cssi/SciSciGPT-SciSciNet huggingface dataset:
Heterogeneous Responses of Hematopoietic Stem Cells to Inflammatory Stimuli Are Altered with Age.

Long-term hematopoietic stem cells (LT-HSCs) maintain hematopoietic output throughout an animal’s lifespan. However, with age, the balance is disrupted, and LT-HSCs produce a myeloid-biased output, resulting in poor immune responses to infectious challenge and the development of myeloid leukemias. Here, we show that young and aged LT-HSCs respond differently to inflammatory stress, such that aged LT-HSCs produce a cell-intrinsic, myeloid-biased expression program. Using single-cell RNA sequencing (scRNA-seq), we identify a myeloid-biased subset within the LT-HSC population (mLT-HSCs) that is prevalent among aged LT-HSCs. We identify CD61 as a marker of mLT-HSCs and show that CD61-high LT-HSCs are uniquely primed to respond to acute inflammatory challenge. We predict that several transcription factors regulate the mLT-HSCs gene program and show that Klf5, Ikzf1, and Stat3 play an important role in age-related inflammatory myeloid bias. We have therefore identified and isolated an LT-HSC subset that regulates myeloid versus lymphoid balance under inflammatory challenge and with age.
Mutational mimics of allosteric effectors: a genome editing design to validate allosteric drug targets

Development of drugs that allosterically regulate enzyme functions to treat disease is a costly venture. Amino acid susbstitutions that mimic allosteric effectors in vitro will identify therapeutic regulatory targets enhancing the likelihood of developing a disease treatment at a reasonable cost. We demonstrate the potential of this approach utilizing human liver pyruvate kinase (hLPYK) as a model. Inhibition of hLPYK was the first desired outcome of this study. We identified individual point mutations that: 1) mimicked allosteric inhibition by alanine, 2) mimicked inhibition by protein phosphorylation, and 3) prevented binding of fructose-1,6-bisphosphate (Fru-1,6-BP). Our second desired outcome was activation of hLPYK. We identified individual point mutations that: 1) prevented hLPYK from binding alanine, the allosteric inhibitor, 2) prevented inhibitory protein phosphorylation, or 3) mimicked allosteric activation by Fru-1,6-BP. Combining the three activating point mutations produced a constitutively activated enzyme that was unresponsive to regulators. Expression of a mutant hLPYK transgene containing these three mutations in a mouse model was not lethal. Thus, mutational mimics of allosteric effectors will be useful to confirm whether allosteric activation of hLPYK will control glycolytic flux in the diabetic liver to reduce hepatic glucose production and, in turn, reduce or prevent hyperglycemia.
Synthesis and Evaluation of Trimetoquinol Derivatives: Novel Thromboxane A2/Prostaglandin H2 Antagonists with Diminished β-Adrenergic Agonist Activity

Trimetoquinol (TMQ, 1) is a unique catecholamine with a strong stereodependence for agonism at β-adrenergic (S ≫ R) and antagonism at thromboxane A2/prostaglandin H2 (TP; R ≫ S) receptors. Our laboratory has reported the effects of N-alkylation and modification of the trisubstituted benzyl group in these receptor systems. For iodinated derivative 5, maintaining potency in TP receptor systems (112%) was coupled with maintaining limited potency in β-adrenergic receptor systems (34% for β1 and 47% for β2). In this study, several diverse TMQ derivatives were prepared to probe for binding interactions specific to a particular receptor system. Planar amidine 2, which was designed to explore the importance of TMQ's chiral center, showed a dramatic loss of potency (<1%) in each receptor system. Likewise, the homologation of a previously described N-benzyl derivative (3) to the N-phenylethyl derivative 4 also showed reduced potency (<3%) in both receptor systems. However, modification of the trimethoxybenzyl group...
Cladogenetic and Anagenetic Models of Chromosome Number Evolution: A Bayesian Model Averaging Approach.

ABSSTRACT Chromosome number is a key feature of the higher-order organization of the genome, and changes in chromosome number play a fundamental role in evolution. Dysploid gains and losses in chromosome number, as well as polyploidization events, may drive reproductive isolation and lineage diversification. The recent development of probabilistic models of chromosome number evolution in the groundbreaking work by Mayrose et al. (2010, ChromEvol) have enabled the inference of ancestral chromosome numbers over molecular phylogenies and generated new interest in studying the role of chromosome changes in evolution. However, the ChromEvol approach assumes all changes occur anagenetically (along branches), and does not model events that are specifically cladogenetic. Cladogenetic changes may be expected if chromosome changes result in reproductive isolation. Here we present a new class of models of chromosome number evolution (called ChromoSSE) that incorporate both anagenetic and cladogenetic change. The ChromoSSE models allow us to determine the mode of chromosome number evolution; is chromosome evolution occurring primarily within lineages, primarily at lineage splitting, or in clade-specific combinations of both? Furthermore, we can estimate the location and timing of possible chromosome speciation events over the phylogeny. We implemented ChromoSSE in a Bayesian statistical framework, specifically in the software RevBayes, to accommodate uncertainty in parameter estimates while leveraging the full power of likelihood based methods. We tested ChromoSSE's accuracy with simulations and re-examined chromosomal evolution in Aristolochia, Carex section Spirostachyae, Helianthus, Mimulus sensu lato (s.l.), and Primula section Aleuritia, finding evidence for clade-specific combinations of anagenetic and cladogenetic dysploid and polyploid modes of chromosome evolution. [Anagenetic; Bayes factors; chromosome evolution; chromosome speciation; chromoSSE; cladogenetic; dysploidy; phylogenetic models; polyploidy; reversible-jump Markov chain Monte Carlo; whole genome duplication.].
Two-layer locally repairable codes for distributed storage systems

In this paper, we propose locally repairable codes (LRCs) with optimal minimum distance for distributed storage systems (DSS). A two-layer encoding structure is employed to ensure data reconstruction and the designated repair locality. The data is first encoded in the first layer by any existing maximum distance separable (MDS) codes, and then the encoded symbols are divided into non-overlapping groups and encoded by an MDS array code in the second layer. The encoding in the second layer provides enough redundancy for local repair, while the overall code performs recovery of the data based on redundancy from both layers. Our codes can be constructed over a finite field with size growing linearly with the total number of nodes in the DSS, and facilitate efficient degraded reads.
Assessing significance in a Markov chain without mixing.

We present a statistical test to detect that a presented state of a reversible Markov chain was not chosen from a stationary distribution. In particular, given a value function for the states of the Markov chain, we would like to show rigorously that the presented state is an outlier with respect to the values, by establishing a p value under the null hypothesis that it was chosen from a stationary distribution of the chain. A simple heuristic used in practice is to sample ranks of states from long random trajectories on the Markov chain and compare these with the rank of the presented state; if the presented state is a 0.1 % outlier compared with the sampled ranks (its rank is in the bottom 0.1 % of sampled ranks), then this observation should correspond to a p value of 0.001 . This significance is not rigorous, however, without good bounds on the mixing time of the Markov chain. Our test is the following: Given the presented state in the Markov chain, take a random walk from the presented state for any number of steps. We prove that observing that the presented state is an e -outlier on the walk is significant at p = 2 e under the null hypothesis that the state was chosen from a stationary distribution. We assume nothing about the Markov chain beyond reversibility and show that significance at p ≈ e is best possible in general. We illustrate the use of our test with a potential application to the rigorous detection of gerrymandering in Congressional districting.
Probabilistic Cross-identification of Multiple Catalogs in Crowded Fields

Matching astronomical catalogs in crowded regions of the sky is challenging both statistically and computationally due to the many possible alternative associations. Budav\'ari and Basu (2016) modeled the two-catalog situation as an Assignment Problem and used the famous Hungarian algorithm to solve it. Here we treat cross-identification of multiple catalogs by introducing a different approach based on integer linear programming. We first test this new method on problems with two catalogs and compare with the previous results. We then test the efficacy of the new approach on problems with three catalogs. The performance and scalability of the new approach is discussed in the context of large surveys.
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning

In this paper we aim to formally explain the phenomenon of fast convergence of SGD observed in modern machine learning. The key observation is that most modern learning architectures are over-parametrized and are trained to interpolate the data by driving the empirical loss (classification and regression) close to zero. While it is still unclear why these interpolated solutions perform well on test data, we show that these regimes allow for fast convergence of SGD, comparable in number of iterations to full gradient descent. For convex loss functions we obtain an exponential convergence bound for {\it mini-batch} SGD parallel to that for full gradient descent. We show that there is a critical batch size $m^*$ such that: (a) SGD iteration with mini-batch size $m\leq m^*$ is nearly equivalent to $m$ iterations of mini-batch size $1$ (\emph{linear scaling regime}). (b) SGD iteration with mini-batch $m> m^*$ is nearly equivalent to a full gradient descent iteration (\emph{saturation regime}). Moreover, for the quadratic loss, we derive explicit expressions for the optimal mini-batch and step size and explicitly characterize the two regimes above. The critical mini-batch size can be viewed as the limit for effective mini-batch parallelization. It is also nearly independent of the data size, implying $O(n)$ acceleration over GD per unit of computation. We give experimental evidence on real data which closely follows our theoretical analyses. Finally, we show how our results fit in the recent developments in training deep neural networks and discuss connections to adaptive rates for SGD and variance reduction.
Recombination and peak jumping.

We show that genetic recombination can be a powerful mechanism for escaping suboptimal peaks. Recent studies of empirical fitness landscapes reveal complex gene interactions and multiple peaks. However, classical work on recombination largely ignores the effect of complex gene interactions. Briefly, we restrict to fitness landscapes where the global peak is difficult to access. If the optimal genotype can be generated by shuffling genes present in the population, then recombination will produce the genotype. If, in addition, recombination is sufficiently rare, then the proportion of the genotype is expected to increase. Specifically, we consider landscapes where shuffling of suboptimal peak genotypes can produce the global peak genotype. The advantage of recombination we identify has no correspondence for 2-locus systems or for smooth landscapes. The effect of recombination indicated is sometimes extreme, also for rare recombination, in the sense that shutting off recombination could result in the organism failing to adapt. A standard question about recombination is whether the mechanism tends to accelerate or decelerate adaptation. However, we argue that extreme effects may be more important than how the majority falls. In a limited sense, our result can be considered a support for Sewall Wright’s view that adaptation sometimes works better in subdivided populations.
Adversarial Reprogramming of Neural Networks

Deep neural networks are susceptible to \emph{adversarial} attacks. In computer vision, well-crafted perturbations to images can cause neural networks to make mistakes such as confusing a cat with a computer. Previous adversarial attacks have been designed to degrade performance of models or cause machine learning models to produce specific outputs chosen ahead of time by the attacker. We introduce attacks that instead {\em reprogram} the target model to perform a task chosen by the attacker---without the attacker needing to specify or compute the desired output for each test-time input. This attack finds a single adversarial perturbation, that can be added to all test-time inputs to a machine learning model in order to cause the model to perform a task chosen by the adversary---even if the model was not trained to do this task. These perturbations can thus be considered a program for the new task. We demonstrate adversarial reprogramming on six ImageNet classification models, repurposing these models to perform a counting task, as well as classification tasks: classification of MNIST and CIFAR-10 examples presented as inputs to the ImageNet model.
Notes on supersymmetric and holomorphic field theories in dimensions 2 and 4

These notes explore some aspects of formal derived geometry related to classical field theory. One goal is to explain how many important classical field theories in physics -- such as supersymmetric gauge theories and supersymmetric sigma-models -- can be described very cleanly using derived geometry. In particular, I describe a mathematically natural construction of Kapustin-Witten's P^1 of twisted supersymmetric gauge theories.
Optimizing quality of experience of dynamic video streaming over fading wireless networks

We address the problem of video streaming packets from an Access Point (AP) to multiple clients over a shared wireless channel with fading. In such systems, each client maintains a buffer of packets from which to play the video, and an outage occurs in the streaming whenever the buffer is empty. Clients can switch to a lower-quality of video packet, or request packet transmission at a higher energy level in order to minimize the number of outages plus the number of outage periods and the number of low quality video packets streamed, while there is an average power constraint on the AP. We pose the problem of choosing the video quality and transmission power as a Constrained Markov Decision Process (CMDP). We show that the problem involving N clients decomposes into N MDPs, each involving only a single client, and furthermore that the optimal policy has a threshold structure, in which the decision to choose the video-quality and power-level of transmission depends solely on the buffer-level.
The Enumeration of Permutations Avoiding 3124 and 4312

We find the generating function for the class of all permutations that avoid the patterns 3124 and 4312 by showing that it is an inflation of the union of two geometric grid classes.
Performance of Johnson-Lindenstrauss transform for k-means and k-medians clustering

Consider an instance of Euclidean k-means or k-medians clustering. We show that the cost of the optimal solution is preserved up to a factor of (1+e) under a projection onto a random O(log(k /e) / e2)-dimensional subspace. Further, the cost of every clustering is preserved within (1+e). More generally, our result applies to any dimension reduction map satisfying a mild sub-Gaussian-tail condition. Our bound on the dimension is nearly optimal. Additionally, our result applies to Euclidean k-clustering with the distances raised to the p-th power for any constant p. For k-means, our result resolves an open problem posed by Cohen, Elder, Musco, Musco, and Persu (STOC 2015); for k-medians, it answers a question raised by Kannan.
Stability Enhanced Large-Margin Classifier Selection

Stability is an important aspect of a classification procedure because unstable predictions can potentially reduce users' trust in a classification system and also harm the reproducibility of scientific conclusions. The major goal of our work is to introduce a novel concept of classification instability, i.e., decision boundary instability (DBI), and incorporate it with the generalization error (GE) as a standard for selecting the most accurate and stable classifier. Specifically, we implement a two-stage algorithm: (i) initially select a subset of classifiers whose estimated GEs are not significantly different from the minimal estimated GE among all the candidate classifiers; (ii) the optimal classifier is chosen as the one achieving the minimal DBI among the subset selected in stage (i). This general selection principle applies to both linear and nonlinear classifiers. Large-margin classifiers are used as a prototypical example to illustrate the above idea. Our selection method is shown to be consistent in the sense that the optimal classifier simultaneously achieves the minimal GE and the minimal DBI. Various simulations and real examples further demonstrate the advantage of our method over several alternative approaches.
Topological Eulerian Synthesis of Slow Motion Periodic Videos

We consider the problem of taking a video that is comprised of multiple periods of repetitive motion, and reordering the frames of the video into a single period, producing a detailed, single cycle video of motion. This problem is challenging, as such videos often contain noise, drift due to camera motion and from cycle to cycle, and irrelevant background motion/occlusions, and these factors can confound the relevant periodic motion we seek in the video. To address these issues in a simple and efficient manner, we introduce a tracking free Eulerian approach for synthesizing a single cycle of motion. Our approach is geometric: we treat each frame as a point in high-dimensional Euclidean space, and analyze the sliding window embedding formed by this sequence of points, which yields samples along a topological loop regardless of the type of periodic motion. We combine tools from topological data analysis and spectral geometric analysis to estimate the phase of each window, and we exploit the sliding window structure to robustly reorder frames. We show quantitative results that highlight the robustness of our technique to camera shake, noise, and occlusions, and qualitative results of single-cycle motion synthesis across a variety of scenarios.
On separated solutions of logistic population equation with harvesting

Abstract We provide a surprising answer to a question raised in S. Ahmad and A.C. Lazer [2] , and extend the results of that paper.
Solid-immersion metalenses for infrared focal plane arrays

Optical components based on metasurfaces (metalenses) offer an alternative methodology for microlens arrays. In particular, metalens arrays have the potential of being monolithically integrated with infrared focal plane arrays (IR FPAs) to increase the operating temperature and sensitivity of the latter. In this work, we demonstrate a type of transmissive metalens that focuses the incident light (λ = 3–5 μm) on the detector plane after propagating through the substrate, i.e., solid-immersion type of focusing. The metalens is fabricated by etching the backside of the detector substrate material (GaSb here), making this approach compatible with the architecture of back-illuminated FPAs. In addition, our designs work for all incident polarizations. We fabricate a 10 × 10 metalens array that proves the scalability of this approach for FPAs. In the future, these solid-immersion metalenses arrays will be monolithically integrated with IR FPAs.
Protest Activity Detection and Perceived Violence Estimation from Social Media Images

We develop a novel visual model which can recognize protesters, describe their activities by visual attributes and estimate the level of perceived violence in an image. Studies of social media and protests use natural language processing to track how individuals use hashtags and links, often with a focus on those items' diffusion. These approaches, however, may not be effective in fully characterizing actual real-world protests (e.g., violent or peaceful) or estimating the demographics of participants (e.g., age, gender, and race) and their emotions. Our system characterizes protests along these dimensions. We have collected geotagged tweets and their images from 2013-2017 and analyzed multiple major protest events in that period. A multi-task convolutional neural network is employed in order to automatically classify the presence of protesters in an image and predict its visual attributes, perceived violence and exhibited emotions. We also release the UCLA Protest Image Dataset, our novel dataset of 40,764 images (11,659 protest images and hard negatives) with various annotations of visual attributes and sentiments. Using this dataset, we train our model and demonstrate its effectiveness. We also present experimental results from various analysis on geotagged image data in several prevalent protest events. Our dataset will be made accessible at https://www.sscnet.ucla.edu/comm/jjoo/mm-protest/.
Global weak solution for a coupled compressible Navier-Stokes and $Q$-tensor system

In this paper, we study a coupled compressible Navier-Stokes/Q-tensor system modeling the nematic liquid crystal flow in a three-dimensional bounded spatial domain. The existence and long time dynamics of globally defined weak solutions for the coupled system are established, using weak convergence methods, compactness and interpolation arguments. The symmetry and traceless properties of the Q-tensor play key roles in this process.
The smallest part of the generic partition of the nilpotent commutator of a nilpotent matrix

Abstract Let k be an infinite field. Fix a Jordan nilpotent n × n Jordan matrix B with entries in k and associated Jordan type P. Let Q ( P ) be the Jordan type of a generic nilpotent matrix commuting with B. In this paper, we use the combinatorics of a poset associated to the partition P, to give an explicit formula for the smallest part of Q ( P ) , which is independent of the characteristic of k . This, in particular, leads to a complete description of Q ( P ) when it has at most three parts.
Diagrams and Essential Sets for Signed Permutations

We introduce diagrams and essential sets for signed permutations, extending the analogous notions for ordinary permutations. In particular, we show that the essential set provides a minimal list of rank conditions defining the Schubert variety or degeneracy locus corresponding to a signed permutation. Our essential set is in bijection with the poset-theoretic version defined by Reiner, Woo, and Yong, and thus gives an explicit, diagrammatic method for computing the latter.
Deep Learning-Based Video System for Accurate and Real-Time Parking Measurement

Parking spaces are costly to build, parking payments are difficult to enforce, and drivers waste an excessive amount of time searching for empty lots. Accurate quantification would inform developers and municipalities in space allocation and design, while real-time measurements would provide drivers and parking enforcement with information that saves time and resources. In this paper, we propose an accurate and real-time video system for future Internet of Things (IoT) and smart cities applications. Using recent developments in deep convolutional neural networks (DCNNs) and a novel vehicle tracking filter, we combine information across multiple image frames in a video sequence to remove noise introduced by occlusions and detection failures. We demonstrate that our system achieves higher accuracy than pure image-based instance segmentation, and is comparable in performance to industry benchmark systems that utilize more expensive sensors, such as radar. Furthermore, our system shows significant potential in its scalability to a city-wide scale and also in the richness of its output that goes beyond traditional binary occupancy statistics.
Isolation, equidistribution, and orbit closures for the SL(2;R) action on moduli space

We prove results about orbit closures and equidistribution for the SL(2;R) action on the moduli space of compact Riemann surfaces, which are analogous to the theory of unipotent ows. The proofs of the main theorems rely on the measure classication theorem of the rst two authors and a certain isolation property of closed SL(2;R) invariant manifolds developed in this paper.
Arithmetic Euler top

Abstract The theory of differential equations has an arithmetic analogue in which derivatives of functions are replaced by Fermat quotients of numbers. Many classical differential equations (Riccati, Weierstrass, Painleve, etc.) were previously shown to possess arithmetic analogues. The paper introduces and studies an arithmetic analogue of the Euler differential equations for the rigid body.
VLBA Survey of OH Masers in Star-Forming Regions II: Satellite Lines

Using the Very Long Baseline Array (VLBA) we performed a high resolution OH maser survey in Galactic star-forming regions (SFRs). We observed all the ground state spectral lines: the main lines at 1665 and 1667 MHz and the satellite lines at 1612 and 1720 MHz. Due to the exceptionality of finding satellite lines in SFRs, we will focus our discussion on those lines. In our sample of 41 OH maser sources, five (12%) showed the 1612 MHz line and ten (24%) showed the 1720 MHz line, with only one source showing both lines. We find that 1720 MHz emission is correlated with the presence of HII regions, suggesting that this emission could be used to diagnose or trace high-mass star formation. We include an analysis of the possible mechanisms that could be causing this correlation as well as assessing the possible relationships between lines in our sample. In particular, the presence of magnetic fields seems to play an important role, as we found Zeeman splitting in four of our sources (W75 N, W3(OH), W51 and NGC 7538). Our results have implications for current understanding of the formation of high-mass stars as well as on the masing processes present in SFRs.
On the limits of sequential testing in high dimensions

This paper presents results pertaining to sequential methods for support recovery of sparse signals in noise. Specifically, we show that any sequential measurement procedure fails provided the average number of measurements per dimension grows slower then D(f 0 ∥f 1 )−1 log s where s is the level of sparsity, and D(f 0 ∥f 1 ) the Kullback-Leibler divergence between the underlying distributions. For comparison, we show any non-sequential procedure fails provided the number of measurements grows at a rate less than D(f 1 ∥f 0 −1 log n, where n is the total dimension of the problem. Lastly, we show that a simple procedure termed sequential thresholding guarantees exact support recovery provided the average number of measurements per dimension grows faster than D(f 0 ∥f 1 −1(log s+loglog n), a mere additive factor more than the lower bound.
A bug's eye view: the Riemannian exponential map on polyhedral surfaces

We explore the perspective of a bug living on the two-dimensional surface of a polyhedron. Images of various kinds of effects like lensing and cloaking are shown via color pictures of three viewpoints: the first person perspective of the bug, a map of the bug's viewpoint, and a look at the bug on the embedded polyhedron from a three-dimensional exterior viewer. The pictures were constructed by computing the exponential map of a polyhedron by cutting and rotating faces into the tangent plane of the bug.
Dropping Convexity for Faster Semi-definite Optimization

We study the minimization of a convex function $f(X)$ over the set of $n\times n$ positive semi-definite matrices, but when the problem is recast as $\min_U g(U) := f(UU^\top)$, with $U \in \mathbb{R}^{n \times r}$ and $r \leq n$. We study the performance of gradient descent on $g$---which we refer to as Factored Gradient Descent (FGD)---under standard assumptions on the original function $f$. We provide a rule for selecting the step size and, with this choice, show that the local convergence rate of FGD mirrors that of standard gradient descent on the original $f$: i.e., after $k$ steps, the error is $O(1/k)$ for smooth $f$, and exponentially small in $k$ when $f$ is (restricted) strongly convex. In addition, we provide a procedure to initialize FGD for (restricted) strongly convex objectives and when one only has access to $f$ via a first-order oracle; for several problem instances, such proper initialization leads to global convergence guarantees. FGD and similar procedures are widely used in practice for problems that can be posed as matrix factorization. To the best of our knowledge, this is the first paper to provide precise convergence rate guarantees for general convex functions under standard convex assumptions.
On the Feasibility of Sharing Spectrum Licenses in mmWave Cellular Systems

The highly directional and adaptive antennas used in mmWave communication open up the possibility of uncoordinated sharing of spectrum licenses between commercial cellular operators. There are several advantages to sharing including a reduction in license costs and an increase in spectrum utilization. In this paper, we establish the theoretical feasibility of spectrum license sharing among mmWave cellular operators. We consider a heterogeneous multi-operator system containing multiple independent cellular networks, each owned by an operator. We then compute the signal-to-interference-and-noise ratio and rate distribution for downlink mobile users of each network. Using the analysis, we compare the systems with fully shared licenses and exclusive licenses for different access rules and explore the trade-offs between system performance and spectrum cost. We show that sharing spectrum licenses increases the per-user rate when antennas have narrow beams and is also favored when there is a low density of users. We also consider a multi-operator system where BSs of all the networks are co-located to show that the simultaneous sharing of spectrum and infrastructure is also feasible. We show that all networks can share licenses with less bandwidth and still achieve the same per-user median rate as if they each had an exclusive license to spectrum with more bandwidth.
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits.

Regret bounds in online learning compare the player's performance to $L^*$, the optimal performance in hindsight with a fixed strategy. Typically such bounds scale with the square root of the time horizon $T$. The more refined concept of first-order regret bound replaces this with a scaling $\sqrt{L^*}$, which may be much smaller than $\sqrt{T}$. It is well known that minor variants of standard algorithms satisfy first-order regret bounds in the full information and multi-armed bandit settings. In a COLT 2017 open problem, Agarwal, Krishnamurthy, Langford, Luo, and Schapire raised the issue that existing techniques do not seem sufficient to obtain first-order regret bounds for the contextual bandit problem. In the present paper, we resolve this open problem by presenting a new strategy based on augmenting the policy space.
An Efficient Approach to Encoding Context for Spoken Language Understanding

In task-oriented dialogue systems, spoken language understanding, or SLU, refers to the task of parsing natural language user utterances into semantic frames. Making use of context from prior dialogue history holds the key to more effective SLU. State of the art approaches to SLU use memory networks to encode context by processing multiple utterances from the dialogue at each turn, resulting in significant trade-offs between accuracy and computational efficiency. On the other hand, downstream components like the dialogue state tracker (DST) already keep track of the dialogue state, which can serve as a summary of the dialogue history. In this work, we propose an efficient approach to encoding context from prior utterances for SLU. More specifically, our architecture includes a separate recurrent neural network (RNN) based encoding module that accumulates dialogue context to guide the frame parsing sub-tasks and can be shared between SLU and DST. In our experiments, we demonstrate the effectiveness of our approach on dialogues from two domains.
On distinct consecutive r-differences

Abstract Suppose A ⊂ R of size k has distinct consecutive r-differences, that is for 1 ≤ i ≤ k − r , the r-tuples ( a i + 1 − a i , … , a i + r − a i + r − 1 ) are distinct. Then for any finite B ⊂ R , one has | A + B | ≫ r | A | | B | 1 / ( r + 1 ) . Utilizing de Bruijn sequences, we show this inequality is sharp up to the constant. Moreover, for the sequence { n α } , a sharp upper bound for the size of the distinct consecutive r-differences is obtained, which generalizes Steinhaus' three gap theorem. A dual problem on the consecutive r-differences of the returning times for some ϕ ∈ R defined by { T : { T θ } ϕ } is also considered, which generalizes a result of Slater.
Determining hyperbolic 3-manifolds by their surfaces

In this article, we prove that the commensurability class of a closed, orientable, hyperbolic 3-manifold is determined by the surface subgroups of its fundamental group. Moreover, we prove that there can be only finitely many closed, orientable, hyperbolic 3-manifolds that have the same set of surfaces.
LIFTING TORSION GALOIS REPRESENTATIONS

Typos in the abstract have been corrected. Let $\rho_n$ be an ordinary weight two representation of absolute Galois group of the rationals to $GL_2(\mathcal O/\pi^n)$. Here $\mathcal O$ is a ramified DVR with uniformiser $\pi$. If $\rho_n$ satisfies mild hypotheses we lift it to a characteristic zero $\mathcal O$-valued geometric weight two representation. The earlier methods could handle only the unramified case. We show that the deformation ring of a residual representation arising from a newform can be arranged to be a prescribed DVR provided we choose a suitable auxiliary level. We extend earlier proofs of modularity of $p$-adic lifts of modular residual representations, via $p$-adic approximations, to cover cases when the lift is defined over ramified DVR's $\mathcal O$.
The q-AGT–W Relations Via Shuffle Algebras

We construct the action of the q-deformed W-algebra on its level r representation geometrically, using the moduli space of U(r) instantons on the plane and the double shuffle algebra. We give an explicit LDU decomposition for the action of W-algebra currents in the fixed point basis of the level r representation, and prove a relation between the Carlsson–Okounkov Ext operator and intertwiners for the deformed W-algebra. We interpret this result as a q-deformed version of the AGT–W relations.
On second order elliptic and parabolic equations of mixed type

Abstract It is known that solutions to second order uniformly elliptic and parabolic equations, either in divergence or nondivergence (general) form, are Holder continuous and satisfy the interior Harnack inequality. We show that even in the one-dimensional case ( x ∈ R 1 ), these properties are not preserved for equations of mixed divergence–nondivergence structure: for elliptic equations. D i ( a i j 1 D j u ) + a i j 2 D i j u = 0 , and parabolic equations p ∂ t u = D i ( a i j D j u ) , where p = p ( t , x ) is a bounded strictly positive function. The Holder continuity and Harnack inequality are known if p does not depend either on t or on x. We essentially use homogenization techniques in our construction. Bibliography: 22 titles.
Data-driven planning via imitation learning:

Robot planning is the process of selecting a sequence of actions that optimize for a task=specific objective. For instance, the objective for a navigation task would be to find collision-free paths...
An interacting replica approach applied to the traveling salesman problem

We present a physics inspired heuristic method for solving combinatorial optimization problems. Our approach is specifically motivated by the desire to avoid trapping in metastable local minima-a common occurrence in hard problems with multiple extrema. Our method involves (i) coupling otherwise independent simulations of a system (“replicas”) via geometrical distances as well as (ii) probabilistic inference applied to the solutions found by individual replicas. The ensemble of replicas evolves as to maximize the inter-replica correlation while simultaneously minimize the local intra-replica cost function (e.g., the total path length in the Traveling Salesman Problem within each replica). We demonstrate how our method improves the performance of rudimentary local optimization schemes long applied to the NP hard Traveling Salesman Problem. In particular, we apply our method to the well-known “k-opt” algorithm and examine two particular cases-k = 2 and k = 3. With the aid of geometrical coupling alone, we are able to determine for the optimum tour length on systems up to 280 cities (an order of magnitude larger than the largest systems typically solved by the bare k = 3 opt). The probabilistic replica-based inference approach improves k — opt even further and determines the optimal solution of a problem with 318 cities and find tours whose total length is close to that of the optimal solutions for other systems with a larger number of cities.
SpotTune: Transfer Learning Through Adaptive Fine-Tuning

Transfer learning, which allows a source task to affect the inductive bias of the target task, is widely used in computer vision. The typical way of conducting transfer learning with deep neural networks is to fine-tune a model pretrained on the source task using data from the target task. In this paper, we propose an adaptive fine-tuning approach, called SpotTune, which finds the optimal fine-tuning strategy per instance for the target data. In SpotTune, given an image from the target task, a policy network is used to make routing decisions on whether to pass the image through the fine-tuned layers or the pre-trained layers. We conduct extensive experiments to demonstrate the effectiveness of the proposed approach. Our method outperforms the traditional fine-tuning approach on 12 out of 14 standard datasets. We also compare SpotTune with other state-of-the-art fine-tuning strategies, showing superior performance. On the Visual Decathlon datasets, our method achieves the highest score across the board without bells and whistles.
On bilinear biquandles

We define a type of biquandle which is a generalization of symplectic quandles. We use the extra structure of these bilinear biquandles to define new knot and link invariants and give some examples.
Linearized GMM Kernels and Normalized Random Fourier Features

The method of "random Fourier features (RFF)" has become a popular tool for approximating the "radial basis function (RBF)" kernel. The variance of RFF is actually large. Interestingly, the variance can be substantially reduced by a simple normalization step as we theoretically demonstrate. We name the improved scheme as the "normalized RFF (NRFF)", and we provide a technical proof of the asymptotic variance of NRFF, as validated by simulations. We also propose the "generalized min-max (GMM)" kernel as a measure of data similarity, where data vectors can have both positive and negative entries. GMM is positive definite as there is an associate hashing method named "generalized consistent weighted sampling (GCWS)" which linearizes this (nonlinear) kernel. We provide an extensive empirical evaluation of the RBF and GMM kernels on more than 50 datasets. For a majority of the datasets, the (tuning-free) GMM kernel outperforms the best-tuned RBF kernel. We then conduct extensive classification experiments for comparing the linearized RBF kernel using NRFF with the linearized GMM kernel using GCWS. We observe that, in order to reach a similar accuracy, GCWS typically requires substantially fewer samples than NRFF, even on datasets where the original RBF kernel outperforms the original GMM kernel. As the training, storage, and processing costs are directly proportional to the sample size, our experiments can help demonstrate that GCWS would be a more practical scheme for large-scale machine learning applications. The empirical success of GCWS (compared to NRFF) can also be explained theoretically, from at least two aspects. Firstly, the relative variance (normalized by the squared expectation) of GCWS is substantially smaller than that of NRFF, except for the very high similarity region (where the variances of both methods approach zero). Secondly, if we are allowed to make a general model assumption on the data, then we can show analytically that GCWS exhibits much smaller variance than NRFF for estimating the same object (e.g., the RBF kernel), except for the very high similarity region.
Transferable Pedestrian Motion Prediction Models at Intersections

One desirable capability of autonomous cars is to accurately predict the pedestrian motion near intersections for safe and efficient trajectory planning. We are interested in developing transfer learning algorithms that can be trained on the pedestrian trajectories collected at one intersection and yet still provide accurate predictions of the trajectories at another, previously unseen intersection. We first discussed the feature selection for transferable pedestrian motion models in general. Following this discussion, we developed one transferable pedestrian motion prediction algorithm based on Inverse Reinforcement Learning (IRL) that infers pedestrian intentions and predicts future trajectories based on observed trajectory. We evaluated our algorithm at three intersections. We used the accuracy of augmented semi-nonnegative sparse coding (ASNSC), trained and tested at the same intersection as a baseline. The result shows that the proposed algorithm improves the baseline accuracy by a statistically significant percentage in both non-transfer task and transfer task.
On the structure of gradient Yamabe solitons

We show that every complete nontrivial gradient Yamabe soliton admits a special global warped product structure with a one-dimensional base. Based on this, we prove a general classification theorem for complete nontrivial locally conformally flat gradient Yamabe solitons.
Stereo magnification: learning view synthesis using multiplane images

The view synthesis problem---generating novel views of a scene from known imagery---has garnered recent attention due in part to compelling applications in virtual and augmented reality. In this paper, we explore an intriguing scenario for view synthesis: extrapolating views from imagery captured by narrow-baseline stereo cameras, including VR cameras and now-widespread dual-lens camera phones. We call this problem stereo magnification, and propose a learning framework that leverages a new layered representation that we call multiplane images (MPIs). Our method also uses a massive new data source for learning view extrapolation: online videos on YouTube. Using data mined from such videos, we train a deep network that predicts an MPI from an input stereo image pair. This inferred MPI can then be used to synthesize a range of novel views of the scene, including views that extrapolate significantly beyond the input baseline. We show that our method compares favorably with several recent view synthesis methods, and demonstrate applications in magnifying narrow-baseline stereo images.
Limited Imitation Contagion on Random Networks: Chaos, Universality, and Unpredictability

We study a family of binary state, socially inspired contagion models which incorporate imitation limited by an aversion to complete conformity. We uncover rich behavior in our models whether operating with either probabilistic or deterministic individual response functions on both dynamic and fixed random networks. In particular, we find significant variation in the limiting behavior of a population's infected fraction, ranging from steady state to chaotic. We show that period doubling arises as we increase the average node degree, and that the universality class of this well-known route to chaos depends on the interaction structure of random networks rather than the microscopic behavior of individual nodes. We find that increasing the fixedness of the system tends to stabilize the infected fraction, yet disjoint, multiple equilibria are possible depending solely on the choice of the initially infected node.
Multi-Version Coding—An Information-Theoretic Perspective of Consistent Distributed Storage

In applications of distributed storage systems to distributed computing and implementation of key-value stores, the following property, usually referred to as consistency in distributed computing, is an important requirement: as the data stored changes, the latest version of the data must be accessible to a client that connects to the storage system. Motivated by technological trends where key-value stores are increasingly implemented in high-speed memory, an information theoretic formulation called multi-version coding is introduced in this paper in order to understand and minimize the memory overhead of consistent distributed storage. Multi-version coding is characterized by $\nu$ totally ordered versions of a message and a storage system with $n$ servers. At each server, values corresponding to an arbitrary subset of the $\nu$ versions are received and encoded. For any subset of $c$ servers in the storage system, the value corresponding to the latest common version or a later version, as per the total ordering, among the $c$ servers is required to be decodable. An achievable multi-version code construction via linear coding and a converse result that shows that the construction is asymptotically tight when $\nu |(c-1)$ are provided. An implication of the converse is that there is an inevitable price, in terms of storage cost, to ensure consistency in distributed storage systems.
Seeing Small Faces from Robust Anchor's Perspective

This paper introduces a novel anchor design principle to support anchor-based face detection for superior scaleinvariant performance, especially on tiny faces. To achieve this, we explicitly address the problem that anchor-based detectors drop performance drastically on faces with tiny sizes, e.g. less than 16 A— 16 pixels. In this paper, we investigate why this is the case. We discover that current anchor design cannot guarantee high overlaps between tiny faces and anchor boxes, which increases the difficulty of training. The new Expected Max Overlapping (EMO) score is proposed which can theoretically explain the low overlapping issue and inspire several effective strategies of new anchor design leading to higher face overlaps, including anchor stride reduction with new network architectures, extra shifted anchors, and stochastic face shifting. Comprehensive experiments show that our proposed method significantly outperforms the baseline anchor-based detector, while consistently achieving state-of-the-art results on challenging face detection datasets with competitive runtime speed.
Heisenberg Groups as Platform for the AAG Key-Exchange Protocol

When the AAG protocol was first introduced, braid groups were proposed as platform group. However, there are studies that successful attack AAG with braid groups, one main attack method is the length-based attack. Searching for a new platform for AAG, Garber, Kahrobaei, and Lam studied polycyclic groups generated by number field and concluded that they are resistant against the length-based attack. Inspired by this result, we ask whether other type of polycyclic groups can be used as platform for AAG. In this paper, we discuss the use of Heisenberg groups, a type of polycyclic group, as a platform group for AAG by submitting them to one of AAG's major attacks, the length-based attack.
On left-orderable fundamental groups and Dehn surgeries on knots

We show that the resulting manifold by $r$-surgery on a large class of two-bridge knots has left-orderable fundamental group if the slope $r$ satisfies certain conditions. This result gives a supporting evidence to a conjecture of Boyer, Gordon and Watson that relates $L$-spaces and the left-orderability of their fundamental groups.