nils-herrmann/clean_scirepeval huggingface dataset:
Accelerated-Cherenkov radiation and signatures of radiation reaction

In this manuscript we examine an accelerated charged particle moving through an optical medium, and explore the emission of accelerated-Cherenkov radiation. The particle’s reaction to acceleration creates a low-frequency spectral cutoff in the Cherenkov emission that has a sharp resonance at the superluminal threshold. Moreover, the effect of recoil on the radiation is incorporated kinematically through the use of an Unruh-DeWitt detector by setting an energy gap, i.e. the change in electron energy, to the recoil energy of the emitted photon. The simultaneous presence of recoil and acceleration conspire to produce a localized resonance peak in the emission. These theoretical considerations could be used to construct high precision tests of radiation reaction using Cherenkov emission under acceleration.
Augmenting the fine beam tube: From hybrid measurements to magnetic field visualization

Since the emergence of augmented reality (AR), it has been a constant subject of educational research, as it can improve conceptual understanding and generally promote learning. In addition, a motivational effect and improved interaction and collaboration through AR were observed. Recently, AR technologies have taken a major leap forward in development, such that head-mounted devices or smartglasses in particular are now finding their first applications in STEM education, especially in experiments. In line with these developments, we here present an AR experiment in electrodynamics for undergraduate laboratory courses in physics using real-time physical data from and virtual tools on mobile devices to both analyze and visualize physical phenomena.Since the emergence of augmented reality (AR), it has been a constant subject of educational research, as it can improve conceptual understanding and generally promote learning. In addition, a motivational effect and improved interaction and collaboration through AR were observed. Recently, AR technologies have taken a major leap forward in development, such that head-mounted devices or smartglasses in particular are now finding their first applications in STEM education, especially in experiments. In line with these developments, we here present an AR experiment in electrodynamics for undergraduate laboratory courses in physics using real-time physical data from and virtual tools on mobile devices to both analyze and visualize physical phenomena.
Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data

Informal settlements are home to the most socially and economically vulnerable people on the planet. In order to deliver effective economic and social aid, non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), require detailed maps of the locations of informal settlements. However, data regarding informal and formal settlements is primarily unavailable and if available is often incomplete. This is due, in part, to the cost and complexity of gathering data on a large scale. To address these challenges, we, in this work, provide three contributions. 1) A brand new machine learning dataset purposely developed for informal settlement detection. 2) We show that it is possible to detect informal settlements using freely available low-resolution (LR) data, in contrast to previous studies that use very-high resolution~(VHR) satellite and aerial imagery, something that is cost-prohibitive for NGOs. 3) We demonstrate two effective classification schemes on our curated data set, one that is cost-efficient for NGOs and another that is cost-prohibitive for NGOs, but has additional utility. We integrate these schemes into a semi-automated pipeline that converts either a LR or VHR satellite image into a binary map that encodes the locations of informal settlements.
Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler

State space models (SSM) have been widely applied for the analysis and visualization of large sequential datasets. Sequential Monte Carlo (SMC) is a very popular particle-based method to sample latent states from intractable posteriors. However, SSM is significantly influenced by the choice of the proposal. Recently Hamiltonian Monte Carlo (HMC) sampling has shown success in many practical problems. In this paper, we propose an SMC augmented by HMC (HSMC) for inference and model learning of nonlinear SSM, which can exempt us from learning proposals and reduce the model complexity significantly. Based on the measure preserving property of HMC, the particles directly generated by transition function can approximate the posterior of latent states arbitrarily well. In order to better adapt to the local geometry of latent space, the HMC is conducted on Riemannian manifold defined by a positive definite metric. In addition, we show that the proposed HSMC method can improve SSMs realized by both Gaussian Processes (GP) and Neural Network (NN).
Host Galaxies of Type Ic and Broad-lined Type Ic Supernovae from the Palomar Transient Factory: Implications for Jet Production

Unlike ordinary supernovae (SNe), some of which are hydrogen and helium deficient (called Type Ic SNe), broad-lined Type Ic SNe (SNe Ic-bl) are very energetic events, and only SNe Ic-bl are coincident with long-duration gamma-ray bursts (GRBs). Understanding the progenitors of SN Ic-bl explosions versus those of their SN Ic cousins is key to understanding the SN–GRB relationship and jet production in massive stars. Here we present the largest existing set of host galaxy spectra of 28 SNe Ic and 14 SNe Ic-bl, all discovered by the same galaxy-untargeted survey, namely, the Palomar Transient Factory (PTF). We carefully measure their gas-phase metallicities, stellar masses (M*), and star formation rates (SFRs). We further reanalyze the hosts of 10 literature SN–GRBs using the same methods and compare them to our PTF SN hosts with the goal of constraining their progenitors from their local environments. We find that the metallicities, SFRs, and M* values of our PTF SN Ic-bl hosts are statistically comparable to those of SN–GRBs but significantly lower than those of the PTF SNe Ic. The mass–metallicity relations as defined by the SNe Ic-bl and SN–GRBs are not significantly different from the same relations as defined by Sloan Digital Sky Survey galaxies, contradicting claims by earlier works. Our findings point toward low metallicity as a crucial ingredient for SN Ic-bl and SN–GRB production since we are able to break the degeneracy between high SFR and low metallicity. We suggest that the PTF SNe Ic-bl may have produced jets that were choked inside the star or were able to break out of the star as unseen low-luminosity or off-axis GRBs.
A Network-based Multimodal Data Fusion Approach for Characterizing Dynamic Multimodal Physiological Patterns

Characterizing the dynamic interactive patterns of complex systems helps gain in-depth understanding of how components interrelate with each other while performing certain functions as a whole. In this study, we present a novel multimodal data fusion approach to construct a complex network, which models the interactions of biological subsystems in the human body under emotional states through physiological responses. Joint recurrence plot and temporal network metrics are employed to integrate the multimodal information at the signal level. A benchmark public dataset of is used for evaluating our model.
Subspace Match Probably Does Not Accurately Assess the Similarity of Learned Representations

Learning informative representations of data is one of the primary goals of deep learning, but there is still little understanding as to what representations a neural network actually learns. To better understand this, subspace match was recently proposed as a method for assessing the similarity of the representations learned by neural networks. It has been shown that two networks with the same architecture trained from different initializations learn representations that at hidden layers show low similarity when assessed with subspace match, even when the output layers show high similarity and the networks largely exhibit similar performance on classification tasks. In this note, we present a simple example motivated by standard results in commutative algebra to illustrate how this can happen, and show that although the subspace match at a hidden layer may be 0, the representations learned may be isomorphic as vector spaces. This leads us to conclude that a subspace match comparison of learned representations may well be uninformative, and it points to the need for better methods of understanding learned representations.
An Interactive Robotic Framework to Facilitate Sensory Experiences for Children with ASD

The diagnosis of Autism Spectrum Disorder (ASD) in children is commonly accompanied by a diagnosis of sensory processing disorders as well. Abnormalities are usually reported in multiple sensory processing domains, showing a higher prevalence of unusual responses, particularly to tactile, auditory and visual stimuli. This paper discusses a novel robot-based framework designed to target sensory difficulties faced by children with ASD in a controlled setting. The setup consists of a number of sensory stations, together with robotic agents that navigate the stations and interact with the stimuli as they are presented. These stimuli are designed to resemble real world scenarios that form a common part of one's everyday experiences. Given the strong interest of children with ASD in technology in general and robots in particular, we attempt to utilize our robotic platform to demonstrate socially acceptable responses to the stimuli in an interactive, pedagogical setting that encourages the child's social, motor and vocal skills, while providing a diverse sensory experience. A user study was conducted to evaluate the efficacy of the proposed framework, with a total of 18 participants (5 with ASD and 13 typically developing) between the ages of 4 and 12 years. We describe our methods of data collection, coding of video data and the analysis of the results obtained from the study. We also discuss the limitations of the current work and detail our plans for the future work to improve the validity of the obtained results.
Imminent Collision Mitigation with Reinforcement Learning and Vision

This work examines the role of reinforcement learning in reducing the severity of on-road collisions by controlling velocity and steering in situations in which contact is imminent. We construct a model, given camera images as input, that is capable of learning and predicting the dynamics of obstacles, cars and pedestrians, and train our policy using this model. Two policies that control both braking and steering are compared against a baseline where the only action taken is (conventional) braking in a straight line. The two policies are trained using two distinct reward structures, one where any and all collisions incur a fixed penalty, and a second one where the penalty is calculated based on already established delta-v models of injury severity. The results show that both policies exceed the performance of the baseline, with the policy trained using injury models having the highest performance.
High-precision Dark Halo Virial Masses from Globular Cluster Numbers: Implications for Globular Cluster Formation and Galaxy Assembly

We confirm that the number of globular clusters (GCs), NGC, is an excellent tracer of their host galaxy’s halo virial mass, Mvir. The simple linear relation Mvir = 5 × 109 M⊙ × NGC fits the data perfectly from Mvir = 1010 M⊙ to Mvir = 2 × 1015 M⊙. This result is independent of galaxy morphology and extends statistically into the dwarf galaxy regime with Mvir = 108–1010 M⊙, including the extreme ultra diffuse galaxy DF44. As this correlation does not depend on GC mass, it is ideally suited for high-precision determinations of Mvir. The linearity is most simply explained by cosmological merging of a high-redshift halo seed population that hosted on average one GC per 5 × 108 M⊙ of dark matter. We show that hierarchical merging is also extremely powerful in restoring a linear correlation and erasing signatures of even a strong secular evolution of GC systems. The cosmological merging scenario also implies a strong decline of the scatter in NGC with increasing virial mass in contrast with the observations that show a roughly constant scatter, independent of virial mass. This discrepancy can be explained if errors in determining virial masses from kinematical tracers and gravitational lensing are on the order of a factor of 2. GCs in dwarf satellite galaxies pose a serious problem for high-redshift GC formation scenarios; the dark halo masses of dwarf galaxies hosting GCs therefore might need to be an order of magnitude larger than currently estimated.
A Model for Learned Bloom Filters and Optimizing by Sandwiching

Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to determine a function that models the data set the Bloom filter is meant to represent. Here we model such learned Bloom filters, with the following outcomes: (1) we clarify what guarantees can and cannot be associated with such a structure; (2) we show how to estimate what size the learning function must obtain in order to obtain improved performance; (3) we provide a simple method, sandwiching, for optimizing learned Bloom filters; and (4) we propose a design and analysis approach for a learned Bloomier filter, based on our modeling approach.
Piezoelectrically Tuned Multimode Cavity Search for Axion Dark Matter.

The μeV axion is a well-motivated extension to the standard model. The Axion Dark Matter eXperiment (ADMX) collaboration seeks to discover this particle by looking for the resonant conversion of dark-matter axions to microwave photons in a strong magnetic field. In this Letter, we report results from a pathfinder experiment, the ADMX "Sidecar," which is designed to pave the way for future, higher mass, searches. This testbed experiment lives inside of and operates in tandem with the main ADMX experiment. The Sidecar experiment excludes masses in three widely spaced frequency ranges (4202-4249, 5086-5799, and 7173-7203 MHz). In addition, Sidecar demonstrates the successful use of a piezoelectric actuator for cavity tuning. Finally, this publication is the first to report data measured using both the TM_{010} and TM_{020} modes.
Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy games

Decision-making problems can be modeled as combinatorial optimization problems with Constraint Programming formalisms such as Constrained Optimization Problems. However, few Constraint Programming formalisms can deal with both optimization and uncertainty at the same time, and none of them are convenient to model problems we tackle in this paper. Here, we propose a way to deal with combinatorial optimization problems under uncertainty within the classical Constrained Optimization Problems formalism by injecting the Rank Dependent Utility from decision theory. We also propose a proof of concept of our method to show it is implementable and can solve concrete decision-making problems using a regular constraint solver, and propose a bot that won the partially observable track of the 2018 µRTS AI competition. Our result shows it is possible to handle uncertainty with regular Constraint Programming solvers, without having to define a new formalism neither to develop dedicated solvers. This brings new perspective to tackle uncertainty in Constraint Programming.
Self-supervised Learning of Image Embedding for Continuous Control

Operating directly from raw high dimensional sensory inputs like images is still a challenge for robotic control. Recently, Reinforcement Learning methods have been proposed to solve specific tasks end-to-end, from pixels to torques. However, these approaches assume the access to a specified reward which may require specialized instrumentation of the environment. Furthermore, the obtained policy and representations tend to be task specific and may not transfer well. In this work we investigate completely self-supervised learning of a general image embedding and control primitives, based on finding the shortest time to reach any state. We also introduce a new structure for the state-action value function that builds a connection between model-free and model-based methods, and improves the performance of the learning algorithm. We experimentally demonstrate these findings in three simulated robotic tasks.
Rogers-Ramanujan-Slater Type Identities

In this survey article, we present an expanded version of Lucy Slater’s famous list of identities of the Rogers-Ramanujan type, including identities of similar type, which were discovered after the publication of Slater’s papers, and older identities (such as those in Ramanujan’s lost notebook) which were not included in Slater’s papers. We attempt to supply the earliest known reference for each identity. Also included are identities of false theta functions, along with their relationship to RogersRamanujan type identities. We also describe several ways in which pairs/larger sets of identities may be related, as well as dependence relationships between identities.
Scalar field dark matter spectator during inflation: the effect of self-interaction

Nowadays cosmological inflation is the most accepted mechanism to explain the primordial seeds that led to the structure formation observed in the Universe. Current observations are in well agreement to initial adiabatic conditions, which imply that single-scalar-field inflation may be enough to describe the early Universe. However, there are several scenarios where the existence of more than a single field could be relevant during this period, for instance, the situation where the so-called spectator is present. Within the spectator scenario we can find the possibility that an ultra-light scalar field dark matter candidate could coexist with the inflaton. In this work we study this possibility where the additional scalar field could be free or self-interacting. We use isocurvature observations to constrain the free parameters of the model.
Space Expansion of Feature Selection for Designing more Accurate Error Predictors

Approximate computing is being considered as a promising design paradigm to overcome the energy and performance challenges in computationally demanding applications. If the case where the accuracy can be configured, the quality level versus energy efficiency or delay also may be traded-off. For this technique to be used, one needs to make sure a satisfactory user experience. This requires employing error predictors to detect unacceptable approximation errors. In this work, we propose a scheduling-aware feature selection method which leverages the intermediate results of the hardware accelerator to improve the prediction accuracy. Additionally, it configures the error predictors according to the energy consumption and latency of the system. The approach enjoys the flexibility of the prediction time for a higher accuracy. The results on various benchmarks demonstrate significant improvements in the prediction accuracy compared to the prior works which used only the accelerator inputs for the prediction.
Unsupervised Learning of Depth and Ego-Motion from Panoramic Video

We introduce a convolutional neural network model for unsupervised learning of depth and ego-motion from cylindrical panoramic video. Panoramic depth estimation is an important technology for applications such as autonomous robotic navigation, virtual reality, and 3D modeling. In contrast to previous approaches for applying convolutional neural networks to panoramic imagery, we use the cylindrical panoramic projection which allows for the use of the traditional CNN layers such as convolutional filters and max pooling without modification. Our evaluation on synthetic and real data shows that unsupervised learning of depth and ego-motion on panoramic images increases depth prediction accuracy in comparison to training on perspective images which have a narrower field-of-view.
Analytically solvable autocorrelation function for weakly correlated interevent times.

Long-term temporal correlations observed in event sequences of natural and social phenomena have been characterized by algebraically decaying autocorrelation functions. Such temporal correlations can be understood not only by heterogeneous interevent times (IETs) but also by correlations between IETs. In contrast to the role of heterogeneous IETs on the autocorrelation function, little is known about the effects due to the correlations between IETs. To rigorously study these effects, we derive an analytical form of the autocorrelation function for the arbitrary IET distribution in the case with weakly correlated IETs, where the Farlie-Gumbel-Morgenstern copula is adopted for modeling the joint probability distribution function of two consecutive IETs. Our analytical results are confirmed by numerical simulations for exponential and power-law IET distributions. For the power-law case, we find a tendency of the steeper decay of the autocorrelation function for the stronger correlation between IETs. Our analytical approach enables us to better understand long-term temporal correlations induced by the correlations between IETs.
Orthogonal graphs modulo power of 2

In this work, we define an orthogonal graph on the set of equivalence classes of tuples over where n and ν are positive integers and or 2. We classify our graph if it is strongly regular or quasi-strongly regular and compute all parameters precisely. We show that our graph is arc transitive. The automorphisms group is given and the chromatic number of the graph except when and ν is odd is determined. Moreover, we work on subconstituents of this orthogonal graph.
A Scalable Framework for Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters with Weight and Workload Balancing

Deep Neural Networks (DNNs) have revolutionized numerous applications, but the demand for ever more performance remains unabated. Scaling DNN computations to larger clusters is generally done by distributing tasks in batch mode using methods such as distributed synchronous SGD. Among the issues with this approach is that to make the distributed cluster work with high utilization, the workload distributed to each node must be large, which implies nontrivial growth in the SGD mini-batch size. 
In this paper, we propose a framework called FPDeep, which uses a hybrid of model and layer parallelism to configure distributed reconfigurable clusters to train DNNs. This approach has numerous benefits. First, the design does not suffer from batch size growth. Second, novel workload and weight partitioning leads to balanced loads of both among nodes. And third, the entire system is a fine-grained pipeline. This leads to high parallelism and utilization and also minimizes the time features need to be cached while waiting for back-propagation. As a result, storage demand is reduced to the point where only on-chip memory is used for the convolution layers. We evaluate FPDeep with the Alexnet, VGG-16, and VGG-19 benchmarks. Experimental results show that FPDeep has good scalability to a large number of FPGAs, with the limiting factor being the FPGA-to-FPGA bandwidth. With 6 transceivers per FPGA, FPDeep shows linearity up to 83 FPGAs. Energy efficiency is evaluated with respect to GOPs/J. FPDeep provides, on average, 6.36x higher energy efficiency than comparable GPU servers.