nils-herrmann/clean_scirepeval huggingface dataset:
Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments

Recent pretrained sentence encoders achieve state of the art results on language understanding tasks, but does this mean they have implicit knowledge of syntactic structures? We introduce a grammatically annotated development set for the Corpus of Linguistic Acceptability (CoLA; Warstadt et al., 2018), which we use to investigate the grammatical knowledge of three pretrained encoders, including the popular OpenAI Transformer (Radford et al., 2018) and BERT (Devlin et al., 2018). We fine-tune these encoders to do acceptability classification over CoLA and compare the models' performance on the annotated analysis set. Some phenomena, e.g. modification by adjuncts, are easy to learn for all models, while others, e.g. long-distance movement, are learned effectively only by models with strong overall performance, and others still, e.g. morphological agreement, are hardly learned by any model.
Learning Undirected Posteriors by Backpropagation through MCMC Updates

The representation of the posterior is a critical aspect of effective variational autoencoders (VAEs). Poor choices for the posterior have a detrimental impact on the generative performance of VAEs due to the mismatch with the true posterior. We extend the class of posterior models that may be learned by using undirected graphical models. We develop an efficient method to train undirected posteriors by showing that the gradient of the training objective with respect to the parameters of the undirected posterior can be computed by backpropagation through Markov chain Monte Carlo updates. We apply these gradient estimators for training discrete VAEs with Boltzmann machine posteriors and demonstrate that undirected models outperform previous results obtained using directed graphical models as posteriors.
Ubiquitous Acoustic Sensing on Commodity IoT Devices: A Survey

With the proliferation of Internet-of-Things (IoT) devices, acoustic sensing attracts significant attention in recent years. It exploits acoustic transceivers such as microphones and speakers beyond their primary functions, namely recording and playing, to enable novel applications and new user experiences. In this paper, we present the first systematic survey on recent advances in ubiquitous acoustic sensing using commodity IoT hardware with a frequency range below 24 kHz. We propose a general framework that categorizes main building blocks of acoustic sensing systems. This framework encompasses three layers, i.e., device, core technique, and application. The device layer includes basic hardware components, acoustic platforms, as well as the air-borne and structure-borne channel characteristics. The core technique layer encompasses key mechanisms to generate acoustic signals (waveforms) and to extract useful temporal, spatial, and spectral information from received signals. The application layer builds upon the functions offered by the core techniques to realize different acoustic sensing applications. We highlight unique challenges due to the limitations of physical devices and acoustic channels and how they are mitigated or overcame by core processing techniques and application-specific solutions. Finally, research opportunities and future directions are discussed to spawn further in-depth investigation on IoT-enabled ubiquitous acoustic sensing.
Dialog System Technology Challenge 7

This paper introduces the Seventh Dialog System Technology Challenges (DSTC), which use shared datasets to explore the problem of building dialog systems. Recently, end-to-end dialog modeling approaches have been applied to various dialog tasks. The seventh DSTC (DSTC7) focuses on developing technologies related to end-to-end dialog systems for (1) sentence selection, (2) sentence generation and (3) audio visual scene aware dialog. This paper summarizes the overall setup and results of DSTC7, including detailed descriptions of the different tracks and provided datasets. We also describe overall trends in the submitted systems and the key results. Each track introduced new datasets and participants achieved impressive results using state-of-the-art end-to-end technologies.
Segmentation of Levator Hiatus Using Multi-Scale Local Region Active contours and Boundary Shape Similarity Constraint

In this paper, a multi-scale framework with local region based active contour and boundary shape similarity constraint is proposed for the segmentation of levator hiatus in ultrasound images. In this paper, we proposed a multiscale active contour framework to segment levator hiatus ultrasound images by combining the local region information and boundary shape similarity constraint. In order to get more precisely initializations and reduce the computational cost, Gaussian pyramid method is used to decompose the image into coarse-to-fine scales. A localized region active contour model is firstly performed on the coarsest scale image to get a rough contour of the levator hiatus, then the segmentation result on the coarse scale is interpolated into the finer scale image as the initialization. The boundary shape similarity between different scales is incorporate into the local region based active contour model so that the result from coarse scale can guide the contour evolution at finer scale. By incorporating the multi-scale and boundary shape similarity, the proposed method can precisely locate the levator hiatus boundaries despite various ultrasound image artifacts. With a data set of 90 levator hiatus ultrasound images, the efficiency and accuracy of the proposed method are validated by quantitative and qualitative evaluations (TP, FP, Js) and comparison with other two state-of-art active contour segmentation methods (C-V model, DRLSE model).
Deep learning for ranking response surfaces with applications to optimal stopping problems

In this paper, we propose deep learning algorithms for ranking response surfaces with applications to optimal stopping problems in financial mathematics. The problem of ranking response surfaces is motivated by estimating optimal feedback policy maps in stochastic control problems, aiming to efficiently find the index associated with the minimal response across the entire continuous input space . By considering points in as pixels and indices of the minimal surfaces as labels, we recast the problem as an image segmentation problem which assigns a label to every pixel in an image with pixels with the same label sharing certain characteristics. This provides an alternative method for efficiently solving the problem instead of using sequential design as in our previous work [R. Hu and M. Ludkovski, Sequential design for ranking response surfaces.  2017, 5, 212–239]. Deep learning algorithms are scalable, parallel and model-free, i.e. no parametric assumptions are needed for response surfaces. Considering ranking response surfaces as image segmentation allows one to use a broad class of deep neural networks (NNs), e.g. feed-forward NNs, UNet, SegNet, DeconvNet, which have been widely applied and numerically proven to possess good performance in the field. We also systematically study the dependence of deep learning algorithms on the input data generated on uniform grids or by sequential design sampling and observe that the performance of deep learning is not sensitive to the noise and location (close to/away from boundaries) of training data. We present a few examples, including synthetic ones and the Bermudan option pricing problem, to show the efficiency and accuracy of this method. We also simulate a 10-dimensional example to demonstrate robustness, while non-learning algorithms in general have difficulties in such high dimensions.
FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction

The basic principles in designing convolutional neural network (CNN) structures for predicting objects on different levels, e.g., image-level, region-level, and pixel-level are diverging. Generally, network structures designed specifically for image classification are directly used as default backbone structure for other tasks including detection and segmentation, but there is seldom backbone structure designed under the consideration of unifying the advantages of networks designed for pixel-level or region-level predicting tasks, which may require very deep features with high resolution. Towards this goal, we design a fish-like network, called FishNet. In FishNet, the information of all resolutions is preserved and refined for the final task. Besides, we observe that existing works still cannot \emph{directly} propagate the gradient information from deep layers to shallow layers. Our design can better handle this problem. Extensive experiments have been conducted to demonstrate the remarkable performance of the FishNet. In particular, on ImageNet-1k, the accuracy of FishNet is able to surpass the performance of DenseNet and ResNet with fewer parameters. FishNet was applied as one of the modules in the winning entry of the COCO Detection 2018 challenge. The code is available at this https URL.
On Event Causality Detection in Tweets

Nowadays, Twitter has become a great source of user-generated information about events. Very often people report causal relationships between events in their tweets. Automatic detection of causality information in these events might play an important role in predictive event analytics. Existing approaches include both rule-based and data-driven supervised methods. However, it is challenging to correctly identify event causality using only linguistic rules due to the highly unstructured nature and grammatical incorrectness of social media short text such as tweets. Also, it is difficult to develop a data-driven supervised method for event causality detection in tweets due to insufficient contextual information. This paper proposes a novel event context word extension technique based on background knowledge. To demonstrate the effectiveness of our proposed event context word extension technique, we develop a feed-forward neural network based approach to detect event causality from tweets. Extensive experiments demonstrate the superiority of our approach.
OMG-VR: Open-source Mudra Gloves for Manipulating Molecular Simulations in VR

As VR finds increasing application in scientific research domains like nanotechnology and biochemistry, we are beginning to better understand the domains in which it brings the most benefit, as well as the gestures and form factors that are most useful for specific applications. Here we describe Open-source Mudra Gloves for Virtual Reality (OMG-VR): etextile gloves designed to facilitate research scientists and students carrying out detailed and complex manipulation of simulated 3d molecular objects in VR. The OMG-VR is designed to sense when a user pinches together their thumb and index finger, or thumb and middle finger, forming a "mudra" position. Tests show that they provide good positional tracking of the point at which a pinch takes place, require no calibration, and are sufficiently accurate and robust to enable scientists to accomplish a range of tasks that involve complex spatial manipulation of molecules. The open source design offers a promising alternative to existing controllers and more costly commercial VR data gloves.
Community matters: heterogenous impacts of a sanitation intervention

We study the effectiveness of a community-level information and mobilization intervention to reduce open defecation (OD) and increase sanitation investments in Nigeria. The results of a cluster-randomized control trial in 246 communities, conducted between 2014 and 2018, suggest that average impacts are exiguous. However, these results hide important community heterogeneity, as the intervention has strong and lasting effects on OD habits in poorer communities. This result is robust across several measures of community socio-economic characteristics, and is not driven by baseline differences in toilet coverage. In poor communities, OD rates decreased by 9pp from a baseline level of 75%, while we find no effect in richer communities. The reduction in OD is achieved mainly through increased toilet ownership (+8pp from a baseline level of 24%). In terms of channels, the intervention appears to have raised the social status attached to toilet ownership among the poorer treated communities, and not in rich communities. Finally, we use data from our study and five other trials of similar interventions and show that estimated impacts on OD are stronger in poorer contexts, rationalizing the wide range of estimates in the literature and providing plausible external validity.
Retrieving Similar E-Commerce Images Using Deep Learning

In this paper, we propose a deep convolutional neural network for learning the embeddings of images in order to capture the notion of visual similarity. We present a deep siamese architecture that when trained on positive and negative pairs of images learn an embedding that accurately approximates the ranking of images in order of visual similarity notion. We also implement a novel loss calculation method using an angular loss metrics based on the problems requirement. The final embedding of the image is combined representation of the lower and top-level embeddings. We used fractional distance matrix to calculate the distance between the learned embeddings in n-dimensional space. In the end, we compare our architecture with other existing deep architecture and go on to demonstrate the superiority of our solution in terms of image retrieval by testing the architecture on four datasets. We also show how our suggested network is better than the other traditional deep CNNs used for capturing fine-grained image similarities by learning an optimum embedding.
Feature Fusion for Robust Patch Matching with Compact Binary Descriptors

This work addresses the problem of learning compact yet discriminative patch descriptors within a deep learning framework. We observe that features extracted by convolutional layers in the pixel domain are largely complementary to features extracted in a transformed domain. We propose a convolutional network framework for learning binary patch descriptors where pixel domain features are fused with features extracted from the transformed domain. In our framework, while convolutional and transformed features are distinctly extracted, they are fused and provided to a single classifier which thus jointly operates on convolutional and transformed features. We experiment at matching patches from three different dataset, showing that our feature fusion approach outperforms multiple state-of-the-art approaches in terms of accuracy, rate and complexity.
Identity types and weak factorization systems in Cauchy complete categories

It has been known that categorical interpretations of dependent type theory with Σ- and Id-types induce weak factorization systems. When one has a weak factorization system $({\cal L},{\cal R})$ on a category $\mathbb{C}$ in hand, it is then natural to ask whether or not $({\cal L},{\cal R})$ harbors an interpretation of dependent type theory with Σ- and Id- (and possibly Π-) types. Using the framework of display map categories to phrase this question more precisely, one would ask whether or not there exists a class ${\cal D}$ of morphisms of $\mathbb{C}$ such that the retract closure of ${\cal D}$ is the class ${\cal R}$ and the pair $(\mathbb{C},{\cal D})$ forms a display map category modeling Σ- and Id- (and possibly Π-) types. In this paper, we show, with the hypothesis that $\cal{C}$ is Cauchy complete, that there exists such a class $\cal{D}$ if and only if $(\mathbb{C},\cal{R})$ itself forms a display map category modeling Σ- and Id- (and possibly Π-) types. Thus, we reduce the search space of our original question from a potentially proper class to a singleton.
Life is Random, Time is Not: Markov Decision Processes with Window Objectives

The window mechanism was introduced by Chatterjee et al. to strengthen classical game objectives with time bounds. It permits to synthesize system controllers that exhibit acceptable behaviors within a configurable time frame, all along their infinite execution, in contrast to the traditional objectives that only require correctness of behaviors in the limit. The window concept has proved its interest in a variety of two-player zero-sum games because it enables reasoning about such time bounds in system specifications, but also thanks to the increased tractability that it usually yields. 
In this work, we extend the window framework to stochastic environments by considering Markov decision processes. A fundamental problem in this context is the threshold probability problem: given an objective it aims to synthesize strategies that guarantee satisfying runs with a given probability. We solve it for the usual variants of window objectives, where either the time frame is set as a parameter, or we ask if such a time frame exists. We develop a generic approach for window-based objectives and instantiate it for the classical mean-payoff and parity objectives, already considered in games. Our work paves the way to a wide use of the window mechanism in stochastic models.
Searching for low-mass dark matter particles with a massive Ge bolometer operated above ground

The EDELWEISS Collaboration has performed a search for dark matter particles with masses below the GeV scale with a 33.4-g germanium cryogenic detector operated in a surface lab. The energy deposits were measured using a neutron-transmutation-doped Ge thermal sensor with a 17.7 eV (rms) baseline heat energy resolution leading to a 60 eV analysis energy threshold. Despite a moderate lead shielding and the high-background environment, the first sub-GeV spin-independent dark matter limit based on a germanium target has been achieved. The experiment provides the most stringent, nuclear-recoil-based, above-ground limit on spin-independent interactions above 600  MeV/c2. The experiment also provides the most stringent limits on spin-dependent interactions with protons and neutrons below 1.3  GeV/c2. Furthermore, the dark matter search results were studied in the context of strongly interacting massive particles, taking into account Earth-shielding effects, for which new regions of the available parameter space have been excluded. Finally, the dark matter search has also been extended to interactions via the Migdal effect, resulting for the first time in the exclusion of particles with masses between 45 and 150  MeV/c2 with spin-independent cross sections ranging from 10-29 to 10-26  cm2.
Spatial concentration and temporal regularities in crime

Though crime is linked to different socio-economic factors, it exhibits remarkable regularities regardless of cities' particularities. In this chapter, we consider two fundamental regularities in crime regarding two essential aspects of criminal activity: time and space. For more than one century, we know that (1) crime occurs unevenly within a city and (2) crime peaks during specific times of the year. Here we describe the tendency of crime to concentrate spatially and to exhibit temporal regularities. We examine these phenomena from the complex-system perspective of cities, accounting for the possibility of both spatial heterogeneity and non-stationarity in urban phenomena.
CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning

In 2018, clinics and hospitals were hit with numerous attacks leading to significant data breaches and interruptions in medical services. An attacker with access to medical records can do much more than hold the data for ransom or sell it on the black market. 
In this paper, we show how an attacker can use deep-learning to add or remove evidence of medical conditions from volumetric (3D) medical scans. An attacker may perform this act in order to stop a political candidate, sabotage research, commit insurance fraud, perform an act of terrorism, or even commit murder. We implement the attack using a 3D conditional GAN and show how the framework (CT-GAN) can be automated. Although the body is complex and 3D medical scans are very large, CT-GAN achieves realistic results which can be executed in milliseconds. 
To evaluate the attack, we focused on injecting and removing lung cancer from CT scans. We show how three expert radiologists and a state-of-the-art deep learning AI are highly susceptible to the attack. We also explore the attack surface of a modern radiology network and demonstrate one attack vector: we intercepted and manipulated CT scans in an active hospital network with a covert penetration test. 
 this https URL 
Source code: this https URL
Exploring the role of interdisciplinarity in physics: Success, talent and luck

Although interdisciplinarity is often touted as a necessity for modern research, the evidence on the relative impact of sectorial versus to interdisciplinary science is qualitative at best. In this paper we leverage the bibliographic data set of the American Physical Society to quantify the role of interdisciplinarity in physics, and that of talent and luck in achieving success in scientific careers. We analyze a period of 30 years (1980-2009) tagging papers and their authors by means of the Physics and Astronomy Classification Scheme (PACS), to show that some degree of interdisciplinarity is quite helpful to reach success, measured as a proxy of either the number of articles or the citations score. We also propose an agent-based model of the publication-reputation-citation dynamics which reproduces the trends observed in the APS data set. On the one hand, the results highlight the crucial role of randomness and serendipity in real scientific research; on the other, they shed light on a counterintuitive effect indicating that the most talented authors are not necessarily the most successful ones.
Escape of Cosmic Rays from the Galaxy and Effects on the Circumgalactic Medium.

The escape of cosmic rays from the Galaxy is expected to shape their spectrum inside the Galaxy. Yet, this phenomenon is very poorly understood and, in the absence of a physical description, it is usually modeled as a free escape from a given boundary, typically located at a few kiloparsec distance from the Galactic disc. We show that the assumption of free escape leads to the conclusion that the cosmic ray current propagating in the circumgalactic medium is responsible for a nonresonant cosmic ray induced instability that in turn leads to the generation of a magnetic field of strength ∼2×10^{-8}  G on a scale ∼10  kpc around our Galaxy. The self-generated diffusion produces large gradients in the particle pressure that induce a displacement of the intergalactic medium with velocity ∼10-100  km/s. Cosmic rays are then carried away by advection. If the overdensity of the intergalactic gas in a region of size ∼10  kpc around our Galaxy is ≳100 with respect to the cosmological baryon density Ω_{b}ρ_{cr}, then the flux of high energy neutrinos due to pion production becomes comparable with the flux of astrophysical neutrinos recently measured by IceCube.