nils-herrmann/clean_scirepeval huggingface dataset:
Storage and executive processes in the frontal lobes.

The human frontal cortex helps mediate working memory, a system that is used for temporary storage and manipulation of information and that is involved in many higher cognitive functions. Working memory includes two components: short-term storage (on the order of seconds) and executive processes that operate on the contents of storage. Recently, these two components have been investigated in functional neuroimaging studies. Studies of storage indicate that different frontal regions are activated for different kinds of information: storage for verbal materials activates Broca's area and left-hemisphere supplementary and premotor areas; storage of spatial information activates the right-hemisphere premotor cortex; and storage of object information activates other areas of the prefrontal cortex. Two of the fundamental executive processes are selective attention and task management. Both processes activate the anterior cingulate and dorsolateral prefrontal cortex.
A 77-GHz MMIC power amplifier for automotive radar applications

A MMIC 77-GHz two-stage power amplifier (PA) is reported in this letter. This MMIC chip demonstrated a measured small signal gain of over 10 dB from 75 GHz to 80 GHz with 18.5-dBm output power at 1 dB compression. The maximum small signal gain is above 12 dB from 77 to 78 GHz. The saturated output power is better than 21.5 dBm and the maximum power added efficiency is 10% between 75 GHz and 78 GHz. This chip is fabricated using 0.1-/spl mu/m AlGaAs/InGaAs/GaAs PHEMT MMIC process on 4-mil GaAs substrate. The output power performance is the highest among the reported 4-mil MMIC GaAs HEMT PAs at this frequency and therefore it is suitable for the 77-GHz automotive radar systems and related transmitter applications in W-band.
Coding polygon meshes as compressable ASCII

Because of the convenience of a text-based format 3D content is often published in form of a gzipped file that contains an ASCII description of the scene graph. While compressed image, audio, and video data is kept in seperate binary files, polygonal data is usually included uncompressed into the ASCII description, as there is no widely-accepted standard for compressed polygon meshes.In this paper we show how to incorporate compression of polygonal data into a purely text-based scene graph description. Our scheme codes polygon meshes as ASCII strings that compress well with standard compression schemes such as gzip. The coder is lossless when only the position and texture coordinate {\em indices} are coded. If loss is acceptable, positions and texture coordinates can be quantized and delta coded, which reduces the file size further. The gzipped scene graph description files decrease by a factor of two (six) in size when the polygon meshes they contain are coded with the lossless (lossy) ASCII coder.Furthermore we describe in detail a proof-of-concept implementation that uses the Shout3D~\cite{shout3d} pure java API---a plugin-less Web3D player that downloads all required java classes on demand. Our prototype is an extremely light-weight implementation of the decoder that can be distributed at minimal additional cost. The size of the compiled decoder class is less than 6KB by itself and less than 3KB if included into a compressed archive of java class files. It makes no use of specific features of the Shout3D API. Hence, our method will work for any scene graph API that allows (a) to extend the node set and (b) to store the scene graph as ASCII.
Introduction to Arabic Natural Language Processing Nizar Y. Habash (Columbia University) Morgan & Claypool (Synthesis Lectures on Human Language Technologies, edited by Graeme Hirst, volume 10), 2010, xvii+167 pp; paperbound, ISBN 978-1-59829-795-9, $40.00; ebook, ISBN 978-1-59829-796-6, $30.00 or b

The Arabic language, which is the mother tongue of more than 300 million people, presents significant challenges to many natural language processing (NLP) applications (Farghaly and Shaalan 2009). Arabic is a highly inflected and derived language. The scale of Arabic computational linguistic research work is now orders of magnitude beyond what was available a decade ago. Hence, it becomes important to find a book that introduces necessary background information for working with Arabic in research or development on NLP and computational linguistics. Habash’s book Introduction to Arabic Natural Language Processing is an important step toward that goal. It introduces Arabic linguistic phenomena, shows how the Arabic language can be handled by computers, and presents resources and tools available for Arabic. The book focuses on introducing the language, as opposed to explaining how to build or conduct research in a specific NLP research area. It is not meant to be an indepth description of how to build specific NLP applications. However, it highlights necessary issues to be aware of in NLP research and application building. As an example, readers will find a solid overview of core tasks including morphological analysis, generation, tokenization, part-of-speech (POS) tagging, and even parsing. But it does not contain detailed instructions on how to build any of these technologies. The author’s approach is to highlight Arabic-specific issues (e.g., how Arabic morphology interacts with different approaches to POS tagging) and to leave language-independent technologies such as core POS tagging or parsing to other resources. Readers will find a nice note on machine translation from and to Arabic (Chapter 8) that addresses the multilingual aspects of working with Arabic. The book is meant for students, researchers, and engineers who are interested in working in Arabic computational linguistics and NLP. No prior knowledge of the Arabic language is required. The book mainly discusses Modern Standard Arabic (MSA), but the author doesn’t hesitate to refer to Arabic dialect occasionally, especially in the early chapters. Readers not familiar with Arabic get a good introduction to the language and how it can be handled by computers. Readers with prior knowledge of Arabic are reminded of the basics needed for computational research, some of which may not be intuitive to a person familiar with the language only linguistically as opposed to computationally.
Social recommendation using Euclidean embedding

Traditional recommender systems assume that all the users are independent, and they usually face the cold start and data sparse problems. To alleviate these problems, social recommender systems use social relations as an additional input to improve recommendation accuracy. Social recommendation follows the intuition that people with social relationships share some kinds of preference towards items. Current social recommendation methods commonly apply the Matrix Factorization (MF) model to incorporate social information into the recommendation process. As an alternative model to MF, we propose a novel social recommendation approach based on Euclidean Embedding (SREE) in this paper. The idea is to embed users and items in a unified Euclidean space, where users are close to both their desired items and social friends. Experimental results conducted on two real-world data sets illustrate that our proposed approach outperforms the state-of-the-art methods in terms of recommendation accuracy.
HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images

In this paper we present an approach to enhance existing maps with fine grained segmentation categories such as parking spots and sidewalk, as well as the number and location of road lanes. Towards this goal, we propose an efficient approach that is able to estimate these fine grained categories by doing joint inference over both, monocular aerial imagery, as well as ground images taken from a stereo camera pair mounted on top of a car. Important to this is reasoning about the alignment between the two types of imagery, as even when the measurements are taken with sophisticated GPS+IMU systems, this alignment is not sufficiently accurate. We demonstrate the effectiveness of our approach on a new dataset which enhances KITTI  with aerial images taken with a camera mounted on an airplane and flying around the city of Karlsruhe, Germany.
REFMAC5 for the refinement of macromolecular crystal structures

This paper describes various components of the macromolecular crystallographic refinement program REFMAC5, which is distributed as part of the CCP4 suite. REFMAC5 utilizes different likelihood functions depending on the diffraction data employed (amplitudes or intensities), the presence of twinning and the availability of SAD/SIRAS experimental diffraction data. To ensure chemical and structural integrity of the refined model, REFMAC5 offers several classes of restraints and choices of model parameterization. Reliable models at resolutions at least as low as 4 Å can be achieved thanks to low-resolution refinement tools such as secondary-structure restraints, restraints to known homologous structures, automatic global and local NCS restraints, `jelly-body' restraints and the use of novel long-range restraints on atomic displacement parameters (ADPs) based on the Kullback-Leibler divergence. REFMAC5 additionally offers TLS parameterization and, when high-resolution data are available, fast refinement of anisotropic ADPs. Refinement in the presence of twinning is performed in a fully automated fashion. REFMAC5 is a flexible and highly optimized refinement package that is ideally suited for refinement across the entire resolution spectrum encountered in macromolecular crystallography.
A UWB based Localization System for Indoor Robot Navigation

For robots to become more popular for domestic applications, the short comings of current indoor navigation technologies have to be overcome. In this paper, we propose the use of UWB-IR for indoor robot navigation. Various parts of an actual implementation of a UWB-IR based robot navigation system such as system architecture, RF sub-system design, antennas and localization algorithms are discussed. It is shown that by properly addressing the various issues, a localization error of less than 25 cm can be achieved at all points within a realistic indoor localization space.
Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering

Open-domain question answering remains a challenging task as it requires models that are capable of understanding questions and answers, collecting useful information, and reasoning over evidence. Previous work typically formulates this task as a reading comprehension or entailment problem given evidence retrieved from search engines. However, existing techniques struggle to retrieve indirectly related evidence when no directly related evidence is provided, especially for complex questions where it is hard to parse precisely what the question asks. In this paper we propose a retriever-reader model that learns to attend on essential terms during the question answering process. We build (1) an essential term selector which first identifies the most important words in a question, then reformulates the query and searches for related evidence; and (2) an enhanced reader that distinguishes between essential terms and distracting words to predict the answer. We evaluate our model on multiple open-domain QA datasets where it outperforms the existing state-of-the-art, notably leading to a relative improvement of 8.1% on the AI2 Reasoning Challenge (ARC) dataset.
Practical Design and Implementation of Metamaterial-Enhanced Magnetic Induction Communication

The wireless communications in complex environments, such as underground and underwater, can enable various applications in the environmental, industrial, homeland security, law enforcement, and military fields. However, conventional electromagnetic wave-based techniques do not work due to the lossy media and complicated structures. Magnetic induction (MI) has been proved to achieve reliable communication in such environments. However, due to the small antenna size, the communication range of MI is still very limited, especially for the portable mobile devices. To this end, Metamaterial-enhanced MI (M2I) communication has been proposed, where the theoretical results predict that it can significantly increase the data rate and range. Nevertheless, there exists a significant gap between the theoretical prediction and the practical realization of M2I; the theoretical model relies on an ideal spherical metamaterial, while it does not exist in nature. In this paper, a practical design is proposed by leveraging a spherical coil array to realize M2I communication. The full-wave simulation is conducted to validate the design objectives. By using the spherical coil array-based M2I communication, the communication range can be significantly extended, exactly as we predicted in the ideal M2I model. Finally, the proposed M2I communication is implemented and tested in various environments.
On automatic differentiation

During these last years, the environmental problems have acquired a growing place in our society. It becomes necessary to find a lasting way to the nuclear waste storage. The waste storage is in relation to the characteristics of wastes according to their activity and the life of the radionuclide. After many studies on the topic, led in particular by ANDRA, the storage in deep level is considered as the reference strategy for wastes with high or medium activity and long-lived. We have to make simulations of the radionucleide transport in underground in order to determine the impact of a possible propagation of radioelements. The modelling of the flow in porous media around the storage requires to know the physical parameters of the different geological layers. Those parameters (porosity and diffusion) are not directly accessible by measurements, hence we have to solve an inverse problem to recover them.
Building a Literal Bridge Between Robotics and Neuroscience using Functional Near Infrared Spectroscopy ( NIRS )

Functional near infrared spectroscopy (NIRS) is a promising new tool for research in human-robot interaction (HRI). The use of NIRS in HRI has already been demonstrated both as a means for investigating brain activity during human-robot interactions, as well as in the development of brain-robot interfaces that passively monitor a person’s cognitive state to adapt robots’ behaviors accordingly. In this paper, we survey the utility of NIRS and its range of applications in HRI. We discuss both some exemplary applications as well as several pressing challenges to the deployment of NIRS in more realistic settings.
A Language Independent Algorithm for Single and Multiple Document Summarization

S S S w S w w S S Sim + ∈ ∧ ∈ = other similarity metrics: cosine, string kernels, etc. 3. r i BC-HurricaneGilbert 09-11 0339 4. BC-Hurricane Gilbert , 0348 5. Hurricane Gilbert Heads Toward Dominican Coast 6. By RUDDY GONZALEZ 7. Associated Press Writer 8. SANTO DOMINGO , Dominican Republic ( AP ) 9. Hurricane Gilbert swept toward the Dominican Republic Sunday , and the Civil Defense alerted its heavily populated south coast to prepare for high winds , heavy rains and high seas . 10. The storm was approaching from the southeast with sustained winds of 75 mph gusting to 92 mph . 11. " There is no need for alarm , " Civil Defense Director Eugenio Cabral said in a television alert shortly before midnight Saturday . 12. Cabral said residents of the province of Barahona should closely follow Gilbert 's movement . 13. An estimated 100,000 people live in the province , including 70,000 in the city of Barahona , about 125 miles west of Santo Domingo . 14. Tropical Storm Gilbert formed in the eastern Caribbean and strengthened into a hurricane Saturday night . 15. The National Hurricane Center in Miami reported its position at 2a.m. Sunday at latitude 16.1 north , longitude 67.5 west , about 140 miles south of Ponce , Puerto Rico , and 200 miles southeast of Santo Domingo . 16. The National Weather Service in San Juan , Puerto Rico , said Gilbert was moving westward at 15 mph with a " broad area of cloudiness and heavy weather " rotating around the center of the storm . 17. The weather service issued a flash flood watch for Puerto Rico and the Virgin Islands until at least 6p.m. Sunday . 18. Strong winds associated with the Gilbert brought coastal flooding , strong southeast winds and up to 12 feet feet to Puerto Rico 's south coast . 19. There were no reports of casualties . 20. San Juan , on the north coast , had heavy rains and gusts Saturday , but they subsided during the night . 21. On Saturday , Hurricane Florence was downgraded to a tropical storm and its remnants pushed inland from theU.S. Gulf Coast . 22. Residents returned home , happy to find little damage from 80 mph winds and sheets of rain . 23. Florence , the sixth named storm of the 1988 Atlantic storm season , was the second hurricane . 24. The first , Debby , reached minimal hurricane strength briefly before hitting the Mexican coast last month . 4 6 5
Human Performance on Insight Problem Solving : A Review

The article provides a review of recent research on insight problem-solving performance. We discuss what insight problems are, the different types of classic and newer insight problems, and how we can classify them. We also explain some of the other aspects that affect insight performance, such as hints, analogs, training, thinking aloud, and individual differences. In addition, we describe some of the main theoretical explanations that have been offered. Finally, we present some measures of insight and relevant neuroscience contributions to the area over the last decade.
Spatial Clustering in the Presence of Obstacles

Clustering in spatial data mining is to group similar objects based on their distance, connectivity, or their relative density in space. In the real world, there exist many physical obstacles such as rivers, lakes and highways, and their presence may affect the result of clustering substantially. In this paper, we study the problem of clustering in the presence of obstacles and define it as a COD (Clustering with Obstructed Distance) problem. As a solution to this problem, we propose a scalable clustering algorithm, called COD-CLARANS . W e discuss various forms of pre-processed information that could enhance the eficiency of COD-CLARANS . In the strictest sense, the CODproblem can be treated as a change an distance function and thus could be handled by current clustering algorithms by changing the distance function. However, we show that by pushing the task of handling obstacles into COD-CLARANS instead of abstracting it a t the distance function level, more optimization can be done in the form of a pruning function E’. W e conduct various performance studies to show that COD-CLARANS is both eficient and effective.
The Epilepsy Spectrum: Targeting Future Research Challenges.

There have been tremendous recent advances in our understanding of the biological underpinnings of epilepsy and associated comorbidities that justify its representation as a spectrum disorder. Advances in genetics, electrophysiology, and neuroimaging have greatly improved our ability to differentiate, diagnose, and treat individuals with epilepsy. However, we have made little overall progress in preventing epilepsy, and the number of patients who are cured remains small. Likewise, the comorbidities of epilepsy are often underdiagnosed or not adequately treated. In this article, we suggest a few areas in which additional research will likely pay big dividends for patients and their families.
Discrete Graph Hashing

Hashing has emerged as a popular technique for fast nearest neighbor search in gigantic databases. In particular, learning based hashing has received considerable attention due to its appealing storage and search efficiency. However, the performance of most unsupervised learning based hashing methods deteriorates rapidly as the hash code length increases. We argue that the degraded performance is due to inferior optimization procedures used to achieve discrete binary codes. This paper presents a graph-based unsupervised hashing model to preserve the neighborhood structure of massive data in a discrete code space. We cast the graph hashing problem into a discrete optimization framework which directly learns the binary codes. A tractable alternating maximization algorithm is then proposed to explicitly deal with the discrete constraints, yielding high-quality codes to well capture the local neighborhoods. Extensive experiments performed on four large datasets with up to one million samples show that our discrete optimization based graph hashing method obtains superior search accuracy over state-of-the-art unsupervised hashing methods, especially for longer codes.
STRADS: a distributed framework for scheduled model parallel machine learning

Machine learning (ML) algorithms are commonly applied to big data, using distributed systems that partition the data across machines and allow each machine to read and update all ML model parameters --- a strategy known as data parallelism. An alternative and complimentary strategy, model parallelism, partitions the model parameters for non-shared parallel access and updates, and may periodically repartition the parameters to facilitate communication. Model parallelism is motivated by two challenges that data-parallelism does not usually address: (1) parameters may be dependent, thus naive concurrent updates can introduce errors that slow convergence or even cause algorithm failure; (2) model parameters converge at different rates, thus a small subset of parameters can bottleneck ML algorithm completion. We propose scheduled model parallelism (SchMP), a programming approach that improves ML algorithm convergence speed by efficiently scheduling parameter updates, taking into account parameter dependencies and uneven convergence. To support SchMP at scale, we develop a distributed framework STRADS which optimizes the throughput of SchMP programs, and benchmark four common ML applications written as SchMP programs: LDA topic modeling, matrix factorization, sparse least-squares (Lasso) regression and sparse logistic regression. By improving ML progress per iteration through SchMP programming whilst improving iteration throughput through STRADS we show that SchMP programs running on STRADS outperform non-model-parallel ML implementations: for example, SchMP LDA and SchMP Lasso respectively achieve 10x and 5x faster convergence than recent, well-established baselines.
Active sampling for entity matching

In entity matching, a fundamental issue while training a classifier to label pairs of entities as either duplicates or non-duplicates is the one of selecting informative training examples. Although active learning presents an attractive solution to this problem, previous approaches minimize the misclassification rate (0-1 loss) of the classifier, which is an unsuitable metric for entity matching due to class imbalance (i.e., many more non-duplicate pairs than duplicate pairs). To address this, a recent paper  proposes to maximize recall of the classifier under the constraint that its precision should be greater than a specified threshold. However, the proposed technique requires the labels of all n input pairs in the worst-case.
 Our main result is an active learning algorithm that approximately maximizes recall of the classifier while respecting a precision constraint with provably sub-linear label complexity (under certain distributional assumptions). Our algorithm uses as a black-box any active learning module that minimizes 0-1 loss. We show that label complexity of our algorithm is at most log n times the label complexity of the black-box, and also bound the difference in the recall of classifier learnt by our algorithm and the recall of the optimal classifier satisfying the precision constraint. We provide an empirical evaluation of our algorithm on several real-world matching data sets that demonstrates the effectiveness of our approach.