cssi/SciSciGPT-SciSciNet huggingface dataset:
Making state civil procedure

State courts matter. Not only do state courts handle more than sixty times the number of civil cases as federal courts, but they also represent an important bulwark against the effects of federal procedural retrenchment. Yet state courts and state procedure are notably absent from the scholarly discourse. In order to evaluate state procedure—and in order to understand the states’ relationship to federal procedural retrenchment—this Article presents the first comprehensive study of who makes state civil procedure. This project begins with a systematic review of the formal processes by which states make their rules of procedure. Many of the relevant sources were not publicly accessible, so this project not only collects important data but in so doing also makes state procedure more accessible. Formal rulemaking authority is only part of the story. At the federal level, scholars have focused on the Advisory Committee on Civil Rules: an elite committee of mostly judges and practitioners, selected by the Chief Justice, that plays a primary role in proposing amendments to the Federal Rules of Civil Procedure. Critics have argued that the advisory commit- tee favors corporate interests, and they have attributed these effects to committee membership. Since the 1960s, there has been a dramatic decline in the share of practitioners on the committee and, simultaneously, an increased homogeneity among its members—i.e., Republican judges and corporate defense attorneys. State advisory committees have gone virtually unstudied. Indeed, in many states, advisory committee membership is not readily accessible. I collected membership information for every state advisory committee, and this Article compares these little-studied state committees to the well-known federal committee. In brief, state committees are notably more diverse. They have far greater representation of practitioners than the federal committee, and those practitioners are more evenly divided between plaintiff- and defense-side lawyers and between individual and corporate lawyers. Partisan effects are less severe among state judge members than at the federal level. State committees have much greater female representation than the federal advisory committee, and at least equal representation of racial and ethnic minorities. But at the same time, many state committees are less accessible to the public than the federal committee is. This Article then makes at least three contributions. First, although these data do not support causal inference, they permit normative engagement with the design of rulemaking institutions. This analysis connects with interdisciplinary re- search on decision-making that suggests that epistemic diversity can produce better and more durable outputs. Second, I argue that civil rulemaking can unite accessibility and diversity. States can be more accessible, and federal rulemaking can be more diverse. Finally, as state procedure becomes more important, this Article helps ensure that relevant information is not limited to those with privileged access and the resources to use it.
Co-option of neurotransmitter signaling for inter-organismal communication in C. elegans

Biogenic amine neurotransmitters play a central role in metazoan biology, and both their chemical structures and cognate receptors are evolutionarily conserved. Their primary roles are in cell-to-cell signaling, as biogenic amines are not normally recruited for communication between separate individuals. Here, we show that in the nematode C. elegans, a neurotransmitter-sensing G protein-coupled receptor, TYRA-2, is required for avoidance responses to osas#9, an ascaroside pheromone that incorporates the neurotransmitter, octopamine. Neuronal ablation, cell-specific genetic rescue, and calcium imaging show that tyra-2 expression in the nociceptive neuron, ASH, is necessary and sufficient to induce osas#9 avoidance. Ectopic expression in the AWA neuron, which is generally associated with attractive responses, reverses the response to osas#9, resulting in attraction instead of avoidance behavior, confirming that TYRA-2 partakes in the sensing of osas#9. The TYRA-2/osas#9 signaling system represents an inter-organismal communication channel that evolved via co-option of a neurotransmitter and its cognate receptor.
A Super-Grover Separation Between Randomized and Quantum Query Complexities

We construct a total Boolean function f satisfying R(f) = Ω(Q(f)), refuting the long-standing conjecture that R(f) = O(Q(f)) for all total Boolean functions. Assuming a conjecture of Aaronson and Ambainis about optimal quantum speedups for partial functions, we improve this to R(f) = Ω(Q(f)). Our construction is motivated by the Goos-Pitassi-Watson function but does not use it.
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks

Many recent papers address reading comprehension, where examples consist of (question, passage, answer) tuples. Presumably, a model must combine information from both questions and passages to predict corresponding answers. However, despite intense interest in the topic, with hundreds of published papers vying for leaderboard dominance, basic questions about the difficulty of many popular benchmarks remain unanswered. In this paper, we establish sensible baselines for the bAbI, SQuAD, CBT, CNN, and Who-did-What datasets, finding that question- and passage-only models often perform surprisingly well. On 14 out of 20 bAbI tasks, passage-only models achieve greater than 50% accuracy, sometimes matching the full model. Interestingly, while CBT provides 20-sentence passages, only the last is needed for accurate prediction. By comparison, SQuAD and CNN appear better-constructed.
Low frame coherence via zero-mean tensor embeddings

This paper is concerned with achieving optimal coherence for highly redundant real unit-norm frames. As the redundancy grows, the number of vectors in the frame becomes too large to admit equiangular arrangements. In this case, other geometric optimality criteria need to be identified. To this end, we use an iteration of the embedding technique by Conway, Hardin and Sloane. As a consequence of their work, a quadratic mapping embeds equiangular lines into a simplex in a real Euclidean space. Here, higher degree polynomial maps embed highly redundant unit-norm frames to simplices in high-dimensional Euclidean spaces. We focus on the lowest degree case in which the embedding is quartic.
EMPIOT: An energy measurement platform for wireless IoT devices

Abstract Profiling and minimizing the energy consumption of resource-constrained devices is an essential step towards employing IoT in various application domains. Due to the large size and high cost of commercial energy measurement platforms, alternative solutions have been proposed by the research community. However, the three main shortcomings of existing tools are complexity, limited measurement range, and low accuracy. Specifically, these tools are not suitable for the energy measurement of new IoT devices such as those supporting the 802.11 technology. In this paper we propose EMPIOT, an accurate, low-cost, easy to build, and flexible, power measurement platform. We present the hardware and software components of this platform and study the effect of various design parameters on accuracy and overhead. In particular, we analyze the effects of driver, bus speed, input voltage, and buffering mechanism on sampling rate, measurement accuracy and processing demand. These extensive experimental studies enable us to configure the system in order to achieve its highest performance. We also propose a novel calibration technique and report the calibration parameters under various settings. Using five different IoT devices performing four types of workloads, we evaluate the performance of EMPIOT against the ground truth obtained from a high-accuracy industrial-grade power measurement tool. Our results show that, for very low-power devices that utilize 802.15.4 wireless standard, the measurement error is less than 3.5%. In addition, for 802.11-based devices that generate short and high power spikes, the error is less than 2.5%.
Syzygy gap fractals—I. Some structural results and an upper bound

k is a field of characteristic p>0, and l1,…,ln are linear forms in k[x,y]. Intending applications to Hilbert–Kunz theory, to each triple C=(F,G,H) of nonzero homogeneous elements of k[x,y] we associate a function δC that encodes the “syzygy gaps” of Fq, Gq, and Hql1a1⋯lnan, for all q=pe and ai⩽q. These are close relatives of functions introduced in [P. Monsky, P. Teixeira, p-Fractals and power series—I. Some 2 variable results, J. Algebra 280 (2004) 505–536]. Like their relatives, the δC exhibit surprising self-similarity related to “magnification by p,” and knowledge of their structure allows the explicit computation of various Hilbert–Kunz functions. We show that these “syzygy gap fractals” are determined by their zeros and have a simple behavior near their local maxima, and derive an upper bound for their local maxima which has long been conjectured by Monsky. Our results will allow us, in a sequel to this paper, to determine the structure of the δC by studying the vanishing of certain determinants.
Limit Theorems for Monotonic Convolution and the Chernoff Product Formula

Bercovici and Pata showed that the correspondence between classically, freely, and Boolean infinitely divisible distributions holds on the level of limit theorems. We extend this correspondence also to distributions infinitely divisible with respect to the additive monotone convolution. Because of non-commutativity of this convolution, we use a new technique based on the Chernoff product formula. In fact, the correspondence between the Boolean and monotone limit theorems extends from probability measures to positive measures of total weight at most one. Finally, we study this correspondence for multiplicative monotone convolution, where the Bercovici-Pata bijection no longer holds.
A principal ideal theorem for compact sets of rank one valuation rings

Abstract Let F be a field, and let Zar ( F ) be the space of valuation rings of F with respect to the Zariski topology. We prove that if X is a quasicompact set of rank one valuation rings in Zar ( F ) whose maximal ideals do not intersect to 0, then the intersection of the rings in X is an integral domain with quotient field F such that every finitely generated ideal is a principal ideal. To prove this result, we develop a duality between (a) quasicompact sets of rank one valuation rings whose maximal ideals do not intersect to 0, and (b) one-dimensional Prufer domains with nonzero Jacobson radical and quotient field F. The necessary restriction in all these cases to collections of valuation rings whose maximal ideals do not intersect to 0 is motivated by settings in which the valuation rings considered all dominate a given local ring.
Phase retrieval versus phaseless reconstruction

Abstract In 2006, Balan, Casazza, and Edidin [1] introduced the frame theoretic study of phaseless reconstruction. Since then, this has turned into a very active area of research. Over the years, many people have replaced the term phaseless reconstruction with phase retrieval. At a meeting in 2012, Casazza asked: Are these really the same? In this paper, we will show that phase retrieval is equivalent to phaseless reconstruction. We then show, more generally, that phase retrieval by projections is equivalent to phaseless reconstruction by projections in the real case.
Contextual Bandits with Latent Confounders: An NMF Approach

Motivated by online recommendation and advertising systems, we consider a causal model for stochastic contextual bandits with a latent low-dimensional confounder. In our model, there are $L$ observed contexts and $K$ arms of the bandit. The observed context influences the reward obtained through a latent confounder variable with cardinality $m$ ($m \ll L,K$). The arm choice and the latent confounder causally determines the reward while the observed context is correlated with the confounder. Under this model, the $L \times K$ mean reward matrix $\mathbf{U}$ (for each context in $[L]$ and each arm in $[K]$) factorizes into non-negative factors $\mathbf{A}$ ($L \times m$) and $\mathbf{W}$ ($m \times K$). This insight enables us to propose an $\epsilon$-greedy NMF-Bandit algorithm that designs a sequence of interventions (selecting specific arms), that achieves a balance between learning this low-dimensional structure and selecting the best arm to minimize regret. Our algorithm achieves a regret of $\mathcal{O}\left(L\mathrm{poly}(m, \log K) \log T \right)$ at time $T$, as compared to $\mathcal{O}(LK\log T)$ for conventional contextual bandits, assuming a constant gap between the best arm and the rest for each context. These guarantees are obtained under mild sufficiency conditions on the factors that are weaker versions of the well-known Statistical RIP condition. We further propose a class of generative models that satisfy our sufficient conditions, and derive a lower bound of $\mathcal{O}\left(Km\log T\right)$. These are the first regret guarantees for online matrix completion with bandit feedback, when the rank is greater than one. We further compare the performance of our algorithm with the state of the art, on synthetic and real world data-sets.
Correlated In Situ Low‐Frequency Noise and Impedance Spectroscopy Reveal Recombination Dynamics in Organic Solar Cells Using Fullerene and Non‐Fullerene Acceptors

Non-fullerene acceptors based on perylenediimides (PDIs) have garnered significant interest as an alternative to fullerene acceptors in organic photovoltaics (OPVs), but their charge transport phenomena are not well understood, especially in bulk heterojunctions (BHJs). Here, charge transport and current fluctuations are investigated by performing correlated low-frequency noise and impedance spectroscopy measurements on two BHJ OPV systems, one employing a fullerene acceptor and the other employing a dimeric PDI acceptor. In the dark, these measurements reveal that PDI-based OPVs have a greater degree of recombination in comparison to fullerene-based OPVs. Furthermore, for the first time in organic solar cells, 1/f noise data are fit to the Kleinpenning model to reveal underlying current fluctuations in different transport regimes. Under illumination, 1/f noise increases by approximately four orders of magnitude for the fullerene-based OPVs and three orders of magnitude for the PDI-based OPVs. An inverse correlation is also observed between noise spectral density and power conversion efficiency. Overall, these results show that low-frequency noise spectroscopy is an effective in situ diagnostic tool to assess charge transport in emerging photovoltaic materials, thereby providing quantitative guidance for the design of next-generation solar cell materials and technologies.
Counter-strategy guided refinement of GR(1) temporal logic specifications

The reactive synthesis problem is to find a finite-state controller that satisfies a given temporal-logic specification regardless of how its environment behaves. Developing a formal specification is a challenging and tedious task and initial specifications are often unrealizable. In many cases, the source of unrealizability is the lack of adequate assumptions on the environment of the system. In this paper, we consider the problem of automatically correcting an unrealizable specification given in the generalized reactivity (1) fragment of linear temporal logic by adding assumptions on the environment. When a temporal-logic specification is unrealizable, the synthesis algorithm computes a counter-strategy as a witness. Our algorithm then analyzes this counter-strategy and synthesizes a set of candidate environment assumptions that can be used to remove the counter-strategy from the environment's possible behaviors. We demonstrate the applicability of our approach with several case studies.
On the Power of Over-parametrization in Neural Networks with Quadratic Activation

We provide new theoretical insights on why over-parametrization is effective in learning neural networks. For a $k$ hidden node shallow network with quadratic activation and $n$ training data points, we show as long as $ k \ge \sqrt{2n}$, over-parametrization enables local search algorithms to find a \emph{globally} optimal solution for general smooth and convex loss functions. Further, despite that the number of parameters may exceed the sample size, using theory of Rademacher complexity, we show with weight decay, the solution also generalizes well if the data is sampled from a regular distribution such as Gaussian. To prove when $k\ge \sqrt{2n}$, the loss function has benign landscape properties, we adopt an idea from smoothed analysis, which may have other applications in studying loss surfaces of neural networks.
EXTRACT: Strong Examples from Weakly-Labeled Sensor Data

Thanks to the rise of wearable and connected devices, sensor-generated time series comprise a large and growing fraction of the world's data. Unfortunately, extracting value from this data can be challenging, since sensors report low-level signals (e.g., acceleration), not the high-level events that are typically of interest (e.g., gestures). We introduce a technique to bridge this gap by automatically extracting examples of real-world events in low-level data, given only a rough estimate of when these events have taken place. By identifying sets of features that repeat in the same temporal arrangement, we isolate examples of such diverse events as human actions, power consumption patterns, and spoken words with up to 96% precision and recall. Our method is fast enough to run in real time and assumes only minimal knowledge of which variables are relevant or the lengths of events. Our evaluation uses numerous publicly available datasets and over 1 million samples of manually labeled sensor data.
A Novel Domain Adaptation Framework for Medical Image Segmentation.

We propose a segmentation framework that uses deep neural networks and introduce two innovations. First, we describe a biophysics-based domain adaptation method. Second, we propose an automatic method to segment white matter, gray matter, glial matter and cerebrospinal fluid, in addition to tumorous tissue. Regarding our first innovation, we use a domain adaptation framework that combines a novel multispecies biophysical tumor growth model with a generative adversarial model to create realistic looking synthetic multimodal MR images with known segmentation. These images are used for the purpose of training time data augmentation. Regarding our second innovation, we propose an automatic approach to enrich available segmentation data by computing the segmentation for healthy tissues. This segmentation, which is done using diffeomorphic image registration between the BraTS training data and a set of pre-labeled atlases, provides more information for training and reduces the class imbalance problem. Our overall approach is not specific to any particular neural network and can be used in conjunction with existing solutions. We demonstrate the performance improvement using a 2D U-Net for the BraTS’18 segmentation challenge. Our biophysics based domain adaptation achieves better results, as compared to the existing state-of-the-art GAN model used to create synthetic data for training.
An Estimation and Analysis Framework for the Rasch Model

The Rasch model is widely used for item response analysis in applications ranging from recommender systems to psychology, education, and finance. While a number of estimators have been proposed for the Rasch model over the last decades, the available analytical performance guarantees are mostly asymptotic. This paper provides a framework that relies on a novel linear minimum mean-squared error (L-MMSE) estimator which enables an exact, nonasymptotic, and closed-form analysis of the parameter estimation error under the Rasch model. The proposed framework provides guidelines on the number of items and responses required to attain low estimation errors in tests or surveys. We furthermore demonstrate its efficacy on a number of real-world collaborative filtering datasets, which reveals that the proposed L-MMSE estimator performs on par with state-of-the-art nonlinear estimators in terms of predictive performance.
FEAST eigensolver for nonlinear eigenvalue problems

Abstract The linear FEAST algorithm is a method for solving linear eigenvalue problems. It uses complex contour integration to calculate the eigenvectors associated with eigenvalues that are located inside some user-defined region in the complex plane. This makes it possible to parallelize the process of solving eigenvalue problems by simply dividing the complex plane into a collection of disjoint regions and calculating the eigenpairs in each region independently of the eigenpairs in the other regions. In this paper we present a generalization of the linear FEAST algorithm that can be used to solve nonlinear eigenvalue problems. Like its linear progenitor, the nonlinear FEAST algorithm can be used to solve nonlinear eigenvalue problems for the eigenpairs corresponding to eigenvalues that lie in a user-defined region in the complex plane, thereby allowing for the calculation of large numbers of eigenpairs in parallel. We describe the nonlinear FEAST algorithm, and use several physically motivated examples to demonstrate its properties.
An improvement of the Beck-Fiala theorem

In 1981 Beck and Fiala proved an upper bound for the discrepancy of a set system of degree d that is independent of the size of the ground set. In the intervening years the bound has been decreased from 2d− 2 to 2d− 4. We improve the bound to 2d− log∗ d. MSC classes: 05D05, 11K38, 05C15
Automorphisms of Dihedral-Like Automorphic Loops

Automorphic loops are loops in which all inner mappings are automorphisms. A large class of automorphic loops is obtained as follows: Let m be a positive even integer, G an abelian group, and α an automorphism of G that satisfies α2 = 1 if m > 2. Then the dihedral-like automorphic loop Dih(m, G, α) is defined on ℤm × G by We prove that two finite dihedral-like automorphic loops Dih(m, G, α), are isomorphic if and only if , , and α is conjugate to in the automorphism group of G. Moreover, for a finite dihedral-like automorphic loop Q, we describe the structure of the automorphism group of Q and its subgroup consisting of inner mappings of Q.
Kernel Density Estimation on Symmetric Spaces

We introduce a novel kernel density estimator for a large class of symmetric spaces and prove a minimax rate of convergence as fast as the minimax rate on Euclidean space. We prove a minimax rate of convergence proven without any compactness assumptions on the space or Holder-class assumptions on the densities. A main tool used in proving the convergence rate is the Helgason-Fourier transform, a generalization of the Fourier transform for semisimple Lie groups modulo maximal compact subgroups. This paper obtains a simplified formula in the special case when the symmetric space is the 2-dimensional hyperboloid.
Boltzmann Enhancements of Biquasile Counting Invariants

In this paper, we build on the biquasiles and dual graph diagrams introduced in [7]. We introduce biquasile Boltzmann weights that enhance the previous knot coloring invariant defined in terms of f...
On the Occurrence of Hecke Eigenvalues and a Lacunarity Question of Serre

Let \pi be a unitary cuspidal automorphic representation for GL(n) over a number field. We establish upper bounds on the number of Hecke eigenvalues of \pi equal to a fixed complex number. For GL(2), we also determine upper bounds on the number of Hecke eigenvalues with absolute value equal to a fixed number \gamma; in the case \gamma=0, this answers a question of Serre. These bounds are then improved upon by restricting to non-dihedral representations. Finally, we obtain analogous bounds for a family of cuspidal automorphic representations for GL(3).
Quasisymmetric rigidity of square Sierpinski carpets

We prove that every quasisymmetric self-homeomorphism of the standard 1/3-Sierpinski carpet S3 is a Euclidean isometry. For carpets in a more general family, the standard 1/p-Sierpinski carpets Sp, p ≥ 3 odd, we show that the groups of quasisymmetric self-maps are finite dihedral. We also establish that Sp and Sq are quasisymmetrically equivalent only if p = q. The main tool in the proof for these facts is a new invariant—a certain discrete modulus of a path family—that is preserved under quasisymmetric
Robust Repeated Auctions under Heterogeneous Buyer Behavior

We study revenue optimization in a repeated auction between a single seller anda single buyer. Traditionally, the design of repeated auctions requires strong modeling assumptions about the bidder behavior, such as it being myopic, infinite lookahead, or some specific form of learning behavior. Is it possible to design mechanisms which are simultaneously optimal against a multitude of possible buyer behaviors? We answer this question by designing a simple state-based mechanism that is simultaneously approximately optimal against a k -lookahead buyer for all k , a buyer who is a no-regret learner, and a buyer who is a policy-regret learner. Against each type of buyer our mechanism attains a constant fraction of the optimal revenue attainable against that type of buyer. We complement our positive results with almost tight impossibility results, showing that the revenue approximation tradeoffs achieved by our mechanism for different lookahead attitudes are near-optimal.
Dynamic Sketching for Graph Optimization Problems with Applications to Cut-Preserving Sketches

In this paper, we introduce a new model for sublinear algorithms called dynamic sketching. In this model, the underlying data is partitioned into a large static part and a small dynamic part and the goal is to compute a summary of the static part (i.e, a sketch) such that given any update for the dynamic part, one can combine it with the sketch to compute a given function. We say that a sketch is compact if its size is bounded by a polynomial function of the length of the dynamic data, (essentially) independent of the size of the static part. A graph optimization problem P in this model is defined as follows. The input is a graph G(V,E) and a set T \subseteq V of k terminals; the edges between the terminals are the dynamic part and the other edges in G are the static part. The goal is to summarize the graph G into a compact sketch (of size poly(k)) such that given any set Q of edges between the terminals, one can answer the problem P for the graph obtained by inserting all edges in Q to G, using only the sketch. We study the fundamental problem of computing a maximum matching and prove tight bounds on the sketch size. In particular, we show that there exists a (compact) dynamic sketch of size O(k^2) for the matching problem and any such sketch has to be of size \Omega(k^2). Our sketch for matchings can be further used to derive compact dynamic sketches for other fundamental graph problems involving cuts and connectivities. Interestingly, our sketch for matchings can also be used to give an elementary construction of a cut-preserving vertex sparsifier with space O(kC^2) for k-terminal graphs, which matches the best known upper bound; here C is the total capacity of the edges incident on the terminals. Additionally, we give an improved lower bound (in terms of C) of Omega(C/log{C}) on size of cut-preserving vertex sparsifiers, and establish that progress on dynamic sketching of the s-t max-flow problem (either upper bound or lower bound) immediately leads to better bounds for size of cut-preserving vertex sparsifiers.
Advances in Very Deep Convolutional Neural Networks for LVCSR.

Very deep CNNs with small 3x3 kernels have recently been shown to achieve very strong performance as acoustic models in hybrid NN-HMM speech recognition systems. In this paper we investigate how to efficiently scale these models to larger datasets. Specifically, we address the design choice of pooling and padding along the time dimension which renders convolutional evaluation of sequences highly inefficient. We propose a new CNN design without timepadding and without timepooling, which is slightly suboptimal for accuracy, but has two significant advantages: it enables sequence training and deployment by allowing efficient convolutional evaluation of full utterances, and, it allows for batch normalization to be straightforwardly adopted to CNNs on sequence data. Through batch normalization, we recover the lost peformance from removing the time-pooling, while keeping the benefit of efficient convolutional evaluation. We demonstrate the performance of our models both on larger scale data than before, and after sequence training. Our very deep CNN model sequence trained on the 2000h switchboard dataset obtains 9.4 word error rate on the Hub5 test-set, matching with a single model the performance of the 2015 IBM system combination, which was the previous best published result.
A new framework for dynamical models on multiplex networks

Many complex systems have natural representations as multi-layer networks. While these formulations retain more information than standard single-layer network models, there is not yet a fully developed theory for computing network metrics and statistics on these objects. We introduce a family of models of multiplex processes motivated by dynamical applications and investigate the properties of their spectra both theoretically and computationally. We study special cases of multiplex diffusion and Markov dynamics, using the spectral results to compute their rates of convergence. We use our framework to define a version of multiplex eigenvector centrality, which generalizes some existing notions in the literature. Last, we compare our operator to structurally-derived models on synthetic and real-world networks, helping delineate the contexts in which the different frameworks are appropriate.
Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition

We study $k$-GenEV, the problem of finding the top $k$ generalized eigenvectors, and $k$-CCA, the problem of finding the top $k$ vectors in canonical-correlation analysis. We propose algorithms $\mathtt{LazyEV}$ and $\mathtt{LazyCCA}$ to solve the two problems with running times linearly dependent on the input size and on $k$. Furthermore, our algorithms are DOUBLY-ACCELERATED: our running times depend only on the square root of the matrix condition number, and on the square root of the eigengap. This is the first such result for both $k$-GenEV or $k$-CCA. We also provide the first gap-free results, which provide running times that depend on $1/\sqrt{\varepsilon}$ rather than the eigengap.
Pixels to Graphs by Associative Embedding

Graphs are a useful abstraction of image content. Not only can graphs represent details about individual objects in a scene but they can capture the interactions between pairs of objects. We present a method for training a convolutional neural network such that it takes in an input image and produces a full graph definition. This is done end-to-end in a single stage with the use of associative embeddings. The network learns to simultaneously identify all of the elements that make up a graph and piece them together. We benchmark on the Visual Genome dataset, and demonstrate state-of-the-art performance on the challenging task of scene graph generation.
Nilpotence and the generalized uncertainty principle(s)

We point out that some of the proposed generalized/modified uncertainty principles originate from solvable, or nilpotent at appropriate limits, &#34deformations&#34 of Lie algebras. We briefly comment on formal aspects related to the well-posedness of one of these algebras. We point out a potential relation of such algebras with Classical Mechanics in the spirit of the symplectic non-squeezing theorem. We also point out their relation to a hierarchy of generalized measure theories emerging in a covariant formalism of quantum gravity.
Homological stability and stable moduli of flat manifold bundles

Abstract We prove that the group homology of the diffeomorphism group of # g S n × S n \ int ( D 2 n ) as a discrete group is independent of g in a range, provided that n > 2 . This answers the high dimensional version of a question posed by Morita about surface diffeomorphism groups made discrete. The stable homology is isomorphic to the homology of a certain infinite loop space related to the Haefliger's classifying space of foliations. One geometric consequence of this description of the stable homology is a splitting theorem that implies certain classes called generalized Mumford–Morita–Miller classes lift to a secondary R / Z -invariants for flat ( # g S n × S n ) -bundles provided g ≫ 0 .
Problems With Evaluation of Word Embeddings Using Word Similarity Tasks

Lacking standardized extrinsic evaluation methods for vector representations of words, the NLP community has relied heavily on word similarity tasks as a proxy for intrinsic evaluation of word vectors. Word similarity evaluation, which correlates the distance between vectors and human judgments of “semantic similarity” is attractive, because it is computationally inexpensive and fast. In this paper we present several problems associated with the evaluation of word vectors on word similarity datasets, and summarize existing solutions. Our study suggests that the use of word similarity tasks for evaluation of word vectors is not sustainable and calls for further research on evaluation methods.
Market Formation as Transitive Closure: the Evolving Pattern of Trade in Music

Where do new markets come from? I construct a network model in which national markets are nodes and flows of recorded music between them are links and conduct a longitudinal analysis of the global pattern of trade in the period 1976–2010. I hypothesize that new export markets are developed through a process of transitive closure in the network of international trade. When two countries' markets experience the same social influences, it brings them close enough together for new homophilous ties to be formed. The implication is that consumption of foreign products helps, not hurts, home-market producers develop overseas markets, but only in those countries that have a history of consuming the same foreign products that were consumed in the home market. Selling in a market changes what is valued in that market, and new market formation is a consequence of having social influences in common.
Optimal Beam-Sweeping and Communication in Mobile Millimeter-Wave Networks

Millimeter-wave (mm-wave) communications incur a high beam alignment cost in mobile scenarios such as vehicular networks. Therefore, an efficient beam alignment mechanism is required to mitigate the resulting overhead. In this paper, a one-dimensional mobility model is proposed where a mobile user (MU), such as a vehicle, moves along a straight road with time-varying and random speed, and communicates with base stations (BSs) located on the roadside over the mm-wave band. To compensate for location uncertainty, the BS widens its transmission beam and, when a critical beamwidth is achieved, it performs beam-sweeping to refine the MU position estimate, followed by data communication over a narrow beam. The average rate and average transmission power are computed in closed form and the optimal beamwidth for communication, number of sweeping beams, and transmission power allocation are derived so as to maximize the average rate under an average power constraint. Structural properties of the optimal design are proved, and a bisection algorithm to determine the optimal sweeping - communication parameters is designed. It is shown numerically that an adaptation of the IEEE 802.11ad standard to the proposed model exhibits up to 90\% degradation in spectral efficiency compared to the proposed scheme.
Weakly supervised scalable audio content analysis

Audio Event Detection is an important task for content analysis of multimedia data. Most of the current works on detection of audio events is driven through supervised learning approaches. We propose a weakly supervised learning framework which can make use of the tremendous amount of web multimedia data with significantly reduced annotation effort and expense. Specifically, we use several multiple instance learning algorithms to show that audio event detection through weak labels is feasible. We also propose a novel scalable multiple instance learning algorithm and show that its competitive with other multiple instance learning algorithms for audio event detection tasks.
Comparing Kalman Filters and Observers for Power System Dynamic State Estimation With Model Uncertainty and Malicious Cyber Attacks

Kalman filters (KFs) and dynamic observers are two main classes of the dynamic state estimation (DSE) routines. The Power system DSE has been implemented by various KFs, such as the extended KF (EKF) and the unscented KF (UKF). In this paper, we discuss two challenges for an effective power system DSE: 1) model uncertainty and 2) potential cyber attacks and measurement faults. To address this, the cubature KF (CKF) and a nonlinear observer are introduced and implemented. Various KFs and the dynamic observer are then tested on the 16-machine 68-bus system given realistic scenarios under model uncertainty and different types of cyber attacks against synchrophasor measurements. It is shown that the CKF and the observer are more robust to model uncertainty and cyber attacks than their counterparts. Based on the tests, a thorough qualitative comparison is also performed for KF routines and observers.
Evidence for Magnetoreception in Red Drum (Sciaenops ocellatus), Black Drum (Pogonias cromis), and Sea Catfish (Ariopsis felis)

Over the past few decades, magnetoreception has been discovered in several species of teleost and elasmobranch fishes by employing varied experimental methods including conditioning experiments, observations of alignment with external fields, and experiments with magnetic deterrents. Biogenic magnetite has been confirmed to be an important receptor mechanism in some species, but there is ongoing debate regarding whether other mechanisms are at work. This paper presents evidence for magnetoreception in three additional species, red drum ( Sciaenops ocellatus ), black drum ( Pogonias cromis ), and sea catfish ( Ariopsis felis ), by employing experiments to test whether fish respond differently to bait on a magnetic hook than on a control. In red drum, the control hook outcaught the magnetic hook by 32-18 for Χ 2 =3.92 and a P -value of 0.048. Black drum showed a significant attraction for the magnetic hook, which prevailed over the control hook by 11-3 for Χ 2 =4.57 and a P -value of 0.033. Gafftopsail catfish ( Bagre marinus ) showed no preference with a 31-35 split between magnetic hook and control for Χ 2 =0.242 and a P -value of 0.623. In a sample of 100 sea catfish in an analogous experiment using smaller hooks, the control hook was preferred 62-38 for Χ 2 =5.76 and a P -value of < 0.001. Such a simple method for identifying magnetoreceptive species may quickly expand the number of known magnetoreceptive species and allow for easier access to magnetoreceptive species and thus facilitate testing of magnetoreceptive hypotheses.
Frequency regulation in Islanded microgrid using demand response

Introducing more Distributed Generation (DG) into power grid infrastructure drives more attention to understand how large scale DG affects grid operation. Islanding is an important concern in this area. Islanding refers to the condition that DGs within a microgrid continue energizing while the microgrid has been disconnected from the main grid. Considering the adverse effects of Islanding, it should be detected and managed in a proper way. After Islanding has been detected, even though the first option is tripping all DG units and shutting down the system, the microgrid has this option to work as a standalone system. For this, the microgrid frequency regulation is a must. This paper comprises two parts. First proposing an islanding detection method based on communication between the microgrid and the main grid. In the second part, after detectiong the islanding using the first part, it presents an effective implementation Demand Response (DR) to regulate the frequency in islanded microgrid as an Ancillary Service (AS) considering the transient constraints of frequency in inverter-based generations.
Optimal Approximate Matrix Product in Terms of Stable Rank

We prove, using the subspace embedding guarantee in a black box way, that one can achieve the spectral norm guarantee for approximate matrix multiplication with a dimensionality-reducing map having m = O(˜r/epsilon^2) rows. Here r˜ is the maximum stable rank, i.e., the squared ratio of Frobenius and operator norms, of the two matrices being multiplied. This is a quantitative improvement over previous work of [Magen and Zouzias, SODA, 2011] and [Kyrillidis et al., arXiv, 2014] and is also optimal for any oblivious dimensionality-reducing map. Furthermore, due to the black box reliance on the subspace embedding property in our proofs, our theorem can be applied to a much more general class of sketching matrices than what was known before, in addition to achieving better bounds. For example, one can apply our theorem to efficient subspace embeddings such as the Subsampled Randomized Hadamard Transform or sparse subspace embeddings, or even with subspace embedding constructions that may be developed in the future. Our main theorem, via connections with spectral error matrix multiplication proven in previous work, implies quantitative improvements for approximate least squares regression and low rank approximation, and implies faster low rank approximation for popular kernels in machine learning such as the gaussian and Sobolev kernels. Our main result has also already been applied to improve dimensionality reduction guarantees for k-means clustering, and also implies new results for nonparametric regression. Lastly, we point out that the proof of the "BSS" deterministic row-sampling result of [Batson et al., SICOMP, 2012] can be modified to obtain deterministic row-sampling for approximate matrix product in terms of the stable rank of the matrices. The original "BSS" proof was in terms of the rank rather than the stable rank.
Modeling neural dynamics during speech production using a state space variational autoencoder

Characterizing the neural encoding of behavior remains a challenging task in many research areas due in part to complex and noisy spatiotemporal dynamics of evoked brain activity. An important aspect of modeling these neural encodings involves separation of robust, behaviorally relevant signals from background activity, which often contains signals from irrelevant brain processes and decaying information from previous behavioral events. To achieve this separation, we develop a two-branch State Space Variational AutoEncoder (SSVAE) model to individually describe the instantaneous evoked foreground signals and the context-dependent background signals. We modeled the spontaneous speech-evoked brain dynamics using smoothed Gaussian mixture models. By applying the proposed SSVAE model to track ECoG dynamics in one participant over multiple hours, we find that the model can predict speech-related dynamics more accurately than other latent factor inference algorithms. Our results demonstrate that separately modeling the instantaneous speech-evoked and slow context-dependent brain dynamics can enhance tracking performance, which has important implications for the development of advanced neural encoding and decoding models in various neuroscience sub-disciplines.
Truncated fractional moments of stable laws

Abstract Expressions are given for the truncated fractional moments E ( X − a ) + p of a general stable law. These involve families of special functions that arose out of the study of multivariate stable densities and probabilities. This main result yields expressions for E ( X − a ) + when the index of stability α > 1 , and expressions for fractional absolute and signed moments for any stable law.
Signed Lozenge Tilings

It is well-known that plane partitions, lozenge tilings of a hexagon, perfect matchings on a honeycomb graph, and families of non-intersecting lattice paths in a hexagon are all in bijection. In this work we consider regions that are more general than hexagons. They are obtained by further removing upward-pointing triangles. We call the resulting shapes triangular regions. We establish signed versions of the latter three bijections for triangular regions. We first investigate the tileability of triangular regions by lozenges. Then we use perfect matchings and families of non-intersecting lattice paths to define two signs of a lozenge tiling. Using a new method that we call resolution of a puncture, we show that the two signs are in fact equivalent. As a consequence, we obtain the equality of determinants, up to sign, that enumerate signed perfect matchings and signed families of lattice paths of a triangular region, respectively. We also describe triangular regions, for which the signed enumerations agree with the unsigned enumerations.
An iterative construction of irreducible polynomials reducible modulo every prime

Abstract We give a method of constructing polynomials of arbitrarily large degree irreducible over a global field F but reducible modulo every prime of F . The method consists of finding quadratic f ∈ F [ x ] whose iterates have the desired property, and it depends on new criteria ensuring all iterates of f are irreducible. In particular when F is a number field in which the ideal (2) is not a square, we construct infinitely many families of quadratic f such that every iterate f n is irreducible over F , but f n is reducible modulo all primes of F for n ⩾ 2 . We also give an example for each n ⩾ 2 of a quadratic f ∈ Z [ x ] whose iterates are all irreducible over Q , whose ( n − 1 ) st iterate is irreducible modulo some primes, and whose n th iterate is reducible modulo all primes. From the perspective of Galois theory, this suggests that a well-known rigidity phenomenon for linear Galois representations does not exist for Galois representations obtained by polynomial iteration. Finally, we study the number of primes p for which a given quadratic f defined over a global field has f n irreducible modulo p for all n ⩾ 1 .
Gonality of expander graphs

Abstract We provide lower bounds on the gonality of a graph in terms of its spectral and edge expansion. As a consequence, we see that the gonality of a random 3-regular graph is asymptotically almost surely greater than one seventh its genus.
Continuum Deformation of a Multiple Quadcopter Payload Delivery Team without Inter-Agent Communication

This paper proposes continuum deformation as a strategy for controlling the collective motion of a multiple quad-copter system (MQS) carrying a common payload. Continuum deformation allows expansion and contraction of inter-agent distances in a 2- D motion plane to follow desired motions of three team leaders. The remaining quadcopter followers establish the desired continuum deformation only by knowing leaders' positions at desired sample time waypoints without the need for inter-agent communication over the intermediate intervals. Each quadcopter applies a linear-quadratic Gaussian (LQG) controller to track the desired trajectory given by the continuum deformation in the presence of disturbance and measurement noise. Results of simulated cooperative aerial payload transport in the presence of uncertainty illustrate application of continuum deformation for coordinated transport through a narrow channel.