nils-herrmann/clean_scirepeval huggingface dataset:
Multi-factory capacity planning in semiconductor assembly and test manufacturing with multiple-chip products

Capacity planning determines the optimal product mix based on the available tool sets and allocates production capacity according to the forecasted demands for the next few months. MaxIt is the previous capacity planning system for Intel's Flash Product Group (FPG) Assembly & Test Manufacturing (ATM). It only applied to single product family scenarios with simple process routing. However, new Celluar Handhold Group (CHG) products need to go through flexible and reentrant ATM routes. In this paper, we introduce MaxItPlus, which is an enhanced MaxIt using MILP (mixed integer linear programming) to conduct capacity planning of multiple product families with mixed process routes in a multifactory ATM environment. We also present the detailed mathematical formulation, the system architecture, and implementation results. The project will help Intel global Flash ATM to achieve a single and efficient capacity planning process for all FPG and CHG products and gain $10 M in marginal profit (as determined by the finance department)
Towards unsupervised physical activity recognition using smartphone accelerometers

The development of smartphones equipped with accelerometers gives a promising way for researchers to accurately recognize an individual’s physical activity in order to better understand the relationship between physical activity and health. However, a huge challenge for such sensor-based activity recognition task is the collection of annotated or labelled training data. In this work, we employ an unsupervised method for recognizing physical activities using smartphone accelerometers. Features are extracted from the raw acceleration data collected by smartphones, then an unsupervised classification method called MCODE is used for activity recognition. We evaluate the effectiveness of our method on three real-world datasets, i.e., a public dataset of daily living activities and two datasets of sports activities of race walking and basketball playing collected by ourselves, and we find our method outperforms other existing methods. The results show that our method is viable to recognize physical activities using smartphone accelerometers.
An Advanced Power Electronics Interface for Electric Vehicles Applications

Power electronics interfaces play an increasingly important role in the future clean vehicle technologies. This paper proposes a novel integrated power electronics interface (IPEI) for battery electric vehicles (BEVs) in order to optimize the performance of the powertrain. The proposed IPEI is responsible for the power-flow management for each operating mode. In this paper, an IPEI is proposed and designed to realize the integration of the dc/dc converter, on-board battery charger, and dc/ac inverter together in the BEV powertrain with high performance. The proposed concept can improve the system efficiency and reliability, can reduce the current and voltage ripples, and can reduce the size of the passive and active components in the BEV drivetrains compared to other topologies. In addition, low electromagnetic interference and low stress in the power switching devices are expected. The proposed topology and its control strategy are designed and analyzed by using MATLAB/Simulink. The simulation results related to this research are presented and discussed. Finally, the proposed topology is experimentally validated with results obtained from the prototypes that have been built and integrated in our laboratory based on TMS320F2808 DSP.
A Survey on Assessment and Ranking Methodologies for User-Generated Content on the Web

User-generated content (UGC) on the Web, especially on social media platforms, facilitates the association of additional information with digital resources; thus, it can provide valuable supplementary content. However, UGC varies in quality and, consequently, raises the challenge of how to maximize its utility for a variety of end-users. This study aims to provide researchers and Web data curators with comprehensive answers to the following questions: What are the existing approaches and methods for assessing and ranking UGC? What features and metrics have been used successfully to assess and predict UGC value across a range of application domains? What methods can be effectively employed to maximize that value? This survey is composed of a systematic review of approaches for assessing and ranking UGC: results are obtained by identifying and comparing methodologies within the context of short text-based UGC on the Web. Existing assessment and ranking approaches adopt one of four framework types: the community-based framework takes into consideration the value assigned to content by a crowd of humans, the end-user--based framework adapts and personalizes the assessment and ranking process with respect to a single end-user, the designer-based framework encodes the software designer’s values in the assessment and ranking method, and the hybrid framework employs methods from more than one of these types. This survey suggests a need for further experimentation and encourages the development of new approaches for the assessment and ranking of UGC.
Mapping real-time communicating tasks on a distributed IMA architecture

Current avionics architectures implemented on large aircraft use complex processors, which are shared by many avionics applications according Integrated Modular Avionics (IMA) concepts. Using less complex processors on smaller aircraft such as helicopters leads to a distributed IMA architecture. Allocation of the avionics applications on a distributed architecture has to deal with two main challenges. A first problem is about the feasibility of a static allocation of partitions on each processing element. The second problem is the worst-case end-to-end communication delay analysis: due to the scheduling of partitions on processing elements which are not synchronized, some allocation schemes are not valid. This paper first presents a mapping algorithm using an integrated approach taking into account these two issues. In a second step, we evaluate, on a realistic helicopter case study, the feasibility of mapping a given application on a variable number of processing elements. Finally, we present a scalability analysis of the proposed mapping algorithm.
What Is a Knowledge Representation?

central and, in some ways, most familiar concepts in AI, the most fundamental question about it—What is it?—has rarely been answered directly. Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, and still others have focused on properties that are important to the notion of representation in general. In this article, we go back to basics to address the question directly. We believe that the answer can best be understood in terms of five important and distinctly different roles that a representation plays, each of which places different and, at times, conflicting demands on the properties a representation should have. We argue that keeping in mind all five of these roles provides a usefully broad perspective that sheds light on some long-standing disputes and can invigorate both research and practice in the field.
Biomimetic cardiovascular platforms for in vitro disease modeling and therapeutic validation.

Bioengineered tissues have become increasingly more sophisticated owing to recent advancements in the fields of biomaterials, microfabrication, microfluidics, genetic engineering, and stem cell and developmental biology. In the coming years, the ability to engineer artificial constructs that accurately mimic the compositional, architectural, and functional properties of human tissues, will profoundly impact the therapeutic and diagnostic aspects of the healthcare industry. In this regard, bioengineered cardiac tissues are of particular importance due to the extremely limited ability of the myocardium to self-regenerate, as well as the remarkably high mortality associated with cardiovascular diseases worldwide. As novel microphysiological systems make the transition from bench to bedside, their implementation in high throughput drug screening, personalized diagnostics, disease modeling, and targeted therapy validation will bring forth a paradigm shift in the clinical management of cardiovascular diseases. Here, we will review the current state of the art in experimental in vitro platforms for next generation diagnostics and therapy validation. We will describe recent advancements in the development of smart biomaterials, biofabrication techniques, and stem cell engineering, aimed at recapitulating cardiovascular function at the tissue- and organ levels. In addition, integrative and multidisciplinary approaches to engineer biomimetic cardiovascular constructs with unprecedented human and clinical relevance will be discussed. We will comment on the implementation of these platforms in high throughput drug screening, in vitro disease modeling and therapy validation. Lastly, future perspectives will be provided on how these biomimetic platforms will aid in the transition towards patient centered diagnostics, and the development of personalized targeted therapeutics.
Selecting Near-Optimal Learners via Incremental Data Allocation

We study a novel machine learning (ML) problem setting of sequentially allocating small subsets of training data amongst a large set of classifiers. The goal is to select a classifier that will give near-optimal accuracy when trained on all data, while also minimizing the cost of misallocated samples. This is motivated by large modern datasets and ML toolkits with many combinations of learning algorithms and hyperparameters. Inspired by the principle of “optimism under uncertainty,” we propose an innovative strategy, Data Allocation using Upper Bounds (DAUB), which robustly achieves these objectives across a variety of real-world datasets. We further develop substantial theoretical support for DAUB in an idealized setting where the expected accuracy of a classifier trained on n samples can be known exactly. Under these conditions we establish a rigorous sub-linear bound on the regret of the approach (in terms of misallocated data), as well as a rigorous bound on suboptimality of the selected classifier. Our accuracy estimates using real-world datasets only entail mild violations of the theoretical scenario, suggesting that the practical behavior of DAUB is likely to approach the idealized behavior.
FAST REGISTRATION OF LASER SCANS WITH 4-POINTS CONGRUENT SETS – WHAT WORKS AND WHAT

Sampling-based algorithms in the mould of RANSAC have emerged as one of the most successful methods for the fully automated registration of point clouds acquired by terrestrial laser scanning (TLS). Sampling methods in conjunction with 3D keypoint extraction, have shown promising results, e.g. the recent K-4PCS (Theiler et al., 2013). However, they still exhibit certain improbable failures, and are computationally expensive and slow if the overlap between scans is low. Here, we examine several variations of the basic K-4PCS framework that have the potential to improve its runtime and robustness. Since the method is inherently parallelizable, straight-forward multi-threading already brings down runtimes to a practically acceptable level (seconds to minutes). At a conceptual level, replacing the RANSAC error function with the more principled MSAC function (Torr and Zisserman, 2000) and introducing a minimum-distance prior to counter the near-field bias reduce failure rates by a factor of up to 4. On the other hand, replacing the repeated evaluation of the RANSAC error function with a voting scheme over the transformation parameters proved not to be generally applicable for the scan registration problem. All these possible extensions are tested experimentally on multiple challenging outdoor and indoor scenarios.
Architecture design and implementation of image based autonomous car: THUNDER-1

Autonomous driving with high velocity is a research hotspot which challenges the scientists and engineers all over the world. This paper proposes a scheme of indoor autonomous car based on ROS which combines the method of Deep Learning using Convolutional Neural Network (CNN) with statistical approach using liDAR images and achieves a robust obstacle avoidance rate in cruise mode. In addition, the design and implementation of autonomous car are also presented in detail which involves the design of Software Framework, Hector Simultaneously Localization and Mapping (Hector SLAM) by Teleoperation, Autonomous Exploration, Path Plan, Pose Estimation, Command Processing, and Data Recording (Co- collection). what’s more, the schemes of outdoor autonomous car, communication, and security are also discussed. Finally, all functional modules are integrated in nVidia Jetson TX1.
Image Analysis and Recognition

In this work we determine how well the common objective image quality measures (Mean Squared Error (MSE), local MSE, Signalto-Noise Ratio (SNR), Structural Similarity Index (SSIM), Visual Signalto-Noise Ratio (VSNR) and Visual Information Fidelity (VIF)) predict subjective radiologists’ assessments for brain and body computed tomography (CT) images. A subjective experiment was designed where radiologists were asked to rate the quality of compressed medical images in a setting similar to clinical. We propose a modified Receiver Operating Characteristic (ROC) analysis method for comparison of the image quality measures where the “ground truth” is considered to be given by subjective scores. The best performance was achieved by the SSIM index and VIF for brain and body CT images. The worst results were observed for VSNR. We have utilized a logistic curve model which can be used to predict the subjective assessments with an objective criteria. This is a practical tool that can be used to determine the quality of medical images.
Paradoxical block of parathormone secretion is mediated by increased activity of G alpha subunits.

The paradox of blunted parathormone (PTH) secretion in patients with severe hypomagnesemia has been known for more than 20 years, but the underlying mechanism is not deciphered. We determined the effect of low magnesium on in vitro PTH release and on the signals triggered by activation of the calcium-sensing receptor (CaSR). Analogous to the in vivo situation, PTH release from dispersed parathyroid cells was suppressed under low magnesium. In parallel, the two major signaling pathways responsible for CaSR-triggered block of PTH secretion, the generation of inositol phosphates, and the inhibition of cAMP were enhanced. Desensitization or pertussis toxin-mediated inhibition of CaSR-stimulated signaling suppressed the effect of low magnesium, further confirming that magnesium acts within the axis CaSR-G-protein. However, the magnesium binding site responsible for inhibition of PTH secretion is not identical with the extracellular ion binding site of the CaSR, because the magnesium deficiency-dependent signal enhancement was not altered on CaSR receptor mutants with increased or decreased affinity for calcium and magnesium. By contrast, when the magnesium affinity of the G alpha subunit was decreased, CaSR activation was no longer affected by magnesium. Thus, the paradoxical block of PTH release under magnesium deficiency seems to be mediated through a novel mechanism involving an increase in the activity of G alpha subunits of heterotrimeric G-proteins.
Pacman dysplasia: a lethal skeletal dysplasia with variable radiographic features

Background. Punctate or stippled cartilaginous calcifications are associated with many conditions, including chromosomal, infectious, endocrine, and teratogenic etiologies. Some of these conditions are clinically mild, while others are lethal. Accurate diagnosis can prove instrumental in clinical management and in genetic counseling.  To describe the diagnostic radiographic features seen in Pacman dysplasia, a distinct autosomal recessive, lethal skeletal dysplasia.  We present the fourth reported case of Pacman dysplasia and compare the findings seen in our patient with the three previously described patients.  Invariable and variable radiographic findings were seen in all four cases of histologically proven Pacman dysplasia.  Pacman dysplasia presents both constant and variable diagnostic radiographic features.
Nanyang Technological University Sound Event Recognition and Classification in Unstructured Environments

The objective of this research is to develop feature extraction and classification techniques for the task of acoustic event recognition (AER) in unstructured environments, which are those where adverse effects such as noise, distortion and multiple sources are likely to occur. The goal is to design a system that can achieve human-like sound recognition performance on a variety of hearing tasks in different environments. The research is important, as the field is commonly overshadowed by the more popular area of automatic speech recognition (ASR), and typical AER systems are often based on techniques taken directly from this. However, direct application presents difficulties, as the characteristics of acoustic events are less well defined than those of speech, and there is no sub-word dictionary available like the phonemes in speech. In addition, the performance of ASR systems typically degrades dramatically in such adverse, unstructured environments. Therefore, it is important to develop a system that can perform well for this challenging task. In this work, two novel feature extraction methods are proposed for recognition of environmental sounds in severe noisy conditions, based on the visual signature of the sounds. The first method is called the Spectrogram Image Feature (SIF), and is based on the timefrequency spectrogram of the sound. This is captured through an image-processing inspired quantisation and mapping of the dynamic range prior to feature extraction. Experimental results show that the feature based on the raw-power spectrogram has a good performance, and is particularly suited to severe mismatched conditions. The second proposed method is the Spectral Power Distribution Image Feature (SPD-IF), which uses the same image feature approach, but is based on an SPD image derived from the stochastic distribution of power over the sound clip. This is combined with a missing feature classification system, which marginalises the image regions containing only noise, and experiments show the method achieves the high accuracy of the baseline methods in clean conditions combined with robust results in mismatched noise.
Ergonomics in laparoscopic surgery

Laparoscopic surgery provides patients with less painful surgery but is more demanding for the surgeon. The increased technological complexity and sometimes poorly adapted equipment have led to increased complaints of surgeon fatigue and discomfort during laparoscopic surgery. Ergonomic integration and suitable laparoscopic operating room environment are essential to improve efficiency, safety, and comfort for the operating team. Understanding ergonomics can not only make life of surgeon comfortable in the operating room but also reduce physical strains on surgeon.
An Overview of Reconfigurable Hardware in Embedded Systems

Over the past few years, the realm of embedded systems has expanded to include a wide variety of products, ranging from digital cameras, to sensor networks, to medical imaging systems. Consequently, engineers strive to create ever smaller and faster products, many of which have stringent power requirements. Coupled with increasing pressure to decrease costs and time-to-market, the design constraints of embedded systems pose a serious challenge to embedded systems designers. Reconfigurable hardware can provide a flexible and efficient platform for satisfying the area, performance, cost, and power requirements of many embedded systems. This article presents an overview of reconfigurable computing in embedded systems, in terms of benefits it can provide, how it has already been used, design issues, and hurdles that have slowed its adoption.
Cloud forensics: Evidence collection and preliminary analysis

Cloud computing systems host most of today's commercial business applications yielding it high revenue which makes it a target of cyber attacks. This emphasizes the need for a digital forensic mechanism for the cloud environment. Conventional digital forensics cannot be directly presented as a cloud forensic solution due to the multi tenancy and virtualization of resources prevalent in cloud. While we do cloud forensics, the data to be inspected are cloud component logs, virtual machine disk images, volatile memory dumps, console logs and network captures. In this paper, we have come up with a remote evidence collection and pre-processing framework using Struts and Hadoop distributed file system. Collection of VM disk images, logs etc., are initiated through a pull model when triggered by the investigator, whereas cloud node periodically pushes network captures to HDFS. Pre-processing steps such as clustering and correlation of logs and VM disk images are carried out through Mahout and Weka to implement cross drive analysis.
A Comprehensive Review on Cyclodextrin-Based Carriers for Delivery of Chemotherapeutic Cytotoxic Anticancer Drugs

Most of the cytotoxic chemotherapeutic agents have poor aqueous solubility. These molecules are associated with poor physicochemical and biopharmaceutical properties, which makes the formulation difficult. An important approach in this regard is the use of combination of cyclodextrin and nanotechnology in delivery system. This paper provides an overview of limitations associated with anticancer drugs, their complexation with cyclodextrins, loading/encapsulating the complexed drugs into carriers, and various approaches used for the delivery. The present review article aims to assess the utility of cyclodextrin-based carriers like liposomes, niosomes, nanoparticles, micelles, millirods, and siRNA for delivery of antineoplastic agents. These systems based on cyclodextrin complexation and nanotechnology will camouflage the undesirable properties of drug and lead to synergistic or additive effect. Cyclodextrin-based nanotechnology seems to provide better therapeutic effect and sustain long life of healthy and recovered cells. Still, considerable study on delivery system and administration routes of cyclodextrin-based carriers is necessary with respect to their pharmacokinetics and toxicology to substantiate their safety and efficiency. In future, it would be possible to resolve the conventional and current issues associated with the development and commercialization of antineoplastic agents.
Mobile Augmented Reality Art and the Politics of Re-assembly

Experimental art deployed in the Augmented Reality (AR) medium is contributing to a reconfiguration of traditional perceptions of interface, audience participation, and perceptual experience. Artists, critical engineers, and programmers, have developed AR in an experimental topology that diverges from both industrial and commercial uses of the medium. In a general technical sense, AR is considered as primarily an information overlay, a datafied window that situates virtual information in the physical world. In contradistinction, AR as experimental art practice activates critical inquiry, collective participation, and multimodal perception. As an emergent hybrid form that challenges and extends already established 'fine art' categories, augmented reality art deployed on Portable Media Devices (PMD’s) such as tablets & smartphones fundamentally eschews models found in the conventional 'art world.' It should not, however, be considered as inscribing a new 'model:' rather, this paper posits that the unique hybrids advanced by mobile augmented reality art–– also known as AR(t)–– are closely related to the notion of the 'machinic assemblage' ( Deleuze & Guattari 1987), where a deep capacity to re-assemble marks each new artevent. This paper develops a new formulation, the 'software assemblage,’ to explore some of the unique mixed reality situations that AR(t) has set in motion.