nils-herrmann/clean_scirepeval huggingface dataset:
Putting Machine Translation in Context with the Noisy Channel Model

We show that Bayes’ rule provides a compelling mechanism for controlling unconditional document language models, using the long-standing challenge of effectively leveraging document context in machine translation. In our formulation, we estimate the probability of a candidate translation as the product of the unconditional probability of the candidate output document and the “reverse translation probability” of translating the candidate output back into the input source language document—the so-called “noisy channel” decomposition. A particular advantage of our model is that it requires only parallel sentences to train, rather than parallel documents, which are not always available. Using a new beam search reranking approximation to solve the decoding problem, we find that document language models outperform language models that assume independence between sentences, and that using either a document or sentence language model outperforms comparable models that directly estimate the translation probability. We obtain the best-published results on the NIST Chinese–English translation task, a standard task for evaluating document translation. Our model also outperforms the benchmark Transformer model by approximately 2.5 BLEU on the WMT19 Chinese–English translation task.
End-to-end learning of energy-based representations for irregularly-sampled signals and images

For numerous domains, including for instance earth observation, medical imaging, astrophysics,..., available image and signal datasets often involve irregular space-time sampling patterns and large missing data rates. These sampling properties may be critical to apply state-of-the-art learning-based (e.g., auto-encoders, CNNs,...), fully benefit from the available large-scale observations and reach breakthroughs in the reconstruction and identification of processes of interest. In this paper, we address the end-to-end learning of representations of signals, images and image sequences from irregularly-sampled data, i.e. when the training data involved missing data. From an analogy to Bayesian formulation, we consider energy-based representations. Two energy forms are investigated: one derived from auto-encoders and one relating to Gibbs priors. The learning stage of these energy-based representations (or priors) involve a joint interpolation issue, which amounts to solving an energy minimization problem under observation constraints. Using a neural-network-based implementation of the considered energy forms, we can state an end-to-end learning scheme from irregularly-sampled data. We demonstrate the relevance of the proposed representations for different case-studies: namely, multivariate time series, 2D images and image sequences.
The Hochschild complex of a finite tensor category

We express the Hochschild complex of a finite tensor category using a specific projective resolution of the canonical coend of the finite tensor category. This leads to our main result that the Hochschild complex of a not necessarily semisimple modular category carries a canonical homotopy coherent projective action of the mapping class group $\text{SL}(2,\mathbb{Z})$ of the torus. This supports the idea to think of the Hochschild complex of a modular category as a derived conformal block for the torus. In the semisimple case, the (underived) conformal block is known to carry a commutative multiplication. For the derived conformal block (i.e. in the non-semisimple case), we generalize this to an $E_2$-structure. Our results allow us obtain a notion of Hochschild chains for braided crossed monoidal categories in the sense of Turaev. We prove that these admit an action of an operad built from Hurwitz spaces.
Probing bosonic stars with atomic clocks

Dark Matter could potentially manifest itself in the form of asymmetric dark stars. In this paper we entertain the possibility of probing such asymmetric bosonic dark matter stars by the use of atomic clocks. If the dark sector connects to the standard model sector via a Higgs or photon portal, the interior of boson stars that are in a Bose-Einstein condensate state can change the values of physical constants that control the timing of atomic clock devices. Dilute asymmetric dark matter boson stars passing through the Earth can induce frequency shifts that can be observed in separated Earth based atomic clocks. This gives the opportunity to probe a class of dark matter candidates that for the moment cannot be detected with any different conventional method.
Structural Language Models for Any-Code Generation

We address the problem of Any-Code Generation (AnyGen) - generating code without any restriction on the vocabulary or structure. The state-of-the-art in this problem is the sequence-to-sequence (seq2seq) approach, which treats code as a sequence and does not leverage any structural information. We introduce a new approach to AnyGen that leverages the strict syntax of programming languages to model a code snippet as a tree - structural language modeling (SLM). SLM estimates the probability of the program's abstract syntax tree (AST) by decomposing it into a product of conditional probabilities over its nodes. We present a neural model that computes these conditional probabilities by considering all AST paths leading to a target node. Unlike previous structural techniques that have severely restricted the kinds of expressions that can be generated, our approach can generate arbitrary expressions in any programming language. Our model significantly outperforms both seq2seq and a variety of existing structured approaches in generating Java and C# code. We make our code, datasets, and models available online.
On the Parameterized Complexity of Reconfiguration of Connected Dominating Sets

In a reconfiguration version of a decision problem $$\mathcal {Q}$$ Q the input is an instance of $$\mathcal {Q}$$ Q and two feasible solutions S and T . The objective is to determine whether there exists a step-by-step transformation between S and T such that all intermediate steps also constitute feasible solutions. In this work, we study the parameterized complexity of the Connected Dominating Set Reconfiguration problem ( CDS-R) . It was shown in previous work that the Dominating Set Reconfiguration problem ( DS-R ) parameterized by k , the maximum allowed size of a dominating set in a reconfiguration sequence, is fixed-parameter tractable on all graphs that exclude a biclique $$K_{d,d}$$ K d , d as a subgraph, for some constant $$d \ge 1$$ d ≥ 1 . We show that the additional connectivity constraint makes the problem much harder, namely, that CDS-R is W [1]-hard parameterized by $$k+\ell $$ k + ℓ , the maximum allowed size of a dominating set plus the length of the reconfiguration sequence, already on 5-degenerate graphs. On the positive side, we show that CDS-R parameterized by k is fixed-parameter tractable, and in fact admits a polynomial kernel on planar graphs.
Identifying Cancer Patients at Risk for Heart Failure Using Machine Learning Methods

Cardiotoxicity related to cancer therapies has become a serious issue, diminishing cancer treatment outcomes and quality of life. Early detection of cancer patients at risk for cardiotoxicity before cardiotoxic treatments and providing preventive measures are potential solutions to improve cancer patients' quality of life. This study focuses on predicting the development of heart failure in cancer patients after cancer diagnoses using historical electronic health record (EHR) data. We examined four machine learning algorithms using 143,199 cancer patients from the University of Florida Health (UF Health) Integrated Data Repository (IDR). We identified a total number of 1,958 qualified cases and matched them to 15,488 controls by gender, age, race, and major cancer type. Two feature encoding strategies were compared to encode variables as machine learning features. The gradient boosting (GB) based model achieved the best AUC score of 0.9077 (with a sensitivity of 0.8520 and a specificity of 0.8138), outperforming other machine learning methods. We also looked into the subgroup of cancer patients with exposure to chemotherapy drugs and observed a lower specificity score (0.7089). The experimental results show that machine learning methods are able to capture clinical factors that are known to be associated with heart failure and that it is feasible to use machine learning methods to identify cancer patients at risk for cancer therapy-related heart failure.
The physics of climate variability and climate change

The climate system is a forced, dissipative, nonlinear, complex and heterogeneous system that is out of thermodynamic equilibrium. The system exhibits natural variability on many scales of motion, in time as well as space, and it is subject to various external forcings, natural as well as anthropogenic. This paper reviews the observational evidence on climate phenomena and the governing equations of planetary-scale flow, as well as presenting the key concept of a hierarchy of models as used in the climate sciences. Recent advances in the application of dynamical systems theory, on the one hand, and of nonequilibrium statistical physics, on the other, are brought together for the first time and shown to complement each other in helping understand and predict the system's behavior. These complementary points of view permit a self-consistent handling of subgrid-scale phenomena as stochastic processes, as well as a unified handling of natural climate variability and forced climate change, along with a treatment of the crucial issues of climate sensitivity, response, and predictability.
CWAE-IRL: Formulating a supervised approach to Inverse Reinforcement Learning problem

Inverse reinforcement learning (IRL) is used to infer the reward function from the actions of an expert running a Markov Decision Process (MDP). A novel approach using variational inference for learning the reward function is proposed in this research. Using this technique, the intractable posterior distribution of the continuous latent variable (the reward function in this case) is analytically approximated to appear to be as close to the prior belief while trying to reconstruct the future state conditioned on the current state and action. The reward function is derived using a well-known deep generative model known as Conditional Variational Auto-encoder (CVAE) with Wasserstein loss function, thus referred to as Conditional Wasserstein Auto-encoder-IRL (CWAE-IRL), which can be analyzed as a combination of the backward and forward inference. This can then form an efficient alternative to the previous approaches to IRL while having no knowledge of the system dynamics of the agent. Experimental results on standard benchmarks such as objectworld and pendulum show that the proposed algorithm can effectively learn the latent reward function in complex, high-dimensional environments.
Variations in the Width, Density, and Direction of the Palomar 5 Tidal Tails

Stars that escape globular clusters form tidal tails that are predominantly shaped by the global distribution of mass in the Galaxy, but also preserve a historical record of small-scale perturbations. Using deep grz photometry from DECaLS, we present highly probable members of the tidal tails associated with the evaporating globular cluster Palomar 5. These data yield the cleanest view of a stellar stream beyond ≈20 kpc and reveal: (1) a wide, low surface-brightness extension of the leading tail; (2) significant density variations along the stream; and (3) sharp changes in the direction of both the leading and the trailing tail. In the fiducial Milky Way model, a rotating bar perturbs the Palomar 5 tails and can produce streams with similar width and density profiles to those observed. However, the deviations of the stream track in this simple model do not match those observed in the Palomar 5 trailing tail, indicating the need for an additional source of perturbation. These discoveries open up the possibility of measuring the population of perturbers in the Milky Way, including dark-matter subhalos, with an ensemble of stellar streams and deep photometry alone.
Cosmological baryon transfer in the simba simulations

We present a framework for characterizing the large-scale movement of baryons relative to dark matter in cosmological simulations, requiring only the initial conditions and final state of the simulation. This is performed using the spread metric that quantifies the distance in the final conditions between initially neighbouring particles, and by analysing the baryonic content of final haloes relative to that of the initial Lagrangian regions (LRs) defined by their dark matter component. Applying this framework to the simba cosmological simulations, we show that 40 per cent (10 per cent) of cosmological baryons have moved $\gt 1\, h^{-1}\, {\rm Mpc}{}$ ($3\, h^{-1}\, {\rm Mpc}{}$) by z = 0, primarily due to entrainment of gas by jets powered by an active galactic nucleus, with baryons moving up to $12\, h^{-1}\, {\rm Mpc}{}$ away in extreme cases. Baryons decouple from the dynamics of the dark matter component due to hydrodynamic forces, radiative cooling, and feedback processes. As a result, only 60 per cent of the gas content in a given halo at z = 0 originates from its LR, roughly independent of halo mass. A typical halo in the mass range Mvir = 1012–1013 M⊙ only retains 20 per cent of the gas originally contained in its LR. We show that up to 20 per cent of the gas content in a typical Milky Way-mass halo may originate in the region defined by the dark matter of another halo. This inter-Lagrangian baryon transfer may have important implications for the origin of gas and metals in the circumgalactic medium of galaxies, as well as for semi-analytic models of galaxy formation and ‘zoom-in’ simulations.
Kinematics of the Palomar 5 Stellar Stream from RR Lyrae Stars

Thin stellar streams, formed from the tidal disruption of globular clusters, are important gravitational tools, sensitive to both global and small-scale properties of dark matter. The Palomar 5 stellar stream (Pal 5) is an exemplar stream within the Milky Way: its ∼20° tidal tails connect back to the progenitor cluster, and the stream has been used to study the shape, total mass, and substructure fraction of the dark matter distribution of the Galaxy. However, most details of the phase-space distribution of the stream are not fully explained, and dynamical models that use the stream for other inferences are therefore incomplete. Here we aim to measure distance and kinematic properties along the Pal 5 stream in order to motivate improved models of the system. We use a large catalog of RR Lyrae-type stars (RRLs) with astrometric data from the Gaia mission to probabilistically identify RRLs in the Pal 5 stream. RRLs are useful because they are intrinsically luminous standard candles and their distances can be inferred with small relative precision (∼3%). By building a probabilistic model of the Pal 5 cluster and stream in proper motion and distance, we find 27 RRLs consistent with being members of the cluster (10) and stream (17). Using these RRLs, we detect gradients in distance and proper motion along the stream, and provide an updated measurement of the distance to the Pal 5 cluster using the RRLs, . We provide a catalog of Pal 5 RRLs with inferred membership probabilities for future modeling work.
DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs

Data-driven, knowledge-grounded neural conversation models are capable of generating more informative responses. However, these models have not yet demonstrated that they can zero-shot adapt to updated, unseen knowledge graphs. This paper proposes a new task about how to apply dynamic knowledge graphs in neural conversation model and presents a novel TV series conversation corpus (DyKgChat) for the task. Our new task and corpus aids in understanding the influence of dynamic knowledge graphs on responses generation. Also, we propose a preliminary model that selects an output from two networks at each time step: a sequence-to-sequence model (Seq2Seq) and a multi-hop reasoning model, in order to support dynamic knowledge graphs. To benchmark this new task and evaluate the capability of adaptation, we introduce several evaluation metrics and the experiments show that our proposed approach outperforms previous knowledge-grounded conversation models. The proposed corpus and model can motivate the future research directions.
Multicomponent dark matter in extended U(1)B−L: neutrino mass and high scale validity

Standard Model with right handed neutrinos charged under additional U(1)B−L gauge symmetry offer solutions to both dark matter (DM) problem and neutrino mass generation, although constrained severely from relic density, direct search and Higgs vacuum stability. We therefore investigate a multicomponent DM scenario augmented by an extra inert scalar doublet, that is neutral under U(1)B−L, which aids to enlarge parameter space allowed by DM constraints and Higgs vacuum stability. The lightest right-handed neutrino and the CP-even inert scalar are taken as the dark matter candidates and constitute a two component dark matter framework as they are rendered stable by an unbroken ℤ2 × ℤ2′ symmetry. DM-DM conversion processes turn out crucial to render requisite relic abundance in mass regions of the RH neutrino that do not appear in the stand-alone U(1)B−L scenario. In addition, the one-loop renormalisation group (RG) equations in this model demonstrate that the electroweak (EW) vacuum can be stabilised till ∼ 109 GeV in a parameter region compatible with the observed relic, the direct detection bound and other relevant constraints. We finally comment on the possibility of including the freeze-in mechanism in the same set-up.
The Choice Function Framework for Online Policy Improvement

There are notable examples of online search improving over hand-coded or learned policies (e.g. AlphaZero) for sequential decision making. It is not clear, however, whether or not policy improvement is guaranteed for many of these approaches, even when given a perfect leaf evaluation function and transition model. Indeed, simple counterexamples show that seemingly reasonable online search procedures can hurt performance compared to the original policy. To address this issue, we introduce the choice function framework for analyzing online search procedures for policy improvement. A choice function specifies the actions to be considered at every node of a search tree, with all other actions being pruned. Our main contribution is to give sufficient conditions for stationary and non-stationary choice functions to guarantee that the value achieved by online search is no worse than the original policy. In addition, we describe a general parametric class of choice functions that satisfy those conditions and present an illustrative use case of the empirical utility of the framework.
Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video

Pushing is a fundamental robotic skill. Existing work has shown how to exploit models of pushing to achieve a variety of tasks, including grasping under uncertainty, in-hand manipulation and clearing clutter. Such models, however, are approximate, which limits their applicability.Learning-based methods can reason directly from raw sensory data with accuracy, and have the potential to generalize to a wider diversity of scenarios. However, developing and testing such methods requires rich-enough datasets. In this paper we introduce Omnipush, a dataset with high variety of planar pushing behavior.In particular, we provide 250 pushes for each of 250 objects, all recorded with RGB-D and a high precision tracking system. The objects are constructed so as to systematically explore key factors that affect pushing–the shape of the object and its mass distribution–which have not been broadly explored in previous datasets, and allow to study generalization in model learning.Omnipush includes a benchmark for meta-learning dynamic models, which requires algorithms that make good predictions and estimate their own uncertainty. We also provide an RGB video prediction benchmark and propose other relevant tasks that can be suited with this dataset. Data and code are available at https://web.mit.edu/mcube/omnipush-dataset/.
Generalized non-minimal derivative coupling: application to inflation and primordial black hole production

We introduce inflationary models where the inflaton features a field dependent non-minimal derivative coupling to the Einstein tensor, that we name GNMDC. This Horndeski term gives new and distinguishable inflationary predictions in a framework that ameliorates possible problems related with gradient instabilities during the reheating stage. We examine the inflationary phenomenology using power law monomial and exponential potentials. We further elaborate on the implications of the inflaton-modulated GNMDC and construct GNMDC terms that can amplify the power spectrum of primordial perturbations at small scales, triggering PBH production. An attractive feature of the GNMDC is that inflation, as well as PBH production, can be implemented utilizing the Higgs potential.
A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications

Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the number of reaction intermediates can be very large and the cost of calculating the adsorption energies by DFT for all surface intermediates even for one active site model can become prohibitive. In this paper, we have identified appropriate descriptors and machine learning models that can be used to predict a significant part of these adsorption energies given data on the rest of them. Moreover, our investigations also included the case when the species data used to train the predictive model is of different size relative to the species the model tries to predict - this is an extrapolation in the data space which is typically difficult with regular machine learning models. Due to the relative size of the available datasets, we have attempted to extrapolate from the larger species to the smaller ones in the current work. Here, we have developed a neural network based predictive model that combines an established additive atomic contribution based model with the concepts of a convolutional neural network that, when extrapolating, achieves a statistically significant improvement over the previous models.
Gated Recurrent Fusion to Learn Driving Behavior from Temporal Multimodal Data

The Tactical Driver Behavior modeling problem requires an understanding of driver actions in complicated urban scenarios from rich multimodal signals including video, LiDAR and CAN signal data streams. However, the majority of deep learning research is focused either on learning the vehicle/environment state (sensor fusion) or the driver policy (from temporal data), but not both. Learning both tasks jointly offers the richest distillation of knowledge but presents challenges in the formulation and successful training. In this work, we propose promising first steps in this direction. Inspired by the gating mechanisms in Long Short-Term Memory units (LSTMs), we propose Gated Recurrent Fusion Units (GRFU) that learn fusion weighting and temporal weighting simultaneously. We demonstrate it's superior performance over multimodal and temporal baselines in supervised regression and classification tasks, all in the realm of autonomous navigation. On tactical driver behavior classification using Honda Driving Dataset (HDD), we report <inline-formula><tex-math notation="LaTeX">$10\%$</tex-math></inline-formula> improvement in mean Average Precision (mAP) score, and similarly, for steering angle regression on TORCS dataset, we note a <inline-formula><tex-math notation="LaTeX">$20\%$</tex-math></inline-formula> drop in Mean Squared Error (MSE) over the state-of-the-art.