nils-herrmann/clean_scirepeval huggingface dataset:
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior

This paper proposes a framework for single-image super-resolution. The underlying idea is to learn a map from input low-resolution images to target high-resolution images based on example pairs of input and output images. Kernel ridge regression (KRR) is adopted for this purpose. To reduce the time complexity of training and testing for KRR, a sparse solution is found by combining the ideas of kernel matching pursuit and gradient descent. As a regularized solution, KRR leads to a better generalization than simply storing the examples as has been done in existing example-based algorithms and results in much less noisy images. However, this may introduce blurring and ringing artifacts around major edges as sharp changes are penalized severely. A prior model of a generic image class which takes into account the discontinuity property of images is adopted to resolve this problem. Comparison with existing algorithms shows the effectiveness of the proposed method.
Time-of-Flight sensor calibration for accurate range sensing

Over the past years Time-of-Flight (ToF) sensors have become a considerable alternative to conventional distance sensing techniques like laser scanners or image based stereo-vision. Due to the ability to provide full-range distance information at high frame-rates, ToF sensors achieve a significant impact onto current research areas like online object recognition, collision prevention or scene and object reconstruction. Nevertheless, ToF-cameras like the Photonic Mixer Device (PMD) still exhibit a number of error sources that affect the accuracy of measured distance information. For this reason, major error sources for ToFcameras will be discussed, along with a new calibration approach that combines intrinsic, distance as well as a reflectivity related error calibration in an overall, easy to use system and thus significantly reduces the number of necessary reference images. The main contribution, in this context, is a new intensity-based calibration model that requires less input data compared to other models and thus significantly contributes to the reduction of calibration data.
Accelerating and Compressing LSTM Based Model for Online Handwritten Chinese Character Recognition

With the development of deep learning tools, the online handwritten Chinese character recognition (HCCR) performance has been greatly improved by using deep neural networks (DNNs) especially for long short-term memory (LSTM). However, DNNs suffer from large consumption of computation and storage resources, which may cause problems for service providers, such as server pressure, longer service latency and higher energy consumption. To solve these problems, we propose a framework that combines singular value decomposition (SVD) and adaptive drop weight (ADW) to accelerate and compress LSTM based models. We first build an LSTM based model that achieves an accuracy of 97.83% on the ICDAR2013 online HCCR competition dataset. After restructuring the model with SVD and ADW, it can reduce the FLOPs (floating point operations per second) of the forward process by approximately 10 times and compress the model with 1/30 of the original size with only a 0.5% decrease in accuracy. Finally, integrated with our efficient forward implementation, the recognition of an online character requires only 2.7 ms in average on a CPU with a single thread, while requiring only 0.45 MB for model storage.
Venture-Capital Financing and the Growth of Startup Firms

Venture capital firms have unique capabilities in terms of dealing with high uncertainty, high degrees of information asymmetry, and providing access to a strategic network. This study examines the association between the presence of venture capital and the growth of startups. It explores whether venture capital leads to growth or, alternatively growth signals the need for venture capital. It also investigates the impact if any of venture capital financing events and the growth of these firms. Finally, it documents the relationship between growth in startup financial valuation and changes in the number of employees over successive rounds of financing.
A Driving Right Leg Circuit (DgRL) for Improved Common Mode Rejection in Bio-Potential Acquisition Systems

The paper presents a novel Driving Right Leg (DgRL) circuit designed to mitigate the effect of common mode signals deriving, say, from power line interferences. The DgRL drives the isolated ground of the instrumentation towards a voltage which is fixed with respect to the common mode potential on the subject, therefore minimizing common mode voltage at the input of the front-end. The paper provides an analytical derivation of the common mode rejection performances of DgRL as compared to the usual grounding circuit or Driven Right Leg (DRL) loop. DgRL is integrated in a bio-potential acquisition system to show how it can reduce the common mode signal of more than 70 dB with respect to standard patient grounding. This value is at least 30 dB higher than the reduction achievable with DRL, making DgRL suitable for single-ended front-ends, like those based on active electrodes. EEG signal acquisition is performed to show how the system can successfully cancel power line interference without any need for differential acquisition, signal post-processing or filtering.
A systematic review of the survival and complication rates of all-ceramic and metal-ceramic reconstructions after an observation period of at least 3 years. Part I: Single crowns.

The objective of this systematic review was to assess the 5-year survival rates of all-ceramic single crowns and to compare it with the survival rates of metal-ceramic crowns and to describe the incidence of biological and technical complications.



An electronic Medline and Dental Global Publication Research System search complemented by manual searching was conducted to identify prospective and retrospective cohort studies on all-ceramic and metal-ceramic crowns with a mean follow-up time of at least 3 years. Patients had to have been examined clinically at the follow-up visit. Assessment of the identified studies and data abstraction was performed independently by two reviewers. Annual failure rates were analyzed using standard and random-effects Poisson regression models to obtain summary estimates of survival proportions.



The search provided 3473 titles and 177 abstracts. Full-text analysis was performed for 86 articles, resulting in 34 studies that met the inclusion criteria. In meta-analysis, the 5-year survival of all-ceramic crowns was estimated at 93.3% (95% confidence interval (CI): 91.1%-95%) and 95.6% (95% CI: 92.4%-97.5%) for metal-ceramic crowns. All-ceramic crowns were also analyzed according to the material utilized. Densely sintered alumina (Procera technique) crowns showed the highest 5-year survival rate of 96.4%, followed by reinforced glass-ceramic crowns (Empress technique) and InCeram-crowns with survival rates of 95.4% and 94.5%, respectively. A significantly lower survival rate of 87.5% was calculated for glass-ceramic crowns after 5 years. All-ceramic crowns were also grouped and analyzed regarding the position in the mouth. All four types of all-ceramic crowns showed lower survival rates when seated on posterior teeth. Moreover, for glass-ceramic crowns (84.4%) and InCeram-crowns (90.4%), this difference reached statistical significance (P = 0.009, P = 0.028).



Based on the present systematic review, all-ceramic crowns, when used for anterior teeth, showed survival rates at 5 years comparable to those seen for metal-ceramic crowns. When used for posterior teeth, the survival rates at 5 years of densely sintered alumina crowns (94.9%) and reinforced glass-ceramic crowns (93.7%) were similar to those obtained for metal-ceramic crowns. Furthermore, lower survival rates of 90.4% and 84.4% can be expected for InCeram crowns and glass-ceramic crowns when utilized for premolars and molars.
Millimeter-Wave Low-Noise Amplifier Design in 28-nm Low-Power Digital CMOS

This paper presents the design of a 60-GHz low-noise amplifier (LNA) in a 28-nm low-power (LP) bulk CMOS process. As the technology is optimized for digital LP applications, the design of millimeter-wave (mm-wave) circuits requires high-frequency design and modeling of all active and passive devices. This includes the development of a suitable RF-transistor layout, as well as transmission lines and high- Q capacitors. The mm-wave circuit design aspects are further discussed with considerations about possible dc-distribution approaches, broadband matching networks, and optimum transistor loads. The proposed approach and device models have been validated with the fabrication and characterization of a two-stage 60-GHz LNA. This circuit exhibits 13.8 dB of power gain, 18 GHz of bandwidth, 4 dB of minimum noise figure, and an input referred 1-dB compression point at -12.5 dBm consuming 24 mW of dc power. Based on this performance and to the authors' best knowledge, the presented amplifier shows the highest reported value for a commonly used figure-of-merit of 60-GHz LNAs.
Fuzzy PID control of a flexible-joint robot arm with uncertainties from time-varying loads

This paper presents the design and experiment of a fuzzy proportional integral derivative (PID) controller for a flexible-joint robot arm with uncertainties from time-varying loads. Experimental results have shown remarkable tracking performance of this fuzzy PID controller, and have convincingly demonstrated that fuzzy logic control can be used for flexiblejoint robot arms with uncertainties and it is quite robust. In this paper, the fuzzy PID controller is first described briefly, using a simple and practical PD+I controller configuration. This configuration preserves the linear structure of the conventional PD+I controller, but has nonconstant gains: the proportional, integral, and derivative gains are nonlinear functions of their input signals, which have self-tuning (adaptive) capabilities in setpoint tracking performance. Moreover, these variable gains make the fuzzy PID controller robust with faster response time and less overshoot than its conventional counterpart. The proposed design was tested using a flexible-joint robot arm driven by a dc motor in a laboratory, where the arm was experienced with time-varying loads. Control performance by the conventional and fuzzy PID controllers for such a laboratory robotic system are both included in this paper for comparison.
Writing a narrative biomedical review: considerations for authors, peer reviewers, and editors

Review articles comprehensively covering a specific topic are crucial for successful research and academic projects. Most editors consider review articles for special and regular issues of journals. Writing a review requires deep knowledge and understanding of a field. The aim of this review is to analyze the main steps in writing a narrative biomedical review and to consider points that may increase the chances of success. We performed a comprehensive search through MEDLINE, EMBASE, Scopus, and Web of Science using the following keywords: review of the literature, narrative review, title, abstract, authorship, ethics, peer review, research methods, medical writing, scientific writing, and writing standards. Opinions expressed in the review are also based on personal experience as authors, peer reviewers, and editors.
A GPS based traffic light pre-emption control system for emergency vehicles

Emergency vehicles such as ambulances, police force vehicles and fire fighting vehicles are required to reach their destination as quickly as possible. Any unnecessary delays experienced on them along their way could eventually cause dangerous events; leading to damages in lives or properties. One of the most important delays is the time that is consumed on travelling between the starting point and the destination point of these emergency vehicles; and in intersections with traffic lights, especially when these intersections are congested. In this paper we discuss the design and implementation of an automatic preemption traffic control system, based on the global positioning system satellites. It ensures the arrival of emergency vehicles to their destinations on the minimum time possible. The results indicate that the proposed system has an optimum solution for the delay time experienced on the emergency vehicles along their path.
DEEP LEARNING AND INTELLIGENT AUDIO MIXING

Mixing multitrack audio is a crucial part of music production. With recent advances in machine learning techniques such as deep learning, it is of great importance to conduct research on the applications of these methods in the field of automatic mixing. In this paper, we present a survey of intelligent audio mixing systems and their recent incorporation of deep neural networks. We propose to the community a research trajectory in the field of deep learning applied to intelligent music production systems. We conclude with a proof of concept based on stem audio mixing as a contentbased transformation using a deep autoencoder.
Fine-Grained Acceleration of Binary Neural Networks Using Intel® Xeon® Processor with Integrated FPGA

Binary weighted networks (BWN) for imageclassification reduce computation for convolutional neuralnetworks (CNN) from multiply-adds to accumulates with littleto no accuracy loss. Hardware architectures such as FPGA cantake full advantage of BWN computations because of theirability to express weights represented as 0 and 1 efficientlythrough customizable logic. In this paper, we present animplementation on Intel®'s Xeon® processor with integratedFPGA to accelerate binary weighted networks. We interfaceIntel's Accelerator Abstraction Layer (AAL) with Caffe toprovide a robust framework used for accelerating CNN. Utilizing the low latency Quick Path Interconnect (QPI) between the Broadwell Xeon® processor and Arria10 FPGA, we can perform fine-grained offloads for specific portions ofthe network. Due to convolution layers making up most of thecomputation in our experiments, we offload the feature andweight data to customized binary hardware in the FPGA forfaster execution. An initial proof of concept design shows thatby using both the Xeon processor and FPGA together we canimprove the throughput by 2x on some layers and by 1.3xoverall while utilizing only a small percentage of FPGA core logic.
Liquids permeation into HTV silicone rubber under AC/DC electric field

As shed and sheath materials, high temperature vulcanization (HTV) silicone rubber suffers from various types of environmental stresses in actual use. Liquids permeation, as a kind of silicone rubber aging factor, has been investigated by many researchers. However, most of the studies are conducted under the condition of no electric field. Since that composite insulators, especially the high voltage (HV) side, are under high electric field, it is worthy of attention on liquids permeation under AC/DC voltages. In this study, we utilized a new-designed liquids permeation test unit under AC/DC electric field to measure the weight of HTV silicone rubber specimens during the test process. Deionized water, as well as basic fuchsine ethanol solutions were used to verify the effects of electric field on liquids permeation. Besides, the influence of ATH content on the liquids permeation was involved.
Multi-level Lasso for Sparse Multi-task Regression

We present a flexible formulation for variable selection in multi-task regression to allow for discrepancies in the estimated sparsity patterns accross the multiple tasks, while leveraging the common structure among them. Our approach is based on an intuitive decomposition of the regression coefficients into a product between a component that is common to all tasks and another component that captures task-specificity. This decomposition yields the Multi-level Lasso objective that can be solved efficiently via alternating optimization. The analysis of the “orthonormal design” case reveals some interesting insights on the nature of the shrinkage performed by our method, compared to that of related work. Theoretical guarantees are provided on the consistency of Multi-level Lasso. Simulations and empirical study of micro-array data further demonstrate the value of our framework.
Don't mind me touching my wrist: a case study of interacting with on-body technology in public

Wearable technology, specifically e-textiles, offers the potential for interacting with electronic devices in a whole new manner. However, some may find the operation of a system that employs non-traditional on-body interactions uncomfortable to perform in a public setting, impacting how readily a new form of mobile technology may be received. Thus, it is important for interaction designers to take into consideration the implications of on-body gesture interactions when designing wearable interfaces. In this study, we explore the third-party perceptions of a user's interactions with a wearable e-textile interface. This two-prong evaluation examines the societal perceptions of a user interacting with the textile interface at different on-body locations, as well as the observer's attitudes toward on-body controller placement. We performed the study in the United States and South Korea to gain cultural insights into the perceptions of on-body technology usage.
Denial-of-Service attacks on 6 LoWPAN-RPL networks : Threats and an intrusion detection system proposition

RPL (Routing Protocol for Low-power and lossy networks) is a specific routing protocol designed to optimize 6LoWPAN (IPv6 over Low power Wireless Personal Area Network) operation. As 6LoWPAN suffers from resource constraints on battery, processor, memory and bandwidth, it affects the performance of the RPL protocol. From security point of view, this will make RPL vulnerable to several threats directly or indirectly. Thus, cryptographic systems are not sufficient to protect the RPL from internal attacks; a compromised node from the network may cause undesired operation without being detected by these systems. An intrusion detection system (IDS) should be used, but it is not easy given the nature of 6LoWPAN; on a side its resource constraints, and on the other side its opening to the outside world through the Internet. In this paper we focus on denial of service (DoS) attacks, we determine the elements to be taken into account in selecting a compatible IDS and we give some solutions that we consider effective and valid for 6LoWPAN-RPL based networks.
The LED Block Cipher

We present a new block cipher LED. While dedicated to compact hardware implementation, and offering the smallest silicon footprint among comparable block ciphers, the cipher has been designed to simultaneously tackle three additional goals. First, we explore the role of an ultra-light (in fact non-existent) key schedule. Second, we consider the resistance of ciphers, and LED in particular, to related-key attacks: we are able to derive simple yet interesting AES-like security proofs for LED regarding relatedor single-key attacks. And third, while we provide a block cipher that is very compact in hardware, we aim to maintain a reasonable performance profile for software implementation.
ULDBs: Databases with Uncertainty and Lineage

This paper introduces ULDBs, an extension of relational databases with simple yet expressive constructs for representing and manipulating both lineage and uncertainty. Uncertain data and data lineage are two important areas of data management that have been considered extensively in isolation, however many applications require the features in tandem. Fundamentally, lineage enables simple and consistent representation of uncertain data, it correlates uncertainty in query results with uncertainty in the input data, and query processing with lineage and uncertainty together presents computational benefits over treating them separately.We show that the ULDB representation is complete, and that it permits straightforward implementation of many relational operations. We define two notions of ULDB minimality--data-minimal and lineage-minimal--and study minimization of ULDB representations under both notions. With lineage, derived relations are no longer self-contained: their uncertainty depends on uncertainty in the base data. We provide an algorithm for the new operation of extracting a database subset in the presence of interconnected uncertainty. Finally, we show how ULDBs enable a new approach to query processing in probabilistic databases.ULDBs form the basis of the Trio system under development at Stanford.
Fabrication of zero-speed sensor using weakly coupled NiFe/CoFe multilayer films

A magnetic sensor consisting of NiFe/Al/sub 2/O/sub 3//CoFe thin film was fabricated. Magnetization reversal of the NiFe soft layer was asymmetric in switching to parallel and antiparallel alignments to magnetization of CoFe because of magnetostatic coupling between the NiFe and CoFe layers. When the magnetization of the NiFe was paralleled to that of CoFe, a steep switching accompanied a large Barkhausen jump-induced pulse voltage in a pickup coil as sensor output. Amplitude of the pulse was not dependent on frequency of an external magnetic field. The pulse voltage output was obtained from the sensor element consisting of the optimized film structure excited by the magnetic field at almost zero frequency. These features are also achieved by twisted FeCoV and other wire materials. However, an integrated on-chip sensor with electronic circuits can be fabricated with film-based materials. The film-based sensor elements fabricated in this study can be utilized for speed sensor, rotation sensor, flow meter, and other applications.
I See What You Say (ISWYS): Arabic lip reading system

The ability of communicating easily with everyone is a blessing people with hearing impairment do not have. They completely rely on their vision around healthy individuals to difficultly read lips. This paper proposes a solution for this problem, ISWYS (I See What You Say) is a research-oriented speech recognition system for the Arabic language that interprets lips movements into readable text. It is accomplished by analyzing a video of lips movements that resemble utterances, and then converting it to readable characters using video analysis and motion estimation. Our algorithm involves dividing the video into n number of frames to generate n-1 image frame which is produced by taking the difference between consecutive frames. Then, video features are extracted to be used by our error function which provided recognition of approximately 70%.
On Preprocessing the ALT-Algorithm Student

In this thesis, we study the preprocessing phase of the ALT algorithm. ALT is a well known preprocessing-based speed-up technique for Dijkstra’s algorithm, which allows fast computations of shortest paths in large scale road networks. The preprocessing of the ALT algorithm has some degree of freedom, in that it must select a subset of nodes in the graph, called landmarks, that fulfill a special role; optimally choosing these landmarks is NP-hard, hence no effective exact solution algorithm exists. In this thesis, we study the landmark selection process; we propose several solution methods, including a greedy algorithm as well as a new heuristic. Furthermore, we propose a new model for the search space of an ALT shortest path computation, which allows us to reduce the problem of optimally choosing k landmarks to the maximum coverage problem, for which approximation results are known, and to formulate the landmark selection problem as an integer linear program.