nils-herrmann/clean_scirepeval huggingface dataset:
A convex optimization approach to smooth trajectories for motion planning with car-like robots

In the recent past, several sampling-based algorithms have been proposed to compute trajectories that are collision-free and dynamically-feasible. However, the outputs of such algorithms are notoriously jagged. In this paper, by focusing on robots with car-like dynamics, we present a fast and simple heuristic algorithm, named Convex Elastic Smoothing (CES) algorithm, for trajectory smoothing and speed optimization. The CES algorithm is inspired by earlier work on elastic band planning and iteratively performs shape and speed optimization. The key feature of the algorithm is that both optimization problems can be solved via convex programming, making CES particularly fast. A range of numerical experiments show that the CES algorithm returns high-quality solutions in a matter of a few hundreds of milliseconds and hence appears amenable to a real-time implementation.
Subreddit Recommendations within Reddit Communities

We describe the creation of a recommendation system for Reddit, a social news and entertainment site where community members can submit content in the form of text posts, links, or images. Our dataset consists of 23,091,688 votes from 43,976 users over 3,436,063 links in 11,675 subreddits. Using this network, we constructed a weighted graph of subreddits, partitioned it into 217 distinct subcommunities, and created both an item-based and user-based recommendation algorithm. Given a user and a subreddit, our algorithm recommends to the user novel subreddits within the same subcommunity. User-based recommendation was found to outperform item-based recommendation.
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking

Model compression is essential for serving large deep neural nets on devices with limited resources or applications that require real-time responses. As a case study, a neural language model usually consists of one or more recurrent layers sandwiched between an embedding layer used for representing input tokens and a softmax layer for generating output tokens. For problems with a very large vocabulary size, the embedding and the softmax matrices can account for more than half of the model size. For instance, the bigLSTM model achieves great performance on the One-Billion-Word (OBW) dataset with around 800k vocabulary, and its word embedding and softmax matrices use more than 6GBytes space, and are responsible for over 90% of the model parameters. In this paper, we propose GroupReduce, a novel compression method for neural language models, based on vocabulary-partition (block) based low-rank matrix approximation and the inherent frequency distribution of tokens (the power-law distribution of words). The experimental results show our method can significantly outperform traditional compression methods such as low-rank approximation and pruning. On the OBW dataset, our method achieved 6.6 times compression rate for the embedding and softmax matrices, and when combined with quantization, our method can achieve 26 times compression rate, which translates to a factor of 12.8 times compression for the entire model with very little degradation in perplexity.
Interpretation of Natural Language Rules in Conversational Machine Reading

Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. However, many real-world question answering problems require the reading of text not because it contains the literal answer, but because it contains a recipe to derive an answer together with the reader’s background knowledge. One example is the task of interpreting regulations to answer “Can I...?” or “Do I have to...?” questions such as “I am working in Canada. Do I have to carry on paying UK National Insurance?” after reading a UK government website about this topic. This task requires both the interpretation of rules and the application of background knowledge. It is further complicated due to the fact that, in practice, most questions are underspecified, and a human assistant will regularly have to ask clarification questions such as “How long have you been working abroad?” when the answer cannot be directly derived from the question and text. In this paper, we formalise this task and develop a crowd-sourcing strategy to collect 32k task instances based on real-world rules and crowd-generated questions and scenarios. We analyse the challenges of this task and assess its difficulty by evaluating the performance of rule-based and machine-learning baselines. We observe promising results when no background knowledge is necessary, and substantial room for improvement whenever background knowledge is needed.
Semantic Segmentation from Limited Training Data

We present our approach for robotic perception in cluttered scenes that led to winning the recent Amazon Robotics Challenge (ARC) 2017. Next to small objects with shiny and transparent surfaces, the biggest challenge of the 2017 competition was the introduction of unseen categories. In contrast to traditional approaches which require large collections of annotated data and many hours of training, the task here was to obtain a robust perception pipeline with only few minutes of data acquisition and training time. To that end, we present two strategies that we explored. One is a deep metric learning approach that works in three separate steps: semantic-agnostic boundary detection, patch classification and pixel-wise voting. The other is a fully-supervised semantic segmentation approach with efficient dataset collection. We conduct an extensive analysis of the two methods on our ARC 2017 dataset. Interestingly, only few examples of each class are sufficient to fine-tune even very deep convolutional neural networks for this specific task.
Robust Incremental Neural Semantic Graph Parsing

Parsing sentences to linguisticallyexpressive semantic representations is a key goal of Natural Language Processing. Yet statistical parsing has focussed almost exclusively on bilexical dependencies or domain-specific logical forms. We propose a neural encoder-decoder transition-based parser which is the first full-coverage semantic graph parser for Minimal Recursion Semantics (MRS). The model architecture uses stack-based embedding features, predicting graphs jointly with unlexicalized predicates and their token alignments. Our parser is more accurate than attention-based baselines on MRS, and on an additional Abstract Meaning Representation (AMR) benchmark, and GPU batch processing makes it an order of magnitude faster than a high-precision grammar-based parser. Further, the 86.69% Smatch score of our MRS parser is higher than the upper-bound on AMR parsing, making MRS an attractive choice as a semantic representation.
The Chaos Within Sudoku

The mathematical structure of Sudoku puzzles is akin to hard constraint satisfaction problems lying at the basis of many applications, including protein folding and the ground-state problem of glassy spin systems. Via an exact mapping of Sudoku into a deterministic, continuous-time dynamical system, here we show that the difficulty of Sudoku translates into transient chaotic behavior exhibited by this system. We also show that the escape rate κ, an invariant of transient chaos, provides a scalar measure of the puzzle's hardness that correlates well with human difficulty ratings. Accordingly, η = -log₁₀κ can be used to define a "Richter"-type scale for puzzle hardness, with easy puzzles having 0 < η ≤ 1, medium ones 1 < η ≤ 2, hard with 2 < η ≤ 3 and ultra-hard with η > 3. To our best knowledge, there are no known puzzles with η > 4.
Multimodal Attention for Neural Machine Translation

The attention mechanism is an important part of the neural machine translation (NMT) where it was reported to produce richer source representation compared to fixed-length encoding sequence-to-sequence models. Recently, the effectiveness of attention has also been explored in the context of image captioning. In this work, we assess the feasibility of a multimodal attention mechanism that simultaneously focus over an image and its natural language description for generating a description in another language. We train several variants of our proposed attention mechanism on the Multi30k multilingual image captioning dataset. We show that a dedicated attention for each modality achieves up to 1.6 points in BLEU and METEOR compared to a textual NMT baseline.
Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms.



ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms.



Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm.



These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.
Evaluation of Mindful Sport Performance Enhancement ( MSPE ) : A New Approach to Promote Flow in Athletes

The construct of mindfulness appears to be compatible with theories of !ow and peak performance in sport. The present study assessed how Mindful Sport Performance Enhancement (MSPE), a new 4-week program, affected !ow states, performance, and psychological characteristics of 11 archers and 21 golfers from the community. Participants completed trait measures of anxiety, perfectionism, thought disruption, con"dence, mindfulness, and !ow. They additionally provided data on their performances and state levels of mindfulness and !ow. Analyses revealed that some signi"cant changes in dimensions of the trait variables occurred during the training. Levels of state !ow attained by the athletes also increased between the "rst and "nal sessions. The "ndings suggest that MSPE is a promising intervention to enhance !ow, mindfulness, and aspects of sport con"dence. An expanded workshop to allot more time for mindfulness practice is recommended for future studies.
Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks

This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks (CNNs). In particular, this is achieved by leveraging a feature-centric voting scheme to implement novel convolutional layers which explicitly exploit the sparsity encountered in the input. To this end, we examine the trade-off between accuracy and speed for different architectures and additionally propose to use an L1 penalty on the filter activations to further encourage sparsity in the intermediate representations. To the best of our knowledge, this is the first work to propose sparse convolutional layers and L1 regularisation for efficient large-scale processing of 3D data. We demonstrate the efficacy of our approach on the KITTI object detection benchmark and show that VoteSDeep models with as few as three layers outperform the previous state of the art in both laser and laser-vision based approaches by margins of up to 40% while remaining highly competitive in terms of processing time.
Interpretable Set Functions

We propose learning flexible but interpretable functions that aggregate a variablelength set of permutation-invariant feature vectors to predict a label. We use a deep lattice network model so we can architect the model structure to enhance interpretability, and add monotonicity constraints between inputs-and-outputs. We then use the proposed set function to automate the engineering of dense, interpretable features from sparse categorical features, which we call semantic feature engine. Experiments on real-world data show the achieved accuracy is similar to deep sets or deep neural networks, and is easier to debug and understand.
RECON - A Controlled English for Business Rules

Capturing business rules in a formal logic representation supports the enterprise in two important ways: it enables the evaluation of logs and audit records for conformance to, or violation of, the rules; and it enables the conforming automation of some enterprise activities. The problem is that formal logic representations of the rules are very difficult for an industry expert to read and even more difficult to write, and translating the natural language of the enterprise to formal logic is an unsolved problem. RECON – Restricted English for Constructing Ontologies – is a subset of English that can be easily read by an industry expert, while having a formal grammar and an unambiguous translation to formal logic. This paper describes the principal features of the RECON language, with examples, and shows the corresponding formal logic constructs that are produced by the RECON tool.
Real-time Nodes Coordinator Nodes Broker Nodes

data store designed for real-time exploratory analytics on large data sets. The system combines a column-oriented storage layout, a distributed, shared-nothing architecture, and an advanced indexing structure to allow for the arbitrary exploration of billion-row tables with sub-second latencies. In this paper, we describe Druid’s architecture, and detail how it supports fast aggregations, flexible filters, and low latency data ingestion.
ESPnet: End-to-End Speech Processing Toolkit

This paper introduces a new open source platform for end-toend speech processing named ESPnet. ESPnet mainly focuses on end-to-end automatic speech recognition (ASR), and adopts widely-used dynamic neural network toolkits, Chainer and PyTorch, as a main deep learning engine. ESPnet also follows the Kaldi ASR toolkit style for data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments. This paper explains a major architecture of this software platform, several important functionalities, which differentiate ESPnet from other open source ASR toolkits, and experimental results with major ASR benchmarks.
AHyDA: Automatic Hypernym Detection with Feature Augmentation

English. Several unsupervised methods for hypernym detection have been investigated in distributional semantics. Here we present a new approach based on a smoothed version of the distributional inclusion hypothesis. The new method is able to improve hypernym detection after testing on the BLESS dataset. Italiano. Sulla base dei metodi non supervisionati presenti in letteratura, affrontiamo il task di riconoscimento di iperonimi nello spazio distribuzionale. Introduciamo una nuova misura direzionale, basata su un’espansione dell’ipotesi di inclusione distribuzionale, che migliora il riconoscimento degli iperonimi, testandola sul dataset BLESS.
Ensemble One-Dimensional Convolution Neural Networks for Skeleton-Based Action Recognition

This letter proposes an ensemble neural network (Ensem-NN) for skeleton-based action recognition. The Ensem-NN is introduced based on the idea of ensemble learning, “two heads are better than one.” According to the property of skeleton sequences, we design one-dimensional convolution neural network with residual structure as Base-Net. From entirety to local, from focus to motion, we designed four different subnets based on the Base-Net to extract diverse features. The first subnet is a Two-stream Entirety Net , which performs on the entirety skeleton and explores both temporal and spatial features. The second is a Body-part Net, which can extract fine-grained spatial and temporal features. The third is an  Attention Net, in which a channel-wised attention mechanism can learn important frames and feature channels. Frame-difference Net, as the fourth subnet, aims at exploring motion features. Finally, the four subnets are fused as one ensemble network. Experimental results show that the proposed Ensem-NN performs better than state-of-the-art methods on three widely used datasets.
Biomechatronic Design and Control of an Anthropomorphic Artificial Hand for Prosthetic and Robotic Applications

This paper proposes a biomechatronic approach to the design of an anthropomorphic artificial hand able to mimic the natural motion of the human fingers. The hand is conceived to be applied to prosthetics as well as to humanoid and personal robotics; hence, anthropomorphism is a fundamental requirement to be addressed both in the physical aspect and in the functional behavior. In this paper, a biomechatronic approach is addressed to harmonize the mechanical design of the anthropomorphic artificial hand with the design of the hand control system. More in detail, this paper focuses on the control system of the hand and on the optimization of the hand design in order to obtain a human-like kinematics and dynamics. By evaluating the simulated hand performance, the mechanical design is iteratively refined. The mechanical structure and the ratio between number of actuators and number of degrees of freedom (DOFs) have been optimized in order to cope with the strict size and weight constraints that are typical of application of artificial hands to prosthetics and humanoid robotics. The proposed hand has a kinematic structure similar to the natural hand featuring three articulated fingers (thumb, index, and middle finger with 3 DOF for each finger and 1 DOF for the abduction/adduction of the thumb) driven by four dc motors. A special underactuated transmission has been designed that allows keeping the number of motors as low as possible while achieving a self-adaptive grasp, as a result of the passive compliance of the distal DOF of the fingers. A proper hand control scheme has been designed and implemented for the study and optimization of hand motor performance in order to achieve a human-like motor behavior. To this aim, available data on motion of the human fingers are collected from the neuroscience literature in order to derive a reference input for the control. Simulation trials and computer-aided design (CAD) mechanical tools are used to obtain a finger model including its dynamics. Also the closed-loop control system is simulated in order to study the effect of iterative mechanical redesign and to define the final set of mechanical parameters for the hand optimization. Results of the experimental tests carried out for validating the model of the robotic finger, and details on the process of integrated refinement and optimization of the mechanical structure and of the hand motor control scheme are extensively reported in the paper.
Metal oxides for solid-state gas sensors : What determines our choice ?

The analysis of various parameters of metal oxides and the search of criteria, which could be used during material selection for solid-state gas sensor pplications, were the main objectives of this review. For these purposes the correlation between electro-physical (band gap, electroconductivity, ype of conductivity, oxygen diffusion), thermodynamic, surface, electronic, structural properties, catalytic activity and gas-sensing characteristics f metal oxides designed for solid-state sensors was established. It has been discussed the role of metal oxide manufacturability, chemical activity, nd parameter’s stability in sensing material choice as well.
An Introduction to Deep Learning for the Physical Layer

We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process. We show how this idea can be extended to networks of multiple transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. This paper is concluded with a discussion of open challenges and areas for future investigation.
Classification of pornographic content on Twitter using support vector machine and Naive Bayes

The Internet has many benefits, some of them are to gain knowledge and gain the latest information. The internet can be used by anyone and can contain any information, including negative content such as pornographic content, radicalism, racial intolerance, violence, fraud, gambling, security and drugs. Those contents cause the number of children victims of pornography on social media increasing every year. Based on that, it needs a system that detects pornographic content on social media. This study aims to determine the best model to detect the pornographic content. Model selection is determined based on unigram and bigram features, classification algorithm, k-fold cross validation. The classification algorithm used is Support Vector Machine and Naive Bayes. The highest F1-score is yielded by the model with combination of Support Vector Machine, most common words, and combination of unigram and bigram, which returns F1-Score value of 91.14%.
Latent Molecular Optimization for Targeted Therapeutic Design

We devise an approach for targeted molecular design, a problem of interest in computational drug discovery: given a target protein site, we wish to generate a chemical with both high binding affinity to the target and satisfactory pharmacological properties. This problem is made difficult by the enormity and discreteness of the space of potential therapeutics, as well as the graph-structured nature of biomolecular surface sites. Using a dataset of protein-ligand complexes, we surmount these issues by extracting a signature of the target site with a graph convolutional network and by encoding the discrete chemical into a continuous latent vector space. The latter embedding permits gradient-based optimization in molecular space, which we perform using learned differentiable models of binding affinity and other pharmacological properties. We show that our approach is able to efficiently optimize these multiple objectives and discover new molecules with potentially useful binding properties, validated via docking methods.