nils-herrmann/clean_scirepeval huggingface dataset:
Extending the Soar Cognitive Architecture

One approach in pursuit of general intelligent agents has been to concentrate on the underlying cognitive architecture, of which Soar is a prime example. In the past, Soar has relied on a minimal number of architectural modules together with purely symbolic representations of knowledge. This paper presents the cognitive architecture approach to general intelligence and the traditional, symbolic Soar architecture. This is followed by major additions to Soar: nonsymbolic representations, new learning mechanisms, and long-term memories.
Complete residential urban area reconstruction from dense aerial LiDAR point clouds

We present an automatic system to reconstruct 3D urban models for residential areas from aerial LiDAR scans. The key difference between downtown area modeling and residential area modeling is that the latter usually contains rich vegetation. Thus, we propose a robust classification algorithm that effectively classifies LiDAR points into trees, buildings, and ground. The classification algorithm adopts an energy minimization scheme based on the 2.5D characteristic of building structures: buildings are composed of opaque skyward roof surfaces and vertical walls, making the interior of building structures invisible to laser scans; in contrast, trees do not possess such characteristic and thus point samples can exist underneath tree crowns. Once the point cloud is successfully classified, our system reconstructs buildings and trees respectively, resulting in a hybrid model representing the 3D urban reality of residential areas.
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference

We present a novel deep learning architecture to address the natural language inference (NLI) task. Existing approaches mostly rely on simple reading mechanisms for independent encoding of the premise and hypothesis. Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and inference. We also introduce a sophisticated ensemble strategy to combine our proposed models, which noticeably improves final predictions. Finally, we demonstrate how the results can be improved further with an additional preprocessing step. Our evaluation shows that DR-BiLSTM obtains the best single model and ensemble model results achieving the new state-of-the-art scores on the Stanford NLI dataset.
Ring Theoretic Properties of Certain Hecke Algebras

In the work of one of us (A.W.) on the conjecture that all elliptic curves defined over Q are modular, the importance of knowing that certain Hecke algebras are complete intersections was established. The purpose of this article is to provide the missing ingredient in [W2] by establishing that the Hecke algebras considered there are complete intersections. As is recorded in [W2], a method going back to Mazur [M] allows one to show that these algebras are Gorenstein, but this seems to be too weak for the purposes of that paper. The methods of this paper are related to those of chapter 3 of [W2]. We would like to thank Henri Darmon, Fred Diamond and Gerd Faltings for carefully reading the first version of this article. Gerd Faltings has also suggested a simplification of our argument and we would like to thank him for allowing us to reproduce this in the appendix to this paper. R.T. would like to thank A.W. for his invitation to collaborate on these problems and for sharing his many insights into the questions considered. R.T. would also like to thank Princeton University, Université de Paris 7 and Harvard University for their hospitality during some of the work on this paper. A.W. was supported by an NSF grant.
Learning Semantic Hierarchies via Word Embeddings

Semantic hierarchy construction aims to build structures of concepts linked by hypernym–hyponym (“is-a”) relations. A major challenge for this task is the automatic discovery of such relations. This paper proposes a novel and effective method for the construction of semantic hierarchies based on word embeddings, which can be used to measure the semantic relationship between words. We identify whether a candidate word pair has hypernym–hyponym relation by using the word-embedding-based semantic projections between words and their hypernyms. Our result, an F-score of 73.74%, outperforms the state-of-theart methods on a manually labeled test dataset. Moreover, combining our method with a previous manually-built hierarchy extension method can further improve Fscore to 80.29%.
Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks

Chest X-Rays are widely used for diagnosing abnormalities in the heart and lung area. Automatically detecting these abnormalities with high accuracy could greatly enhance real world diagnosis processes. Lack of standard publicly available dataset and benchmark studies, however, makes it difficult to compare various detection methods. In order to overcome these difficulties, we have used the publicly available Indiana chest X-Ray dataset and studied the performance of known deep convolutional network (DCN) architectures on different abnormalities. We find that the same DCN architecture doesn’t perform well across all abnormalities. Shallow features or earlier layers consistently provide higher detection accuracy compared to deep features. We have also found ensemble models to improve classification significantly compared to single model. Combining these insight, we report the highest accuracy on chest X-Ray abnormality detection on this dataset. Our localization experiments using these trained classifiers show that for spatially spread out abnormalities like cardiomegaly and pulmonary edema, the network can localize the abnormalities successfully most of the time. We find that in the cardiomegaly classification task, the deep learning method improves the accuracy by a staggering 17 percentage point compared to rule based methods. One remarkable result of the cardiomegaly localization is that the heart and its surrounding region is most responsible for cardiomegaly detection, in contrast to the rule based models where the ratio of heart and lung area is used as the measure. We believe that through deep learning based classification and localization, we will discover many more interesting features in medical image diagnosis that are not considered traditionally.
Autonomous Underwater Navigation and Optical Mapping in Unknown Natural Environments

We present an approach for navigating in unknown environments while, simultaneously, gathering information for inspecting underwater structures using an autonomous underwater vehicle (AUV). To accomplish this, we first use our pipeline for mapping and planning collision-free paths online, which endows an AUV with the capability to autonomously acquire optical data in close proximity. With that information, we then propose a reconstruction pipeline to create a photo-realistic textured 3D model of the inspected area. These 3D models are also of particular interest to other fields of study in marine sciences, since they can serve as base maps for environmental monitoring, thus allowing change detection of biological communities and their environment over time. Finally, we evaluate our approach using the Sparus II, a torpedo-shaped AUV, conducting inspection missions in a challenging, real-world and natural scenario.
Small planar UWB antennas in proximity of the human head

The effects of a human head on the performance of small planar ultra-wideband (UWB) antennas in proximity of the head are investigated numerically and experimentally. In simulation, a numerical head model is used in the XFDTD software package. The head model developed by REMCOM is with the frequency-dependent dielectric constant and conductivity obtained from the average data of anatomical human heads. Two types of planar antennas printed on printed circuit board (PCB) are designed to cover the UWB band. The impedance and radiation performance of the antennas are examined when the antennas are placed very close to the human head. The study shows that the human head slightly affects the impedance performance of the antennas. The radiated field distributions and the gain of the antennas demonstrate that the human head significantly blocks and absorbs the radiation from the antennas so that the radiation patterns are directional in the horizontal planes and the average gain greatly decreases. The information derived from the study is helpful to engineers who are applying UWB devices around/on human heads.
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.
No country for old members: user lifecycle and linguistic change in online communities

Vibrant online communities are in constant flux. As members join and depart, the interactional norms evolve, stimulating further changes to the membership and its social dynamics. Linguistic change --- in the sense of innovation that becomes accepted as the norm --- is essential to this dynamic process: it both facilitates individual expression and fosters the emergence of a collective identity.
 We propose a framework for tracking linguistic change as it happens and for understanding how specific users react to these evolving norms. By applying this framework to two large online communities we show that users follow a determined two-stage lifecycle with respect to their susceptibility to linguistic change: a linguistically innovative learning phase in which users adopt the language of the community followed by a conservative phase in which users stop changing and the evolving community norms pass them by.
 Building on this observation, we show how this framework can be used to detect, early in a user's career, how long she will stay active in the community. Thus, this work has practical significance for those who design and maintain online communities. It also yields new theoretical insights into the evolution of linguistic norms and the complex interplay between community-level and individual-level linguistic change.
Personalized next-song recommendation in online karaokes

In this paper, we propose Personalized Markov Embedding (PME), a next-song recommendation strategy for online karaoke users. By modeling the sequential singing behavior, we first embed songs and users into a Euclidean space in which distances between songs and users reflect the strength of their relationships. Then, given each user's last song, we can generate personalized recommendations by ranking the candidate songs according to the embedding. Moreover, PME can be trained without any requirement of content information. Finally, we perform an experimental evaluation on a real world data set provided by ihou.com which is an online karaoke website launched by iFLYTEK, and the results clearly demonstrate the effectiveness of PME.
Unregulated use of laptops over time in large lecture classes

Students often have their own individual laptop computers in university classes, and researchers debate the potential benefits and drawbacks of laptop use. In the presented research, we used a combination of surveys and in-class observations to study how students use their laptops in an unmonitored and unrestricted class setting—a large lecture-based university class with nearly 3000 enrolled students. By analyzing computer use over the duration of long (165 minute) classes, we demonstrate how computer use changes over time. The observations and studentreports provided similar descriptions of laptop activities. Note taking was the most common use for the computers, followed by the use of social media web sites. Overall, the data show that students engaged in off-task computer activities for nearly two-thirds of the time. An analysis of the frequency of the various laptop activities over time showed that engagement in individual activities varied significantly over the duration of the class.
Where and when to pay attention: the neural systems for directing attention to spatial locations and to time intervals as revealed by both PET and fMRI.

Although attention is distributed across time as well as space, the temporal allocation of attention has been less well researched than its spatial counterpart. A temporal analog of the covert spatial orientation task [Posner MI, Snyder CRR, Davidson BJ (1980) Attention and the detection of signals.  was developed to compare the neural systems involved in directing attention to spatial locations versus time intervals. We asked whether there exists a general system for allocating attentional resources, independent of stimulus dimension, or whether functionally specialized brain regions are recruited for directing attention toward spatial versus temporal aspects of the environment. We measured brain activity in seven healthy volunteers by using positron emission tomography (PET) and in eight healthy volunteers by using functional magnetic resonance imaging (fMRI). The task manipulated cued attention to spatial locations (S) and temporal intervals (T) in a factorial design. Symbolic central cues oriented subjects toward S only (left or right), toward T only (300 msec or 1500 msec), toward both S and T simultaneously, or provided no information regarding S or T. Subjects also were scanned during a resting baseline condition. Behavioral data showed benefits and costs for performance during temporal attention similar to those established for spatial attention. Brain-imaging data revealed a partial overlap between neural systems involved in the performance of spatial versus temporal orientation of attention tasks. Additionally, hemispheric asymmetries revealed preferential right and left parietal activation for spatial and temporal attention, respectively. Parietal cortex was activated bilaterally by attending to both dimensions simultaneously. This is the first direct comparison of the neural correlates of attending to spatial versus temporal cues.
Forex trend classification using machine learning techniques

(Forex) is a highly volatile complex time series for which predicting the daily trend is a challenging problem. In this paper, we investigate the prediction of the High exchange rate daily trend as a binary classification problem, with uptrend and downtrend outcomes. A large number of basic features driven from the time series data, including technical analysis features are generated using multiple history time windows. Various feature selection and feature extraction techniques are used to find best subsets for the classification problem. Machine learning systems are tested for each feature subset and results are analyzed. Four important Forex currency pairs are investigated and the results show consistent success in the daily prediction and in the expected profit.
Efficient spotlight SAR raw signal simulation of extended scenes

Synthetic aperture radar (SAR) raw signal simulation is a powerful tool for designing new sensors, testing processing algorithms, planning missions, and devising inversion algorithms. In this paper, a spotlight SAR raw signal simulator for distributed targets is presented. The proposed procedure is based on a Fourier domain analysis: a proper analytical reformulation of the spotlight SAR raw signal expression is presented. It is shown that this reformulation allows us to design a very efficient simulation scheme that employs fast Fourier transform codes. Accordingly, the computational load is dramatically reduced with respect to a time-domain simulation and this, for the first time, makes spotlight simulation of extended scenes feasible.
Annotating Discourse Connectives and Their Arguments

This paper describes a new, large scale discourse-level annotation project – the Penn Discourse TreeBank (PDTB). We present an approach to annotating a level of discourse structure that is based on identifying discourse connectives and their arguments. The PDTB is being built directly on top of the Penn TreeBank and Propbank, thus supporting the extraction of useful syntactic and semantic features and providing a richer substrate for the development and evaluation of practical algorithms. We provide a detailed preliminary analysis of inter-annotator agreement – both the level of agreement and the types of inter-annotator variation.
Motion planning in urban environments: Part II

We present the motion planning framework for an autonomous vehicle navigating through urban environments. Such environments present a number of motion planning challenges, including ultra-reliability, high-speed operation, complex inter-vehicle interaction, parking in large unstructured lots, and constrained maneuvers. Our approach combines a model-predictive trajectory generation algorithm for computing dynamically-feasible actions with two higher-level planners for generating long range plans in both on-road and unstructured areas of the environment. In this Part II of a two-part paper, we describe the unstructured planning component of this system used for navigating through parking lots and recovering from anomalous on-road scenarios. We provide examples and results from ldquoBossrdquo, an autonomous SUV that has driven itself over 3000 kilometers and competed in, and won, the Urban Challenge.
Alternating-Time Temporal Logic

Temporal logic comes in two varieties: linear-time temporal logic assumes implicit universal quantification over all paths that are generated by system moves; branching-time temporal logic allows explicit existential and universal quantification over all paths. We introduce a third, more general variety of temporal logic: alternating-time temporal logic offers selective quantification over those paths that are possible outcomes of games, such as the game in which the system and the environment alternate moves. While linear-time and branching-time logics are natural specification languages for closed systems, alternative-time logics are natural specification languages for open systems. For example, by preceding the temporal operator "eventually" with a selective path quantifier, we can specify that in the game between the system and the environment, the system has a strategy to reach a certain state. Also, the problems of receptiveness, realizability, and controllability can be formulated as model-checking problems for alternating-time formulas. Depending on whether we admit arbitrary nesting of selective path quantifiers and temporal operators, we obtain the two alternating-time temporal logics ATL and ATL*. We interpret the formulas of ATL and ATL* over alternating transition systems. While in ordinary transitory systems, each transition corresponds to a possible step of the system, in alternating transition systems, each transition corresponds to a possible move in the game between the system and the environment. Fair alternating transition systems can capture both synchronous and asynchronous compositions f open systems. For synchronous systems, the expressive power of ATL beyond CTL comes at no cost: the model-checking complexity of synchronous ATL is linear in the size of the system and the length of the formula. The symbolic model-checking algorithm for CTL extends with few modifications to synchronous ATL, and with some work, also to asynchronous to ATL, whose model-checking complexity is quadratic. This makes ATL an obvious candidate for the automatic verification of open systems. In the case of ATL*, the model-checking problem is closely related to the synthesis problem for linear-time formulas, and requires doubly exponential time for both synchronous and asynchronous
Learning Common and Feature-Specific Patterns: A Novel Multiple-Sparse-Representation-Based Tracker

The use of multiple features has been shown to be an effective strategy for visual tracking because of their complementary contributions to appearance modeling. The key problem is how to learn a fused representation from multiple features for appearance modeling. Different features extracted from the same object should share some commonalities in their representations while each feature should also have some feature-specific representation patterns which reflect its complementarity in appearance modeling. Different from existing multi-feature sparse trackers which only consider the commonalities among the sparsity patterns of multiple features, this paper proposes a novel multiple sparse representation framework for visual tracking which jointly exploits the shared and feature-specific properties of different features by decomposing multiple sparsity patterns. Moreover, we introduce a novel online multiple metric learning to efficiently and adaptively incorporate the appearance proximity constraint, which ensures that the learned commonalities of multiple features are more representative. Experimental results on tracking benchmark videos and other challenging videos demonstrate the effectiveness of the proposed tracker.
Malware detection using statistical analysis of byte-level file content

Commercial anti-virus software are unable to provide protection against newly launched (a.k.a "zero-day") malware. In this paper, we propose a novel malware detection technique which is based on the analysis of byte-level file content. The novelty of our approach, compared with existing content based mining schemes, is that it does not memorize specific byte-sequences or strings appearing in the actual file content. Our technique is non-signature based and therefore has the potential to detect previously unknown and zero-day malware. We compute a wide range of statistical and information-theoretic features in a block-wise manner to quantify the byte-level file content. We leverage standard data mining algorithms to classify the file content of every block as normal or potentially malicious. Finally, we correlate the block-wise classification results of a given file to categorize it as benign or malware. Since the proposed scheme operates at the byte-level file content; therefore, it does not require any a priori information about the filetype. We have tested our proposed technique using a benign dataset comprising of six different filetypes --- DOC, EXE, JPG, MP3, PDF and ZIP and a malware dataset comprising of six different malware types --- backdoor, trojan, virus, worm, constructor and miscellaneous. We also perform a comparison with existing data mining based malware detection techniques. The results of our experiments show that the proposed nonsignature based technique surpasses the existing techniques and achieves more than 90% detection accuracy.