nils-herrmann/clean_scirepeval huggingface dataset:
A Sequential Guiding Network with Attention for Image Captioning

The recent advances of deep learning in both computer vision (CV) and natural language processing (NLP) provide us a new way of understanding semantics, by which we can deal with more challenging tasks such as automatic description generation from natural images. In this challenge, the encoder-decoder framework has achieved promising performance when a convolutional neural network (CNN) is used as image encoder and a recurrent neural network (RNN) as decoder. In this paper, we introduce a sequential guiding network that guides the decoder during word generation. The new model is an extension of the encoder-decoder framework with attention that has an additional guiding long short-term memory (LSTM) and can be trained in an end-to-end manner by using image/descriptions pairs. We validate our approach by conducting extensive experiments on a benchmark dataset, i.e., MS COCO Captions. The proposed model achieves significant improvement comparing to the other state-of-the-art deep learning models.
Horizon: Facebook's Open Source Applied Reinforcement Learning Platform

In this paper we present Horizon, Facebook's open source applied reinforcement learning (RL) platform. Horizon is an end-to-end platform designed to solve industry applied RL problems where datasets are large (millions to billions of observations), the feedback loop is slow (vs. a simulator), and experiments must be done with care because they don't run in a simulator. Unlike other RL platforms, which are often designed for fast prototyping and experimentation, Horizon is designed with production use cases as top of mind. The platform contains workflows to train popular deep RL algorithms and includes data preprocessing, feature transformation, distributed training, counterfactual policy evaluation, optimized serving, and a model-based data understanding tool. We also showcase and describe real examples where reinforcement learning models trained with Horizon significantly outperformed and replaced supervised learning systems at Facebook.
Towards Linear Time Neural Machine Translation with Capsule Networks

In this study, we first investigate a novel capsule network with dynamic routing for linear time Neural Machine Translation (NMT), referred as CapsNMT. CapsNMT uses an aggregation mechanism to map the source sentence into a matrix with pre-determined size, and then applys a deep LSTM network to decode the target sequence from the source representation. Unlike the previous work (CITATION) to store the source sentence with a passive and bottom-up way, the dynamic routing policy encodes the source sentence with an iterative process to decide the credit attribution between nodes from lower and higher layers. CapsNMT has two core properties: it runs in time that is linear in the length of the sequences and provides a more flexible way to aggregate the part-whole information of the source sentence. On WMT14 English-German task and a larger WMT14 English-French task, CapsNMT achieves comparable results with the Transformer system. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for sequence to sequence problems.
γ
-ray strength function for thallium isotopes relevant to the 
Pb205−Tl205
 chronometry

Photoneutron cross sections were measured for $^{203}\mathrm{Tl}$ and $^{205}\mathrm{Tl}$ at energies between the one- and two-neutron thresholds using quasimonochromatic $\ensuremath{\gamma}$-ray beams produced in laser Compton scattering at the NewSUBARU synchrotron radiation facility. Our measurement results in cross sections significantly different from the previously reported bremsstrahlung experiment, leading to rather different giant dipole resonance (GDR) parameters, in particular to lower GDR peak energies and higher peak cross sections. The photoneutron data are used to constrain the $\ensuremath{\gamma}$-ray strength function on the basis of the Hartree-Fock-Bogolyubov plus quasiparticle random-phase approximation using the Gogny D1M interaction. Supplementing the experimentally constrained $\ensuremath{\gamma}$-ray strength function with the zero-limit $E1$ and $M1$ contributions for the de-excitation mode, we estimate the Maxwellian-averaged cross section for the s-process branching-point nucleus $^{204}\mathrm{Tl}$ in the context of the $^{205}\mathrm{Pb}\ensuremath{-}^{205}\mathrm{Tl}$ chronometry.
Excessive Invariance Causes Adversarial Vulnerability

Despite their impressive performance, deep neural networks exhibit striking failures on out-of-distribution inputs. One core idea of adversarial example research is to reveal neural network errors under such distribution shifts. We decompose these errors into two complementary sources: sensitivity and invariance. We show deep networks are not only too sensitive to task-irrelevant changes of their input, as is well-known from epsilon-adversarial examples, but are also too invariant to a wide range of task-relevant changes, thus making vast regions in input space vulnerable to adversarial attacks. We show such excessive invariance occurs across various tasks and architecture types. On MNIST and ImageNet one can manipulate the class-specific content of almost any image without changing the hidden activations. We identify an insufficiency of the standard cross-entropy loss as a reason for these failures. Further, we extend this objective based on an information-theoretic analysis so it encourages the model to consider all task-dependent features in its decision. This provides the first approach tailored explicitly to overcome excessive invariance and resulting vulnerabilities.
A Corpus for Reasoning about Natural Language Grounded in Photographs

We introduce a new dataset for joint reasoning about natural language and images, with a focus on semantic diversity, compositionality, and visual reasoning challenges. The data contains 107,292 examples of English sentences paired with web photographs. The task is to determine whether a natural language caption is true about a pair of photographs. We crowdsource the data using sets of visually rich images and a compare-and-contrast task to elicit linguistically diverse language. Qualitative analysis shows the data requires compositional joint reasoning, including about quantities, comparisons, and relations. Evaluation using state-of-the-art visual reasoning methods shows the data presents a strong challenge.
Zipf’s, Heaps’ and Taylor’s Laws are Determined by the Expansion into the Adjacent Possible

Zipf’s, Heaps’ and Taylor’s laws are ubiquitous in many different systems where innovation processes are at play. Together, they represent a compelling set of stylized facts regarding the overall statistics, the innovation rate and the scaling of fluctuations for systems as diverse as written texts and cities, ecological systems and stock markets. Many modeling schemes have been proposed in literature to explain those laws, but only recently a modeling framework has been introduced that accounts for the emergence of those laws without deducing the emergence of one of the laws from the others or without ad hoc assumptions. This modeling framework is based on the concept of adjacent possible space and its key feature of being dynamically restructured while its boundaries get explored, i.e., conditional to the occurrence of novel events. Here, we illustrate this approach and show how this simple modeling framework, instantiated through a modified Pólya’s urn model, is able to reproduce Zipf’s, Heaps’ and Taylor’s laws within a unique self-consistent scheme. In addition, the same modeling scheme embraces other less common evolutionary laws (Hoppe’s model and Dirichlet processes) as particular cases.
What evidence does deep learning model use to classify Skin Lesions?

Melanoma is a type of skin cancer with the most rapidly increasing incidence. Early detection of melanoma using dermoscopy images significantly increases patients' survival rate. However, accurately classifying skin lesions by eye, especially in the early stage of melanoma, is extremely challenging for the dermatologists. Hence, the discovery of reliable biomarkers will be meaningful for melanoma diagnosis. Recent years, the value of deep learning empowered computer-assisted diagnose has been shown in biomedical imaging based decision making. However, much research focuses on improving disease detection accuracy but not exploring the evidence of pathology. In this paper, we propose a method to interpret the deep learning classification findings. Firstly, we propose an accurate neural network architecture to classify skin lesions. Secondly, we utilize a prediction difference analysis method that examines each patch on the image through patch-wised corrupting to detect the biomarkers. Lastly, we validate that our biomarker findings are corresponding to the patterns in the literature. The findings can be significant and useful to guide clinical diagnosis.
Augmentic Compositional Models for Knowledge Base Completion Using Gradient Representations

Neural models of Knowledge Base data have typically employed compositional representations of graph objects: entity and relation embeddings are systematically combined to evaluate the truth of a candidate Knowedge Base entry. Using a model inspired by Harmonic Grammar, we propose to tokenize triplet embeddings by subjecting them to a process of optimization with respect to learned well-formedness conditions on Knowledge Base triplets. The resulting model, known as Gradient Graphs, leads to sizable improvements when implemented as a companion to compositional models. Also, we show that the "supracompositional" triplet token embeddings it produces have interpretable properties that prove helpful in performing inference on the resulting triplet representations.
Optimal sequence length requirements for phylogenetic tree reconstruction with indels

We consider the phylogenetic tree reconstruction problem with insertions and deletions (indels). Phylogenetic algorithms proceed under a model where sequences evolve down the model tree, and given sequences at the leaves, the problem is to reconstruct the model tree with high probability. Traditionally, sequences mutate by substitution-only processes, although some recent work considers evolutionary processes with insertions and deletions. In this paper, we improve on previous work by giving a reconstruction algorithm that simultaneously has O(poly logn) sequence length and tolerates constant indel probabilities on each edge. Our recursively-reconstructed distance-based technique provably outputs the model tree when the model tree has O(poly logn) diameter and discretized branch lengths, allowing for the probability of insertion and deletion to be non-uniform and asymmetric on each edge. Our polylogarithmic sequence length bounds improve significantly over previous polynomial sequence length bounds and match sequence length bounds in the substitution-only models of phylogenetic evolution, thereby challenging the idea that many global misalignments caused by insertions and deletions when pindel is large are a fundamental obstruction to reconstruction with short sequences. We build upon a signature scheme for sequences, introduced by Daskalakis and Roch, that is robust to insertions and deletions. Our main contribution is to show that an averaging procedure gives an accurate reconstruction of signatures for ancestors, even while the explicit ancestral sequences cannot be reconstructed due to misalignments. Because these signatures are not as sensitive to indels, we can bound the noise that arise from indel-induced shifts and provide a novel analysis that provably reconstructs the model tree with O(poly logn) sequence length as long as the rate of mutation is less than the well known Kesten-Stigum threshold. The upper bound on the rate of mutation is optimal as beyond this threshold, an information-theoretic lower bound of Ω(poly(n)) sequence length requirement exists.
Exploiting Explicit Paths for Multi-hop Reading Comprehension

We propose a novel, path-based reasoning approach for the multi-hop reading comprehension task where a system needs to combine facts from multiple passages to answer a question. Although inspired by multi-hop reasoning over knowledge graphs, our proposed approach operates directly over unstructured text. It generates potential paths through passages and scores them without any direct path supervision. The proposed model, named PathNet, attempts to extract implicit relations from text through entity pair representations, and compose them to encode each path. To capture additional context, PathNet also composes the passage representations along each path to compute a passage-based representation. Unlike previous approaches, our model is then able to explain its reasoning via these explicit paths through the passages. We show that our approach outperforms prior models on the multi-hop Wikihop dataset, and also can be generalized to apply to the OpenBookQA dataset, matching state-of-the-art performance.
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings

Machine translation is highly sensitive to the size and quality of the training data, which has led to an increasing interest in collecting and filtering large parallel corpora. In this paper, we propose a new method for this task based on multilingual sentence embeddings. In contrast to previous approaches, which rely on nearest neighbor retrieval with a hard threshold over cosine similarity, our proposed method accounts for the scale inconsistencies of this measure, considering the margin between a given sentence pair and its closest candidates instead. Our experiments show large improvements over existing methods. We outperform the best published results on the BUCC mining task and the UN reconstruction task by more than 10 F1 and 30 precision points, respectively. Filtering the English-German ParaCrawl corpus with our approach, we obtain 31.2 BLEU points on newstest2014, an improvement of more than one point over the best official filtered version.
Closed-Loop GAN for continual Learning

Sequential learning of tasks using gradient descent leads to an unremitting decline in the accuracy of tasks for which training data is no longer available, termed catastrophic forgetting. Generative models have been explored as a means to approximate the distribution of old tasks and bypass storage of real data. Here we propose a cumulative closed-loop memory replay GAN (CloGAN) provided with external regularization by a small memory unit selected for maximum sample diversity. We evaluate incremental class learning using a notoriously hard paradigm, single-headed learning, in which each task is a disjoint subset of classes in the overall dataset, and performance is evaluated on all previous classes. First, we show that when constructing a dynamic memory unit to preserve sample heterogeneity, model performance asymptotically approaches training on the full dataset. We then show that using a stochastic generator to continuously output fresh new images during training increases performance significantly further meanwhile generating quality images. We compare our approach to several baselines including fine-tuning by gradient descent (FGD), Elastic Weight Consolidation (EWC), Deep Generative Replay (DGR) and Memory Replay GAN (MeRGAN). Our method has very low long-term memory cost, the memory unit, as well as negligible intermediate memory storage.
Adversarial Black-Box Attacks for Automatic Speech Recognition Systems Using Multi-Objective Genetic Optimization

Fooling deep neural networks with adversarial input have exposed a significant vulnerability in current state-of-the-art systems in multiple domains. Both black-box and white-box approaches have been used to either replicate the model itself or to craft examples which cause the model to fail. In this work, we use a multi-objective genetic algorithm based approach to perform both targeted and un-targeted black-box attacks on automatic speech recognition (ASR) systems. The main contribution of this research is the proposal of a generic framework which can be used to attack any ASR system, even if it's internal working is hidden. 
During the un-targeted attacks, the Word Error Rates (WER) of the ASR degrades from 0.5 to 5.4, indicating the potency of our approach. In targeted attacks, our solution reaches a WER of 2.14. In both attacks, the adversarial samples maintain a high acoustic similarity of 0.98 and 0.97.
A function fitting method

In this article, we describe a function fitting method that has potential applications in machine learning and also prove relevant theorems. The described function fitting method is a convex minimization problem which can be solved using a gradient descent algorithm. We also provide qualitative analysis on fitness to data of this function fitting method. The function fitting problem is also shown to be a solution of a linear, weak partial differential equation (PDE). We describe a simple numerical solution using a gradient descent algorithm that converges uniformly to the actual solution. As the functional of the minimization problem is a quadratic form, there also exists a numerical method using linear algebra.
The problems of $\eta'\rightarrow\pi^0\gamma\gamma$ decay and the New Physics.

BES-III Collaboration reported the observation of the rare decay $\eta'\rightarrow\pi^0\gamma\gamma$. The observed decay width sharply disagrees with the preliminary theoretical estimations. 
The aim of our paper is to reduce the theoretical uncertainty by taking the coupling constants extracted from the known meson decays without any assumptions. For the L$\sigma$M(ChPT) contributions we consider several values of the mixing angles extracted in different ways thus making our predictions more solid. 
We show that both our result and the preliminary estimations disagree with the BES-III result. 
We consider several possible explanations of this discrepancy including the New Physics.
Deep Multiple Description Coding by Learning Scalar Quantization

In this paper, we propose a deep multiple description coding framework, whose quantizers are adaptively learned via the minimization of multiple description compressive loss. Firstly, our framework is built upon auto-encoder networks, which have multiple description multi-scale dilated encoder network and multiple description decoder networks. Secondly, two entropy estimation networks are learned to estimate the informative amounts of the quantized tensors, which can further supervise the learning of multiple description encoder network to represent the input image delicately. Thirdly, a pair of scalar quantizers accompanied by two importance-indicator maps is automatically learned in an end-to-end self-supervised way. Finally, multiple description structural dissimilarity distance loss is imposed on multiple description decoded images in pixel domain for diversified multiple description generations rather than on feature tensors in feature domain, in addition to multiple description reconstruction loss. Through testing on two commonly used datasets, it is verified that our method is beyond several state-of-the-art multiple description coding approaches in terms of coding efficiency.
The inner engine of GeV-radiation-emitting gamma-ray bursts

We motivate how the most recent progress in the understanding the nature of the GeV radiation in most energetic gamma-ray bursts (GRBs), the binary-driven hypernovae (BdHNe), has led to the solution of a forty years unsolved problem in relativistic astrophysics: how to extract the rotational energy from a Kerr black hole for powering synchrotron emission and ultra high-energy cosmic rays. The "inner engine" is identified in the proper use of a classical solution introduced by Wald in 1974 duly extended to the most extreme conditions found around the newborn black hole in a BdHN. The energy extraction process occurs in a sequence impulsive processes each accelerating protons to $10^{21}$ eV in a timescale of $10^{-6}$ s and in presence of an external magnetic field of $10^{14}$ G. Specific example is given for a black hole of initial angular momentum $J=0.3\,M^2$ and mass $M\approx 3\,M_\odot$ leading to the GeV radiation of $10^{49}$ erg$\cdot$s$^{-1}$. The process can energetically continue for thousands of years.
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control

We propose a plan online and learn offline (POLO) framework for the setting where an agent, with an internal model, needs to continually act and learn in the world. Our work builds on the synergistic relationship between local model-based control, global value function learning, and exploration. We study how local trajectory optimization can cope with approximation errors in the value function, and can stabilize and accelerate value function learning. Conversely, we also study how approximate value functions can help reduce the planning horizon and allow for better policies beyond local solutions. Finally, we also demonstrate how trajectory optimization can be used to perform temporally coordinated exploration in conjunction with estimating uncertainty in value function approximation. This exploration is critical for fast and stable learning of the value function. Combining these components enable solutions to complex simulated control tasks, like humanoid locomotion and dexterous in-hand manipulation, in the equivalent of a few minutes of experience in the real world.
Machine learning for molecular dynamics with strongly correlated electrons

We use machine learning to enable large-scale molecular dynamics (MD) of a correlated electron model under the Gutzwiller approximation scheme. This model exhibits a Mott transition as a function of on-site Coulomb repulsion $U$. The repeated solution of the Gutzwiller self-consistency equations would be prohibitively expensive for large-scale MD simulations. We show that machine learning models of the Gutzwiller potential energy can be remarkably accurate. The models, which are trained with $N=33$ atoms, enable highly accurate MD simulations at much larger scales ($N\ensuremath{\gtrsim}{10}^{3}$). We investigate the physics of the smooth Mott crossover in the fluid phase.
Tidal disruption events from massive black hole binaries: predictions for ongoing and future surveys

We compute the expected cosmic rates of tidal disruption events (TDEs) induced by individual massive black holes (MBHs) and by MBH binaries (MBHBs) – with a specific focus on the latter class – to explore the potential of TDEs to probe the cosmic population of sub-pc MBHBs. Rates are computed by combining MBH and MBHB population models derived from large cosmological simulations with estimates of the induced TDE rates for each class of objects. We construct empirical TDE spectra that fit a large number of observations in the optical, UV, and X-ray and consider their observability by current and future survey instruments. Consistent with results in the literature, and depending on the detailed assumption of the model, we find that LSST and Gaia in optical and eROSITA in X-ray will observe a total of 3000–6000, 80–180, and 600–900 TDEs per year, respectively. Depending on the survey, 1 to several per cent of these are prompted by MBHBs. In particular, both LSST and eROSITA are expected to see 150–450 MBHB-induced TDEs in their respective mission lifetimes, including 5–100 repeated flares. The latter provide an observational sample of binary candidates with relatively low contamination and have the potential of unveiling the sub-pc population of MBHBs in the mass range $10^5\lt M\lt 10^7\, \mathrm{M}_\odot$, thus informing future low-frequency gravitational wave observatories.