nils-herrmann/clean_scirepeval huggingface dataset:
Gluon polarization measurements from longitudinally polarized proton-proton collisions at STAR

The gluon polarization contribution to the proton spin is an integral part to solve the longstanding proton spin puzzle. At the Relativistic Heavy Ion Collider (RHIC), the STAR experiment has measured jets produced in mid-pseudo-rapidity, |η| < 1.0, and full azimuth, ϕ, from longitudinally polarized pp collisions to study the gluon polarization in the proton. At center of mass energies s = 200 and 510 GeV, jet production is dominated by hard QCD scattering processes such as gluon-gluon (gg) and quark-gluon (qg), thus making the longitudinal double-spin asymmetry (ALL ) sensitive to the gluon polarization. Early STAR inclusive jet ALL results at s = 200 GeV provided the first evidence of the non-zero gluon polarization at momentum fraction x > 0.05. The higher center of mass energy s = 510 GeV allows to explore the gluon polarization as low as x ∼ 0.015. In this talk we will present the recent STAR inclusive jet and dijet ALL results at s = 510 GeV, and discuss the relevant new analysis techniques for the estimation of trigger bias and reconstruction uncertainty, the underlying event correction on the jet energy and its effect on jet ALL . Dijet results are shown for different topologies in regions of pseudo-rapidity, effectively scanning the x-dependence of the gluon polarization.
Distributed Attack-Robust Submodular Maximization for Multi-Robot Planning

We aim to guard swarm-robotics applications against denial-of-service (DoS) attacks that result in withdrawals of robots. We focus on applications requiring the selection of actions for each robot, among a set of available ones, e.g., which trajectory to follow. Such applications are central in large-scale robotic applications, e.g., multi-robot motion planning for target tracking. But the current attack-robust algorithms are centralized, and scale quadratically with the problem size (e.g., number of robots). In this paper, we propose a general-purpose distributed algorithm towards robust optimization at scale, with local communications only. We name it distributed robust maximization (DRM). DRM proposes a divide-and-conquer approach that distributively partitions the problem among K cliques of robots. The cliques optimize in parallel, independently of each other. That way, DRM also offers computational speed-ups up to 1/K2 the running time of its centralized counterparts. K depends on the robots’ communication range, which is given as input to DRM. DRM also achieves a close-to-optimal performance. We demonstrate DRM’s performance in Gazebo and MATLAB simulations, in scenarios of active target tracking with multiple robots. We observe DRM achieves significant computational speed-ups (it is 3 to 4 orders faster) and, yet, nearly matches the tracking performance of its centralized counterparts.
MASS-UMAP: Fast and accurate analog ensemble search in weather radar archive

The use of analog-similar weather patterns for weather forecasting and analysis is an established method in meteorology. The most challenging aspect of using this approach in the context of operational radar applications is to be able to perform a fast and accurate search for similar spatiotemporal precipitation patterns in a large archive of historical records. In this context, sequential pairwise search is too slow and computationally expensive. Here, we propose an architecture to significantly speed up spatiotemporal analog retrieval by combining nonlinear geometric dimensionality reduction (UMAP) with the fastest known Euclidean search algorithm for time series (MASS) to find radar analogs in constant time, independently of the desired temporal length to match and the number of extracted analogs. We show that UMAP, combined with a grid search protocol over relevant hyperparameters, can find analog sequences with lower mean square error (MSE) than principal component analysis (PCA). Moreover, we show that MASS is 20 times faster than brute force search on the UMAP embedding space. We test the architecture on real dataset and show that it enables precise and fast operational analog ensemble search through more than 2 years of radar archive in less than 3 seconds on a single workstation.
IIITM Face: A Database for Facial Attribute Detection in Constrained and Simulated Unconstrained Environments

This paper addresses the challenges of face attribute detection specifically in the Indian context. While there are numerous face datasets in unconstrained environments, none of them captures emotions in different facial orientations. Moreover, there is an under-representation of people of Indian ethnicity in these datasets since they have been scraped from popular search engines. As a result, the performance of state-of-the-art techniques can't be evaluated on Indian faces. In this work, we introduce a new dataset IIITM Face for scientific community to address these challenges. Our dataset includes 107 participants who exhibit 6 emotions in 3 different face orientations. Each of theses images are further labelled on attributes like gender, presence of moustache, beard or eyeglasses, clothes worn by the subjects and the density of their hair. Moreover, the images are captured in high resolution with specific background colors which can be easily replaced by cluttered backgrounds to simulate 'in the Wild' behavior. We demonstrate the same by constructing IIITM Face-SUE. Both IIITM Face and IIITM Face-SUE have been benchmarked across key multi-label metrics for the research community to compare their results.
DeepMark: One-Shot Clothing Detection

The one-shot approach, DeepMark, for fast clothing detection as a modification of a multi-target network, CenterNet, is proposed in the paper. The state-of-the-art accuracy of 0.723 mAP for bounding box detection task and 0.532 mAP for landmark detection task on the DeepFashion2 Challenge dataset were achieved. The proposed architecture can be used effectively on the low-power devices.
Persistent and Unforgeable Watermarks for Deep Neural Networks

As deep learning classifiers continue to mature, model providers with sufficient data and computation resources are exploring approaches to monetize the development of increasingly powerful models. Licensing models is a promising approach, but requires a robust tool for owners to claim ownership of models, i.e. a watermark. Unfortunately, current watermarks are all vulnerable to piracy attacks, where attackers embed forged watermarks into a model to dispute ownership. We believe properties of persistence and piracy resistance are critical to watermarks, but are fundamentally at odds with the current way models are trained and tuned. In this work, we propose two new training techniques (out-of-bound values and null-embedding) that provide persistence and limit the training of certain inputs into trained models. We then introduce "wonder filters", a new primitive that embeds a persistent bit-sequence into a model, but only at initial training time. Wonder filters enable model owners to embed a bit-sequence generated from their private keys into a model at training time. Attackers cannot remove wonder filters via tuning, and cannot add their own filters to pretrained models. We provide analytical proofs of key properties, and experimentally validate them over a variety of tasks and models. Finally, we explore a number of adaptive counter-measures, and show our watermark remains robust.
N = 4 Superconformal Algebras and Diagonal Cosets

Coset constructions of ${{\mathcal{W}}}$-algebras have many applications and were recently given for principal ${ In this paper, we give coset constructions of the large and small $N=4$ superconformal algebras, which are the minimal ${ From these realizations, one finds a remarkable connection between the large $N=4$ algebra and the diagonal coset $C^{k_1, k_2} = \textrm{Com}(V^{k_1+k_2}({{\mathfrak{s}}}{{\mathfrak{l}}}_2), V^{k_1}({{\mathfrak{s}}}{{\mathfrak{l}}}_2) \otimes V^{k_2}({{\mathfrak{s}}}{{\mathfrak{l}}}_2))$, namely, as two-parameter vertex algebras, $C^{k_1, k_2}$ coincides with the coset of the large $N=4$ algebra by its affine subalgebra. We also show that at special points in the parameter space, the simple quotients of these cosets are isomorphic to various ${{\mathcal{W}}}$-algebras. As a corollary, we give new examples of strongly rational principal ${{\mathcal{W}}}$-algebras of type $C$ at degenerate admissible levels.
Linking artificial and human neural representations of language

What information from an act of sentence understanding is robustly represented in the human brain? We investigate this question by comparing sentence encoding models on a brain decoding task, where the sentence that an experimental participant has seen must be predicted from the fMRI signal evoked by the sentence. We take a pre-trained BERT architecture as a baseline sentence encoding model and fine-tune it on a variety of natural language understanding (NLU) tasks, asking which lead to improvements in brain-decoding performance. We find that none of the sentence encoding tasks tested yield significant increases in brain decoding performance. Through further task ablations and representational analyses, we find that tasks which produce syntax-light representations yield significant improvements in brain decoding performance. Our results constrain the space of NLU models that could best account for human neural representations of language, but also suggest limits on the possibility of decoding fine-grained syntactic information from fMRI human neuroimaging.
Optimal Patrolling of High Priority Segments while Visiting the Unit Interval with a Set of Mobile Robots

Consider a region that requires to be protected from unauthorized penetrations. The border of the region, modeled as a unit line segment, consists of high priority segments that require the highest level of protection separated by low priority segments that require to be visited infinitely often. We study the problem of patrolling the border with a set of k robots. The goal is to obtain a strategy that minimizes the maximum idle time (the time that a point is left unattended) of the high priority points while visiting the low priority points infinitely often. We use the concept of single lid cover (segments of fixed length) where each high priority point is covered with at least one lid, and then we extend it to strong double-lid cover where each high priority point is covered with at least two lids, and the unit line segment is fully covered. Let λk-1 be the minimum lid length that accepts a single λk-1-lid cover with k - 1 lids and Λ2k be the minimum lid length that accepts a strong double Λ2k-lid cover with 2k lids. We show that 2min(Λ2k, λk-1) is the lower bound of the idle time when the max speed of the robots is one. To compute Λ2k and λk-1, we present an algorithm with time complexity O(max(k, n) logn) where n is the number of high priority sections. Our algorithm improves by a factor of min(n, k) the previous O(knlogn) running time algorithm. For the upper bound, first we present a strategy with idle time λk-1 where one robot covers the unit line, and the remaining robots cover the lids of a single λk-1-lid cover with k - 1 lids. Then, we present a simple strategy with idle time 3Λ2k that splits the unit line into not-disjoint k segments of equal length that robots synchronously cover, i.e., reaching the leftmost and rightmost point simultaneously. Then, we present a complex strategy that split the unit line into k non-disjoint segments that robots asynchronously cover. We show that combining strategies one and two attain an approximation of 1.5 the optimal idle time and combining strategy one and third attain optimal idle time.
Distillation ≈ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network

Distillation is a method to transfer knowledge from one model to another and often achieves higher accuracy with the same capacity. In this paper, we aim to provide a theoretical understanding on what mainly helps with the distillation. Our answer is "early stopping". Assuming that the teacher network is overparameterized, we argue that the teacher network is essentially harvesting dark knowledge from the data via early stopping. This can be justified by a new concept, {Anisotropic Information Retrieval (AIR)}, which means that the neural network tends to fit the informative information first and the non-informative information (including noise) later. Motivated by the recent development on theoretically analyzing overparameterized neural networks, we can characterize AIR by the eigenspace of the Neural Tangent Kernel(NTK). AIR facilities a new understanding of distillation. With that, we further utilize distillation to refine noisy labels. We propose a self-distillation algorithm to sequentially distill knowledge from the network in the previous training epoch to avoid memorizing the wrong labels. We also demonstrate, both theoretically and empirically, that self-distillation can benefit from more than just early stopping. Theoretically, we prove convergence of the proposed algorithm to the ground truth labels for randomly initialized overparameterized neural networks in terms of $\ell_2$ distance, while the previous result was on convergence in $0$-$1$ loss. The theoretical result ensures the learned neural network enjoy a margin on the training data which leads to better generalization. Empirically, we achieve better testing accuracy and entirely avoid early stopping which makes the algorithm more user-friendly.
Kidney Recognition in CT Using YOLOv3

Organ localization can be challenging considering the heterogeneity of medical images and the biological diversity from one individual to another. The contribution of this paper is to overview the performance of the object detection model, YOLOv3, on kidney localization in 2D and in 3D from CT scans. The model obtained a 0.851 Dice score in 2D and 0.742 in 3D. The SSD, a similar state-of-the-art object detection model, showed similar scores on the test set. YOLOv3 and SSD demonstrated the ability to detect kidneys on a wide variety of CT scans including patients suffering from different renal conditions.
Learning Point Embeddings from Shape Repositories for Few-Shot Segmentation

User generated 3D shapes in online repositories contain rich information about surfaces, primitives, and their geometric relations, often arranged in a hierarchy. We present a framework for learning representations of 3D shapes that reflect the information present in this meta data and show that it leads to improved generalization for semantic segmentation tasks. Our approach is a point embedding network that generates a vectorial representation of the 3D points such that it reflects the grouping hierarchy and tag data. The main challenge is that the data is noisy and highly variable. To this end, we present a tree-aware metric-learning approach and demonstrate that such learned embeddings offer excellent transfer to semantic segmentation tasks, especially when training data is limited. Our approach reduces the relative error by 10.2% with 8 training examples, by 11.72% with 120 training examples on the ShapeNet semantic segmentation benchmark, in comparison to the network trained from scratch. By utilizing tag data the relative error is reduced by 12.8% with 8 training examples, in comparison to the network trained from scratch. These improvements come at no additional labeling cost as the meta data is freely available.
YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection

Object detection remains an active area of research in the field of computer vision, and considerable advances and successes has been achieved in this area through the design of deep convolutional neural networks for tackling object detection. Despite these successes, one of the biggest challenges to widespread deployment of such object detection networks on edge and mobile scenarios is the high computational and memory requirements. As such, there has been growing research interest in the design of efficient deep neural network architectures catered for edge and mobile usage. In this study, we introduce YOLO Nano, a highly compact deep convolutional neural network for the task of object detection. A human-machine collaborative design strategy is leveraged to create YOLO Nano, where principled network design prototyping, based on design principles from the YOLO family of single-shot object detection network architectures, is coupled with machine-driven design exploration to create a compact network with highly customized module-level macroarchitecture and microarchitecture designs tailored for the task of embedded object detection. The proposed YOLO Nano possesses a model size of ∼4.0MB (>15.1× and >8.3× smaller than Tiny YOLOv2 and Tiny YOLOv3, respectively) and requires 4.57B operations for inference (>34% and ∼17% lower than Tiny YOLOv2 and Tiny YOLOv3, respectively) while still achieving an mAP of ∼69.1% on the VOC 2007 dataset (∼12% and ∼10.7% higher than Tiny YOLOv2 and Tiny YOLOv3, respectively). Experiments on inference speed and power efficiency on a Jetson AGX Xavier embedded module at different power budgets further demonstrate the efficacy of YOLO Nano for embedded scenarios.
Extracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models

Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction. We present the first attempt to apply state-of-the-art entity recognition approaches on a newly released dataset, MedMentions. This dataset contains over 4000 biomedical abstracts, annotated for UMLS semantic types. In comparison to existing datasets, MedMentions contains a far greater number of entity types, and thus represents a more challenging but realistic scenario in a real-world setting. We explore a number of relevant dimensions, including the use of contextual versus non-contextual word embeddings, general versus domain-specific unsupervised pre-training, and different deep learning architectures. We contrast our results against the well-known i2b2 2010 entity recognition dataset, and propose a new method to combine general and domain-specific information. While producing a state-of-the-art result for the i2b2 2010 task (F1 = 0.90), our results on MedMentions are significantly lower (F1 = 0.63), suggesting there is still plenty of opportunity for improvement on this new data.
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions. In this paper, we develop a novel post-hoc visual explanation method called Score-CAM based on class activation mapping. Unlike previous class activation mapping based approaches, Score-CAM gets rid of the dependence on gradients by obtaining the weight of each activation map through its forward passing score on target class, the final result is obtained by a linear combination of weights and activation maps. We demonstrate that Score-CAM achieves better visual performance and fairness for interpreting the decision making process. Our approach outperforms previous methods on both recognition and localization tasks, it also passes the sanity check. We also indicate its application as debugging tools. The implementation is available1.
Updated constraints on asteroid-mass primordial black holes as dark matter

Author(s): Smyth, Nolan; Profumo, Stefano; English, Samuel; Jeltema, Tesla; McKinnon, Kevin; Guhathakurta, Puragra |  Microlensing of stars places significant constraints on sub-planetary-mass compact objects, including primordial black holes, as dark matter candidates. As the lens' Einstein radius in the source plane becomes comparable to the size of the light source, however, source amplification is strongly suppressed, making it challenging to constrain lenses with a mass at or below $10^{-10}$ solar masses, i.e. asteroid-mass objects. Current constraints, using Subaru HSC observations of M31, assume a fixed source size of one solar radius. Here we point out that the actual stars in M31 bright enough to be used for microlensing are typically much larger. We correct the HSC constraints by constructing a source size distribution based on the M31 PHAT survey and on a synthetic stellar catalogue, and by correspondingly weighing the finite-size source effects. We find that the actual HSC constraints are weaker by up to almost three orders of magnitude in some cases, broadening the range of masses for which primordial black holes can be the totality of the cosmological dark matter by almost one order of magnitude.
Efficient Local Causal Discovery Based on Markov Blanket

We study the problem of local causal discovery learning which identifies direct causes and effects of a target variable of interest in a causal network. The existing constraint-based local causal discovery approaches are inefficient, since these approaches do not take a triangular structure formed by a given variable and its child variables into account in learning local causal structure, and hence need to spend much time in distinguishing several direct effects. Additionally, these approaches depend on the standard MB (Markov Blanket) or PC (Parent and Children) discovery algorithms which demand to conduct lots of conditional independence tests to obtain the MB or PC sets. To overcome the above problems, in this paper, we propose a novel Efficient Local Causal Discovery algorithm via MB (ELCD) to identify direct causes and effects of a given variable. More specifically, we design a new algorithm for Efficient Oriented MB discovery, name EOMB. EOMB not only utilizes fewer conditional independence tests to identify MB, but also is able to identify more direct effects of a given variable with the help of triangular causal structures and determine several direct causes as much as possible. In addition, based on the proposed EOMB, ELCD is presented to learn a local causal structure around a target variable. The benefits of ELCD are that it not only can determine the direct causes and effects of a given variable accurately, but also runs faster than other local causal discovery algorithms. Experimental results on eight Bayesian networks (BNs) show that our proposed approach performs better than state-of-the-art baseline methods.
Neural Zero-Inflated Quality Estimation Model For Automatic Speech Recognition System

The performances of automatic speech recognition (ASR) systems are usually evaluated by the metric word error rate (WER) when the manually transcribed data are provided, which are, however, expensively available in the real scenario. In addition, the empirical distribution of WER for most ASR systems usually tends to put a significant mass near zero, making it difficult to simulate with a single continuous distribution. In order to address the two issues of ASR quality estimation (QE), we propose a novel neural zero-inflated model to predict the WER of the ASR result without transcripts. We design a neural zero-inflated beta regression on top of a bidirectional transformer language model conditional on speech features (speech-BERT). We adopt the pre-training strategy of token level mask language modeling for speech-BERT as well, and further fine-tune with our zero-inflated layer for the mixture of discrete and continuous outputs. The experimental results show that our approach achieves better performance on WER prediction in the metrics of Pearson and MAE, compared with most existed quality estimation algorithms for ASR or machine translation.