nils-herrmann/clean_scirepeval huggingface dataset:
Main-memory foreign key joins on advanced processors: design and re-evaluations for OLAP workloads

The hash join algorithm family is one of the leading techniques for equi-join performance evaluation. OLAP systems borrow this line of research to efficiently implement foreign key joins between dimension tables and big fact tables. From data warehouse schema and workload feature perspective, the hash join algorithm can be further simplified with multidimensional mapping, and the foreign key join algorithms can be evaluated from multiple perspectives instead of single performance perspective. In this paper, we introduce the surrogate key index oriented foreign key join as schema-conscious and OLAP workload customized design foreign key join to comprehensively evaluate how state-of-the-art join algorithms perform in OLAP workloads. Our experiments and analysis gave the following insights: (1) customized foreign key join algorithm for OLAP workload can make join performance step forward than general-purpose hash joins; (2) each join algorithm shows strong and weak performance regions dominated by the cache locality ratio of input_size/cache_size with a fine-grained micro join benchmark; (3) the simple hardware-oblivious shared hash table join outperforms complex hardware-conscious radix partitioning hash join in most benchmark cases; (4) the customized foreign key join algorithm with surrogate key index simplified the algorithm complexity for hardware accelerators and make it easy to be implemented for different hardware accelerators. Overall, we argue that improving join performance is a systematic work opposite to merely hardware-conscious algorithm optimizations, and the OLAP domain knowledge enables surrogate key index to be effective for foreign key joins in data warehousing workloads for both CPU and hardware accelerators.
Coarse-to-fine volumetric segmentation of teeth in Cone-Beam CT

We consider the problem of localizing and segmenting individual teeth inside 3D Cone-Beam Computed Tomography (CBCT) images. To handle large image sizes we approach this task with a coarse-to-fine framework, where the whole volume is first analyzed as a 33-class semantic segmentation (adults have up to 32 teeth) in coarse resolution, followed by binary semantic segmentation of the cropped region of interest in original resolution. To improve the performance of the challenging 33-class segmentation, we first train the Coarse step model on a large weakly labeled dataset, then fine-tune it on a smaller precisely labeled dataset. The Fine step model is trained with precise labels only. Experiments using our inhouse dataset show significant improvement for both weaklysupervised pretraining and for the addition of the Fine step. Empirically, this framework yields precise teeth masks with low localization errors sufficient for many real-world applications.
The almost untreatable narcissistic patient.

Clinical experience in the Personality Disorders Institute at Weill Cornell Medical College suggests that patients with borderline personality organization and a narcissistic personality disorder have a more serious prognosis than all other personality disorders functioning at the borderline level, and that those who in addition present significant antisocial behavior have an even worse prognosis (Clarkin, Yeomans, and Kernberg 1999; Stone 1990). This negative trend culminates in a group of practically untreatable patients with antisocial personality disorder, who represent the most severe cases of pathological narcissism. There are also patients with severe narcissistic personality disorder, functioning at an overt borderline level with significant antisocial features, but not presenting an antisocial personality disorder proper, who at times respond to treatment, while others do not. These patients are explored here, with a focus on particular psychotherapeutic techniques that have proven helpful, as well as on the limits of these technical approaches.
Airgapless Electric Motors With an External Rotor

This paper proposes a new family of electric motors based on the concept of an airgapless device. In this type of device, the rotor is allowed to touch the stator at a contact point, which maximizes the internal flux and, therefore, the electromagnetic torque. A higher torque density motor is proposed in this paper due to a reduced reluctance caused by zero airgap situation. A comparison with other high-torque-density electric motors demonstrates the advantages of the proposed airgapless motors. The proposed motor will maximize the generated torque, allowing these types of motors to be competitive in applications where hydraulic motors are prevalent, i.e., low-speed and high-torque requirements. Hydraulic motor systems face two major problems with their braking system and with low efficiency due to a large number of energy conversion stages (i.e., a motor pump, hydraulic connections, and the hydraulic motor itself). The proposed electric motor, unlike hydraulic motors, converts electrical energy directly to mechanical energy with no extra braking system necessary and with higher efficiency. The modeling of this machine with a minimum number of poles is discussed before a generalization is presented. A proof-of-concept electric motor with nine magnetic bipoles is built to validate the theoretical assumptions proposed in this paper.
A Meta-plugin for Bespoke Data Management in WordPress

WordPress is a powerful and extensible platform for webbased information publishing and management. While the WordPress core is targeted to the publication of chronologically ordered textual articles typical of blogs, users have developed plugins as well as themes to support the data management requirements of specific domains such as e-commerce or e-learning. However, the creation of such plugins requires development skills and effort. We present a meta-plugin that automatically generates bespoke plugins for data management based on user-defined ER models. We illustrate the approach using an example of creating a WordPress site for managing information about courses.
Modeling IoT-Based Solutions Using Human-Centric Wireless Sensor Networks

The Internet of Things (IoT) has inspired solutions that are already available for addressing problems in various application scenarios, such as healthcare, security, emergency support and tourism. However, there is no clear approach to modeling these systems and envisioning their capabilities at the design time. Therefore, the process of designing these systems is ad hoc and its real impact is evaluated once the solution is already implemented, which is risky and expensive. This paper proposes a modeling approach that uses human-centric wireless sensor networks to specify and evaluate models of IoT-based systems at the time of design, avoiding the need to spend time and effort on early implementations of immature designs. It allows designers to focus on the system design, leaving the implementation decisions for a next phase. The article illustrates the usefulness of this proposal through a running example, showing the design of an IoT-based solution to support the first responses during medium-sized or large urban incidents. The case study used in the proposal evaluation is based on a real train crash. The proposed modeling approach can be used to design IoT-based systems for other application scenarios, e.g., to support security operatives or monitor chronic patients in their homes.
MedScan, a natural language processing engine for MEDLINE abstracts

The importance of extracting biomedical information from scientific publications is well recognized. A number of information extraction systems for the biomedical domain have been reported, but none of them have become widely used in practical applications. Most proposals to date make rather simplistic assumptions about the syntactic aspect of natural language. There is an urgent need for a system that has broad coverage and performs well in real-text applications.



We present a general biomedical domain-oriented NLP engine called MedScan that efficiently processes sentences from MEDLINE abstracts and produces a set of regularized logical structures representing the meaning of each sentence. The engine utilizes a specially developed context-free grammar and lexicon. Preliminary evaluation of the system's performance, accuracy, and coverage exhibited encouraging results. Further approaches for increasing the coverage and reducing parsing ambiguity of the engine, as well as its application for information extraction are discussed.
Efficacy and safety of a hyaluronic acid filler in subjects treated for correction of midface volume deficiency: a 24 month study

Hyaluronic acid (HA) fillers are an established intervention for correcting facial volume deficiency. Few studies have evaluated treatment outcomes for longer than 6 months. The purpose of this study was to determine the durability of an HA filler in the correction of midface volume deficiency over 24 months, as independently evaluated by physician investigators and subjects.



Subjects received treatment with Juvéderm(™) Voluma(™) to the malar area, based on the investigators' determination of baseline severity and aesthetic goals. The treatment was administered in one or two sessions over an initial 4-week period. Supplementary treatment was permissible at week 78, based on protocol-defined criteria. A clinically meaningful response was predefined as at least a one-point improvement on the MidFace Volume Deficit Scale (MFVDS) and on the Global Aesthetic Improvement Scale (GAIS).



Of the 103 subjects enrolled, 84% had moderate or significant volume deficiency at baseline. At the first post-treatment evaluation (week 8), 96% were documented to be MFVDS responders, with 98% and 100% graded as GAIS responders when assessed by the subjects and investigators, respectively. At week 78, 81.7% of subjects were still MFVDS responders, with 73.2% and 78.1% being GAIS responders, respectively. Seventy-two subjects completed the 24-month study, of whom 45 did not receive supplementary Voluma(™) at week 78. Forty-three of the 45 (95.6%) subjects were MFVDS responders, with 82.2% and 91.1% being GAIS responders, respectively. At end of the study, 66/72 subjects were either satisfied or very satisfied with Voluma(™), with 70/72 indicating that they would recommend the product to others. Adverse events were transient and infrequent, with injection site bruising and swelling being the most commonly reported.



Voluma(™) is safe and effective in the correction of mild to severe facial volume deficiency, achieving long-term clinically meaningful results. There was a high degree of satisfaction with the treatment outcome over the 24 months of the study.
Improved text-detection methods for a camera-based text reading system for blind persons

Automatic text recognition from natural images receives a growing attention because of potential applications in image retrieval, robotics and intelligent transport system. Camera-based document analysis becomes a real possibility with the increasing resolution and availability of digital cameras. Our research objective is a system that reads the text encountered in natural scenes with the aim to provide assistance to visually impaired persons. In the case of a blind person, finding the text region is the first important problem that must be addressed, because it cannot be assumed that the acquired image contains only characters. In a previous paper (N. Ezaki et al., 2004), we propose four text-detection methods based on connected components. Finding small characters needed significant improvement. This paper describes a new text-detection method geared for small text characters. This method uses Fisher's discriminant rate (FDR) to decide whether an image area should be binarized using local or global thresholds. Fusing the new method with a previous morphology-based one yields improved results. Using a controllable Webcam and a laptop PC, we developed a prototype that works in real time. At first, our system tries to find in the image areas with small characters. Then it zooms into the found areas to retake higher resolution images necessary for character recognition. Going from this proof-of-concept to a complete system requires further research effort.
A Shortest Path Dependency Kernel for Relation Extraction

We present a novel approach to relation extraction, based on the observation that the information required to assert a relationship between two named entities in the same sentence is typically captured by the shortest path between the two entities in the dependency graph. Experiments on extracting top-level relations from the ACE (Automated Content Extraction) newspaper corpus show that the new shortest path dependency kernel outperforms a recent approach based on dependency tree kernels.
Can we build language-independent OCR using LSTM networks?

Language models or recognition dictionaries are usually considered an essential step in OCR. However, using a language model complicates training of OCR systems, and it also narrows the range of texts that an OCR system can be used with. Recent results have shown that Long Short-Term Memory (LSTM) based OCR yields low error rates even without language modeling. In this paper, we explore the question to what extent LSTM models can be used for multilingual OCR without the use of language models. To do this, we measure cross-language performance of LSTM models trained on different languages. LSTM models show good promise to be used for language-independent OCR. The recognition errors are very low (around 1%) without using any language model or dictionary correction.
Compact CPW-Fed Planar Monopole Antenna With Wide Circular Polarization Bandwidth

The design and implementation of a coplanar waveguide-fed planar monopole antenna with circular polarization and broadband operation is presented. The antenna operates in the Industrial, Scientific, and Medical (ISM) and wireless local area network (WLAN) (5 GHz) bands with circular polarization (CP) in both bands. It is demonstrated that a fractional bandwidth for CP larger than 33% can be attained simply by introducing an inverted L-shaped slot in the ground plane and parallel-aligning an inverted-L-shaped strip. The advantages of the proposed antenna are the simple yet efficient design of the radiator, a wide 3-dB axial-ratio operating band, and a compact size.
TextBoxes: A Fast Text Detector with a Single Deep Neural Network

This paper presents an end-to-end trainable fast scene text detector, named TextBoxes, which detects scene text with both high accuracy and efficiency in a single network forward pass, involving no post-process except for a standard nonmaximum suppression. TextBoxes outperforms competing methods in terms of text localization accuracy and is much faster, taking only 0.09s per image in a fast implementation. Furthermore, combined with a text recognizer, TextBoxes significantly outperforms state-of-the-art approaches on word spotting and end-to-end text recognition tasks.
Span, CRUNCH, and Beyond: Working Memory Capacity and the Aging Brain

Neuroimaging data emphasize that older adults often show greater extent of brain activation than younger adults for similar objective levels of difficulty. A possible interpretation of this finding is that older adults need to recruit neuronal resources at lower loads than younger adults, leaving no resources for higher loads, and thus leading to performance decrements [Compensation-Related Utilization of Neural Circuits Hypothesis; e.g., Reuter-Lorenz, P. A., & Cappell, K. A. Neurocognitive aging and the compensation hypothesis.  2008]. The Compensation-Related Utilization of Neural Circuits Hypothesis leads to the prediction that activation differences between younger and older adults should disappear when task difficulty is made subjectively comparable. In a Sternberg memory search task, this can be achieved by assessing brain activity as a function of load relative to the individual's memory span, which declines with age. Specifically, we hypothesized a nonlinear relationship between load and both performance and brain activity and predicted that asymptotes in the brain activation function should correlate with performance asymptotes (corresponding to working memory span). The results suggest that age differences in brain activation can be largely attributed to individual variations in working memory span. Interestingly, the brain activation data show a sigmoid relationship with load. Results are discussed in terms of Cowan's [Cowan, N. The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87–114, 2001] model of working memory and theories of impaired inhibitory processes in aging.
An Edge Detection Algorithm for Online Image Analysis

Online image analysis is used in a wide variety of applications. Edge detection is a fundamental tool used to obtain features of objects as a prerequisite step to object segmentation. This paper presents a simple and relatively fast online edge detection algorithm based on second derivative. The proposed edge detector is less sensitive to noise and may be applied on color, gray and binary images without preprocessing requirements. The merits of the algorithm are demonstrated by comparison with Canny’s and Sobel’s edge detectors.
Automatic Chinese food identification and quantity estimation

Computer-aided food identification and quantity estimation have caught more attention in recent years because of the growing concern of our health. The identification problem is usually defined as an image categorization or classification problem and several researches have been proposed. In this paper, we address the issues of feature descriptors in the food identification problem and introduce a preliminary approach for the quantity estimation using depth information. Sparse coding is utilized in the SIFT and Local binary pattern feature descriptors, and these features combined with gabor and color features are used to represent food items. A multi-label SVM classifier is trained for each feature, and these classifiers are combined with multi-class Adaboost algorithm. For evaluation, 50 categories of worldwide food are used, and each category contains 100 photographs from different sources, such as manually taken or from Internet web albums. An overall accuracy of 68.3% is achieved, and success at top-N candidates achieved 80.6%, 84.8%, and 90.9% accuracy accordingly when N equals 2, 3, and 5, thus making mobile application practical. The experimental results show that the proposed methods greatly improve the performance of original SIFT and LBP feature descriptors. On the other hand, for quantity estimation using depth information, a straight forward method is proposed for certain food, while transparent food ingredients such as pure water and cooked rice are temporarily excluded.
A Robust Digital Image Watermarking Scheme Using Hybrid DWT-DCT-SVD Technique

Protection of digital multimedia content has become an increasingly important issue for content owners and service providers. As watermarking is identified as a major technology to achieve copyright protection, the relevant literature includes several distinct approaches for embedding data into a multimedia element (primarily images, audio, and video). In this paper, we present a hybrid watermarking scheme based on Discrete Wavelet Transform – Discrete Cosine Transform – Singular Value Decomposition (DWT-DCT-SVD). Robustness is achieved by taking DCT of the DWT coefficients of the HL band of DWT. After applying DCT we map the DCT coefficients in a zig – zag order into four quadrants and apply the SVD to each quadrant. These four quadrants represent frequency bands from the lowest to the highest. The singular values in each quadrant are then modified by the singular values of the DWT-DCT transformed visual watermark. We show that embedding data in lowest frequencies is resistant to most of the attacks and some attacks are resistant to other frequency bands.
The Open Motion Planning Library

The open motion planning library (OMPL) is a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms. The library is designed in a way that it allows the user to easily solve a variety of complex motion planning problems with minimal input. OMPL facilitates the addition of new motion planning algorithms, and it can be conveniently interfaced with other software components. A simple graphical user interface (GUI) built on top of the library, a number of tutorials, demos, and programming assignments are designed to teach students about sampling-based motion planning. The library is also available for use through Robot Operating System (ROS).
Genetic Algorithms for Cutting Stock Problems: With and Without Contiguity

A number of optimisation problems involve the optimal grouping of a finite set of items into a number of categories subject to one or more constraints. Such problems raise interesting issues in mapping solutions in genetic algorithms. These problems range from the knapsack problem to bin packing and cutting stock problems. This paper describes research involving cutting stock problems. Results show that the mapping that is used affects the solution in terms of both quality of the solution found and time taken to find solutions, and that different mappings are suitable for different variants of the problem.
Facade Segmentation in the Wild

Urban façade segmentation from automatically acquired imagery, in contrast to traditional image segmentation, poses several unique challenges. 360◦ photospheres captured from vehicles are an effective way to capture a large number of images, but this data presents difficult-to-model warping and stitching artifacts. In addition, each pixel can belong to multiple façade elements, and different facade elements (e.g., window, balcony, sill, etc.) are correlated and vary wildly in their characteristics. In this paper, we propose three network architectures of varying complexity to achieve multilabel semantic segmentation of façade images while exploiting their unique characteristics. Specifically, we propose a MULTIFACSEGNET architecture to assign multiple labels to each pixel, a SEPARABLE architecture as a low-rank formulation that encourages extraction of rectangular elements, and a COMPATIBILITY network that simultaneously seeks segmentation across facade element types allowing the network to ‘see’ intermediate output probabilities of the various façade element classes. Our results on benchmark datasets show significant improvements over existing façade segmentation approaches for the typical façade elements. For example, on one commonly used dataset the accuracy scores for window (the most important architectural element) increases from 0.91 to 0.97 percent compared to the best competing method, and comparable improvements on other element types.
The ONE simulator for DTN protocol evaluation

Delay-tolerant Networking (DTN) enables communication in sparse mobile ad-hoc networks and other challenged environments where traditional networking fails and new routing and application protocols are required. Past experience with DTN routing and application protocols has shown that their performance is highly dependent on the underlying mobility and node characteristics. Evaluating DTN protocols across many scenarios requires suitable simulation tools. This paper presents the Opportunistic Networking Environment (ONE) simulator specifically designed for evaluating DTN routing and application protocols. It allows users to create scenarios based upon different synthetic movement models and real-world traces and offers a framework for implementing routing and application protocols (already including six well-known routing protocols). Interactive visualization and post-processing tools support evaluating experiments and an emulation mode allows the ONE simulator to become part of a real-world DTN testbed. We show sample simulations to demonstrate the simulator’s flexible support for DTN protocol evaluation.