nils-herrmann/clean_scirepeval huggingface dataset:
Learning to Conceal: A Deep Learning Based Method for Preserving Privacy and Avoiding Prejudice

In this paper, we introduce a learning model able to conceals personal information (e.g. gender, age, ethnicity, etc.) from an image, while maintaining any additional information present in the image (e.g. smile, hair-style, brightness). Our trained model is not provided the information that it is concealing, and does not try learning it either. Namely, we created a variational autoencoder (VAE) model that is trained on a dataset including labels of the information one would like to conceal (e.g. gender, ethnicity, age). These labels are directly added to the VAE's sampled latent vector. Due to the limited number of neurons in the latent vector and its appended noise, the VAE avoids learning any relation between the given images and the given labels, as those are given directly. Therefore, the encoded image lacks any of the information one wishes to conceal. The encoding may be decoded back into an image according to any provided properties (e.g. a 40 year old woman). 
The proposed architecture can be used as a mean for privacy preserving and can serve as an input to systems, which will become unbiased and not suffer from prejudice. We believe that privacy and discrimination are two of the most important aspects in which the community should try and develop methods to prevent misuse of technological advances.
Towards a UV model of kinetic mixing and portal matter

The nature of dark matter (DM) and how it might interact with the particles of the Standard Model (SM) is an ever-growing mystery. It is possible that the existence of new `dark sector' forces, yet undiscovered, are the key to solving this fundamental problem, and one might hope that in the future such forces might even be `unified' with the ones we already know in some UV-complete framework. In this paper, following a bottom-up approach, we attempt to take the first steps in the construction of such a framework. The much-discussed possibility of the kinetic mixing (KM) of the `dark photon' with the hypercharge gauge boson of the SM via loops of portal matter (PM) fields, charged in both sectors, offers an attractive starting point for these efforts. Given the anticipated finite strength of the KM in a UV-complete theory, the absence of anomalies, and the lifetime constraints on the PM fields arising from CMB and nucleosynthesis constraints, PM must behave as vector-like copies of the known SM fermion fields, such as those which appear naturally in $E_6$-type models. Within such a setup, the SM and their corresponding partner PM fields would be related by a new $SU(2)_I$ gauge symmetry. With this observation as a springboard, we construct a generalization of these ideas where $SU(2)_I$ is augmented by an additional $U(1)_{I_Y}$ factor so that the light dark photon is the result of a symmetry breaking analogous to the SM, \ie, $SU(2)_I\times U(1)_{I_Y}\to U(1)_D$, but with $U(1)_D$ now also broken at the $\lesssim$ GeV scale. While SM fields are $U(1)_D$ singlets, as in the conventional dark photon approach, they transform nontrivially under the full $SU(2)_I\times U(1)_{I_Y}$ gauge group. This approach leads to numerous interesting signatures, both at low energies and at colliders, and can be viewed as an initial step in the construction of a more UV-complete framework.
Two-, three-, and four-body decays in the constructive standard model

We further develop the massive constructive theory of the Standard Model and use it to calculate the amplitude and squared amplitude for all two-body decays, a collection of weak three-body decays, as well as Higgs decay to four neutrinos. We compare our results with those from Feynman diagrams and find complete agreement. We show that in all the cases considered here, the amplitudes of massive constructive theories are significantly simpler than those resulting from Feynman diagrams. In fact, a naive counting of the number of calculations required for a matrix-element generator to compute a phase-space point is orders-of-magnitude smaller for the result coming from the constructive method suggesting that these generators might benefit from this method in the future, even in the case of massive weak amplitudes. We also anticipate that our simpler expressions will produce numerically more stable expressions.
Directional dark matter detection in anisotropic Dirac materials

Dirac materials, because of unique symmetries near the Fermi surface, are a promising target for dark photon-mediated scattering and absorption of light dark matter. In this paper, we characterize the daily modulation rate of dark matter interacting with a Dirac material due to anisotropies in the crystal structure of typical Dirac materials, and we show that daily modulation is ${\cal O}(1)$ of the total rate for the Dirac material ZrTe$_5$. These modulation effects depend on the orientation of the material's Fermi velocity and permittivity tensor with respect to the dark matter wind, and they are maximized when the crystal is oriented such that the dark matter wind is completely aligned with the largest and smallest components of the Fermi velocity at two different times of the day. Because of the large modulation, any putative dark matter signal could be rapidly verified or ruled out by changing the orientation of the crystal with respect to the wind and observing how the daily modulation pattern changes.
Robot Sound Interpretation: Combining Sight and Sound in Learning-Based Control

We explore the interpretation of sound for robot decision making, inspired by human speech comprehension. While previous methods separate sound processing unit and robot controller, we propose an end-to-end deep neural network which directly interprets sound commands for visual-based decision making. The network is trained using reinforcement learning with auxiliary losses on the sight and sound networks. We demonstrate our approach on two robots, a TurtleBot3 and a Kuka-IIWA arm, which hear a command word, identify the associated target object, and perform precise control to reach the target. For both robots, we show the effectiveness of our network in generalization to sound types and robotic tasks empirically. We successfully transfer the policy learned in simulator to a real-world TurtleBot3.
Physical velocity of particles in relativistic curved momentum space

We show in general that for a relativistic theory with curved momentum space, i.e.~a theory with deformed relativistic symmetries, the physical velocity of particles coincides with their group velocity. This clarifies a long-standing question about the discrepancy between coordinate and group velocity for this kind of theories. The first evidence that this was the case had been obtained at linear order in the deformation parameter in  The proof was based on the recent understanding of how relative locality affects these scenarios. We here rely again on a careful implementation of relative locality effects, and obtain our result for a generic (relativistic) curved momentum space framework at all orders in the deformation/curvature parameter. We also discuss the validity of this result when the deformation depends on the coordinates as well as on the momenta.
Neural Network-Assisted Nonlinear Multiview Component Analysis: Identifiability and Algorithm

Multiview analysis aims at extracting shared latent components from data samples that are acquired in different domains, e.g., image, text, and audio. Classic multiview analysis, e.g., canonical correlation analysis (CCA), tackles this problem via matching the linearly transformed views in a certain latent domain. More recently, powerful nonlinear learning tools such as kernel methods and neural networks are utilized for enhancing the classic CCA. However, unlike linear CCA whose theoretical aspects are clearly understood, nonlinear CCA approaches are largely intuition-driven. In particular, it is unclear under what conditions the shared latent components across the veiws can be identified—while identifiability plays an essential role in many applications. In this work, we revisit nonlinear multiview analysis and address both the theoretical and computational aspects. We take a nonlinear multiview mixture learning viewpoint, which is a natural extension of the classic generative models for linear CCA. From there, we derive a nonlinear multiview analysis criteron. We show that minimizing this criterion leads to identification of the latent shared components up to certain ambiguities, under reasonable conditions. Our derivation and formulation also offer new insights and interpretations to existing deep neural networkbased CCA formulations. On the computation side, we propose an effective algorithm with simple and scalable update rules. A series of simulations and real-data experiments corroborate our theoretical analysis.
Revisit Policy Optimization in Matrix Form

In tabular case, when the reward and environment dynamics are known, policy evaluation can be written as $\bm{V}_{\bm{\pi}} = (I - \gamma P_{\bm{\pi}})^{-1} \bm{r}_{\bm{\pi}}$, where $P_{\bm{\pi}}$ is the state transition matrix given policy ${\bm{\pi}}$ and $\bm{r}_{\bm{\pi}}$ is the reward signal given ${\bm{\pi}}$. What annoys us is that $P_{\bm{\pi}}$ and $\bm{r}_{\bm{\pi}}$ are both mixed with ${\bm{\pi}}$, which means every time when we update ${\bm{\pi}}$, they will change together. In this paper, we leverage the notation from \cite{wang2007dual} to disentangle ${\bm{\pi}}$ and environment dynamics which makes optimization over policy more straightforward. We show that policy gradient theorem \cite{sutton2018reinforcement} and TRPO \cite{schulman2015trust} can be put into a more general framework and such notation has good potential to be extended to model-based reinforcement learning.
Ultra high energy cosmic rays from super-heavy dark matter in the context of large exposure observatories

The origin of the ultra high energy cosmic rays (UHECRs, E>1018 eV) is still uncertain. However, great progress has been achieved due to the data taken by The Pierre Auger and Telescope Array observatories. The UHECR flux presents two main features, a hardening of the spectrum known as the ankle and a suppression at higher energies. The experimental data suggest that above the ankle the UHECRs flux is dominated by an extragalactic component of astrophysical origin. However, a minority component of exotic origin that dominates the flux beyond the suppression is still compatible with current data. Therefore, there exist the possibility that part of the UHECR flux originates from the decay of super-heavy dark matter particles clustered in the halos of the galaxies. In these scenarios the main contribution comes from the halo of our galaxy. In this article the possibility of identifying these scenarios in the context of the future very large exposure cosmic rays observatories is studied. It is worth mentioning that the contribution of the extragalactic halos located in the nearby universe is also included in these studies.
Learning Sparse Mixture of Experts for Visual Question Answering

There has been a rapid progress in the task of Visual Question Answering with improved model architectures. Unfortunately, these models are usually computationally intensive due to their sheer size which poses a serious challenge for deployment. We aim to tackle this issue for the specific task of Visual Question Answering (VQA). A Convolutional Neural Network (CNN) is an integral part of the visual processing pipeline of a VQA model (assuming the CNN is trained along with entire VQA model). In this project, we propose an efficient and modular neural architecture for the VQA task with focus on the CNN module. Our experiments demonstrate that a sparsely activated CNN based VQA model achieves comparable performance to a standard CNN based VQA model architecture.
Methods, Models, and the Evolution of Moral Psychology

Why are we good? Why are we bad? Questions regarding the evolution of morality have spurred an astoundingly large interdisciplinary literature. Some significant subset of this body of work addresses questions regarding our moral psychology: how did humans evolve the psychological properties which underpin our systems of ethics and morality? Here I do three things. First, I discuss some methodological issues, and defend particularly effective methods for addressing many research questions in this area. Second, I give an in-depth example, describing how an explanation can be given for the evolution of guilt---one of the core moral emotions---using the methods advocated here. Last, I lay out which sorts of strategic scenarios generally are the ones that our moral psychology evolved to `solve', and thus which models are the most useful in further exploring this evolution.
An Empirical Mass–Radius Relation for Cool Giant Planets

Probabilistic relationships between the mass and radius of planets are useful for a variety of purposes, including estimating the yields of planet discovery efforts and the radii of discovered planets given their masses. Previous work on giant planets often do not make the important distinction between cool and (inflated) hot giants. This can lead to needlessly large uncertainties and overestimated radii for the cooler objects. Using hierarchical bayesian modeling and the AIC model selection criterion, we identify an empirical mass-radius relationship for cool giant planets above 15 Earth masses. It takes the form of a quadratic function in log mass, with a power-law dispersion. Parametric uncertainties from MCMC are provided, and we compare our results with the outputs of interior structure models and the radii of the solar system giant planets, which fit comfortably within our relation.
A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming

Recent successes of Reinforcement Learning (RL) allow an agent to learn policies that surpass human experts but suffers from being time-hungry and data-hungry. By contrast, human learning is significantly faster because prior and general knowledge and multiple information resources are utilized. In this paper, we propose a Planner-Actor-Critic architecture for huMAN-centered planning and learning (PACMAN), where an agent uses its prior, high-level, deterministic symbolic knowledge to plan for goal-directed actions, and also integrates the Actor-Critic algorithm of RL to fine-tune its behavior towards both environmental rewards and human feedback. This work is the first unified framework where knowledge-based planning, RL, and human teaching jointly contribute to the policy learning of an agent. Our experiments demonstrate that PACMAN leads to a significant jump-start at the early stage of learning, converges rapidly and with small variance, and is robust to inconsistent, infrequent, and misleading feedback.
Experimenting with Constraint Programming on GPU

The focus of my PhD thesis is on exploring parallel approaches to efficiently solve problems modeled by constraints and presenting a new proposal. Current solvers are very advanced; they are carefully designed to effectively manage the high-level problems' description and include refined strategies to avoid useless work. Despite this, finding a solution can take an unacceptable amount of time. Parallelization can mitigate this problem when the instance of the problem modeled is large, as it happens in real world problems. It is done by propagating constraints in parallel and concurrently exploring different parts of the search space. I am developing on a constraint solver that exploits the many cores available on Graphics Processing Units (GPU) to speed up the search.
Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation

Hierarchical neural networks are often used to model inherent structures within dialogues. For goal-oriented dialogues, these models miss a mechanism adhering to the goals and neglect the distinct conversational patterns between two interlocutors. In this work, we propose Goal-Embedded Dual Hierarchical Attentional Encoder-Decoder (G-DuHA) able to center around goals and capture interlocutor-level disparity while modeling goal-oriented dialogues. Experiments on dialogue generation, response generation, and human evaluations demonstrate that the proposed model successfully generates higher-quality, more diverse and goal-centric dialogues. Moreover, we apply data augmentation via goal-oriented dialogue generation for task-oriented dialog systems with better performance achieved.
Non-Bayesian Social Learning With Uncertain Models

Non-Bayesian social learning theory provides a framework that models distributed inference for a group of agents interacting over a network. Agents iteratively form and communicate beliefs about an unknown state of the world with their neighbors using a learning rule. Existing approaches assume agents have access to precise statistical models (in the form of likelihoods) for the state of the world. However, in many situations, such models must be learned from finite data. We propose a social learning rule that takes into account uncertainty in the statistical models using second-order probabilities. Therefore, beliefs derived from uncertain models are sensitive to the amount of past evidence collected for each hypothesis. These beliefs characterize whether or not the hypotheses are consistent with the true state of the world. We explicitly show the dependency of the generated beliefs with respect to the amount of prior evidence. Furthermore, as the amount of prior evidence goes to infinity, learning occurs and is consistent with traditional social learning theory.
Dark 
CP
 violation through the 
Z
 portal

Despite great agreement with experiment, the Standard Model (SM) of particle physics lacks a viable Dark Matter (DM) candidate and sufficient amount of CP-violation to account for the observed baryon excess in the universe. Non-minimal Higgs frameworks are economic extensions of the SM which could remedy these shortcomings. Within the framework of a three Higgs doublet model, we introduce an extended dark sector which accommodates both DM and CP-violation. Such dark sources of CP-violation do not contribute to the Electric Dipole Moments and are therefore unconstrained. We present a novel mechanism in which the CP-violating dark particles only interact with the SM through the gauge bosons, primarily the $Z$ boson. Such $Z$-portal dark CP violation is realised in the regions of the parameter space where Higgs-mediated (co)annihilation processes are sub-dominant and have negligible contributions to the DM relic density. We show that such $Z$-portal CP violating DM can still thermalise and satisfy all experimental and observational bounds and discuss the implications of such phenomena for electroweak baryogenesis.
The influence of cut vertices and eigenvalues on character graphs of solvable groups

Given a finite group $G$, the character graph, denoted by $\Delta(G)$, for its irreducible character degrees is a graph with vertex set $\rho(G)$ which is the set of prime numbers that divide the irreducible character degrees of $G$, and with $\{p,q\}$ being an edge if there exist a non-linear $\chi\in {\rm Irr}(G)$ whose degree is divisible by $pq$. In this paper, we discuss the influences of cut vertices and eigenvalues of $\Delta(G)$ on the group structure of $G$. Recently, Lewis and Meng proved the character graph of each solvable group has at most one cut vertex. Now, we determine the structure of character graphs of solvable groups with a cut vertex and diameter $3$. Furthermore, we study solvable groups whose character graphs have at most two distinct eigenvalues. Moreover, we investigate the solvable groups whose character graphs are regular with three distinct eigenvalues. In addition, we give some lower bounds for the number of edges of $\Delta(G)$.
Improved Variational Neural Machine Translation by Promoting Mutual Information

Posterior collapse plagues VAEs for text, especially for conditional text generation with strong autoregressive decoders. In this work, we address this problem in variational neural machine translation by explicitly promoting mutual information between the latent variables and the data. Our model extends the conditional variational autoencoder (CVAE) with two new ingredients: first, we propose a modified evidence lower bound (ELBO) objective which explicitly promotes mutual information; second, we regularize the probabilities of the decoder by mixing an auxiliary factorized distribution which is directly predicted by the latent variables. We present empirical results on the Transformer architecture and show the proposed model effectively addressed posterior collapse: latent variables are no longer ignored in the presence of powerful decoder. As a result, the proposed model yields improved translation quality while demonstrating superior performance in terms of data efficiency and robustness.
Systemic Cascades On Inhomogeneous Random Financial Networks

This systemic risk paper introduces inhomogeneous random financial networks (IRFNs). Such models are intended to describe parts, or the entirety, of a highly heterogeneous network of banks and their interconnections, in the global financial system. Both the balance sheets and the stylized crisis behaviour of banks are ingredients of the network model. A systemic crisis is pictured as triggered by a shock to banks' balance sheets, which then leads to the propagation of damaging shocks and the potential for amplification of the crisis, ending with the system in a cascade equilibrium. Under some conditions the model has ``locally tree-like independence (LTI)'', where a general percolation theoretic argument leads to an analytic fixed point equation describing the cascade equilibrium when the number of banks $N$ in the system is taken to infinity. This paper focusses on mathematical properties of the framework in the context of Eisenberg-Noe solvency cascades generalized to account for fractional bankruptcy charges. New results including a definition and proof of the ``LTI property'' of the Eisenberg-Noe solvency cascade mechanism lead to explicit $N=\infty$ fixed point equations that arise under very general model specifications. The essential formulas are shown to be implementable via well-defined approximation schemes, but numerical exploration of some of the wide range of potential applications of the method is left for future work.
Nguyen’s tridents and the classification of semigraphical translators for mean curvature flow

We construct a one-parameter family of singly periodic translating solutions to mean curvature flow that converge as the period tends to 0 to the union of a grim reaper surface and a plane that bisects it lengthwise. The surfaces are semigraphical: they are properly embedded, and, after removing a discrete collection of vertical lines, they are graphs. We also provide a nearly complete classification of semigraphical translators.
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models

Neural NLP models are increasingly accurate but are imperfect and opaque—they break in counterintuitive ways and leave end users puzzled at their behavior. Model interpretation methods ameliorate this opacity by providing explanations for specific model predictions. Unfortunately, existing interpretation codebases make it difficult to apply these methods to new models and tasks, which hinders adoption for practitioners and burdens interpretability researchers. We introduce AllenNLP Interpret, a flexible framework for interpreting NLP models. The toolkit provides interpretation primitives (e.g., input gradients) for any AllenNLP model and task, a suite of built-in interpretation methods, and a library of front-end visualization components. We demonstrate the toolkit’s flexibility and utility by implementing live demos for five interpretation methods (e.g., saliency maps and adversarial attacks) on a variety of models and tasks (e.g., masked language modeling using BERT and reading comprehension using BiDAF). These demos, alongside our code and tutorials, are available at https://allennlp.org/interpret.