nils-herrmann/clean_scirepeval huggingface dataset:
Quantum walk search on the complete bipartite graph

The coined quantum walk is a discretization of the Dirac equation of relativistic quantum mechanics, and it is the basis of many quantum algorithms. We investigate how it searches the complete bipartite graph of $N$ vertices for one of $k$ marked vertices with different initial states. We prove intriguing dependence on the number of marked and unmarked vertices in each partite set. For example, when the graph is irregular and the initial state is the typical uniform superposition over the vertices, then the success probability can vary greatly from one timestep to the next, even alternating between 0 and 1, so the precise time at which measurement occurs is crucial. When the initial state is a uniform superposition over the edges, however, the success probability evolves smoothly. As another example, if the complete bipartite graph is regular, then the two initial states are equivalent. Then if two marked vertices are in the same partite set, the success probability reaches 1/2, but if they are in different partite sets, it instead reaches $1$. This differs from the complete graph, which is the quantum walk formulation of Grover's algorithm, where the success probability with two marked vertices is 8/9. This reveals a contrast to the continuous-time quantum walk, whose evolution is governed by Schr\"odinger's equation, which asymptotically searches the regular complete bipartite graph with any arrangement of marked vertices in the same manner as the complete graph.
Coupled Representation Learning for Domains, Intents and Slots in Spoken Language Understanding

Representation learning is an essential problem in a wide range of applications and it is important for performing downstream tasks successfully. In this paper, we propose a new model that learns coupled representations of domains, intents, and slots by taking advantage of their hierarchical dependency in a Spoken Language Understanding system. Our proposed model learns the vector representation of intents based on the slots tied to these intents by aggregating the representations of the slots. Similarly, the vector representation of a domain is learned by aggregating the representations of the intents tied to a specific domain. To the best of our knowledge, it is the first approach to jointly learning the representations of domains, intents, and slots using their hierarchical relationships. The experimental results demonstrate the effectiveness of the representations learned by our model, as evidenced by improved performance on the contextual cross-domain reranking task.
Probing spectral properties of the QGP with real-time lattice simulations

We present a new method to obtain spectral properties of a non-Abelian gauge theory in the region where occupation numbers are high. The method to measure the (single-particle) spectral function is based on linear response theory and classical-statistical lattice simulations. Although we apply it to a system far from equilibrium in a self-similar regime, the extracted spectral function can be understood within the hard thermal loop (HTL) formalism and can thus be connected to thermal equilibrium at high temperatures. This allows us to obtain quantities like the lifetime of quasiparticles that are beyond the leading order and difficult to compute within HTL. The approach has the potential to measure transport coefficients, to study the earliest stages of heavy-ion collisions in a controlled way and it can be employed beyond the range of validity of HTL.
Minimal length effect on the broadening of free wave packets and its physical implications

We study the Generalized Uncertainty Principle (GUP) modified time evolution for the width of wave-packets for a scalar potential. Free particle case is solved exactly where the wave-packet broadening is modified by a coupling between the GUP parameter and higher order moments in the probability distribution in momentum space. We consider two popular forms of deformations widely used in the literature - one of which modifies the commutator with a quadratic term in momentum, while the other modifies it with terms both linear and quadratic in momentum. Unlike the standard case, satisfying Heisenberg uncertainty, here the GUP modified broadening rates, for both deformations, not only depend on the initial size (both in position and momentum space) of the wave-packet, but also on the initial probability distribution and momentum of the particle. The new rates of wave-packet broadening, for both situations, are modified by a handful of new terms - such as the skewness and kurtosis coefficients, as well as the (constant) momentum of the particle. Comparisons with the standard Heisenberg Uncertainty Principle (HUP)-based results show potentially measurable differences in the rates of free wave-packet broadening for physical systems such as the $C_{60}$ and $C_{176}$ molecules, and more so for large organic molecular wave-packets. In doing so, we open a path to scan the GUP parameter space by several orders of magnitude inside the best existing upper bounds for both forms of GUP.
Simulation to scaled city: zero-shot policy transfer for traffic control via autonomous vehicles

Using deep reinforcement learning, we successfully train a set of two autonomous vehicles to lead a fleet of vehicles onto a round-about and then transfer this policy from simulation to a scaled city without fine-tuning. We use Flow, a library for deep reinforcement learning in microsimulators, to train two policies, (1) a policy with noise injected into the state and action space and (2) a policy without any injected noise. In simulation, the autonomous vehicles learn an emergent metering behavior for both policies which allows smooth merging. We then directly transfer this policy without any tuning to the University of Delaware's Scaled Smart City (UDSSC), a 1:25 scale testbed for connected and automated vehicles. We characterize the performance of the transferred policy based on how thoroughly the ramp metering behavior is captured in UDSSC. We show that the noise-free policy results in severe slowdowns and only, occasionally, it exhibits acceptable metering behavior. On the other hand, the noise-injected policy consistently performs an acceptable metering behavior, implying that the noise eventually aids with the zero-shot policy transfer. Finally, the transferred, noise-injected policy leads to a 5% reduction of average travel time and a reduction of 22% in maximum travel time in the UDSSC. Videos of the proposed self-learning controllers can be found at https://sites.google.com/view/iccps-policy-transfer.
Probing neutron star structure via 
f
-mode oscillations and damping in dynamical spacetime models

Gravitational wave and electromagnetic observations can provide new insights into the nature of matter at supra-nuclear densities inside neutron stars. Improvements in electromagnetic and gravitational wave sensing instruments continue to enhance the accuracy with which they can measure the masses, radii, and tidal deformability of neutron stars. These better measurements place tighter constraints on the equation of state of cold matter above nuclear density. In this article, we discuss a complementary approach to get insights into the structure of neutron stars by providing a model prediction for non-linear fundamental eigenmodes (f-modes) and their decay over time, which are thought to be induced by time-dependent tides in neutron star binaries. Building on pioneering studies that relate the properties of f-modes to the structure of neutron stars, we systematically study this link in the non-perturbative regime using models that utilize numerical relativity. Using a suite of fully relativistic numerical relativity simulations of oscillating TOV stars, we establish blueprints for the numerical accuracy needed to accurately compute the frequency and damping times of f-mode oscillations, which we expect to be a good guide for the requirements in the binary case. We show that the resulting f-mode frequencies match established results from linear perturbation theory, but the damping times within numerical errors depart from linear predictions. This work lays the foundation for upcoming studies aimed at a comparison of theoretical models of f-mode signatures in gravitational waves, and their uncertainties with actual gravitational wave data, searching for neutron star binaries on highly eccentric orbits, and probing neutron star structure at high densities.
Bias Mitigation Post-processing for Individual and Group Fairness

Whereas previous post-processing approaches for increasing the fairness of predictions of biased classifiers address only group fairness, we propose a method for increasing both individual and group fairness. Our novel framework includes an individual bias detector used to prioritize data samples in a bias mitigation algorithm aiming to improve the group fairness measure of disparate impact. We show superior performance to previous work in the combination of classification accuracy, individual fairness and group fairness on several real-world datasets in applications such as credit, employment, and criminal justice.
A better lower bound on the family complexity of binary Legendre sequence

In this paper we study a family of binary Legendre sequences and its family complexity. Family complexity is a pseudorandomness measure introduced by Ahlswede et.~al.~in 2003. A lower bound on the family complexity of a family based on the Legendre symbol of polynomials over a finite field was given by Gyarmati in 2015. In this article we improve the bound given by Gyarmati on family complexity of binary Legendre sequences. The bound depends on the Lambert W function and the number of elements in a finite field belonging to its proper subfield. Moreover, we present a fast method for calculating the bound.
An Experimental Information Gathering and Utilization Systems (IGUS) Robot to Demonstrate the Physics of Now

The past, present, and future are not fundamental properties of Minkowski spacetime. It has been suggested that they are properties of a class of information gathering and utilizing systems (IGUSs). A human is a model IGUS robot. We provide a demonstration that what is perceived as past, present, and future is not uniquely determined from the laws of physics by constructing a robot that process information differently and therefore experience different “presents.” We construct a customized virtual reality (VR) system which allows an observer to switch between present and past moments. This “robot” (human with VR system) can experience immersion in the immediate past ad libitum. Being able to actually construct an IGUS that has the same “present” at two different coordinates along the worldline lends support to the IGUS approach for explaining the psychological nature of past/present/future.
Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking

Recently, the region proposal networks (RPN) have been combined with the Siamese network for tracking, and shown excellent accuracy with high efficiency. Nevertheless, previously proposed one-stage Siamese-RPN trackers degenerate in presence of similar distractors and large scale variation. Addressing these issues, we propose a multi-stage tracking framework, Siamese Cascaded RPN (C-RPN), which consists of a sequence of RPNs cascaded from deep high-level to shallow low-level layers in a Siamese network. Compared to previous solutions, C-RPN has several advantages: (1) Each RPN is trained using the outputs of RPN in the previous stage. Such process stimulates hard negative sampling, resulting in more balanced training samples. Consequently, the RPNs are sequentially more discriminative in distinguishing difficult background (i.e.,, similar distractors). (2) Multi-level features are fully leveraged through a novel feature transfer block (FTB) for each RPN, further improving the discriminability of C-RPN using both high-level semantic and low-level spatial information. (3) With multiple steps of regressions, C-RPN progressively refines the location and shape of the target in each RPN with adjusted anchor boxes in the previous stage, which makes localization more accurate. C-RPN is trained end-to-end with the multi-task loss function. In inference, C-RPN is deployed as it is, without any temporal adaption, for real-time tracking. In extensive experiments on OTB-2013, OTB-2015, VOT-2016, VOT-2017, LaSOT and TrackingNet, C-RPN consistently achieves state-of-the-art results and runs in real-time.
A Parametric Top-View Representation of Complex Road Scenes

In this paper, we address the problem of inferring the layout of complex road scenes given a single camera as input. To achieve that, we first propose a novel parameterized model of road layouts in a top-view representation, which is not only intuitive for human visualization but also provides an interpretable interface for higher-level decision making. Moreover, the design of our top-view scene model allows for efficient sampling and thus generation of large-scale simulated data, which we leverage to train a deep neural network to infer our scene model's parameters. Specifically, our proposed training procedure uses supervised domain-adaptation techniques to incorporate both simulated as well as manually annotated data. Finally, we design a Conditional Random Field (CRF) that enforces coherent predictions for a single frame and encourages temporal smoothness among video frames. Experiments on two public data sets show that: (1) Our parametric top-view model is representative enough to describe complex road scenes; (2) The proposed method outperforms baselines trained on manually-annotated or simulated data only, thus getting the best of both; (3) Our CRF is able to generate temporally smoothed while semantically meaningful results.
Few-shot classification in named entity recognition task

For many natural language processing (NLP) tasks the amount of annotated data is limited. This urges a need to apply semi-supervised learning techniques, such as transfer learning or meta-learning. In this work we tackle Named Entity Recognition (NER) task using Prototypical Network --- a metric learning technique. It learns intermediate representations of words which cluster well into named entity classes. This property of the model allows classifying words with extremely limited number of training examples, and can potentially be used as a zero-shot learning method. By coupling this technique with transfer learning we achieve well-performing classifiers trained on only 20 instances of a target class.
Specification-Guided Safety Verification for Feedforward Neural Networks

This paper presents a specification-guided safety verification method for feedforward neural networks with general activation functions. As such feedforward networks are memoryless, they can be abstractly represented as mathematical functions, and the reachability analysis of the neural network amounts to interval analysis problems. In the framework of interval analysis, a computationally efficient formula which can quickly compute the output interval sets of a neural network is developed. Then, a specification-guided reachability algorithm is developed. Specifically, the bisection process in the verification algorithm is completely guided by a given safety specification. Due to the employment of the safety specification, unnecessary computations are avoided and thus the computational cost can be reduced significantly. Experiments show that the proposed method enjoys much more efficiency in safety verification with significantly less computational cost.
An Empirical Model of Large-Batch Training

In an increasing number of domains it has been demonstrated that deep learning models can be trained using relatively large batch sizes without sacrificing data efficiency. However the limits of this massive data parallelism seem to differ from domain to domain, ranging from batches of tens of thousands in ImageNet to batches of millions in RL agents that play the game Dota 2. To our knowledge there is limited conceptual understanding of why these limits to batch size differ or how we might choose the correct batch size in a new domain. In this paper, we demonstrate that a simple and easy-to-measure statistic called the gradient noise scale predicts the largest useful batch size across many domains and applications, including a number of supervised learning datasets (MNIST, SVHN, CIFAR-10, ImageNet, Billion Word), reinforcement learning domains (Atari and Dota), and even generative model training (autoencoders on SVHN). We find that the noise scale increases as the loss decreases over a training run and depends on the model size primarily through improved model performance. Our empirically-motivated theory also describes the tradeoff between compute-efficiency and time-efficiency, and provides a rough model of the benefits of adaptive batch-size training.
Ordering the smallest claim amounts from two sets of interdependent heterogeneous portfolios

Let $ X_{\lambda_1},\ldots,X_{\lambda_n}$ be a set of dependent and non-negative random variables share a survival copula and let $Y_i= I_{p_i}X_{\lambda_i}$, $i=1,\ldots,n$, where $I_{p_1},\ldots,I_{p_n}$ be independent Bernoulli random variables independent of $X_{\lambda_i}$'s, with ${\rm E}[I_{p_i}]=p_i$, $i=1,\ldots,n$. In actuarial sciences, $Y_i$ corresponds to the claim amount in a portfolio of risks. This paper considers comparing the smallest claim amounts from two sets of interdependent portfolios, in the sense of usual and likelihood ratio orders, when the variables in one set have the parameters $\lambda_1,\ldots,\lambda_n$ and $p_1,\ldots,p_n$ and the variables in the other set have the parameters $\lambda^{*}_1,\ldots,\lambda^{*}_n$ and $p^*_1,\ldots,p^*_n$. Also, we present some bounds for survival function of the smallest claim amount in a portfolio. To illustrate validity of the results, we serve some applicable models.
Bootstrapping Conversational Agents With Weak Supervision

Many conversational agents in the market today follow a standard bot development framework which requires training intent classifiers to recognize user input. The need to create a proper set of training examples is often the bottleneck in the development process. In many occasions agent developers have access to historical chat logs that can provide a good quantity as well as coverage of training examples. However, the cost of labeling them with tens to hundreds of intents often prohibits taking full advantage of these chat logs. In this paper, we present a framework called search, label, and propagate (SLP) for bootstrapping intents from existing chat logs using weak supervision. The framework reduces hours to days of labeling effort down to minutes of work by using a search engine to find examples, then relies on a data programming approach to automatically expand the labels. We report on a user study that shows positive user feedback for this new approach to build conversational agents, and demonstrates the effectiveness of using data programming for autolabeling. While the system is developed for training conversational agents, the framework has broader application in significantly reducing labeling effort for training text classifiers.
Nonabelian Hodge theory and vector valued
 modular forms

We examine the relationship between nonabelian Hodge theory for Riemann surfaces and the theory of vector valued modular forms. In particular, we explain how one might use this relationship to prove a conjectural three-term inequality on the weights of free bases of vector valued modular forms associated to complex, finite dimensional, irreducible representations of the modular group. This conjecture is known for irreducible unitary representations and for all irreducible representations of dimension at most 12. We prove new instances of the three-term inequality for certain nonunitary representations, corresponding to a class of maximally-decomposed variations of Hodge structure, by considering the same inequality with respect to a new type of modular form, called a "Higgs form", that arises naturally on the Dolbeault side of nonabelian Hodge theory. The paper concludes with a discussion of a strategy for reducing the general case of nilpotent Higgs bundles to the case under consideration in our main theorem.
Systemic risk measures with markets volatility

As systemic risk has become a hot topic in the financial markets, how to measure, allocate and regulate the systemic risk are becoming especially important. However, the financial markets are becoming more and more complicate, which makes the usual study of systemic risk to be restricted. In this paper, we will study the systemic risk measures on a special space $L^{p(\cdot)}$ where the variable exponent $p(\cdot)$ is no longer a given real number like the space $L^{p}$, but a random variable, which reflects the possible volatility of the financial markets. Finally, the dual representation for this new systemic risk measures will be studied. Our results show that every this new systemic risk measure can be decomposed into a convex certain function and a simple-systemic risk measure, which provides a new ideas for dealing with the systemic risk.
Learning Latent Subspaces in Variational Autoencoders

Variational autoencoders (VAEs) are widely used deep generative models capable of learning unsupervised latent representations of data. Such representations are often difficult to interpret or control. We consider the problem of unsupervised learning of features correlated to specific labels in a dataset. We propose a VAE-based generative model which we show is capable of extracting features correlated to binary labels in the data and structuring it in a latent subspace which is easy to interpret. Our model, the Conditional Subspace VAE (CSVAE), uses mutual information minimization to learn a low-dimensional latent subspace associated with each label that can easily be inspected and independently manipulated. We demonstrate the utility of the learned representations for attribute manipulation tasks on both the Toronto Face and CelebA datasets.
Extraction of the CKM phase 
γ
 using charmless three-body decays of 
B
 mesons

The weak phase $\gamma$ is extracted from three-body charmless decays of $B$ mesons following a method proposed by Bhattacharya, Imbeault \& London. The result is obtained by combining the BABAR amplitude analyses for the processes $B^0 \rightarrow K^+ \pi^0 \pi^-$, $B^0 \rightarrow K_S^0 \pi^+ \pi^-$, $B^0 \rightarrow K_S^0 K_S^0 K_S^0$, $B^0 \rightarrow K^+ K_S^0 K^-$ and $B^+ \rightarrow K^+ \pi^+ \pi^-$, under the assumption of $\alpha_{\rm{SU(3)}}$ flavour symmetry. Six possible solutions are found. One solution is compatible with the Standard Model while the others are not. It is also found that, when averaged over the entire Dalitz plane, the effect of $\alpha_{\rm{SU(3)}}$ breaking on the analysis is only at the percent level.
Inter-Sentence Relation Extraction for Associating Biological Context with Events in Biomedical Texts

We present an analysis of the problem of identifying biological context and associating it with biochemical events in biomedical texts. This constitutes a non-trivial, inter-sentential relation extraction task. We focus on biological context as descriptions of the species, tissue type and cell type that are associated with biochemical events. We describe the properties of an annotated corpus of context-event relations and present and evaluate several classifiers for context-event association trained on syntactic, distance and frequency features.