cssi/SciSciGPT-SciSciNet huggingface dataset:
Excess membrane cholesterol is an early contributing reversible aspect of skeletal muscle insulin resistance in C57BL/6NJ mice fed a Western-style high-fat diet

Skeletal muscle insulin resistance manifests shortly after high-fat feeding, yet mechanisms are not known. Here we set out to determine whether excess skeletal muscle membrane cholesterol and cytos...
DART-ID increases single-cell proteome coverage

Analysis by liquid chromatography and tandem mass spectrometry (LC-MS/MS) can identify and quantify thousands of proteins in microgram-level samples, such as those comprised of thousands of cells. This process, however, remains challenging for smaller samples, such as the proteomes of single mammalian cells, because reduced protein levels reduce the number of confidently sequenced peptides. To alleviate this reduction, we developed Data-driven Alignment of Retention Times for IDentification (DART-ID). DART-ID implements principled Bayesian frameworks for global retention time (RT) alignment and for incorporating RT estimates towards improved confidence estimates of peptide-spectrum-matches. When applied to bulk or to single-cell samples, DART-ID increased the number of data points by 30-50% at 1% FDR, and thus decreased missing data. Benchmarks indicate excellent quantification of peptides upgraded by DART-ID and support their utility for quantitative analysis, such as identifying cell types and cell-type specific proteins. The additional datapoints provided by DART-ID boost the statistical power and double the number of proteins identified as differentially abundant in monocytes and T-cells. DART-ID can be applied to diverse experimental designs and is freely available at http://dart-id.slavovlab.net.
Food-web interaction strength distributions are conserved by greater variation between than within predator–prey pairs

Species interactions in food webs are usually recognized as dynamic, varying across species, space, and time because of biotic and abiotic drivers. Yet food webs also show emergent properties that appear consistent, such as a skewed frequency distribution of interaction strengths (many weak, few strong). Reconciling these two properties requires an understanding of the variation in pairwise interaction strengths and its underlying mechanisms. We estimated stream sculpin feeding rates in three seasons at nine sites in Oregon to examine variation in trophic interaction strengths both across and within predator-prey pairs. Predator and prey densities, prey body mass, and abiotic factors were considered as putative drivers of within-pair variation over space and time. We hypothesized that consistently skewed interaction strength distributions could result if individual interaction strengths show relatively little variation, or alternatively, if interaction strengths vary but shift in ways that conserve their overall frequency distribution. Feeding rate distributions remained consistently and positively skewed across all sites and seasons. The mean coefficient of variation in feeding rates within each of 25 focal species pairs across surveys was less than half the mean coefficient of variation seen across species pairs within a survey. The rank order of feeding rates also remained conserved across streams, seasons and individual surveys. On average, feeding rates on each prey taxon nonetheless varied by a hundredfold, with some feeding rates showing more variation in space and others in time. In general, feeding rates increased with prey density and decreased with high stream flows and low water temperatures, although for nearly half of all species pairs, factors other than prey density explained the most variation. Our findings show that although individual interaction strengths exhibit considerable variation in space and time, they can nonetheless remain relatively consistent, and thus predictable, compared to the even larger variation that occurs across species pairs. These results highlight how the ecological scale of inference can strongly shape conclusions about interaction strength consistency and help reconcile how the skewed nature of interaction strength distributions can persist in highly dynamic food webs.
Domination ratio of integer distance digraphs

Abstract An integer distance digraph is the Cayley graph Γ ( Z , S ) of the additive group Z of all integers with respect to some finite subset S ⊆ Z . The domination ratio of Γ ( Z , S ) is the minimum density of a dominating set in Γ ( Z , S ) . We establish some basic results on the domination ratio of Γ ( Z , S ) and precisely determine it when S = { s , t } with s dividing t .
Training Set Debugging Using Trusted Items.

Training set bugs are flaws in the data that adversely affect machine learning. The training set is usually too large for manual inspection, but one may have the resources to verify a few trusted items. The set of trusted items may not by itself be adequate for learning, so we propose an algorithm that uses these items to identify bugs in the training set and thus improves learning. Specifically, our approach seeks the smallest set of changes to the training set labels such that the model learned from this corrected training set predicts labels of the trusted items correctly. We flag the items whose labels are changed as potential bugs, whose labels can be checked for veracity by human experts. To find the bugs in this way is a challenging combinatorial bilevel optimization problem, but it can be relaxed into a continuous optimization problem.Experiments on toy and real data demonstrate that our approach can identify training set bugs effectively and suggest appropriate changes to the labels. Our algorithm is a step toward trustworthy machine learning.
Linear Dependence Between Hereditary Quasirandomness Conditions

Answering a question of Simonovits and Sos, Conlon, Fox, and Sudakov proved that for any nonempty graph $H$, and any $\varepsilon>0$, there exists $\delta>0$ polynomial in $\varepsilon$, such that if $G$ is an $n$-vertex graph with the property that every $U\subseteq V(G)$ contains $p^{e(H)}|U|^{v(H)}\pm\delta n^{v(H)}$ labeled copies of $H$, then $G$ is $(p,\varepsilon)$-quasirandom in the sense that every subset $U\subseteq G$ contains $\frac{1}{2}p|U|^{2}\pm\varepsilon n^{2}$ edges. They conjectured that $\delta$ may be taken to be linear in $\varepsilon$ and proved this in the case that $H$ is a complete graph. We study a labelled version of this quasirandomness property proposed by Reiher and Schacht. Let $H$ be any nonempty graph on $r$ vertices $v_{1},\ldots,v_{r}$, and $\varepsilon>0$. We show that there exists $\delta=\delta(\varepsilon)>0$ linear in $\varepsilon$, such that if $G$ is an $n$-vertex graph with the property that every sequence of $r$ subsets $U_{1},\ldots,U_{r}\subseteq V(G)$, the number of copies of $H$ with each $v_{i}$ in $U_{i}$ is $p^{e(H)}\prod|U_{i}|\pm\delta n^{v(H)}$, then $G$ is $(p,\varepsilon)$-quasirandom.
Second cohomology groups for algebraic groups and their Frobenius kernels

Let G be a simple simply connected algebraic group scheme defined over an algebraically closed field of characteristic p>0. Let T be a maximal split torus in G, B⊃T be a Borel subgroup of G and U its unipotent radical. Let F:G→G be the Frobenius morphism. For r⩾1 define the Frobenius kernel, Gr, to be the kernel of F iterated with itself r times. Define Ur (respectively Br) to be the kernel of the Frobenius map restricted to U (respectively B). Let X(T) be the integral weight lattice and X(T)+ be the dominant integral weights. The computations of particular importance are H2(U1,k), H2(Br,λ) for λ∈X(T), H2(Gr,H0(λ)) for λ∈X(T)+, and H2(B,λ) for λ∈X(T). The above cohomology groups for the case when the field has characteristic 2 are computed in this paper. These computations complete the picture started by Bendel, Nakano, and Pillen (2007) [5] for p⩾3. Furthermore, the computations show H2(Gr,H0(λ)) has a good filtration.
An empirical process interpretation of a model of species survival

Abstract We study a model of species survival recently proposed by Michael and Volkov. We interpret it as a variant of empirical processes, in which the sample size is random and when decreasing, samples of smallest numerical values are removed. Micheal and Volkov proved that the empirical distributions converge to the sample distribution conditioned not to be below a certain threshold. We prove a functional central limit theorem for the fluctuations. There exists a threshold above which the limit process is Gaussian with variance bounded below by a positive constant, while at the threshold it is half-Gaussian.
A gluing construction for polynomial invariants

Abstract We give a polynomial gluing construction of two groups G X ⊆ GL ( l , F ) and G Y ⊆ GL ( m , F ) which results in a group G ⊆ GL ( l + m , F ) whose ring of invariants is isomorphic to the tensor product of the rings of invariants of G X and G Y . In particular, this result allows us to obtain many groups with polynomial rings of invariants, including all p-groups whose rings of invariants are polynomial over F p , and the finite subgroups of GL ( n , F ) defined by sparsity patterns, which generalize many known examples.
The Space {B^{-1}_{\infty, \infty}}, Volumetric Sparseness, and 3D NSE

In the context of the $${L^\infty}$$ -theory of the 3D NSE, it is shown that smallness of a solution in Besov space $${B^{-1}_{\infty, \infty}}$$ suffices to prevent a possible blow-up. In particular, it is revealed that the aforementioned condition implies a particular local spatial structure of the regions of high velocity magnitude, namely, the structure of local volumetric sparseness on the scale comparable to the radius of spatial analyticity measured in $${L^\infty}$$ .
Conformal blocks and rational normal curves

We prove that the Chow quotient parametrizing configurations of n points in $\mathbb{P}^d$ which generically lie on a rational normal curve is isomorphic to $\overline{M}_{0,n}$, generalizing the well-known $d = 1$ result of Kapranov. In particular, $\overline{M}_{0,n}$ admits birational morphisms to all the corresponding geometric invariant theory (GIT) quotients. For symmetric linearizations the polarization on each GIT quotient pulls back to a divisor that spans the same extremal ray in the symmetric nef cone of $\overline{M}_{0,n}$ as a conformal blocks line bundle. A symmetry in conformal blocks implies a duality of point-configurations that comes from Gale duality and generalizes a result of Goppa in algebraic coding theory. In a suitable sense, $\overline{M}_{0,2m}$ is fixed pointwise by the Gale transform when $d=m-1$ so stable curves correspond to self-associated configurations.
Further time regularity for fully non-linear parabolic equations

We establish H\"older estimates for the time derivative of solutions of fully non-linear parabolic equations that does not necessarily have $C^{2,\alpha}$ estimates.
Bifurcation to Coherent Structures in Nonlocally Coupled Systems

We show bifurcation of localized spike solutions from spatially constant states in systems of nonlocally coupled equations in the whole space. The main assumptions are a generic bifurcation of saddle-node or transcritical type for spatially constant profiles, and a symmetry and second moment condition on the convolution kernel. The results extend well known results for spots, spikes, and fronts, in locally coupled systems on the real line, and for radially symmetric profiles in higher space dimensions. Rather than relying on center manifolds, we pursue a more direct approach, deriving leading order asymptotics and Newton corrections for error terms. The key ingredient is smoothness of Fourier multipliers arising from discrepancies between nonlocal operators and their local long-wavelength approximations.
HNC IN PROTOPLANETARY DISKS

The distributions and abundances of small organics in protoplanetary disks are potentially powerful probes of disk physics and chemistry. HNC is a common probe of dense interstellar regions and the target of this study. We use the Submillimeter Array (SMA) to observe HNC 3–2 toward the protoplanetary disks around the T Tauri star TW Hya and the Herbig Ae star HD 163296. HNC is detected toward both disks, constituting the first spatially resolved observations of HNC in disks. We also present SMA observations of HCN 3–2 and IRAM 30 m observations of HCN and HNC 1–0 toward HD 163296. The disk-averaged HNC/HCN emission ratio is 0.1–0.2 toward both disks. Toward TW Hya, the HNC emission is confined to a ring. The varying HNC abundance in the TW Hya disk demonstrates that HNC chemistry is strongly linked to the disk physical structure. In particular, the inner rim of the HNC ring can be explained by efficient destruction of HNC at elevated temperatures, similar to what is observed in the ISM. However, to realize the full potential of HNC as a disk tracer requires a combination of high SNR spatially resolved observations of HNC and HCN and disk-specific HNC chemical modeling.
Infinitely split Nash equilibrium problems in repeated games

In this paper, we introduce the concept of infinitely split Nash equilibrium in repeated games in which the profile sets are chain-complete posets. Then by using a fixed point theorem on posets in (J. Math. Anal. Appl. 409:1084–1092, 2014), we prove an existence theorem. As an application, we study the repeated extended Bertrant duopoly model of price competition.
Reciprocity of Dedekind sums and the Euler class

Dedekind sums are arithmetic sums that were first introduced by Dedekind in the context of elliptic functions and modular forms, and later recognized to be surprisingly ubiquitous. Among the variations and generalizations introduced since, there is a construction of Dedekind sums for lattices in $\mathrm{SL}_2(\mathbf{R}). Building upon work of Asai, we prove the reciprocity law for these Dedekind sums, based on a concrete realization of the Euler class. As an application, we obtain an explicit formula expressing Dedekind sums for Hecke triangle groups in terms of continued fractions.
On abstract homomorphisms of Chevalley groups over the coordinate rings of affine curves

The goal of this paper is to establish a general rigidity statement for abstract representations of elementary subgroups of Chevalley groups of rank ≥ 2 over a class of commutative rings that includes the localizations of 1-generated rings and the coordinate rings of affine curves. Our main result implies, for example, that any finite-dimensional representation of SLn(ℤ[X]) (n ≥ 3) over an algebraically closed field of characteristic zero has a standard description, yielding thereby the first unconditional rigidity statement for finitely generated linear groups other than arithmetic groups/lattices.
A Curved Brunn-Minkowski Inequality for the Symmetric Group

In this paper, we construct an injection $A \times B \rightarrow M \times M$ from the product of any two nonempty subsets of the symmetric group into the square of their midpoint set, where the metric is that corresponding to the conjugacy class of transpositions. If $A$ and $B$ are disjoint, our construction allows to inject two copies of $A \times B$ into $M \times M$. These injections imply a positively curved Brunn-Minkowski inequality for the symmetric group analogous to that obtained by Ollivier and Villani for the hypercube. However, while Ollivier and Villani's inequality is optimal, we believe that the curvature term in our inequality can be improved. We identify a hypothetical concentration inequality in the symmetric group and prove that it yields an optimally curved Brunn-Minkowski inequality.
Evidence of ballistic thermal transport in lithium niobate at room temperature

Abstract In ballistic transport, heat carriers such as phonons travel through the solid without any scattering or interaction. Therefore, there is no temperature gradient in the solid, suggesting zero thermal conductivity by Fourier's law. Ballistic transport is typically seen in high purity crystals at either temperatures below ~ 10 K, or physical size below ~ 100 nm, where the mean free path of the carrier is larger than the solid itself. In this letter, we show evidence of ballistic transport at room temperature in lithium niobate wafers in the in-plane and cross-plane directions under both steady state and high frequency heating that are monitored using both infrared and resistance thermometry. We report phonon mean free path in lithium niobate around 425 μm, which is about 50 times higher than the largest phonon mean free path in the literature at room temperature.
Half-spectral space–time covariance models

Abstract We develop a new class of space–time Gaussian process models by specifying covariance functions using what we call a half-spectral representation. We establish general properties of half-spectral models and appeal to results on a screening effect analysis to claim more natural space–time interaction properties in the class of models we develop. To test this claim and to more generally test this class of models, we fit models we develop in this paper to a wind power dataset. We show our models fit these data better than other common separable and non-separable space–time models.
On representation schemes and Grassmanians of finite dimensional algebras and a construction of Lusztig

Let I be a finite set and CI be the algebra of functions on I. For a finite dimensional C algebra A with \CI contained in A we show that certain moduli spaces of finite dimsional modules are isomorphic to certain Grassmannian (quot-type) varieties. There is a special case of interest in representation theory. Lusztig defined two varieties related to a quiver and gave a bijection between their C-points (citation in article). Savage and Tingley raised the question (citation in article) of whether these varieties are isomorphic as algebraic varieties. This question has been open since Lusztig's original work. It follows from the result of this note that the two varieties are indeed isomorphic.
LASS: a simple assignment model with Laplacian smoothing

We consider the problem of learning soft assignments of N items to K categories given two sources of information: an item-category similarity matrix, which encourages items to be assigned to categories they are similar to (and to not be assigned to categories they are dissimilar to), and an item-item similarity matrix, which encourages similar items to have similar assignments. We propose a simple quadratic programming model that captures this intuition. We give necessary conditions for its solution to be unique, define an out-of-sample mapping, and derive a simple, effective training algorithm based on the alternating direction method of multipliers. The model predicts reasonable assignments from even a few similarity values, and can be seen as a generalization of semisupervised learning. It is particularly useful when items naturally belong to multiple categories, as for example when annotating documents with keywords or pictures with tags, with partially tagged items, or when the categories have complex interrelations (e.g. hierarchical) that are unknown.
Continuity properties of vectors realizing points in the classical field of values

For an n-by-n matrix A, let f A be its ‘field of values generating function’ defined as f A : x ↦ x*Ax. We consider two natural versions of the continuity, which we call strong and weak, of (which is of course multi-valued) on the field of values F(A). The strong continuity holds, in particular, on the interior of F(A), and at such points z ∈ ∂F(A) which are either corner points, belong to the relative interior of flat portions of ∂F(A), or whose preimage under f A is contained in a one-dimensional set. Consequently, is continuous in this sense on the whole F(A) for all normal, 2-by-2, and unitarily irreducible 3-by-3 matrices. Nevertheless, we show by example that the strong continuity of fails at certain points of ∂F(A) for some (unitarily reducible) 3-by-3 and (unitarily irreducible) 4-by-4 matrices. The weak continuity, in its turn, fails for some unitarily reducible 4-by-4 and untiarily irreducible 6-by-6 matrices.
The Singular Values of Convolutional Layers

We characterize the singular values of the linear transformation associated with a standard 2D multi-channel convolutional layer, enabling their efficient computation. This characterization also leads to an algorithm for projecting a convolutional layer onto an operator-norm ball. We show that this is an effective regularizer; for example, it improves the test error of a deep residual network using batch normalization on CIFAR-10 from 6.2\% to 5.3\%.
Anisotropic Agglomerative Adaptive Mean-Shift

Mean Shift today, is widely used for mode detection and clustering. The technique though, is challenged in practice due to assumptions of isotropicity and homoscedasticity. We present an adaptive Mean Shift methodology that allows for full anisotropic clustering, through unsupervised local bandwidth selection. The bandwidth matrices evolve naturally, adapting locally through agglomeration, and in turn guiding further agglomeration. The online methodology is practical and effecive for low-dimensional feature spaces, preserving better detail and clustering salience. Additionally, conventional Mean Shift either critically depends on a per instance choice of bandwidth, or relies on offline methods which are inflexible and/or again data instance specific. The presented approach, due to its adaptive design, also alleviates this issue - with a default form performing generally well. The methodology though, allows for effective tuning of results.
Many-body wave scattering problems in the case of small scatterers

Formulas are derived for solutions of many-body wave scattering problems by small particles in the case of acoustically soft, hard, and impedance particles embedded in an inhomogeneous medium. The case of transmission (interface) boundary conditions is also studied in detail. The limiting case is considered, when the size a of small particles tends to zero while their number tends to infinity at a suitable rate. Equations for the limiting effective (self-consistent) field in the medium are derived. The theory is based on a study of integral equations and asymptotics of their solutions as a→0. The case of wave scattering by many small particles embedded in an inhomogeneous medium is also studied.
Collecting telemetry data privately

The collection and analysis of telemetry data from user's devices is routinely performed by many software companies. Telemetry collection leads to improved user experience but poses significant risks to users' privacy. Locally differentially private (LDP) algorithms have recently emerged as the main tool that allows data collectors to estimate various population statistics, while preserving privacy. The guarantees provided by such algorithms are typically very strong for a single round of telemetry collection, but degrade rapidly when telemetry is collected regularly. In particular, existing LDP algorithms are not suitable for repeated collection of counter data such as daily app usage statistics. In this paper, we develop new LDP mechanisms geared towards repeated collection of counter data, with formal privacy guarantees even after being executed for an arbitrarily long period of time. For two basic analytical tasks, mean estimation and histogram estimation, our LDP mechanisms for repeated data collection provide estimates with comparable or even the same accuracy as existing single-round LDP collection mechanisms. We conduct empirical evaluation on real-world counter datasets to verify our theoretical results. Our mechanisms have been deployed by Microsoft to collect telemetry across millions of devices.
Weighted Rogers–Ramanujan partitions and Dyson crank

In this paper, we refine a weighted partition identity of Alladi. We write formulas for generating functions for the number of partitions grouped with respect to a partition statistic other than the norm. We tie our weighted results as well as the different statistics with the crank of a partition. In particular, we prove that the number of partitions into even number of distinct parts whose odd-indexed parts’ sum is n is equal to the number of partitions of n with non-negative crank.
Uncoupled isotonic regression via minimum Wasserstein deconvolution

Isotonic regression is a standard problem in shape-constrained estimation where the goal is to estimate an unknown nondecreasing regression function $f$ from independent pairs $(x_i, y_i)$ where $\mathbb{E}[y_i]=f(x_i), i=1, \ldots n$. While this problem is well understood both statistically and computationally, much less is known about its uncoupled counterpart where one is given only the unordered sets $\{x_1, \ldots, x_n\}$ and $\{y_1, \ldots, y_n\}$. In this work, we leverage tools from optimal transport theory to derive minimax rates under weak moments conditions on $y_i$ and to give an efficient algorithm achieving optimal rates. Both upper and lower bounds employ moment-matching arguments that are also pertinent to learning mixtures of distributions and deconvolution.
The Bergman Kernel on Some Hartogs Domains

We obtain new explicit formulas for the Bergman kernel function on two families of Hartogs domains. To do so, we first compute the Bergman kernels on the slices of these Hartogs domains with some coordinates fixed, evaluate these kernel functions at certain points off the diagonal, and then apply a first order differential operator to them. We find, for example, explicit formulas for the kernel function on $$\begin{aligned} \left\{ (z_1,z_2,w)\in \mathbb {C}^3:e^{|w|^2}|z_1|^2+|z_2|^2<1\right\} \end{aligned}$$ and on $$\begin{aligned} \left\{ (z_1,z_2,w)\in \mathbb {C}^3:|z_1|^2+|z_2|^2+|w|^2<1+|z_2w|^2\;\mathrm{and} \;|w|<1\right\} . \end{aligned}$$ We use our formulas to determine the boundary behavior of the kernel function of these domains on the diagonal.
The utility of Naturalness, and how its application to Quantum Electrodynamics envisages the Standard Model and Higgs boson

Abstract With the Higgs boson discovery and no new physics found at the LHC, confidence in Naturalness as a guiding principle for particle physics is under increased pressure. We wait to see if it proves its mettle in the LHC upgrades ahead, and beyond. In the meantime, I present a justification a posteriori of the Naturalness criterion by suggesting that uncompromising application of the principle to Quantum Electrodynamics leads toward the Standard Model and Higgs boson without additional experimental input. Potential lessons for today and future theory building are commented upon.
Beamforming with Multiple One-Bit Wireless Transceivers

Classical beamforming techniques rely on highly linear transmitters and receivers to allow phase-coherent combining at the transmitter and receiver. The transmitter uses beamforming to steer signal power towards the receiver, and the receiver uses beamforming to gather and coherently combine the signals from multiple receiver antennas. When the transmitters and receivers are instead constrained for power and cost reasons to be nonlinear one-bit devices, the potential advantages and performance metrics associated with beamforming are not as well understood. We define beamforming at the transmitter as a codebook design problem to maximize the minimum distance between codewords. We define beamforming at the receiver as the maximum likelihood detector of the transmitted codeword. We show that beamforming with one-bit transceivers is a constellation design problem, and that we can come within a few dB SNR of the capacity attained by linear transceivers.
Effects of Wall-Normal and Angular Momentum Injections in Airfoil Separation Control

The objective of this computational study is to quantify the influence of wall-normal and angular momentum injections in suppressing laminar flow separation over a canonical airfoil. Open-loop cont...
Real-Time Human Detection as an Edge Service Enabled by a Lightweight CNN

Edge computing allows more computing tasks to take place on the decentralized nodes at the edge of networks. Today many delay sensitive, mission-critical applications can leverage these edge devices to reduce the time delay or even to enable real-time, online decision making thanks to their onsite presence. Human objects detection, behavior recognition and prediction in smart surveillance fall into that category, where a transition of a huge volume of video streaming data can take valuable time and place heavy pressure on communication networks. It is widely recognized that video processing and object detection are computing intensive and too expensive to be handled by resource-limited edge devices. Inspired by the depthwise separable convolution and Single Shot Multi-Box Detector (SSD), a lightweight Convolutional Neural Network (L-CNN) is introduced in this paper. By narrowing down the classifier's searching space to focus on human objects in surveillance video frames, the proposed L-CNN algorithm is able to detect pedestrians with an affordable computation workload to an edge device. A prototype has been implemented on an edge node (Raspberry PI 3) using openCV libraries, and satisfactory performance is achieved using real-world surveillance video streams. The experimental study has validated the design of L-CNN and shown it is a promising approach to computing intensive applications at the edge.
Learning social affordance for human-robot interaction

In this paper, we present an approach for robot learning of social affordance from human activity videos. We consider the problem in the context of human-robot interaction: Our approach learns structural representations of human-human (and human-object-human) interactions, describing how body-parts of each agent move with respect to each other and what spatial relations they should maintain to complete each sub-event (i.e., sub-goal). This enables the robot to infer its own movement in reaction to the human body motion, allowing it to naturally replicate such interactions. We introduce the representation of social affordance and propose a generative model for its weakly supervised learning from human demonstration videos. Our approach discovers critical steps (i.e., latent sub-events) in an interaction and the typical motion associated with them, learning what body-parts should be involved and how. The experimental results demonstrate that our Markov Chain Monte Carlo (MCMC) based learning algorithm automatically discovers semantically meaningful social affordance from RGB-D videos, which allows us to generate appropriate full body motion for an agent.
Computing Maximum Flow with Augmenting Electrical Flows

We present an eO m 10 7 U 17 -time algorithm for the maximum s-t flow problem (and the minimum s-t cut problem) in directed graphs with m arcs and largest integer capacity U. This matches the running time of the eO (mU) 10 7 - time algorithm of M , adry [30] in the unit-capacity case, and improves over it, as well as over the eO (mpn log U)-time algorithm of Lee and Sidford [25], whenever U is moderately large and the graph is sufficiently sparse. By well-known reductions, this also implies similar running time improvements for the maximum-cardinality bipartite b-matching problem. One of the advantages of our algorithm is that it is significantly simpler than the ones presented in [30] and [25]. In particular, these algorithms employ a sophisticated interiorpoint method framework, while our algorithm is cast directly in the classic augmenting path setting that almost all the combinatorial maximum flow algorithms use. At a high level, the presented algorithm takes a primal dual approach in which each iteration uses electrical flows computations both to find an augmenting s-t flow in the current residual graph and to update the dual solution. We show that by maintain certain careful coupling of these primal and dual solutions we are always guaranteed to make significant progress.
The Cognitive Compressive Sensing Problem

In the Cognitive Compressive Sensing (CCS) problem, a Cognitive Receiver (CR) seeks to optimize the reward obtained by sensing an underlying $N$ dimensional random vector, by collecting at most $K$ arbitrary projections of it. The $N$ components of the latent vector represent sub-channels states, that change dynamically from "busy" to "idle" and vice versa, as a Markov chain that is biased towards producing sparse vectors. To identify the optimal strategy we formulate the Multi-Armed Bandit Compressive Sensing (MAB-CS) problem, generalizing the popular Cognitive Spectrum Sensing model, in which the CR can sense $K$ out of the $N$ sub-channels, as well as the typical static setting of Compressive Sensing, in which the CR observes $K$ linear combinations of the $N$ dimensional sparse vector. The CR opportunistic choice of the sensing matrix should balance the desire of revealing the state of as many dimensions of the latent vector as possible, while not exceeding the limits beyond which the vector support is no longer uniquely identifiable.
The Optimal Hard Threshold for Singular Values is $4/\sqrt {3}$

We consider recovery of low-rank matrices from noisy data by hard thresholding of singular values, in which empirical singular values below a threshold λ are set to 0. We study the asymptotic mean squared error (AMSE) in a framework, where the matrix size is large compared with the rank of the matrix to be recovered, and the signal-to-noise ratio of the low-rank piece stays constant. The AMSE-optimal choice of hard threshold, in the case of n-by-n matrix in white noise of level σ, is simply (4/√3)√nσ ≈ 2.309√nσ when σ is known, or simply 2.858 · y med when σ is unknown, where y med is the median empirical singular value. For nonsquare, m by n matrices with m ≠ n the thresholding coefficients 4/√3 and 2.858 are replaced with different provided constants that depend on m/n. Asymptotically, this thresholding rule adapts to unknown rank and unknown noise level in an optimal manner: it is always better than hard thresholding at any other value, and is always better than ideal truncated singular value decomposition (TSVD), which truncates at the true rank of the low-rank matrix we are trying to recover. Hard thresholding at the recommended value to recover an n-by-n matrix of rank r guarantees an AMSE at most 3 nrσ 2 . In comparison, the guarantees provided by TSVD, optimally tuned singular value soft thresholding and the best guarantee achievable by any shrinkage of the data singular values are 5 nrσ 2 , 6 nrσ 2 , and 2 nrσ 2 , respectively. The recommended value for hard threshold also offers, among hard thresholds, the best possible AMSE guarantees for recovering matrices with bounded nuclear norm. Empirical evidence suggests that performance improvement over TSVD and other popular shrinkage rules can be substantial, for different noise distributions, even in relatively small n.
Invariant tubular neighborhoods in infinite-dimensional Riemannian geometry, with applications to Yang-Mills theory

We present a new construction of tubular neighborhoods in (possibly infinite dimensional) Riemannian manifolds M, which allows us to show that if G is an arbitrary group acting isometrically on M, then every G-invariant submanifold with locally trivial normal bundle has a G-invariant total tubular neighborhood. We apply this result to the Morse strata of the Yang-Mills functional over a closed surface. The resulting neighborhoods play an important role in calculations of gauge-equivariant cohomology for moduli spaces of flat connections over non-orientable surfaces.
Coexistence of radar and communication systems in CBRS bands through downlink power control

Citizen Broadband Radio Service band (3550–3700 GHz) is seen as one of the key frequency bands to enable improvements in the performance of wireless broadband and cellular systems. A careful study of interference caused by a secondary cellular communication system coexisting with an incumbent naval radar is required to establish a pragmatic protection distance, which not only protects the incumbent from harmful interference but also can increase spectrum access opportunity for the coexisting secondary system. In this context, this paper investigates the co-channel and adjacent channel coexistence of a ship-borne naval radar with a wide-area cellular communication system and presents the analysis of interference caused by downlink transmissions of the cellular system on the naval radar for different values of radar protection distance. The results of such analysis suggest that maintaining a protection distance of 30 km from the radar will ensure the required INR protection criterion of −6 dB at the radar receiver with > 0.9 probability, even when the secondary network operates on the same channel as the radar. Novel power control algorithms to assign operating powers to the coexisting cellular devices are also proposed to further reduce the protection distance from radar while still meeting the radar INR protection requirement.
Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation

Existing research studies on vision and language grounding for robot navigation focus on improving model-free deep reinforcement learning (DRL) models in synthetic environments. However, model-free DRL models do not consider the dynamics in the real-world environments, and they often fail to generalize to new scenes. In this paper, we take a radical approach to bridge the gap between synthetic studies and real-world practices—We propose a novel, planned-ahead hybrid reinforcement learning model that combines model-free and model-based reinforcement learning to solve a real-world vision-language navigation task. Our look-ahead module tightly integrates a look-ahead policy model with an environment model that predicts the next state and the reward. Experimental results suggest that our proposed method significantly outperforms the baselines and achieves the best on the real-world Room-to-Room dataset. Moreover, our scalable method is more generalizable when transferring to unseen environments.
Towards A Model Theory for Distributed Representations.

Distributed representations (such as those based on embeddings) and discrete representations (such as those based on logic) have complementary strengths. We explore one possible approach to combining these two kinds of representations. We present a model theory/semantics for first order logic based on vectors of reals. We describe the model theory, discuss some interesting properties of such a system and present a simple approach to query answering.
Chern character for matrix factorizations via Chern–Weil

Abstract Given a matrix factorization, we use the Atiyah class to give an algebraic Chern–Weil type construction to its Chern character; this allows us to realize the Chern character in an explicit way. It also generalizes the existing result to any smooth k-algebra Q (k a commutative ring containing Q ) and any f ∈ Q , which agrees with a recent result of Platt [9] . We also study some basic properties of the Chern character.
A remark on a result of Xia Chen

Abstract We consider the parabolic Anderson model which is driven by a Gaussian noise fractional in time and having certain scaling property in the spatial variables. Recently, Xia Chen has obtained exact Lyapunov exponent for all moments of positive integer orders. In this note, we explain how to extend Xia Chen’s result for all moments of order p , where p is any real number at least 2.
Context-aware D2D peer selection for load distribution in LTE networks

In this paper we propose a novel context-aware approach for resource allocation in device to device (D2D) communication networks which exploits context information about the users velocity and size of their demanded data to decide whether their traffic load can be transferred to D2D tier and which D2D users should be paired. The problem is modeled as a matching game with externalities and a novel algorithm is proposed to solve the game which converges to a stable matching between the D2D users. Simulation results demonstrate the effectiveness of our model in offloading the cellular networks traffic to the D2D tier.
Multilevel diversity coding with regeneration: Separate coding achieves the MBR point

The problem of multilevel diversity coding with regeneration is considered in this work. Two new outer bounds on the optimal tradeoffs between the normalized storage capacity and repair bandwidth are established, by which the optimality of separate coding at the minimum-bandwidth-regeneration (MBR) point follows immediately. This resolves a question left open in a previous work by Tian and Liu.
New analytic criterion for porous solids with pressure-insensitive matrix

Abstract In this paper, we address the question of how the relative weighting of the two invariants of the plastic deformation of the matrix influence the mechanical response of a porous metallic material. To this end, we first propose a new isotropic potential for description of the plastic behavior of the matrix that depends on both invariants of the strain-rate deviator. The relative weight of the two invariants is described by a material parameter β. Depending on the sign of the parameter β, the new plastic potential for the matrix is either interior to von Mises strain-rate potential (β J 3 Σ on the mechanical response of the porous solid. If the matrix is characterized by β > β*, the response of the porous material for tensile loadings and J 3 Σ ≥ 0 is softer than that for loadings at J 3 Σ ≤ 0 . The reverse holds true for β
Occam’s Quantum Strop: Synchronizing and Compressing Classical Cryptic Processes via a Quantum Channel

A stochastic process' statistical complexity stands out as a fundamental property: the minimum information required to synchronize one process generator to another. How much information is required, though, when synchronizing over a quantum channel? Recent work demonstrated that representing causal similarity as quantum state-indistinguishability provides a quantum advantage. We generalize this to synchronization and offer a sequence of constructions that exploit extended causal structures, finding substantial increase of the quantum advantage. We demonstrate that maximum compression is determined by the process' cryptic order--a classical, topological property closely allied to Markov order, itself a measure of historical dependence. We introduce an efficient algorithm that computes the quantum advantage and close noting that the advantage comes at a cost-one trades off prediction for generation complexity.
Analysis of Ergodic Rate for Transmit Antenna Selection in Low-Resolution ADC Systems

In this paper, we analyze the ergodic rate of single transmit antenna selection (TAS) in low-resolution analog-to-digital converter (ADC) systems. Using low-resolution ADCs is a potential power-reduction solution for multiple antenna systems. Low-resolution ADC systems with TAS can further reduce cost and power consumption in wireless transceivers. Considering such systems, we derive the approximated lower bound of the ergodic rate with TAS. Here, we exploit the approximated distribution of the sum of Weibull random variables to address the challenge involved in analyzing the quantization error. We, then, derive the approximated ergodic rate with TAS for a single receive antenna in closed form, which reveals the TAS gain in low-resolution ADC systems. The upper bound of a single transmit and single receive antenna system under coarse quantization is derived to compare with the ergodic rate of the TAS system. The analysis shows that the TAS method achieves a large improvement in ergodic rate with a moderate number of transmit antennas. Simulation results validate the derived ergodic rates and resulting intuition.
Few-body systems and the pionless effective field theory

The pionless effective field theory (EFT) is the appropriate low-energy EFT for short-range interactions that display a large scattering length. It has been successfully applied in atomic, nuclear and particle physics. We give an overview over recent calculations employing the pionless effective field theory and lay emphasis on applications in the threeand four-body sector where the most exciting developments have occured.
A well-conditioned and sparse estimation of covariance and inverse covariance matrices using a joint penalty

We develop a method for estimating well-conditioned and sparse covariance and inverse covariance matrices from a sample of vectors drawn from a sub-Gaussian distribution in high dimensional setting. The proposed estimators are obtained by minimizing the quadratic loss function and joint penalty of l1 norm and variance of its eigenvalues. In contrast to some of the existing methods of covariance and inverse covariance matrix estimation, where often the interest is to estimate a sparse matrix, the proposed method is flexible in estimating both a sparse and well-conditioned covariance matrix simultaneously. The proposed estimators are optimal in the sense that they achieve the mini-max rate of estimation in operator norm for the underlying class of covariance and inverse covariance matrices. We give a very fast algorithm for computation of these covariance and inverse covariance matrices which is easily scalable to large scale data analysis problems. The simulation study for varying sample sizes and variables shows that the proposed estimators performs better than several other estimators for various choices of structured covariance and inverse covariance matrices. We also use our proposed estimator for tumor tissues classification using gene expression data and compare its performance with some other classification methods.
The M&M Game: From Morsels to Modern Mathematics

To an adult, it's obvious that the day of someone's death is not precisely determined by the day of birth, but it's a very different story for a child. We invented what we call the M&M Game to help...
Demystifying the bias from selective inference: A revisit to Dawid’s treatment selection problem

Abstract We extend the heuristic discussion in Senn (2008) on the bias from selective inference for the treatment selection problem (Dawid, 1994), by deriving the closed-form expression for the selection bias. We illustrate the advantages of our theoretical results through numerical and simulated examples.
Note on a theorem of Bangert

We generalize Bangert’s non-hyperbolicity result for uniformly tamed almost complex structures on standard symplectic ℝ2n to asymptotically standard symplectic manifolds.
Closed-Loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search

In search applications, autonomous unmanned vehicles must be able to efficiently reacquire and localize mobile targets that can remain out of view for long periods of time in large spaces. As such, all available information sources must be actively leveraged - including imprecise but readily available semantic observations provided by humans. To achieve this, this work develops and validates a novel collaborative human-machine sensing solution for dynamic target search. Our approach uses continuous partially observable Markov decision process (CPOMDP) planning to generate vehicle trajectories that optimally exploit imperfect detection data from onboard sensors, as well as semantic natural language observations that can be specifically requested from human sensors. The key innovation is a scalable hierarchical Gaussian mixture model formulation for efficiently solving CPOMDPs with semantic observations in continuous dynamic state spaces. The approach is demonstrated and validated with a real human-robot team engaged in dynamic indoor target search and capture scenarios on a custom testbed.
The Li–Yau–Hamilton estimate and the Yang–Mills heat equation on manifolds with boundary

The paper pursues two connected goals. Firstly, we establish the Li-Yau-Hamilton estimate for the heat equation on a manifold M with nonempty boundary. Results of this kind are typically used to prove monotonicity formulas related to geometric flows. Secondly, we establish bounds for a solution ∇ (t) of the Yang-Mills heat equation in a vector bundle over M. The Li-Yau-Hamilton estimate is utilized in the proofs. Our results imply that the curvature of ∇ (t) does not blow up if the dimension of M is less than 4 or if the initial energy of ∇ (t) is sufficiently small.