cssi/SciSciGPT-SciSciNet huggingface dataset:
Beyond the low-degree algorithm: mixtures of subcubes and their applications

We introduce the problem of learning mixtures of k subcubes over {0,1}n, which contains many classic learning theory problems as a special case (and is itself a special case of others). We give a surprising nO(logk)-time learning algorithm based on higher-order multilinear moments. It is not possible to learn the parameters because the same distribution can be represented by quite different models. Instead, we develop a framework for reasoning about how multilinear moments can pinpoint essential features of the mixture, like the number of components. We also give applications of our algorithm to learning decision trees with stochastic transitions (which also capture interesting scenarios where the transitions are deterministic but there are latent variables). Using our algorithm for learning mixtures of subcubes, we can approximate the Bayes optimal classifier within additive error є on k-leaf decision trees with at most s stochastic transitions on any root-to-leaf path in nO(s + logk)·poly(1/є) time. In this stochastic setting, the classic nO(logk)·poly(1/є)-time algorithms of Rivest, Blum, and Ehrenfreucht-Haussler for learning decision trees with zero stochastic transitions break down because they are fundamentally Occam algorithms. The low-degree algorithm of Linial-Mansour-Nisan is able to get a constant factor approximation to the optimal error (again within an additive є) and runs in time nO(s + log(k/є)). The quasipolynomial dependence on 1/є is inherent to the low-degree approach because the degree needs to grow as the target accuracy decreases, which is undesirable when є is small. In contrast, as we will show, mixtures of k subcubes are uniquely determined by their 2 logk order moments and hence provide a useful abstraction for simultaneously achieving the polynomial dependence on 1/є of the classic Occam algorithms for decision trees and the flexibility of the low-degree algorithm in being able to accommodate stochastic transitions. Using our multilinear moment techniques, we also give the first improved upper and lower bounds since the work of Feldman-O’Donnell-Servedio for the related but harder problem of learning mixtures of binary product distributions.
Cops and robbers on planar directed graphs

Aigner and Fromme initiated the systematic study of the cop number of a graph by proving the elegant and sharp result that in every connected planar graph, three cops are sufficient to win a natural pursuit game against a single robber. This game, introduced by Nowakowski and Winkler, is commonly known as Cops and Robbers in the combinatorial literature. We extend this study to directed planar graphs, and establish separation from the undirected setting. We exhibit a geometric construction which shows that a more sophisticated robber strategy can indefinitely evade three cops on a particular strongly connected planar directed graph.
Belief tree search for active object recognition

Active Object Recognition (AOR) has been approached as an unsupervised learning problem, in which optimal trajectories for object inspection are not known and to be discovered by reducing label uncertainty or training with reinforcement learning. Such approaches suffer from local optima and have no guarantees of the quality of their solution. In this paper, we treat AOR as a Partially Observable Markov Decision Process (POMDP) and find near-optimal values and corresponding action-values of training data using Belief Tree Search (BTS) on the AOR belief Markov Decision Process (MDP). AOR then reduces to the problem of knowledge transfer from these action-values to the test set. We train a Long Short Term Memory (LSTM) network on these values to predict the best next action on the training set rollouts and experimentally show that our method generalizes well to explore novel objects and novel views of familiar objects with high accuracy. We compare this supervised scheme against guided policy search, and show that the LSTM network reaches higher recognition accuracy compared to the guided policy search and guided Neurally Fitted Q-iteration. We further look into optimizing the observation function to increase the total collected reward during active recognition. In AOR, the observation function is known only approximately. We derive a gradient-based update for the observation function to increase the total expected reward. We show that by optimizing the observation function and retraining the supervised LSTM network, the AOR performance on the test set improves significantly.
Game theory for secure critical interdependent gas-power-water infrastructure

A city's critical infrastructure such as gas, water, and power systems, are largely interdependent since they share energy, computing, and communication resources. This, in turn, makes it challenging to endow them with fool-proof security solutions. In this paper, a unified model for interdependent gas- power-water infrastructure is presented and the security of this model is studied using a novel game-theoretic framework. In particular, a zero-sum noncooperative game is formulated between a malicious attacker who seeks to simultaneously alter the states of the gas-power-water critical infrastructure to increase the power generation cost and a defender who allocates communication resources over its attack detection filters in local areas to monitor the infrastructure. At the mixed strategy Nash equilibrium of this game, numerical results show that the expected power generation cost deviation is 35% lower than the one resulting from an equal allocation of resources over the local filters. The results also show that, at equilibrium, the interdependence of the power system on the natural gas and water systems can motivate the attacker to target the states of the water and natural gas systems to change the operational states of the power grid. Conversely, the defender allocates a portion of its resources to the water and natural gas states of the interdependent system to protect the grid from state deviations.
Minimizing Sum of Truncated Convex Functions and Its Applications

In this article, we study a class of problems where the sum of truncated convex functions is minimized. In statistical applications, they are commonly encountered when l0-penalized models are fitte...
Power type asymptotically uniformly smooth and asymptotically uniformly flat norms

We provide a short characterization of p-asymptotic uniform smoothability and asymptotic uniform flatenability of operators and of Banach spaces. We use these characterizations to show that many asymptotic uniform smoothness properties pass to injective tensor products of operators and of Banach spaces. In particular, we prove that the injective tensor product of two asymptotically uniformly smooth Banach spaces is asymptotically uniformly smooth. We prove that for $$1<p<\infty $$ , the class of p-asymptotically uniformly smoothable operators can be endowed with an ideal norm making this class a Banach ideal. We also prove that the class of asymptotically uniformly flattenable operators can be endowed with an ideal norm making this class a Banach ideal.
Rare K decays in a model of quark and lepton masses

An extension of a model of neutrino masses to the quark sector provides an interesting link between these two sectors. A parameter which is important to describe neutrino oscillations and masses is found to be a crucial one appearing in various ``penguin'' operators, in particular the so-called Z penguin. This parameter is severely constrained by the rare decay process ${K}_{L}\ensuremath{\rightarrow}{\ensuremath{\mu}}^{+}{\ensuremath{\mu}}^{\ensuremath{-}}.$ This in turn has interesting implications on the decay rates of other rare processes such as ${K}_{L}\ensuremath{\rightarrow}\ensuremath{\mu}e,$ etc., as well as on the masses of the neutrinos and the masses of the vectorlike quarks and leptons which appear in our model.
Learning local dependence in ordered data

In many applications, data come with a natural ordering. This ordering can often induce local dependence among nearby variables. However, in complex data, the width of this dependence may vary, making simple assumptions such as a constant neighborhood size unrealistic. We propose a framework for learning this local dependence based on estimating the inverse of the Cholesky factor of the covariance matrix. Penalized maximum likelihood estimation of this matrix yields a simple regression interpretation for local dependence in which variables are predicted by their neighbors. Our proposed method involves solving a convex, penalized Gaussian likelihood problem with a hierarchical group lasso penalty. The problem decomposes into independent subproblems which can be solved efficiently in parallel using first-order methods. Our method yields a sparse, symmetric, positive definite estimator of the precision matrix, encoding a Gaussian graphical model. We derive theoretical results not found in existing methods attaining this structure. In particular, our conditions for signed support recovery and estimation consistency rates in multiple norms are as mild as those in a regression problem. Empirical results show our method performing favorably compared to existing methods. We apply our method to genomic data to flexibly model linkage disequilibrium. Our method is also applied to improve the performance of discriminant analysis in sound recording classification.
Building the space elevator: lessons from biological design

One of the biggest perceived challenges in building megastructures, such as the space elevator, is the unavailability of materials with sufficient tensile strength. The presumed necessity of very strong materials stems from a design paradigm which requires structures to operate at a small fraction of their maximum tensile strength (usually, 50% or less). This criterion limits the probability of failure by giving structures sufficient leeway in handling stochastic components, such as variability in material strength and/or external forces. While reasonable for typical engineering structures, low working stress ratios—defined as operating stress as a fraction of ultimate tensile strength—in the case of megastructures are both too stringent and unable to adequately control the failure probability. We draw inspiration from natural biological structures, such as bones, tendons and ligaments, which are made up of smaller substructures and exhibit self-repair, and suggest a design that requires structures to operate at significantly higher stress ratios, while maintaining reliability through a continuous repair mechanism. We outline a mathematical framework for analysing the reliability of structures with components exhibiting probabilistic rupture and repair that depend on their time-in-use (age). Further, we predict time-to-failure distributions for the overall structure. We then apply this framework to the space elevator and find that a high degree of reliability is achievable using currently existing materials, provided it operates at sufficiently high working stress ratios, sustained through an autonomous repair mechanism, implemented via, e.g. robots.
EXAMPLES OF REDUCIBLE AND FINITE DEHN FILLINGS

If a hyperbolic 3-manifold M admits a reducible and a finite Dehn filling, the distance between the filling slopes is known to be 1. This has been proved recently by Boyer, Gordon and Zhang. The first example of a manifold with two such fillings was given by Boyer and Zhang. In this paper, we give examples of hyperbolic manifolds admitting a reducible Dehn filling and a finite Dehn filling of every type: cyclic, dihedral, tetrahedral, octahedral and icosahedral.
Qubit compatible superconducting interconnects

We present a fabrication process for fully superconducting interconnects compatible with superconducting qubit technology. These interconnects allow for the three dimensional integration of quantum circuits without introducing lossy amorphous dielectrics. They are composed of indium bumps several microns tall separated from an aluminum base layer by titanium nitride which serves as a diffusion barrier. We measure the whole structure to be superconducting (transition temperature of 1.1 K), limited by the aluminum. These interconnects have an average critical current of 26.8 mA, and mechanical shear and thermal cycle testing indicate that these devices are mechanically robust. Our process provides a method that reliably yields superconducting interconnects suitable for use with superconducting qubits.
A component lasso

We propose a new sparse regression method called the component lasso, based on a simple idea. The method uses the connected-components structure of the sample covariance matrix to split the problem into smaller ones. It then applies the lasso to each subproblem separately, obtaining a coefficient vector for each one. Finally, it uses non-negative least squares to recombine the different vectors into a single solution. This step is useful in selecting and reweighting components that are correlated with the response. We prove that the component lasso is strongly sign consistent in a block-diagonal setting. Simulated and real data examples show that the component lasso can outperform standard regression methods such as the lasso and elastic net, achieving a lower mean squared error as well as better support recovery. The modular structure of the algorithm also lends itself naturally to parallel computation. The Canadian Journal of Statistics 43: 624–646; 2015 © 2015 Statistical Society of Canada Resume Les auteurs proposent une nouvelle methode de regression eparse intitulee le lasso par composante qui se base sur une idee toute simple. Ils utilisent la structure en composantes connectees de la matrice de covariance empirique afin de fragmenter le probleme. Ils appliquent alors le lasso separement a chaque sous-probleme et obtiennent un vecteur de coefficients pour chacun. Ils utilisent finalement les moindres carres non negatifs afin de recombiner les differents vecteurs en une solution unique. Cette derniere etape s'avere utile dans la selection et la reponderation des composantes qui sont correlees avec la reponse. Les auteurs demontrent la forte convergence en signe du lasso par composante dans le contexte d'une matrice diagonale par blocs. Ils presentent des exemples avec des donnees reelles et simulees qui montrent que le lasso par composante peut surclasser les methodes de regression habituelles telles que le lasso et le filet elastique, tant au niveau de l'erreur quadratique moyenne que de la reconstitution du support. La structure modulaire de l'algorithme s'adapte par ailleurs naturellement au calcul parallele. La revue canadienne de statistique 43: 624–646; 2015 © 2015 Societe statistique du Canada
Total diameter and area of closed submanifolds

The total diameter of a closed planar curve $$C\subset \mathbf {R}^2$$ is the integral of its antipodal chord lengths. We show that this quantity is bounded below by twice the area of $$C$$ . Furthermore, when $$C$$ is convex or centrally symmetric, the lower bound is twice as large. Both inequalities are sharp and the equality holds in the convex case only when $$C$$ is a circle. We also generalize these results to $$m$$ dimensional submanifolds of $$\mathbf {R}^n$$ , where the “area” will be defined in terms of the mod $$2$$ winding numbers of the submanifold about the $$n-m-1$$ dimensional affine subspaces of $$\mathbf {R}^n$$ .
Operator spaces which are one-sided M-ideals in their bidual

We generalize an important class of Banach spaces, namely the $M$-embedded Banach spaces, to the non-commutative setting of operator spaces. The one-sided $M$-embedded operator spaces are the operator spaces which are one-sided $M$-ideals in their second dual. We show that several properties from the classical setting, like the stability under taking subspaces and quotients, unique extension property, Radon Nikod$\acute {\rm{y}}$m Property and many more, are retained in the non-commutative setting. We also discuss the dual setting of one-sided $L$-embedded operator spaces.
Concentration inequalities for a removal-driven thinning process

We prove exponential concentration estimates and a strong law of large numbers for a particle system that is the simplest representative of a general class of models for 2D grain boundary coarsening. The system consists of $n$ particles in $(0,\infty)$ that move at unit speed to the left. Each time a particle hits the boundary point $0$, it is removed from the system along with a second particle chosen uniformly from the particles in $(0,\infty)$. Under the assumption that the initial empirical measure of the particle system converges weakly to a measure with density $f_0(x) \in L^1_+(0,\infty)$, the empirical measure of the particle system at time $t$ is shown to converge to the measure with density $f(x,t)$, where $f$ is the unique solution to the kinetic equation with nonlinear boundary coupling $$\partial_t f (x,t) - \partial_x f(x,t) = -\frac{f(0,t)}{\int_0^\infty f(y,t)\, dy} f(x,t), \quad 0<x < \infty, $$ and initial condition $f(x,0)=f_0(x)$. The proof relies on a concentration inequality for an urn model studied by Pittel, and Maurey's concentration inequality for Lipschitz functions on the permutation group.
TALL: Temporal Activity Localization via Language Query

This paper focuses on temporal localization of actions in untrimmed videos. Existing methods typically train classifiers for a pre-defined list of actions and apply them in a sliding window fashion. However, activities in the wild consist of a wide combination of actors, actions and objects; it is difficult to design a proper activity list that meets users’ needs. We propose to localize activities by natural language queries. Temporal Activity Localization via Language (TALL) is challenging as it requires: (1) suitable design of text and video representations to allow cross-modal matching of actions and language queries; (2) ability to locate actions accurately given features from sliding windows of limited granularity. We propose a novel Cross-modal Temporal Regression Localizer (CTRL) to jointly model text query and video clips, output alignment scores and action boundary regression results for candidate clips. Lor evaluation, we adopt TaCoS dataset, and build a new dataset for this task on top of Charades by adding sentence temporal annotations, called Charades-STA. We also build complex sentence queries in Charades-STA for test. Experimental results show that CTRL outperforms previous methods significantly on both datasets.
Joint Extraction of Events and Entities within a Document Context

Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon. The interpretation of events and entities is highly contextually dependent. Existing work in information extraction typically models events separately from entities, and performs inference at the sentence level, ignoring the rest of the document. In this paper, we propose a novel approach that models the dependencies among variables of events, entities, and their relations, and performs joint inference of these variables across a document. The goal is to enable access to document-level contextual information and facilitate context-aware predictions. We demonstrate that our approach substantially outperforms the state-of-the-art methods for event extraction as well as a strong baseline for entity extraction.
Persistent homology transform for modeling shapes and surfaces

We introduce a statistic, the persistent homology transform (PHT), to model surfaces in R3 and shapes in R2. This statistic is a collection of persistence diagrams—multiscale topological summaries used extensively in topological data analysis. We use the PHT to represent shapes and execute operations such as computing distances between shapes or classifying shapes. We provide a constructive proof that the map from the space of simplicial complexes in R3 into the space spanned by this statistic is injective. This implies that we can use it to determine a metric on the space of piecewise linear shapes. Stability results justify that we can approximate this metric using finitely many persistence diagrams. We illustrate the utility of this statistic on simulated and real data.
Iterative reconstruction of rank-one matrices in noise

We consider the problem of estimating a rank-one matrix in Gaussian noise under a probabilistic model for the left and right factors of the matrix. The probabilistic model can impose constraints on the factors including sparsity and positivity that arise commonly in learning problems. We propose a family of algorithms that reduce the problem to a sequence of scalar estimation computations. These algorithms are similar to approximate message passing techniques based on Gaussian approximations of loopy belief propagation that have been used recently in compressed sensing. Leveraging analysis methods by Bayati and Montanari, we show that the asymptotic behavior of the algorithm is described by a simple scalar equivalent model, where the distribution of the estimates at each iteration is identical to certain scalar estimates of the variables in Gaussian noise. Moreover, the effective Gaussian noise level is described by a set of state evolution equations. The proposed approach to deriving algorithms thus provides a computationally simple and general method for rank-one estimation problems with a precise analysis in certain high-dimensional settings.
A 2D nearest-neighbor quantum architecture for factoring in polylogarithmic depth

We contribute a 2D nearest-neighbor quantum architecture for Shor's algorithm to factor an n-bit number in O(log3 n) depth. Our implementation uses parallel phase estimation, constant-depth fanout and teleportation, and constant-depth carry-save modular addition. We derive upper bounds on the circuit resources of our architecture under a new 2D model which allows a classical controller and parallel, communicating modules. We provide a comparison to all previous nearest-neighbor factoring implementations. Our circuit results in an exponential improvement in nearest-neighbor circuit depth at the cost of a polynomial increase in circuit size and width.
Learning to Track at 100 FPS with Deep Regression Networks

Machine learning techniques are often used in computer vision due to their ability to leverage large amounts of training data to improve performance. Unfortunately, most generic object trackers are still trained from scratch online and do not benefit from the large number of videos that are readily available for offline training. We propose a method for offline training of neural networks that can track novel objects at test-time at 100 fps. Our tracker is significantly faster than previous methods that use neural networks for tracking, which are typically very slow to run and not practical for real-time applications. Our tracker uses a simple feed-forward network with no online training required. The tracker learns a generic relationship between object motion and appearance and can be used to track novel objects that do not appear in the training set. We test our network on a standard tracking benchmark to demonstrate our tracker’s state-of-the-art performance. Further, our performance improves as we add more videos to our offline training set. To the best of our knowledge, our tracker (Our tracker is available at http://davheld.github.io/GOTURN/GOTURN.html) is the first neural-network tracker that learns to track generic objects at 100 fps.
Nonparanormal Information Estimation.

We study the problem of using i.i.d. samples from an unknown multivariate probability distribution $p$ to estimate the mutual information of $p$. This problem has recently received attention in two settings: (1) where $p$ is assumed to be Gaussian and (2) where $p$ is assumed only to lie in a large nonparametric smoothness class. Estimators proposed for the Gaussian case converge in high dimensions when the Gaussian assumption holds, but are brittle, failing dramatically when $p$ is not Gaussian. Estimators proposed for the nonparametric case fail to converge with realistic sample sizes except in very low dimensions. As a result, there is a lack of robust mutual information estimators for many realistic data. To address this, we propose estimators for mutual information when $p$ is assumed to be a nonparanormal (a.k.a., Gaussian copula) model, a semiparametric compromise between Gaussian and nonparametric extremes. Using theoretical bounds and experiments, we show these estimators strike a practical balance between robustness and scaling with dimensionality.
The JKind Model Checker

JKind is an open-source industrial model checker developed by Rockwell Collins and the University of Minnesota. JKind uses multiple parallel engines to prove or falsify safety properties of infinite state models. It is portable, easy to install, performance competitive with other state-of-the-art model checkers, and has features designed to improve the results presented to users: inductive validity cores for proofs and counterexample smoothing for test-case generation. It serves as the back-end for various industrial applications.
Fair k-Center Clustering for Data Summarization

In data summarization we want to choose $k$ prototypes in order to summarize a data set. We study a setting where the data set comprises several demographic groups and we are restricted to choose $k_i$ prototypes belonging to group $i$. A common approach to the problem without the fairness constraint is to optimize a centroid-based clustering objective such as $k$-center. A natural extension then is to incorporate the fairness constraint into the clustering problem. Existing algorithms for doing so run in time super-quadratic in the size of the data set, which is in contrast to the standard $k$-center problem being approximable in linear time. In this paper, we resolve this gap by providing a simple approximation algorithm for the $k$-center problem under the fairness constraint with running time linear in the size of the data set and $k$. If the number of demographic groups is small, the approximation guarantee of our algorithm only incurs a constant-factor overhead.
A nearly instance optimal algorithm for top-k ranking under the multinomial logit model

We study the active learning problem of top-k ranking from multi-wise comparisons under the popular multinomial logit model. Our goal is to identify the top-k items with high probability by adaptively querying sets for comparisons and observing the noisy output of the most preferred item from each comparison. To achieve this goal, we design a new active ranking algorithm without using any information about the underlying items' preference scores. We also establish a matching lower bound on the sample complexity even when the set of preference scores is given to the algorithm. These two results together show that the proposed algorithm is nearly instance optimal (similar to instance optimal [12], but up to polylog factors). Our work extends the existing literature on rank aggregation in three directions. First, instead of studying a static problem with fixed data, we investigate the top-k ranking problem in an active learning setting. Second, we show our algorithm is nearly instance optimal, which is a much stronger theoretical guarantee. Finally, we extend the pairwise comparison to the multi-wise comparison, which has not been fully explored in ranking literature.
Energy-Efficient Power Allocation in Cognitive Radio Systems With Imperfect Spectrum Sensing

This paper studies energy-efficient power allocation schemes for secondary users in sensing-based spectrum sharing cognitive radio systems. It is assumed that secondary users first perform channel sensing possibly with errors and then initiate data transmission with different power levels based on sensing decisions. In this setting, the optimization problem is to maximize energy efficiency (EE) subject to peak/average transmission power constraints and peak/average interference constraints. By exploiting the quasi-concave property of the EE maximization problem, the original problem is transformed into an equivalent parameterized concave problem, and an iterative power allocation algorithm based on Dinkelbach’s method is proposed. The optimal power levels are identified in the presence of different levels of channel side information (CSI) regarding the transmission and interference links at the secondary transmitter, namely, perfect CSI of both transmission and interference links, perfect CSI of the transmission link, imperfect CSI of the interference link, imperfect CSI of both links, or only statistical CSI of both links. Through numerical results, the impact of sensing performance, different types of CSI availability, and transmit and interference power constraints on the EE of the secondary users is analyzed.
Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach

We study the problem of attacking a machine learning model in the hard-label black-box setting, where no model information is revealed except that the attacker can make queries to probe the corresponding hard-label decisions. This is a very challenging problem since the direct extension of state-of-the-art white-box attacks (e.g., CW or PGD) to the hard-label black-box setting will require minimizing a non-continuous step function, which is combinatorial and cannot be solved by a gradient-based optimizer. The only current approach is based on random walk on the boundary, which requires lots of queries and lacks convergence guarantees. We propose a novel way to formulate the hard-label black-box attack as a real-valued optimization problem which is usually continuous and can be solved by any zeroth order optimization algorithm. For example, using the Randomized Gradient-Free method, we are able to bound the number of iterations needed for our algorithm to achieve stationary points. We demonstrate that our proposed method outperforms the previous random walk approach to attacking convolutional neural networks on MNIST, CIFAR, and ImageNet datasets. More interestingly, we show that the proposed algorithm can also be used to attack other discrete and non-continuous machine learning models, such as Gradient Boosting Decision Trees (GBDT).
Finite orbit points for sets of quadratic polynomials

Let S = {x2 + c 1,x2 + c 2,…,x2 + c s} be a set of quadratic polynomials with rational coefficients, and let P be a rational basepoint. We classify the pairs (S,P) for which P has finite orbit for ...
Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation

The analysis of Belief Propagation and other algorithms for the {\em reconstruction problem} plays a key role in the analysis of community detection in inference on graphs, phylogenetic reconstruction in bioinformatics, and the cavity method in statistical physics. We prove a conjecture of Evans, Kenyon, Peres, and Schulman (2000) which states that any bounded memory message passing algorithm is statistically much weaker than Belief Propagation for the reconstruction problem. More formally, any recursive algorithm with bounded memory for the reconstruction problem on the trees with the binary symmetric channel has a phase transition strictly below the Belief Propagation threshold, also known as the Kesten-Stigum bound. The proof combines in novel fashion tools from recursive reconstruction, information theory, and optimal transport, and also establishes an asymptotic normality result for BP and other message-passing algorithms near the critical threshold.
Closed-Loop Memory GAN for Continual Learning.

Sequential learning of tasks using gradient descent leads to an unremitting decline in the accuracy of tasks for which training data is no longer available, termed catastrophic forgetting. Generative models have been explored as a means to approximate the distribution of old tasks and bypass storage of real data. Here we propose a cumulative closed-loop memory replay GAN (CloGAN) provided with external regularization by a small memory unit selected for maximum sample diversity. We evaluate incremental class learning using a notoriously hard paradigm, single-headed learning, in which each task is a disjoint subset of classes in the overall dataset, and performance is evaluated on all previous classes. First, we show that when constructing a dynamic memory unit to preserve sample heterogeneity, model performance asymptotically approaches training on the full dataset. We then show that using a stochastic generator to continuously output fresh new images during training increases performance significantly further meanwhile generating quality images. We compare our approach to several baselines including fine-tuning by gradient descent (FGD), Elastic Weight Consolidation (EWC), Deep Generative Replay (DGR) and Memory Replay GAN (MeRGAN). Our method has very low long-term memory cost, the memory unit, as well as negligible intermediate memory storage.
Reversing the Placebo: Performance‐Branded Experiences Can Undermine Consumer Performance

Products bearing premium brand labels are known to increase perceptions of efficacy and improve objective consumer performance relative to lesser-branded equivalents, in what is traditionally described as a marketing placebo effect. In this paper, we suggest that experiences bearing these highly-regarded brand labels can lead to a reverse effect, such that consumer performance actually declines with their use. Our findings demonstrate across domains of improving mental acuity, learning a new language, and developing financial analysis skills that completing performance-branded training experiences impairs objective performance in related tasks, relative to lower-performance branded or unbranded counterparts. We posit that branded training experiences can evoke a brand-as-master relationship in which consumers take on a subservient role relative to the brand. As a consequence, higher-performance brands may impose greater demands upon consumers, increasing performance-anxiety and interfering with an individual’s ability to perform effectively. These results document an important ramification of applying branding to learning experiences and identify contexts in which traditionally positive marketing actions can backfire for consumers.
Relevancy Classification of Multimodal Social Media Streams for Emergency Services

Social media has become an integral part of our daily lives. During time-critical events, the public shares a variety of posts on social media including reports for resource needs, damages, and help offerings for the affected community. Such posts can be relevant and may contain valuable situational awareness information. However, the information overload of social media challenges the timely processing and extraction of relevant information by the emergency services. Furthermore, the growing usage of multimedia content in the social media posts in recent years further adds to the challenge in timely mining relevant information from social media. In this paper, we present a novel method for multimodal relevancy classification of social media posts, where relevancy is defined with respect to the information needs of emergency management agencies. Specifically, we experiment with the combination of semantic textual features with the image features to efficiently classify a relevant multimodal social media post. We validate our method using an evaluation of classifying the data from three real-world crisis events. Our experiments demonstrate that features based on the proposed hybrid framework of exploiting both textual and image content improve the performance of identifying relevant posts. In the light of these experiments, the application of the proposed classification method could reduce cognitive load on emergency services, in filtering multimodal public posts at large scale.
Sham on You: Too Good to Be True

Of course, many taxpayers manipulate their affairs to minimize their taxes. The new December 2017 limits on state tax deductions evidence, however, the more endemic nature of the sham problem. Some state government officials have joined the loophole quest on behalf of their citizens. For instance, some states have proposed new state tax credits for “voluntary” contributions to state charities. Treasury Secretary Mnuchin aptly challenged this “ridiculous” attempt to “dress up” non-deductible tax payments as deductible charitable contributions. Despite Mnuchin’s convincing rebuke, state officials carry on with their ongoing avoidance efforts.In addition, similar avoidance schemes permeate many other legal areas such as property law, contract law, family law, and copyright law. While some courts strike down the sham attempts, other courts are less vigilant. Occasionally, some courts even endorse the workaround to achieve a desired goal.How should the legal system respond to all these legal gymnastics? First, lawmakers should avoid even sanctioned indirect workarounds. Even though intended as a narrowly circumscribed allowance, these endorsed maneuvers foster disrespect for legal requirements and create an air of legitimacy for other unintended manipulations. Instead, lawmakers should endeavor to achieve their desired ends more directly, without resort to these problematic formalisms.Unsanctioned schemes present a more intricate challenge. After analyzing the reasons behind the current judicial inertia, this Article crafts a new sham defense system sensitive to the root causes. A precise, but limited, sham definition provides the first line of a defense against the most egregious cases. Next, a balanced summary judgment approach further reinforces the new sham definition. Finally, legislative recommendations for vulnerable provisions complete the necessary protection.
Investment in the Shadow of Conflict: Globalization, Capital Control, and State Repression

In conflict-prone societies, the fear of expropriation that accompanies a regime change reduces capital investment. These reductions in investments, in turn, harm the economy, amplifying the likelihood of regime change. This article studies the implications of these feedback channels on the interactions between globalization, capital control, state repression, and regime change. I show that processes that facilitate capital movements (e.g., globalization, economic modernization, and technologies that reduce transportation costs) amplify the likelihood of regime change in conflict-prone societies and strengthen the elite’s demand for a strong coercive state. In particular, to limit their collective action problem and manage the political risk of regime change, capitalists support a state that imposes capital control. We identify two conflicting forces, the Boix Effect and the Marx Effect, which determine when capital control and state repression become complements (Nazi Germany) or substitutes (Latin American military regimes) in right-wing regimes.
Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging

Multiplexed in-situ fluorescent imaging offers several advantages over single-cell assays that do not preserve the spatial characteristics of biological samples. This spatial information, in addition to morphological properties and extensive intracellular or surface marker profiling, comprise promising avenues for rapid advancements in the understanding of disease progression and diagnosis. As protocols for conducting such imaging experiments continue to improve, it is the intent of this study to provide and validate software for processing the large quantity of associated data in kind. Cytokit offers (i) an end-to-end, GPU-accelerated image processing pipeline; (ii) efficient input/output (I/O) strategies for operations specific to high dimensional microscopy; and (iii) an interactive user interface for cross filtering of spatial, graphical, expression, and morphological cell properties within the 100+ GB image datasets common to multiplexed immunofluorescence. Image processing operations supported in Cytokit are generally sourced from existing deep learning models or are at least in part adapted from open source packages to run in a single or multi-GPU environment. The efficacy of these operations is demonstrated through several imaging experiments that pair Cytokit results with those from an independent but comparable assay. A further validation also demonstrates that previously published results can be reproduced from a publicly available multiplexed image dataset. Cytokit is a collection of open source tools for quantifying and analyzing properties of individual cells in large fluorescent microscopy datasets that are often, but not necessarily, generated from multiplexed antibody labeling protocols over many fields of view or time periods. This project is best suited to bioinformaticians or other technical users that wish to analyze such data in a batch-oriented, high-throughput setting. All source code, documentation, and data generated for this article are available under the Apache License 2.0 at https://github.com/hammerlab/cytokit .
The Changing Quality of Nonstandard Work Arrangements: Does Skill Matter?

This article explores the implications of nonstandard employment for types of workers and their change over time. Using data from 1995, 2005, and 2017, we trace the evolving forms of nonstandard employment over the last decade and the associated job-quality patterns for workers with different skills, measured by education levels and occupation tasks. We find that nonstandard employment reduces earnings and weekly work schedule but does not affect the likelihood of feeling insecure about job continuity for workers in general. However, a closer examination reveals considerable variation along these three dimensions: highly educated nonstandard workers have lower earnings and fewer working hours than traditional workers over time and nonstandard routine occupation workers tend to feel greater job insecurity. Variations across gender and race-ethnicity are also discussed.
Ketotifen Modulates Mast Cell Chemotaxis to Kit-Ligand, but Does Not Impact Mast Cell Numbers, Degranulation, or Tumor Behavior in Neurofibromas of Nf1-Deficient Mice.

Neurofibromatosis Type 1 (NF1) is one of the most common genetic tumor predisposition syndromes in humans. Mutant NF1 results in dysregulated RAS allowing neoplasms throughout the neuroaxis. Plexiform neurofibromas (pNF) afflict up to 50% of patients with NF1. They are complex tumors of the peripheral nerve that cause major morbidity via nerve dysregulation and mortality via conversion to malignant sarcoma. Genetically engineered mouse models (GEMM) of NF1 provide valuable insights for the identification of therapies that have utility in people with pNF. Preclinical studies in GEMMs implicate mast cells and the c-Kit/Kit ligand pathway in pNF tumorigenesis. Kit ligand is a potent chemokine secreted by tumorigenic, Nf1-deficient Schwann cells. Ketotifen is an FDA-approved drug for the treatment of allergic conjunctivitis and asthma that promotes mast cell stabilization and has been used in prior case studies to treat or prevent pNFs. This study investigated the effect of ketotifen on mast cell infiltration and degranulation in the presence and absence of Kit ligand provocation and the effect of ketotifen on shrinking or preventing pNF formation in the Nf1flox/flox;PostnCre+ GEMM. Ketotifen decreased mast cell infiltration in response to exogenous Kit ligand administration, but did not affect mast cell degranulation. Importantly, ketotifen did not reduce mast cells numbers or activity in pNF and did not prevent pNF formation or decrease the volume of established pNF despite administration of pharmacologically active doses. These findings suggest that ketotifen has limited use as monotherapy to prevent or reduce pNF burden in the setting of Nf1 mutations.
Feedback graph regret bounds for Thompson Sampling and UCB

We study the stochastic multi-armed bandit problem with the graph-based feedback structure introduced by Mannor and Shamir. We analyze the performance of the two most prominent stochastic bandit algorithms, Thompson Sampling and Upper Confidence Bound (UCB), in the graph-based feedback setting. We show that these algorithms achieve regret guarantees that combine the graph structure and the gaps between the means of the arm distributions. Surprisingly this holds despite the fact that these algorithms do not explicitly use the graph structure to select arms; they observe the additional feedback but do not explore based on it. Towards this result we introduce a "layering technique" highlighting the commonalities in the two algorithms.
Buccal: low-cost cheek sensing for inferring continuous jaw motion in mobile virtual reality.

Teleconferencing is touted to be one of the main and most powerful uses of virtual reality (VR). While subtle facial movements play a large role in human-to-human interactions, current work in the VR space has focused on identifying discrete emotions and expressions through coarse facial cues and gestures. By tracking and representing the fluid movements of facial elements as continuous range values, users are able to more fully express themselves. In this work, we present Buccal, a simple yet effective approach to inferring continuous lip and jaw motions by measuring deformations of the cheeks and temples with only 5 infrared proximity sensors embedded in a mobile VR headset. The signals from these sensors are mapped to facial movements through a regression model trained with ground truth labels recorded from a webcam. For a streamlined user experience, we train a user independent model that requires no setup process. Finally, we demonstrate the use of our technique to manipulate the lips and jaw of a 3D face model in real-time.
Compensation for Loss of Life and Limb

This article discusses the direct economic costs, indirect economic costs, and noneconomic costs associated with injuries and fatalities. Different methods for putting a value on noneconomic costs, often referred to as pain and suffering, are compared, and the types of compensation available for these different costs, including private insurance, public insurance, and the tort system, are examined. Next, a detailed discussion of how the tort system treats compensation for loss of life and limb is given. Finally, this article considers how life and limb are valued in other contexts, such as setting government regulations and assessing health care priorities. The focus is on the United States. Keywords: compensation; personal injury; value of a statistical life (VSL); quality-adjusted life year (QALY); health utility; revealed preferences; tort; economic damages; pain and suffering; punitive damages
RSANet: Recurrent Slice-Wise Attention Network for Multiple Sclerosis Lesion Segmentation

Brain lesion volume measured on T2 weighted MRI images is a clinically important disease marker in multiple sclerosis (MS). Manual delineation of MS lesions is a time-consuming and highly operator-dependent task, which is influenced by lesion size, shape and conspicuity. Recently, automated lesion segmentation algorithms based on deep neural networks have been developed with promising results. In this paper, we propose a novel recurrent slice-wise attention network (RSANet), which models 3D MRI images as sequences of slices and captures long-range dependencies through a recurrent manner to utilize contextual information of MS lesions. Experiments on a dataset with 43 patients show that the proposed method outperforms the state-of-the-art approaches. Our implementation is available online at https://github.com/tinymilky/RSANet.
Real Effects of PCAOB International Inspections

This paper examines the effect of the Public Company Accounting Oversight Board (PCAOB) international inspection program on companies' financing and investing decisions. Estimates from difference-i...