nils-herrmann/clean_scirepeval huggingface dataset:
Efficient and accurate nearest neighbor and closest pair search in high-dimensional space

Nearest Neighbor (NN) search in high-dimensional space is an important problem in many applications. From the database perspective, a good solution needs to have two properties: (i) it can be easily incorporated in a relational database, and (ii) its query cost should increase sublinearly with the dataset size, regardless of the data and query distributions. Locality-Sensitive Hashing (LSH) is a well-known methodology fulfilling both requirements, but its current implementations either incur expensive space and query cost, or abandon its theoretical guarantee on the quality of query results.
 Motivated by this, we improve LSH by proposing an access method called the Locality-Sensitive B-tree (LSB-tree) to enable fast, accurate, high-dimensional NN search in relational databases. The combination of several LSB-trees forms a LSB-forest that has strong quality guarantees, but improves dramatically the efficiency of the previous LSH implementation having the same guarantees. In practice, the LSB-tree itself is also an effective index which consumes linear space, supports efficient updates, and provides accurate query results. In our experiments, the LSB-tree was faster than: (i) iDistance (a famous technique for exact NN search) by two orders of magnitude, and (ii) MedRank (a recent approximate method with nontrivial quality guarantees) by one order of magnitude, and meanwhile returned much better results.
 As a second step, we extend our LSB technique to solve another classic problem, called Closest Pair (CP) search, in high-dimensional space. The long-term challenge for this problem has been to achieve subquadratic running time at very high dimensionalities, which fails most of the existing solutions. We show that, using a LSB-forest, CP search can be accomplished in (worst-case) time significantly lower than the quadratic complexity, yet still ensuring very good quality. In practice, accurate answers can be found using just two LSB-trees, thus giving a substantial reduction in the space and running time. In our experiments, our technique was faster: (i) than distance browsing (a well-known method for solving the problem exactly) by several orders of magnitude, and (ii) than D-shift (an approximate approach with theoretical guarantees in low-dimensional space) by one order of magnitude, and at the same time, outputs better results.
Predicting Sequences of Clinical Events by Using a Personalized Temporal Latent Embedding Model

As a result of the recent trend towards digitization -- which increasingly affects evidence-based medicine, accountable care, personalized medicine, and medical "Big Data" analysis -- growing amounts of clinical data are becoming available for analysis. In this paper, we follow the idea that one can model clinical processes based on clinical data, which can then be the basis for many useful applications. We model the whole clinical evolution of each individual patient, which is composed of thousands of events such as ordered tests, lab results and diagnoses. Specifically, we base our work on a dataset provided by the Charité University Hospital of Berlin which is composed of patients that suffered from kidney failure and either obtained an organ transplant or are still waiting for one. These patients face a lifelong treatment and periodic visits to the clinic. Our goal is to develop a system to predict the sequence of events recorded in the electronic medical record of each patient, and thus to develop the basis for a future clinical decision support system. For modelling, we use machine learning approaches which are based on a combination of the embedding of entities and events in a multidimensional latent space, in combination with Neural Network predictive models. Similar approaches have been highly successful in statistical models for recommendation systems, language models, and knowledge graphs. We extend existing embedding models to the clinical domain, in particular with respect to temporal sequences, long-term memories and personalization. We compare the performance of our proposed models with standard approaches such as K-nearest neighbors method, Naïve Bayes classifier and Logistic Regression, and obtained favorable results with our proposed model.
Civitas: The Smart City Middleware, from Sensors to Big Data

Software development for smart cities bring into light new concerns, such as how to deal with scalability, heterogeneity (sensors, actuators, high performance computing devices, etc.), geolocation information or privacy issues, among some. Traditional approaches to distributed systems fail to address these challenges, because they were mainly devoted to enterprise IT environments. This paper proposes a middleware framework, called Civitas, specially devoted to support the task of service development for the Smart City paradigm. This paper also analyzes the main drawbacks of traditional approaches to middleware service development and how these are overcome in the middleware framework proposed here.
Named Entity Recognition using an HMM-based Chunk Tagger

This paper proposes a Hidden Markov Model (HMM) and an HMM-based chunk tagger, from which a named entity (NE) recognition (NER) system is built to recognize and classify names, times and numerical quantities. Through the HMM, our system is able to apply and integrate four types of internal and external evidences: 1) simple deterministic internal feature of the words, such as capitalization and digitalization; 2) internal semantic feature of important triggers; 3) internal gazetteer feature; 4) external macro context feature. In this way, the NER problem can be resolved effectively. Evaluation of our system on MUC-6 and MUC-7 English NE tasks achieves F-measures of 96.6% and 94.1% respectively. It shows that the performance is significantly better than reported by any other machine-learning system. Moreover, the performance is even consistently better than those based on handcrafted rules.
Treatment planning of the edentulous maxilla

The predictability of successful osseointegrated implant rehabilitation of the edentulous jaw as described by Branemark et al., introduced a new era of management for the edentulous predicament. Implant rehabilitation of the edentulous maxilla remains one of the most complex restorative challenges because of the number of variables that affect both the aesthetic and functional aspect of the prosthesis. Among the prosthesis designs used to treat the edentulous maxilla are fixed or removable implant-supported restorations. Since the aesthetic requirements and preoperative situation of each patient varies, considerable time must be spent on accurate diagnosis to ensure patient desires are satisfied and predictable outcomes are achieved. The purpose of this article is to compare the treatment options and prosthesis designs for the edentulous maxilla. Emphasis will be placed on diagnosis and treatment planning. Criteria will be given to guide the practitioner in deciding whether a fixed or removable restoration should be placed. This objective will be accomplished through the review of cases with regard to varying design considerations and factors that influence the decision-making process.
GLTM: A Global and Local Word Embedding-Based Topic Model for Short Texts

Short texts have become a kind of prevalent source of information, and discovering topical information from short text collections is valuable for many applications. Due to the length limitation, conventional topic models based on document-level word co-occurrence information often fail to distill semantically coherent topics from short text collections. On the other hand, word embeddings as a powerful tool have been successfully applied in natural language processing. Word embeddings trained on large corpus are encoded with general semantic and syntactic information of words, and hence they can be leveraged to guide topic modeling for short text collections as supplementary information for sparse co-occurrence patterns. However, word embeddings are trained on large external corpus and the encoded information is not necessarily suitable for training data set of topic models, which is ignored by most existing models. In this article, we propose a novel global and local word embedding-based topic model (GLTM) for short texts. In the GLTM, we train global word embeddings from large external corpus and employ the continuous skip-gram model with negative sampling (SGNS) to obtain local word embeddings. Utilizing both the global and local word embeddings, the GLTM can distill semantic relatedness information between words which can be further leveraged by Gibbs sampler in the inference process to strengthen semantic coherence of topics. Compared with five state-of-the-art short text topic models on four real-world short text collections, the proposed GLTM exhibits the superiority in most cases.
Document-level Sentiment Inference with Social, Faction, and Discourse Context

We present a new approach for documentlevel sentiment inference, where the goal is to predict directed opinions (who feels positively or negatively towards whom) for all entities mentioned in a text. To encourage more complete and consistent predictions, we introduce an ILP that jointly models (1) sentenceand discourse-level sentiment cues, (2) factual evidence about entity factions, and (3) global constraints based on social science theories such as homophily, social balance, and reciprocity. Together, these cues allow for rich inference across groups of entities, including for example that CEOs and the companies they lead are likely to have similar sentiment towards others. We evaluate performance on new, densely labeled data that provides supervision for all pairs, complementing previous work that only labeled pairs mentioned in the same sentence. Experiments demonstrate that the global model outperforms sentence-level baselines, by providing more coherent predictions across sets of related entities.
Paper mechatronics: a design case study for a young medium

Paper Mechatronics is a novel interdisciplinary design medium for children, enabled by recent advances in craft technologies: the term refers to a reappraisal of traditional educational papercrafts in combination with accessible mechanical, electronic, and computational elements. We present a design case study--building computationally-enhanced paper flowers--and discuss the iterative design process involved in the creation. We also describe a workshop we conducted with teenagers to evaluate paper mechatronics as a creative learning activity for children. We conclude with a discussion of future directions.
Do Supervised Distributional Methods Really Learn Lexical Inference Relations?

Distributional representations of words have been recently used in supervised settings for recognizing lexical inference relations between word pairs, such as hypernymy and entailment. We investigate a collection of these state-of-the-art methods, and show that they do not actually learn a relation between two words. Instead, they learn an independent property of a single word in the pair: whether that word is a “prototypical hypernym”.
The microbiota-gut-brain axis: neurobehavioral correlates, health and sociality

Recent data suggest that the human body is not such a neatly self-sufficient island after all. It is more like a super-complex ecosystem containing trillions of bacteria and other microorganisms that inhabit all our surfaces; skin, mouth, sexual organs, and specially intestines. It has recently become evident that such microbiota, specifically within the gut, can greatly influence many physiological parameters, including cognitive functions, such as learning, memory and decision making processes. Human microbiota is a diverse and dynamic ecosystem, which has evolved in a mutualistic relationship with its host. Ontogenetically, it is vertically inoculated from the mother during birth, established during the first year of life and during lifespan, horizontally transferred among relatives, mates or close community members. This micro-ecosystem serves the host by protecting it against pathogens, metabolizing complex lipids and polysaccharides that otherwise would be inaccessible nutrients, neutralizing drugs and carcinogens, modulating intestinal motility, and making visceral perception possible. It is now evident that the bidirectional signaling between the gastrointestinal tract and the brain, mainly through the vagus nerve, the so called "microbiota-gut-vagus-brain axis," is vital for maintaining homeostasis and it may be also involved in the etiology of several metabolic and mental dysfunctions/disorders. Here we review evidence on the ability of the gut microbiota to communicate with the brain and thus modulate behavior, and also elaborate on the ethological and cultural strategies of human and non-human primates to select, transfer and eliminate microorganisms for selecting the commensal profile.
E-LAMP: integration of innovative ideas for multimedia event detection

Detecting multimedia events in web videos is an emerging hot research area in the fields of multimedia and computer vision. In this paper, we introduce the core methods and technologies of the framework we developed recently for our Event Labeling through Analytic Media Processing (E-LAMP) system to deal with different aspects of the overall problem of event detection. More specifically, we have developed efficient methods for feature extraction so that we are able to handle large collections of video data with thousands of hours of videos. Second, we represent the extracted raw features in a spatial bag-of-words model with more effective tilings such that the spatial layout information of different features and different events can be better captured, thus the overall detection performance can be improved. Third, different from widely used early and late fusion schemes, a novel algorithm is developed to learn a more robust and discriminative intermediate feature representation from multiple features so that better event models can be built upon it. Finally, to tackle the additional challenge of event detection with only very few positive exemplars, we have developed a novel algorithm which is able to effectively adapt the knowledge learnt from auxiliary sources to assist the event detection. Both our empirical results and the official evaluation results on TRECVID MED’11 and MED’12 demonstrate the excellent performance of the integration of these ideas.
Bayesian Unsupervised Word Segmentation with Nested Pitman-Yor Language Modeling

In this paper, we propose a new Bayesian model for fully unsupervised word segmentation and an efficient blocked Gibbs sampler combined with dynamic programming for inference. Our model is a nested hierarchical Pitman-Yor language model, where Pitman-Yor spelling model is embedded in the word model. We confirmed that it significantly outperforms previous reported results in both phonetic transcripts and standard datasets for Chinese and Japanese word segmentation. Our model is also considered as a way to construct an accurate word n-gram language model directly from characters of arbitrary language, without any “word” indications.
Beyond short snippets: Deep networks for video classification

Convolutional neural networks (CNNs) have been extensively applied for image recognition problems giving state-of-the-art results on recognition, detection, segmentation and retrieval. In this work we propose and evaluate several deep neural network architectures to combine image information across a video over longer time periods than previously attempted. We propose two methods capable of handling full length videos. The first method explores various convolutional temporal feature pooling architectures, examining the various design choices which need to be made when adapting a CNN for this task. The second proposed method explicitly models the video as an ordered sequence of frames. For this purpose we employ a recurrent neural network that uses Long Short-Term Memory (LSTM) cells which are connected to the output of the underlying CNN. Our best networks exhibit significant performance improvements over previously published results on the Sports 1 million dataset (73.1% vs. 60.9%) and the UCF-101 datasets with (88.6% vs. 88.0%) and without additional optical flow information (82.6% vs. 73.0%).
On Machine Learning and Structure for Mobile Robots

Due to recent advances compute, data, models the role of learning in autonomous systems has expanded significantly, rendering new applications possible for the first time. While some of the most significant benefits are obtained in the perception modules of the software stack, other aspects continue to rely on known manual procedures based on prior knowledge on geometry, dynamics, kinematics etc. Nonetheless, learning gains relevance in these modules when data collection and curation become easier than manual rule design. Building on this coarse and broad survey of current research, the final sections aim to provide insights into future potentials and challenges as well as the necessity of structure in current practical applications.
Minimum Near-Convex Shape Decomposition.

Shape decomposition is a fundamental problem for part-based shape representation. We propose the Minimum Near-Convex Decomposition (MNCD) to decompose arbitrary shapes into minimum number of &#x0022;near-convex&#x0022; parts. The near-convex shape decomposition is formulated as a discrete optimization problem by minimizing the number of non-intersecting cuts. Two perception rules are imposed as constraints into our objective function to improve the visual naturalness of the decomposition. With the degree of near-convexity a user specified parameter, our decomposition is robust to local distortions and shape deformation. The optimization can be efficiently solved via Binary Integer Linear Programming. Both theoretical analysis and experiment results show that our approach outperforms the state-of-the-art results without introducing redundant parts, and thus leads to robust shape representation.
Real-time multi-feature based fire flame detection in video

In this paper, we present a new approach to detect fire flame by processing and analyzing the stationary camera videos. For a fire detection system, it is desired to be sensitive and reliable. The proposed method improves not only the sensitivity but also the reliability through reducing the susceptibility to false alarms. The proposed approach based on multi-feature, i.e., chromatic features, dynamic features, texture features, and contour features, can both improve the sensitivity and reliability in fire detection. In our approach, we adopt a novel algorithm to extract the moving region and analyze the frequency of flickers. Experimental results show that the proposed method can run in real-time and performs favorably against the state-of-art methods with higher Page 1 of 22 IET Review Copy Only IET Image Processing This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication in an issue of the journal. To cite the paper please use the doi provided on the Digital Library page.
Unsupervised induction and filling of semantic slots for spoken dialogue systems using frame-semantic parsing

Spoken dialogue systems typically use predefined semantic slots to parse users' natural language inputs into unified semantic representations. To define the slots, domain experts and professional annotators are often involved, and the cost can be expensive. In this paper, we ask the following question: given a collection of unlabeled raw audios, can we use the frame semantics theory to automatically induce and fill the semantic slots in an unsupervised fashion? To do this, we propose the use of a state-of-the-art frame-semantic parser, and a spectral clustering based slot ranking model that adapts the generic output of the parser to the target semantic space. Empirical experiments on a real-world spoken dialogue dataset show that the automatically induced semantic slots are in line with the reference slots created by domain experts: we observe a mean averaged precision of 69.36% using ASR-transcribed data. Our slot filling evaluations also indicate the promising future of this proposed approach.
Design Considerations of Charge Pump for Antenna Switch Controller With SOI CMOS Technology

An enhanced charge pump for the antenna switch controller using the silicon-on-insulator (SOI) CMOS technology is presented in this brief. The pseudo cross-coupled technique is proposed to reduce parasitic capacitances at charging/discharging nodes through charge transferring paths, which improves the current drive capability and provides better accuracy of the output voltage. Furthermore, the codesign between the gate control voltages of power MOS transistors and the clock drive signals of pumping capacitors has been investigated to eliminate the reversion loss and reduce the ripple voltage. The pseudo cross-coupled charge pump has been fabricated in the 0.18-  $\mu\text{m}$ SOI CMOS technology with an area of 0.065 mm2. According to the comparison results of the conventional and enhanced charge pumps, the start-up time and the recovery time are typically shortened by 71.4% and 21.7%, owing to the improvement of the current drive capability, and the ripple voltage at no-load condition is greatly reduced by 46.1%.
A survey of circuit innovations in ferroelectric random-access memories

This paper surveys circuit innovations in ferroelectric memories at three circuit levels: memory cell, sensing and architecture. A ferroelectric memory cell consists of at least one ferroelectric capacitor, where binary data are stored, and one or two transistors that either allow access to the capacitor or amplify its contents for a read operation. Once a cell is accessed for a read operation, its data are presented in the form of an analog signal to a sense amplifier, where it is compared against a reference voltage to determine its logic level. The circuit techniques used to generate the reference voltage must be robust to semiconductor processing variations across the chip and the device imperfections of ferroelectric capacitors. We review six methods of generating a reference voltage, two being presented for the first time in this paper. These methods are discussed and evaluated in terms of their accuracy, area overhead and sensing complexity. Ferroelectric memories share architectural features such as addressing schemes and input/output circuitry with other types of random-access memories such as dynamic random-access memories. However, they have distinct features with respect to accessing the stored data, sensing, and overall circuit topology. We review nine different architectures for ferroelectric memories and discuss them in terms of speed, density and power consumption.
The infrastructure problem in HCI

HCI endeavors to create human-centered computer systems, but underlying technological infrastructures often stymie these efforts. We outline three specific classes of user experience difficulties caused by underlying technical infrastructures, which we term constrained possibilities, unmediated interaction, and interjected abstractions. We explore how prior approaches in HCI have addressed these issues, and discuss new approaches that will be required for future progress. We argue that the HCI community must become more deeply involved with the creation of technical infrastructures. Doing so, however, requires a substantial expansion to the methodological toolbox of HCI.