nils-herrmann/clean_scirepeval huggingface dataset:
Multi-Spectral Visual Odometry without Explicit Stereo Matching

Multi-spectral sensors consisting of a standard (visible-light) camera and a long-wave infrared camera can simultaneously provide both visible and thermal images. Since thermal images are independent from environmental illumination, they can help to overcome certain limitations of standard cameras under complicated illumination conditions. However, due to the difference in the information source of the two types of cameras, their images usually share very low texture similarity. Hence, traditional texture-based feature matching methods cannot be directly applied to obtain stereo correspondences. To tackle this problem, a multi-spectral visual odometry method without explicit stereo matching is proposed in this paper. Bundle adjustment of multi-view stereo is performed on the visible and the thermal images using direct image alignment. Scale drift can be avoided by additional temporal observations of map points with the fixed-baseline stereo. Experimental results indicate that the proposed method can provide accurate visual odometry results with recovered metric scale. Moreover, the proposed method can also provide a metric 3D reconstruction in semi-dense density with multi-spectral information, which is not available from existing multi-spectral methods.
Deep Learning Based Chatbot Models

A conversational agent (chatbot) is a piece of software that is able to communicate with humans using natural language. Modeling conversation is an important task in natural language processing and artificial intelligence. While chatbots can be used for various tasks, in general they have to understand users' utterances and provide responses that are relevant to the problem at hand. 
In my work, I conduct an in-depth survey of recent literature, examining over 70 publications related to chatbots published in the last 3 years. Then, I proceed to make the argument that the very nature of the general conversation domain demands approaches that are different from current state-of-of-the-art architectures. Based on several examples from the literature I show why current chatbot models fail to take into account enough priors when generating responses and how this affects the quality of the conversation. In the case of chatbots, these priors can be outside sources of information that the conversation is conditioned on like the persona or mood of the conversers. In addition to presenting the reasons behind this problem, I propose several ideas on how it could be remedied. 
The next section focuses on adapting the very recent Transformer model to the chatbot domain, which is currently state-of-the-art in neural machine translation. I first present experiments with the vanilla model, using conversations extracted from the Cornell Movie-Dialog Corpus. Secondly, I augment the model with some of my ideas regarding the issues of encoder-decoder architectures. More specifically, I feed additional features into the model like mood or persona together with the raw conversation data. Finally, I conduct a detailed analysis of how the vanilla model performs on conversational data by comparing it to previous chatbot models and how the additional features affect the quality of the generated responses.
Fairness in Deep Learning: A Computational Perspective

Fairness in deep learning has attracted tremendous attention recently, as deep learning is increasingly being used in high-stake decision making applications that affect individual lives. We provide a review covering recent progresses to tackle algorithmic fairness problems of deep learning from the computational perspective. Specifically, we show that interpretability can serve as a useful ingredient to diagnose the reasons that lead to algorithmic discrimination. We also discuss fairness mitigation approaches categorized according to three stages of deep learning life-cycle, aiming to push forward the area of fairness in deep learning and build genuinely fair and reliable deep learning systems.
Assessing Knee OA Severity with CNN attention-based end-to-end architectures

This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing state-of-the-art CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST). All code from our experiments will be publicly available on the github repository: this https URL
Studies of helium poisoning of a Hamamatsu R5900-00-M16 photomultiplier

We report results from studies of the helium poisoning of a 16-anode photomultiplier tube R5900-00-M16 manufactured by Hamamatsu Photonics. A tube was immersed in pure helium for a period of about four months and was periodically monitored using a digital oscilloscope. Our results are based on the analysis of waveforms triggered by the dark noise pulses. Collected data yield evidence of after-pulses due to helium contamination of the tube. The probability of after-pulsing increased linearly with the exposure time to helium but the phototube suffered only a small drop in gain, indicating generally strong resilience to helium poisoning.
Observation of the Leptonic Decay D^{+}→τ^{+}ν_{τ}.

We report the first observation of D^{+}→τ^{+}ν_{τ} with a significance of 5.1σ. . Taking the world average B(D^{+}→μ^{+}ν_{μ})=(3.74±0.17)×10^{-4}, we obtain R_{τ/μ}=Γ(D^{+}→τ^{+}ν_{τ})/Γ(D^{+}→μ^{+}ν_{μ})=3.21±0.64_{stat}±0.43_{syst}., which is consistent with the standard model expectation of lepton flavor universality. Using external inputs, our results give values for the D^{+} decay constant f_{D^{+}} and the Cabibbo-Kobayashi-Maskawa matrix element |V_{cd}| that are consistent with, but less precise than, other determinations.
You Can't Publish Replication Studies (and How to Anyways)

Reproducibility has been increasingly encouraged by communities of science in order to validate experimental conclusions, and replication studies represent a significant opportunity to vision scientists wishing contribute new perceptual models, methods, or insights to the visualization community. Unfortunately, the notion of replication of previous studies does not lend itself to how we communicate research findings. Simple put, studies that re-conduct and confirm earlier results do not hold any novelty, a key element to the modern research publication system. Nevertheless, savvy researchers have discovered ways to produce replication studies by embedding them into other sufficiently novel studies. In this position paper, we define three methods -- re-evaluation, expansion, and specialization -- for embedding a replication study into a novel published work. Within this context, we provide a non-exhaustive case study on replications of Cleveland and McGill's seminal work on graphical perception. As it turns out, numerous replication studies have been carried out based on that work, which have both confirmed prior findings and shined new light on our understanding of human perception. Finally, we discuss how publishing a true replication study should be avoided, while providing suggestions for how vision scientists and others can still use replication studies as a vehicle to producing visualization research publications.
Predicting Features of Quantum Systems from Very Few Measurements.

Predicting features of complex, large-scale quantum systems is essential to the characterization and engineering of quantum architectures. We present an efficient approach for constructing an approximate classical description, called the classical shadow, of a quantum system from very few quantum measurements that can later be used to predict a large collection of features. This approach is guaranteed to accurately predict M linear functions with bounded Hilbert-Schmidt norm from only order of log(M) measurements. This is completely independent of the system size and saturates fundamental lower bounds from information theory. We support our theoretical findings with numerical experiments over a wide range of problem sizes (2 to 162 qubits). These highlight advantages compared to existing machine learning approaches.
Region Tracking in an Image Sequence: Preventing Driver Inattention

Driver inattention is a large problem on the roads around the world. The objective of this project was to develop an eye tracking algorithm with sufficient computational efficiency and accuracy, to successfully realize when the driver was looking away from the road for an extended period. The method of tracking involved the minimization of a functional, using the gradient descent and level set methods. The algorithm was then discretized and implemented using C and MATLAB. Multiple synthetic images, grey-scale and colour images were tested using the final design, with a desired region coverage of 82%. Further work is needed to decrease the computation time, increase the robustness of the algorithm, develop a small device capable of running the algorithm, as well as physically implement this device into various vehicles.
Cross-Enhancement Transform Two-Stream 3D ConvNets for Pedestrian Action Recognition of Autonomous Vehicles

Action recognition is an important research topic in machine vision. It is widely used in many fields and is one of the key technologies in pedestrian behavior recognition and intention prediction in the field of autonomous driving. Based on the widely used 3D ConvNets algorithm, combined with Two-Stream Inflated algorithm and transfer learning algorithm, we construct a Cross-Enhancement Transform based Two-Stream 3D ConvNets algorithm. On the datasets with different data distribution characteristics, the performance of the algorithm is different, especially the performance of the RGB and optical flow stream in the two stream is different. For this case, we combine the data distribution characteristics on the specific dataset. As a teaching model, the stream with better performance in the two stream is used to assist in training another stream, and then two stream inference is made. We conducted experiments on the UCF-101, HMDB-51, and Kinetics data sets, and the experimental results confirmed the effectiveness of our algorithm.
DefSLAM: Tracking and Mapping of Deforming Scenes From Monocular Sequences

Monocular simultaneous localization and mapping (SLAM) algorithms perform robustly when observing rigid scenes; however, they fail when the observed scene deforms, for example, in medical endoscopy applications. In this article, we present DefSLAM, the first monocular SLAM capable of operating in deforming scenes in real time. Our approach intertwines Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM) techniques to deal with the exploratory sequences typical of SLAM. A deformation tracking thread recovers the pose of the camera and the deformation of the observed map, at frame rate, by means of SfT processing a template that models the scene shape-at-rest. A deformation mapping thread runs in parallel with the tracking to update the template, at keyframe rate, by means of an isometric NRSfM processing a batch of full perspective keyframes. In our experiments, DefSLAM processes close-up sequences of deforming scenes, both in a laboratory-controlled experiment and in medical endoscopy sequences, producing accurate 3-D models of the scene with respect to the moving camera.
Linear response theory for Google matrix

We develop the linear response theory for the Google matrix PageRank algorithm with respect to a general weak perturbation and a numerical efficient and accurate algorithm, called LIRGOMAX algorithm, to compute the linear response of the PageRank with respect to this perturbation. We illustrate its efficiency on the example of the English Wikipedia network with more than 5 millions of articles (nodes). For a group of initial nodes (or simply a pair of nodes) this algorithm allows to identify the effective pathway between initial nodes thus selecting a particular subset of nodes which are most sensitive to the weak perturbation applied to them (injection or pumping at one node and absorption of probability at another node). The further application of the reduced Google matrix algorithm (REGOMAX) allows to determine the effective interactions between the nodes of this subset. General linear response theory already found numerous applications in various areas of science including statistical and mesoscopic physics. Based on these grounds we argue that the developed LIRGOMAX algorithm will find broad applications in the analysis of complex directed networks.
Lyapunov Exponents for Quantum Channels: An Entropy Formula and Generic Properties

We denote by Mk the set of k by k matrices with complex entries. We consider quantum channels φL of the form: given a measurable function L: Mk → Mk and a measure µ on Mk we define the linear operator φL : Mk → Mk , by the law ρ → φL (ρ) = ∫ Mk L(v)ρL(v)† dµ(v). In a previous work, the authors show that for a fixed measure µ the Φ-Erg property is generic on the function L (also irreducibility). Here we will show that the purification property is also generic on L for a fixed µ. Given L and µ there are two related stochastic processes: one takes values on the projective space P (ℂ k ) and the other on matrices in Mk . The Φ-Erg property and the purification condition are the nice hypothesis for the discrete time evolution given by the natural transition probability. In this way it will follow that generically on L, if ∫ |L(v)|2 log |L(v)| dµ(v) < ∞, the Lyapunov exponents ∞ > γ 1 ≥ γ 2 ≥ … ≥ γk ≥ −∞ are well defined. In a previous work, the concepts of entropy of a channel and Gibbs channel were presented; and also an example (associated to a stationary Markov chain) in which this definition of entropy (for a quantum channel) matches the Kolmogorov-Shanon definition of entropy. We estimate here the larger Lyapunov exponent for the mentioned example and we show that it is equal to −1/2 h, where h is the entropy of the associated Markov invariant probability.
Multimessenger tests of gravity with weakly lensed gravitational waves

General relativity (GR) predicts concordant trajectories for photons and gravitational waves (GW). We propose a new multi-messenger avenue (GW-CMB-CMB) to prove this aspect of fundamental physics by cross-correlating the GW signal of astrophysical origin with the lensing field derived from the cosmic microwave background (CMB). This new window will allow robust measurement of the prediction from GR with high signal-to-noise and will be able to unveil the true nature of gravity using the GW sources detected by missions such as the Laser Interferometer Space Antenna (LISA), Einstein Telescope and Cosmic Explorer.
Probing the theory of gravity with gravitational lensing of gravitational waves and galaxy surveys

The cross-correlation of gravitational wave strain with upcoming galaxy surveys probes theories of gravity in a new way. This method enables testing the theory of gravity by combining the effects from both gravitational lensing of gravitational waves and the propagation of gravitational waves in space–time. We find that within 10 yr the combination of the Advanced LIGO (Laser Interferometer Gravitational-Wave Observatory) and VIRGO (Virgo interferometer) detector networks with planned galaxy surveys should detect weak gravitational lensing of gravitational waves in the low-redshift Universe (z < 0.5). With the next-generation gravitational wave experiments such as Voyager, LISA (Laser Interferometer Space Antenna), Cosmic Explorer, and the Einstein Telescope, we can extend this test of the theory of gravity to larger redshifts by exploiting the synergies between electromagnetic wave and gravitational wave probes.
Gravity and the non-linear growth of structure in the Carnegie-Spitzer-IMACS Redshift Survey

A key obstacle to developing a satisfying theory of galaxy evolution is the difficulty in extending analytic descriptions of early structure formation into full non-linearity, the regime in which galaxy growth occurs. Extant techniques, though powerful, are based on approximate numerical methods whose Monte Carlo-like nature hinders intuition building. Here, we develop a new solution to this problem and its empirical validation. We first derive closed-form analytic expectations for the evolution of fixed percentiles in the real-space cosmic density distribution, averaged over representative volumes observers can track cross-sectionally. Using the Lagrangian forms of the fluid equations, we show that percentiles in δ – the density relative to the median – should grow as $\delta (t)\propto \delta _{0}^{\alpha }\, t^{\beta }$, where α ≡ 2 and β ≡ 2 for Newtonian gravity at epochs after the overdensities transitioned to non-linear growth. We then use 9.5 square degress of Carnegie-Spitzer-IMACS Redshift Survey data to map galaxy environmental densities over 0.2 < z < 1.5 (∼7 Gyr) and infer α = 1.98 ± 0.04 and β = 2.01 ± 0.11 – consistent with our analytic prediction. These findings – enabled by swapping the Eulerian domain of most work on density growth for a Lagrangian approach to real-space volumetric averages – provide some of the strongest evidence that a lognormal distribution of early density fluctuations indeed decoupled from cosmic expansion to grow through gravitational accretion. They also comprise the first exact, analytic description of the non-linear growth of structure extensible to (arbitrarily) low redshift. We hope these results open the door to new modelling of, and insight-building into, galaxy growth and its diversity in cosmological contexts.
Probing correlated compensated isocurvature perturbations using scale-dependent galaxy bias

Compensated isocurvature perturbations (CIPs) are modulations of the relative baryon and dark matter density that leave the total matter density constant. The best current constraints from the primary cosmic microwave background (CMB) are consistent with CIPs some two orders of magnitude larger in amplitude than adiabatic perturbations, suggesting that there may be a huge gap in our knowledge of the early Universe. However, it was recently suggested by Barreira~et.~al. that CIPs which are correlated with the primordial curvature perturbation, as arises in some versions of the curvaton model, lead to a new observable: scale dependent galaxy bias. Combining a galaxy survey with an unbiased tracer of the density field facilitates a measurement of the amplitude of correlated CIPs that is free from cosmic variance, the main limitation on constraints from the primary CMB. Among the most promising tracers to use for this purpose is the remote dipole field, reconstructed using the technique of kinetic Sunyaev Zel'dovich (kSZ) tomography. In this paper, we evaluate the detection significance on the amplitude of correlated CIPs possible with next-generation CMB and galaxy surveys using kSZ tomography. Our analysis includes all relativistic contributions to the observed galaxy number counts and allows for both CIPs and primordial non-Gaussianity, which also gives rise to a scale dependent galaxy bias. We find that kSZ tomography can probe CIPs of comparable amplitude to the adiabatic fluctuations, representing an improvement of over two orders of magnitude upon current constraints, and an order of magnitude over what will be possible using future CMB or galaxy surveys alone.
A Multifactor Polynomial Framework for Long-Term Electricity Forwards with Delivery Period

We propose a multi-factor polynomial framework to model and hedge long-term electricity contracts with delivery period. This framework has various advantages: the computation of forwards and correlation between different forwards are fully explicit, and the model can be calibrated to observed electricity forward curves easily and well. Electricity markets suffer from non-storability and poor medium- to long-term liquidity. Therefore, we suggest a rolling hedge which only uses liquid forward contracts and is risk-minimizing in the sense of F\"ollmer and Schweizer. We calibrate the model to over eight years of German power calendar year forward curves and investigate the quality of the risk-minimizing hedge over various time horizons.
Mass agnostic jet taggers

Searching for new physics in large data sets needs a balance between
two competing effects—signal identification vs background distortion. In
this work, we perform a systematic study of both single variable and
multivariate jet tagging methods that aim for this balance. The methods
preserve the shape of the background distribution by either augmenting
the training procedure or the data itself. Multiple quantitative metrics
to compare the methods are considered, for tagging 2-, 3-, or 4-prong
jets from the QCD background. This is the first study to show that the
data augmentation techniques of Planing and PCA based scaling deliver
similar performance as the augmented training techniques of Adversarial
NN and uBoost, but are both easier to implement and computationally
cheaper.
Neural Text Summarization: A Critical Evaluation

Text summarization aims at compressing long documents into a shorter form that conveys the most important parts of the original document. Despite increased interest in the community and notable research effort, progress on benchmark datasets has stagnated. We critically evaluate key ingredients of the current research setup: datasets, evaluation metrics, and models, and highlight three primary shortcomings: 1) automatically collected datasets leave the task underconstrained and may contain noise detrimental to training and evaluation, 2) current evaluation protocol is weakly correlated with human judgment and does not account for important characteristics such as factual correctness, 3) models overfit to layout biases of current datasets and offer limited diversity in their outputs.
Pareto-Optimal Data Compression for Binary Classification Tasks

The goal of lossy data compression is to reduce the storage cost of a data set X while retaining as much information as possible about something (Y) that you care about. For example, what aspects of an image X contain the most information about whether it depicts a cat? Mathematically, this corresponds to finding a mapping X→Z≡f(X) that maximizes the mutual information I(Z,Y) while the entropy H(Z) is kept below some fixed threshold. We present a new method for mapping out the Pareto frontier for classification tasks, reflecting the tradeoff between retained entropy and class information. We first show how a random variable X (an image, say) drawn from a class Y∈{1,…,n} can be distilled into a vector W=f(X)∈Rn−1 losslessly, so that I(W,Y)=I(X,Y); for example, for a binary classification task of cats and dogs, each image X is mapped into a single real number W retaining all information that helps distinguish cats from dogs. For the n=2 case of binary classification, we then show how W can be further compressed into a discrete variable Z=gβ(W)∈{1,…,mβ} by binning W into mβ bins, in such a way that varying the parameter β sweeps out the full Pareto frontier, solving a generalization of the discrete information bottleneck (DIB) problem. We argue that the most interesting points on this frontier are “corners” maximizing I(Z,Y) for a fixed number of bins m=2,3,… which can conveniently be found without multiobjective optimization. We apply this method to the CIFAR-10, MNIST and Fashion-MNIST datasets, illustrating how it can be interpreted as an information-theoretically optimal image clustering algorithm. We find that these Pareto frontiers are not concave, and that recently reported DIB phase transitions correspond to transitions between these corners, changing the number of clusters.