstract We bound an exponential sum that appears in the study of irregularities of distribution (the low-frequency Fourier energy of the sum of several Dirac measures) by geometric quantities: a special case is that for all { x 1 , … , x N } ⊂ T 2 , X ≥ 1 and a universal c > 0 ∑ i , j = 1 N X 2 1 + X 4 ‖ x i − x j ‖ 4 ≲ ∑ k ∈ Z 2 ‖ k ‖ ≤ X | ∑ n = 1 N e 2 π i 〈 k , x n 〉 | 2 ≲ ∑ i , j = 1 N X 2 e − c X 2 ‖ x i − x j ‖ 2 . Since this exponential sum is intimately tied to rather subtle distribution properties of the points, we obtain nonlocal structural statements for near-minimizers of the Riesz-type energy. For X ≳ N 1 / 2 both upper and lower bound match for maximally-separated point sets satisfying ‖ x i − x j ‖ ≳ N − 1 / 2 .
Photosensor Oculography: Survey and Parametric Analysis of Designs Using Model-Based Simulation

This paper presents a renewed overview of photosensor oculography (PSOG), an eye-tracking technique based on the use of simple photosensors (usually infrared) to measure the overall amount of reflected light while the eye rotates. PSOG can provide high spatial and temporal resolution, low tracking latency, and reduced power consumption. The deeper examination of this technique is particularly important, given the current needs for efficient eye-tracking mechanisms by the emerging interaction devices, e.g., augmented and virtual reality headsets. In our investigation, we employ an adjustable simulation framework to examine the eye-tracking performance when changing the parameters of different PSOG designs. We focus our examination on eye-tracking accuracy and crosstalk, two crucial characteristics for the seamless interaction via eye-tracking. Our experiments demonstrate the parameter values (or ranges) that lead to different levels of performance for accuracy and crosstalk. We also show that the optimization of accuracy and crosstalk is often driven by competing conditions, and thus, we perform a multi-objective optimization procedure and estimate the respective trade-off parameters. Finally, we present the effects from sensor shifts and evaluate the resulting increase in accuracy error. Our results and analysis can be used to facilitate the selection of parameters in future PSOG systems.
Li-Yau gradient bound for collapsing manifolds under integral curvature condition

Let $(\M^n, g_{ij})$ be a complete Riemammnian manifold. For some constants $p,\ r>0$, define $\displaystyle k(p,r)=\sup_{x\in M}r^2\left(\oint_{B(x,r)}|Ric^-|^p dV\right)^{1/p}$, where $Ric^-$ denotes the negative part of the Ricci curvature tensor. We prove that for any $p>\frac{n}{2}$, when $k(p,1)$ is small enough, certain Li-Yau type gradient bound holds for the positive solutions of the heat equation on geodesic balls $B(O,r)$ in $\M$ with $0<r\leq 1$. Here the assumption that $k(p,1)$ being small allows the situation where the manifolds is collapsing. Recall that in \cite{ZZ}, certain Li-Yau gradient bounds was also obtained by the authors, assuming that $|Ric^-|\in L^p(\M)$ and the manifold is noncollaped. Therefore, to some extent, the results in this paper and in \cite{ZZ} complete the picture of Li-Yau gradient bound for the heat equation on manifolds with $|Ric^-|$ being $L^p$ integrable, modulo sharpness of constants.
Time-optimal control with finite bandwidth

Time-optimal control theory provides recipes to achieve quantum operations with high fidelity and speed, as required in quantum technologies such as quantum sensing and computation. While technical advances have achieved the ultrastrong driving regime in many physical systems, these capabilities have yet to be fully exploited for the precise control of quantum systems, as other limitations, such as the generation of higher harmonics or the finite response time of the control apparatus, prevent the implementation of theoretical time-optimal control. Here we present a method to achieve time-optimal control of qubit systems that can take advantage of fast driving beyond the rotating wave approximation. We exploit results from time-optimal control theory to design driving protocols that can be implemented with realistic, finite-bandwidth control fields, and we find a relationship between bandwidth limitations and achievable control fidelity.
Adaptive Boolean Monotonicity Testing in Total Influence Time

Testing monotonicity of a Boolean function f:{0,1}^n -> {0,1} is an important problem in the field of property testing. It has led to connections with many interesting combinatorial questions on the directed hypercube: routing, random walks, and new isoperimetric theorems. Denoting the proximity parameter by epsilon, the best tester is the non-adaptive O~(epsilon^{-2}sqrt{n}) tester of Khot-Minzer-Safra (FOCS 2015). A series of recent results by Belovs-Blais (STOC 2016) and Chen-Waingarten-Xie (STOC 2017) have led to Omega~(n^{1/3}) lower bounds for adaptive testers. Reducing this gap is a significant question, that touches on the role of adaptivity in monotonicity testing of Boolean functions. We approach this question from the perspective of parametrized property testing, a concept recently introduced by Pallavoor-Raskhodnikova-Varma (ACM TOCT 2017), where one seeks to understand performance of testers with respect to parameters other than just the size. Our result is an adaptive monotonicity tester with one-sided error whose query complexity is O(epsilon^{-2}I(f)log^5 n), where I(f) is the total influence of the function. Therefore, adaptivity provably helps monotonicity testing for low influence functions.
Targeted Learning with Daily EHR Data

Electronic health records (EHR) data provide a cost- and time-effective opportunity to conduct cohort studies of the effects of multiple time-point interventions in the diverse patient population found in real-world clinical settings. Because the computational cost of analyzing EHR data at daily (or more granular) scale can be quite high, a pragmatic approach has been to partition the follow-up into coarser intervals of pre-specified length (eg, quarterly or monthly intervals). The feasibility and practical impact of analyzing EHR data at a granular scale has not been previously evaluated. We start filling these gaps by leveraging large-scale EHR data from a diabetes study to develop a scalable targeted learning approach that allows analyses with small intervals. We then study the practical effects of selecting different coarsening intervals on inferences by reanalyzing data from the same large-scale pool of patients. Specifically, we map daily EHR data into four analytic datasets using 90-, 30-, 15-, and 5-day intervals. We apply a semiparametric and doubly robust estimation approach, the longitudinal Targeted Minimum Loss-Based Estimation (TMLE), to estimate the causal effects of four dynamic treatment rules with each dataset, and compare the resulting inferences. To overcome the computational challenges presented by the size of these data, we propose a novel TMLE implementation, the "long-format TMLE," and rely on the latest advances in scalable data-adaptive machine-learning software, xgboost and h2o, for estimation of the TMLE nuisance parameters.
Real class sizes

In this paper, we study the structures of finite groups using some arithmetic conditions on the sizes of real conjugacy classes. We prove that a finite group is solvable if the prime graph on the real class sizes of the group is disconnected. Moreover, we show that if the sizes of all noncentral real conjugacy classes of a finite group G have the same 2-part and the Sylow 2-subgroup of G satisfies a certain condition, then G is solvable.
Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement Learning

We formulate tracking as an online decision-making process, where a tracking agent must follow an object despite ambiguous image frames and a limited computational bud- get. Crucially, the agent must decide where to look in the upcoming frames, when to reinitialize because it believes the target has been lost, and when to update its appearance model for the tracked object. Such decisions are typically made heuristically. Instead, we propose to learn an optimal decision-making policy by formulating tracking as a par- tially observable decision-making process (POMDP). We learn policies with deep reinforcement learning algorithms that need supervision (a reward signal) only when the track has gone awry. We demonstrate that sparse rewards al- low us to quickly train on massive datasets, several orders of magnitude more than past work. Interestingly, by treat- ing the data source of Internet videos as unlimited streams, we both learn and evaluate our trackers in a single, unified computational stream.
Initial value problem for the free boundary magnetohydrodynamics with zero magnetic boundary condition

We show local existence and uniqueness of plasma(fluid)-vaccum free boundary problem of magnetohydrodynamic flow in three-dimensional space with infinite depth setting when magnetic field is zero on the free boundary. We use Sobolev-Slobodetskii space which was used in usual free boundary problem in [3,5,6,7,8,9]. We also show that this solution can be extended as long as we want for sufficiently small initial data. Using the result of this paper we will get a unique solution of (kinematic inviscid) - (magnetic non diffusive) free boundary magnetohydrodynamics problem via (kinematic viscosity) - (magnetic diffusivity) limit in [10].
Scaling Laws for Consensus Protocols Subject to Noise

We study the performance of discrete-time consensus protocols in the presence of additive noise. When the consensus dynamic corresponds to a reversible Markov chain, we give an exact expression for a weighted version of steady-state disagreement in terms of the stationary distribution and hitting times in an underlying graph. We then show how this result can be used to characterize the noise robustness of a class of protocols for formation control in terms of the Kemeny constant of an underlying graph.
On the Cohomology of Congruence Subgroups of GL3 over the Eisenstein Integers

Let F be the imaginary quadratic field of discriminant −3 and OF its ring of integers. Let Γ be the arithmetic group GL3(OF), and for any ideal n⊂OF let Γ0(n) be the congruence subgroup of level n ...
Permissive Barrier Certificates for Safe Stabilization Using Sum-of-squares

Motivated by the need to simultaneously guarantee safety and stability of safety-critical dynamical systems, we construct permissive barrier certificates in this paper that explicitly maximize the region where the system can be stabilized without violating safety constraints. An iterative search algorithm is developed to search for the maximum volume barrier certified region of safe stabilization. The barrier certified region, which is allowed to take any arbitrary shape, is proved to be strictly larger than safe regions generated with Lyapunov sublevel set based methods. The proposed approach effectively unites a Lyapunov function with multiple barrier functions that might not be compatible with each other. Simulation results of the iterative search algorithm demonstrate the effectiveness of the proposed method.
Mosaic representations of odors in the input and output layers of the mouse olfactory bulb

The elementary stimulus features encoded by the olfactory system remain poorly understood. We examined the relationship between 1,666 physical-chemical descriptors of odors and the activity of olfactory bulb inputs and outputs in awake mice. Glomerular and mitral and tufted cell responses were sparse and locally heterogeneous, with only a weak dependence of their positions on physical-chemical properties. Odor features represented by ensembles of mitral and tufted cells were overlapping but distinct from those represented in glomeruli, which is consistent with an extensive interplay between feedforward and feedback inputs to the bulb. This reformatting was well described as a rotation in odor space. The physical-chemical descriptors accounted for a small fraction in response variance, and the similarity of odors in the physical-chemical space was a poor predictor of similarity in neuronal representations. Our results suggest that commonly used physical-chemical properties are not systematically represented in bulbar activity and encourage further searches for better descriptors of odor space.
Atomic Batteries: Energy from Radioactivity

With alternate, sustainable, natural sources of energy being sought after, there is new interest in energy from radioactivity, including natural and waste radioactive materials. A study of various atomic batteries is presented with perspectives of development and comparisons of performance parameters and cost. We discuss radioisotope thermal generators, indirect conversion batteries, direct conversion batteries, and direct charge batteries. We qualitatively describe their principles of operation and their applications. We project possible market trends through our comparative cost analysis. We also explore a future direction for certain atomic batteries by using nanomaterials to improve their performance.
Localized spectrum slicing

Given a sparse Hermitian matrix $A$ and a real number $\mu$, we construct a set of sparse vectors, each approximately spanned only by eigenvectors of $A$ corresponding to eigenvalues near $\mu$. This set of vectors spans the column space of a localized spectrum slicing (LSS) operator, and is called an LSS basis set. The sparsity of the LSS basis set is related to the decay properties of matrix Gaussian functions. We present a divide-and-conquer strategy with controllable error to construct the LSS basis set. This is a purely algebraic process using only submatrices of $A$, and can therefore be applied to general sparse Hermitian matrices. The LSS basis set leads to sparse projected matrices with reduced sizes, which allows the projected problems to be solved efficiently with techniques using sparse linear algebra. As an example, we demonstrate that the LSS basis set can be used to solve interior eigenvalue problems for a discretized second order partial differential operator in one-dimensional and two-dimensional domains, as well as for a matrix of general sparsity pattern.
Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks

Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks.
The Jack Daniels Problem

Abstract In 1975 Ogg offered a bottle of Jack Daniels for an explanation of the fact that the prime divisors of the order of the monster M are the primes p for which the characteristic p supersingular j -invariants are all defined over F p . This coincidence is often suggested as the first hint of monstrous moonshine , the deep unexpected interplay between the monster and modular functions. We revisit Ogg's problem, and we point out (using existing tools) that the moonshine functions for order p elements give the set of characteristic p supersingular j -invariants (apart from 0 and 1728). Furthermore, we discuss this coincidence of the two seemingly unrelated sets of primes using the first principles of moonshine.
Integrable Complex Structures on Twistor Spaces

We introduce integrable complex structures on twistor spaces fibered over complex manifolds. We then show, in particular, that the twistor spaces associated with generalized Kahler, SKT and strong HKT manifolds all naturally admit complex structures. Moreover, in the strong HKT case we construct a metric and three compatible complex structures on the twistor space that have equal torsions.
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations

Much work in Natural Language Processing (NLP) has been for resource-rich languages, making generalization to new, less-resourced languages challenging. We present two approaches for improving generalization to low-resourced languages by adapting continuous word representations using linguistically motivated subword units: phonemes, morphemes and graphemes. Our method requires neither parallel corpora nor bilingual dictionaries and provides a significant gain in performance over previous methods relying on these resources. We demonstrate the effectiveness of our approaches on Named Entity Recognition for four languages, namely Uyghur, Turkish, Bengali and Hindi, of which Uyghur and Bengali are low resource languages, and also perform experiments on Machine Translation. Exploiting subwords with transfer learning gives us a boost of +15.2 NER F1 for Uyghur and +9.7 F1 for Bengali. We also show improvements in the monolingual setting where we achieve (avg.) +3 F1 and (avg.) +1.35 BLEU.
Characterizing Transgender Health Issues in Twitter

Although there are millions of transgender people in the world, a lack of information exists about their health issues. This issue has consequences for the medical field, which only has a nascent understanding of how to identify and meet this population's health-related needs. Social media sites like Twitter provide new opportunities for transgender people to overcome these barriers by sharing their personal health experiences. Our research employs a computational framework to collect tweets from self-identified transgender users, detect those that are health-related, and identify their information needs. This framework is significant because it provides a macro-scale perspective on an issue that lacks investigation at national or demographic levels. Our findings identified 54 distinct health-related topics that we grouped into 7 broader categories. Further, we found both linguistic and topical differences in the health-related information shared by transgender men (TM) as com-pared to transgender women (TW). These findings can help inform medical and policy-based strategies for health interventions within transgender communities. Also, our proposed approach can inform the development of computational strategies to identify the health-related information needs of other marginalized populations.
Sensor placement for optimal Kalman filtering: Fundamental limits, submodularity, and algorithms

In this paper, we focus on sensor placement in linear dynamic estimation, where the objective is to place a small number of sensors in a system of interdependent states so to design an estimator with a desired estimation performance. In particular, we consider a linear time-variant system that is corrupted with process and measurement noise, and study how the selection of its sensors affects the estimation error of the corresponding Kalman filter over a finite observation interval. Our contributions are threefold: First, we prove that the minimum mean square error of the Kalman filter decreases only linearly as the number of sensors increases. That is, adding extra sensors so to reduce this estimation error is ineffective, a fundamental design limit. Similarly, we prove that the number of sensors grows linearly with the system's size for fixed minimum mean square error and number of output measurements over an observation interval; this is another fundamental limit, especially for systems where the system's size is large. Second, we prove that the log det of the error covariance of the Kalman filter, which captures the volume of the corresponding confidence ellipsoid, with respect to the system's initial condition and process noise is a supermodular and non-increasing set function in the choice of the sensor set. Therefore, it exhibits the diminishing returns property. Third, we provide an efficient approximation algorithm that selects a small number sensors so to optimize the Kalman filter with respect to this estimation error —the worst-case performance guarantees of this algorithm are provided as well.
Seismic modeling using the frozen Gaussian approximation

We adopt the frozen Gaussian approximation (FGA) for modeling seismic waves. The FGA method belongs to the category of ray-based beam methods. It decomposes the seismic wavefield into a set of Gaussian functions and propagates these functions along appropriate ray paths. As opposed to the classic Gaussian-beam method, FGA keeps the Gaussians frozen (at a fixed width) during the propagation process and adjusts their amplitudes in order to produce an accurate approximation after summation. We perform the initial decomposition of seismic data using a fast version of the FBI (FourierBros-Iagolnitzer) transform and propagate the frozen Gaussian beams numerically using ray tracing. A test using a smoothed Marmousi model confirms the validity of FGA for accurate modeling of seismic wavefields.
A Trisectrix From a Carpenter's Square

In 1928, Henry Scudder described how to use a carpenter's square to trisect an angle. We use the ideas behind Scudder's technique to define a trisectrix—a curve that can be used to trisect an angle...
Binary and Multi-Bit Coding for Stable Random Projections.

We develop efficient binary (i.e., 1-bit) and multi-bit coding schemes for estimating the scale parameter of α-stable distributions. The work is motivated by the recent work on one scan 1-bit compressed sensing (sparse signal recovery) [12] using α-stable random projections, which requires estimating of the scale parameter at bits-level. Our technique can be naturally applied to data stream computations for estimating the α-th frequency moment. In fact, the method applies to the general scale family of distributions, not limited to α-stable distributions.
On redundancy of memoryless sources over countable alphabets

In this paper, we study the average redundancy incurred by the universal description of strings of positive integers (ℤ + ), the strings being generated independently and identically distributed (i.i.d.) according an unknown distribution p over ℤ + in a known collection P. The redundancy of describing a single symbol is a crucial metric here—if description of a single symbol incurs finite redundancy then the class P is weakly compressible. Namely there is a universal measure q over infinite strings of positive integers such that for all p P, the normalized redundancy of describing length-n strings diminishes to 0 as n → ∞. We show that if redundancy of describing a single symbol is finite, the collection P is tight but that the converse does not always hold. In contrast to worst case formulations, we show that it is possible that the description of a single symbol from an unknown distribution of P incurs finite average redundancy, yet the description of length n i.i.d. strings incurs a constant (> 0) redundancy per symbol encoded. We then show a sufficient condition on single-letter marginals, such that length n i.i.d. samples will incur vanishing redundancy per symbol encoded.
Structural decomposition of monomial resolutions

Abstract We express the multigraded Betti numbers of an arbitrary monomial ideal in terms of the multigraded Betti numbers of two basic classes of ideals. This decomposition has multiple applications. In some concrete cases, we use it to construct minimal resolutions of classes of monomial ideals; in other cases, we use it to compute projective dimensions. To illustrate the effectiveness of the structural decomposition, we give a new proof of a classic theorem by Charalambous that states the following: let k be a field, and M an Artinian monomial ideal in S = k [ x 1 , … , x n ] ; then, for all i, b i ( S / M ) ≥ ( n i ) .
DEVELOPING BCS IDEAS IN THE FORMER SOVIET UNION

The essay below is an attempt to re-create the wonderful scientific atmosphere that emerged after the basic BCS ideas first arrived in Russia in 1957. It summarizes the most significant contributions to the microscopic theory of superconductivity by Russian physicists during the next few years that gave the theory its modern form.
Learning Intrinsic Image Decomposition from Watching the World

Single-view intrinsic image decomposition is a highly ill-posed problem, and so a promising approach is to learn from large amounts of data. However, it is difficult to collect ground truth training data at scale for intrinsic images. In this paper, we explore a different approach to learning intrinsic images: observing image sequences over time depicting the same scene under changing illumination, and learning single-view decompositions that are consistent with these changes. This approach allows us to learn without ground truth decompositions, and to instead exploit information available from multiple images when training. Our trained model can then be applied at test time to single views. We describe a new learning framework based on this idea, including new loss functions that can be efficiently evaluated over entire sequences. While prior learning-based methods achieve good performance on specific benchmarks, we show that our approach generalizes well to several diverse datasets, including MIT intrinsic images, Intrinsic Images in the Wild and Shading Annotations in the Wild.1
Kähler–Ricci flow on blowups along submanifolds

In this short note, we study the behavior of Kahler–Ricci flow on Kahler manifolds which contract divisors to smooth submanifolds. We show that the Kahler potentials are Holder continuous and the flow converges sequentially in Gromov–Hausdorff topology to a compact metric space which is homeomorphic to the base manifold.
Beyond Walkers in Stochastic Quantum Chemistry: Reducing Error Using Fast Randomized Iteration.

We introduce a family of methods for the full configuration interaction problem in quantum chemistry, based on the fast randomized iteration (FRI) framework [Lim, L.-H.; Weare, J. SIAM Rev. 2017, 59, 547; DOI: 10.1137/15M1040827]. These methods, which we term “FCI-FRI”, stochastically impose sparsity during iterations of the power method and can be viewed as a generalization of full configuration interaction quantum Monte Carlo (FCIQMC) without walkers. In addition to the multinomial scheme commonly used to sample excitations in FCIQMC, we present a systematic scheme where excitations are not sampled independently. Performing ground-state calculations on five small molecules at fixed cost, we find that the systematic FCI-FRI scheme is 11–45 times more statistically efficient than the multinomial FCI-FRI scheme, which is in turn 1.4–178 times more statistically efficient than the original FCIQMC algorithm.
Comparing oncologic outcomes in patients undergoing surgery for oncocytic neoplasms, conventional oncocytoma, and chromophobe renal cell carcinoma.

Abstract Introduction Oncocytic neoplasms are renal tumors similar to oncocytoma, but their morphologic variations preclude definitive diagnosis. This somewhat confusing diagnosis can create treatment and surveillance challenges for the treating urologist. We hypothesize that these subtle morphologic variations do not drastically affect the malignant potential of these tumors, and we sought to demonstrate this by comparing clinical outcomes of oncocytic neoplasms to those of classic oncocytoma and chromophobe. Methods We gathered demographic and outcomes data for patients with variant oncocytic tumors. Oncologic surveillance was conducted per institutional protocol in accordance with NCCN guidelines. Descriptive statistics were used to compare incidence of metastasis and death against those for patients with oncocytoma and chromophobe. Three hundred and fifty-one patients were analyzed: 164 patients with oncocytoma, 28 with oncocytic neoplasms, and 159 with chromophobe tumors. Results Median follow-up time for the entire cohort was 32.4 months, (interquartile range 9.2–70.0). Seventeen total patients (17/351, 4.9%) died during the course of the study. In patients with oncocytoma or oncocytic neoplasm, none were known to metastasize or die of their disease. Only chromophobe tumors >6 cm in size in our series demonstrated metastatic progression and approximately half of these metastasized tumors demonstrated sarcomatoid changes. Conclusion Variant oncocytic neoplasms appear to have a natural course similar to classic oncocytoma. These tumors appear to have no metastatic potential, and oncologic surveillance may not be indicated after surgery.
Understanding PCR Processes to Draw Meaningful Conclusions from Environmental DNA Studies.

As environmental DNA (eDNA) studies have grown in popularity for use in ecological applications, it has become clear that their results differ in significant ways from those of traditional, non-PCR-based surveys. In general, eDNA studies that rely on amplicon sequencing may detect hundreds of species present in a sampled environment, but the resulting species composition can be idiosyncratic, reflecting species’ true biomass abundances poorly or not at all. Here, we use a set of simulations to develop a mechanistic understanding of the processes leading to the kinds of results common in mixed-template PCR-based (metabarcoding) studies. In particular, we focus on the effects of PCR cycle number and primer amplification efficiency on the results of diversity metrics in sequencing studies. We then show that proportional indices of amplicon reads capture trends in taxon biomass with high accuracy, particularly where amplification efficiency is high (median correlation up to 0.97). Our results explain much of the observed behavior of PCR-based studies, and lead to recommendations for best practices in the field.
Institutional investment and the changing role of public equity markets: International evidence

Abstract Using country-level data, we study the relation between institutionalization of capital and various measures of reliance on public equity markets. For developed and developing countries, assets under institutional management (mutual funds, pension funds, and insurance companies) are negatively related to the number of listed companies, market capitalization, and trading volume. The negative relationships are estimated on the margin, as other factors such as GDP have countervailing positive partial effects and are generally stronger for more highly developed countries. Results indicate that as direct ownership of equity by retail investors is displaced by investing through institutions, financial systems become less public-market-centric.
Economic policy uncertainty and firm value: the mediating role of intangible assets and R&D

We explore the mechanisms by which intangible assets and R&D intensity mediate the relationship between global economic policy uncertainty and firm value. We find that firms in high intangible-inte...
Application of a Novel Force-Field to Manipulate the Relationship Between Pelvis Motion and Step Width in Human Walking

Motion of the pelvis throughout a step predicts step width during human walking. This behavior is often considered an important component of ensuring bipedal stability, but can be disrupted in populations with neurological injuries. The purpose of this study was to determine whether a novel force-field that exerts mediolateral forces on the legs can manipulate the relationship between pelvis motion and step width, providing proof-of-concept for a future clinical intervention. We designed a force-field able to: 1) minimize the delivered mediolateral forces (Transparent mode); 2) apply mediolateral forces to assist the leg toward mechanically-appropriate step widths (Assistive mode); and 3) apply mediolateral forces to perturb the leg away from mechanically-appropriate step widths (Perturbing mode). Neurologically-intact participants were randomly assigned to either the Assistive group (n = 12) or Perturbing group (n = 12), and performed a series of walking trials in which they interfaced with the force-field. We quantified the step-by-step relationship between mediolateral pelvis displacement and step width using partial correlations. Walking in the Transparent force-field had a minimal effect on this relationship. However, force-field assistance directly strengthened the relationship between pelvis displacement and step width, whereas force-field perturbations weakened this relationship. Both assistance and perturbations were followed by short-lived effects during a wash-out period, in which the relationship between pelvis displacement and step width differed from the baseline value. The present results demonstrate that the link between pelvis motion and step width can be manipulated through mechanical means, which may be useful for retraining gait balance in clinical populations.
Surveying the Sweetpotato Rhizosphere, Endophyte, and Surrounding Soil Microbiomes at Two North Carolina Farms Reveals Underpinnings of Sweetpotato Microbiome Community Assembly

Farmers grow sweetpotatoes worldwide and some sub-Saharan African and Asian diets include sweetpotato as a staple, yet the sweetpotato microbiome is conspicuously less studied relative to crops suc...