nils-herrmann/clean_scirepeval huggingface dataset:
Design and implementation of a 120A resonant inverter for induction furnace

This paper presents the design and implementation of a series resonant inverter operating at high frequency for induction heating applications. The main advantage of this work is focused in applying power electronics, particularly a series resonant inverter based on an IGBT's full bridge and a series RLC Load matched with a high frequency coupling transformer. The series resonant inverter is designed to deliver a current load up to 120A with a one phase AC source. Due to the inherent high speed operation and high efficiency of switching converters, other advantages of the induction furnace presented in this paper are that it require less time to start melting, and require less physical space than other traditional furnaces.
The ability to act--multiple suicidal gunshot wounds.

Multiple self-inflicted gunshot wounds are rare and usually present a challenge to the forensic pathologist in determining the manner of death. Determining a person's capability to act following a gunshot wound can be of major importance in crime scene reconstruction and in differentiation between homicide and suicide. Questions concerning the possibility of physical activity following a given gunshot wound are repeatedly raised in court. We report herein three unusual cases of suicide involving multiple gunshot wounds; all the victims suffered gunshot wounds of the head without immediate incapacitation. In the first two cases, the head was target for two gunshots. Third case was a combination of two gunshots to the head and chest. In the text, we focus on the victim's ability to act after the first shot, with regards to the character and localization of the gunshot wound. Also, we focus on findings that are typical for a repeated suicidal shot.
Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions

We present a practical approach for processing mobile sensor time series data for continual deep learning predictions. The approach comprises data cleaning, normalization, capping, time-based compression, and finally classification with a recurrent neural network. We demonstrate the effectiveness of the approach in a case study with 279 participants. On the basis of sparse sensor events, the network continually predicts whether the participants would attend to a notification within 10 minutes. Compared to a random baseline, the classifier achieves a 40% performance increase (AUC of 0.702) on a withheld test set. This approach allows to forgo resource-intensive, domain-specific, error-prone feature engineering, which may drastically increase the applicability of machine learning to mobile phone sensor data.
Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements

In this work we benchmark, for the first time, the latest Standard Particle Swarm Optimisation algorithm (SPSO-2011) against the 28 test functions designed for the Special Session on Real-Parameter Single Objective Optimisation at CEC-2013. SPSO-2011 is a major improvement over previous PSO versions, with an adaptive random topology and rotational invariance constituting the main advancements. Results showed an outstanding performance of SPSO-2011 for the family of unimodal and separable test functions, with a fast convergence to the global optimum, while good performance was observed for four rotated multimodal functions. Conversely, SPSO-2011 showed the weakest performance for all composition problems (i.e. highly complex functions specially designed for this competition) and certain multimodal test functions. In general, a fast convergence towards the region of the global optimum was achieved, requiring less than 10E+03 function evaluations. However, for most composition and multimodal functions SPSO2011 showed a limited capability to “escape” from sub-optimal regions. Despite this limitation, a desirable feature of SPSO-2011 was its scalable behaviour, which observed up to 50-dimensional problems, i.e. keeping a similar performance across dimensions with no need for increasing the population size. Therefore, it seems advisable that future PSO improvements be focused on enhancing the algorithm's ability to solve non-separable and asymmetrical functions, with a large number of local minima and a second global minimum located far from the true optimum. This work is the first effort towards providing a baseline for a fair comparison of future PSO improvements.
The Off-Switch Game

It is clear that one of the primary tools we can use to mitigate the potential risk from a misbehaving AI system is the ability to turn the system off. As the capabilities of AI systems improve, it is important to ensure that such systems do not adopt subgoals that prevent a human from switching them off. This is a challenge because many formulations of rational agents create strong incentives for self-preservation. This is not caused by a built-in instinct, but because a rational agent will maximize expected utility and cannot achieve whatever objective it has been given if it is dead. Our goal is to study the incentives an agent has to allow itself to be switched off. We analyze a simple game between a human H and a robot R, where H can press R’s off switch but R can disable the off switch. A traditional agent takes its reward function for granted: we show that such agents have an incentive to disable the off switch, except in the special case where H is perfectly rational. Our key insight is that for R to want to preserve its off switch, it needs to be uncertain about the utility associated with the outcome, and to treat H’s actions as important observations about that utility. (R also has no incentive to switch itself off in this setting.) We conclude that giving machines an appropriate level of uncertainty about their objectives leads to safer designs, and we argue that this setting is a useful generalization of the classical AI paradigm of rational agents.
Technical assessment of the CLEERGAS moving grate-based process for energy generation from municipal solid waste.

A technical analysis has been completed for a commercial-scale two-stage gasification-combustion system. The CLEERGAS (Covanta Low Emissions Energy Recovery GASification) process consists of partial combustion and gasification of as-received municipal solid waste (MSW) on a moving grate producing syngas followed by full combustion of the generated syngas in an adjoining chamber and boiler. This process has been in operation since 2009 on a modified 330-tonne day(-1) waste-to-energy (WTE) line in Tulsa, Oklahoma. Material balances determined that the syngas composition is 12.8% H2 and 11.4% CO, the heating value of the gas in the gasifier section is 4098 kJ Nm(-3), and an aggregate molecular formula for the waste is C6H14.5O5. The analysis of gas measurements sampled from the Tulsa unit showed that the gasification-combustion mode fully processed the MSW at an excess air input of only 20% as compared to the 80-100% typically found in conventional WTE moving grate plants. Other important attributes of the CLEERGAS gasification-combustion process are that it has operated on a commercial scale for a period of over two years with 93% availability and utilizes a moving grate technology that is currently used in hundreds of WTE plants around the world.
Programming cells by multiplex genome engineering and accelerated evolution

The breadth of genomic diversity found among organisms in nature allows populations to adapt to diverse environments. However, genomic diversity is difficult to generate in the laboratory and new phenotypes do not easily arise on practical timescales. Although in vitro and directed evolution methods have created genetic variants with usefully altered phenotypes, these methods are limited to laborious and serial manipulation of single genes and are not used for parallel and continuous directed evolution of gene networks or genomes. Here, we describe multiplex automated genome engineering (MAGE) for large-scale programming and evolution of cells. MAGE simultaneously targets many locations on the chromosome for modification in a single cell or across a population of cells, thus producing combinatorial genomic diversity. Because the process is cyclical and scalable, we constructed prototype devices that automate the MAGE technology to facilitate rapid and continuous generation of a diverse set of genetic changes (mismatches, insertions, deletions). We applied MAGE to optimize the 1-deoxy-d-xylulose-5-phosphate (DXP) biosynthesis pathway in Escherichia coli to overproduce the industrially important isoprenoid lycopene. Twenty-four genetic components in the DXP pathway were modified simultaneously using a complex pool of synthetic DNA, creating over 4.3 billion combinatorial genomic variants per day. We isolated variants with more than fivefold increase in lycopene production within 3 days, a significant improvement over existing metabolic engineering techniques. Our multiplex approach embraces engineering in the context of evolution by expediting the design and evolution of organisms with new and improved properties.
Agile Spectrum Imaging: Programmable Wavelength Modulation for Cameras and Projectors

We advocate the use of quickly-adjustable, computer-controlled color spectra in photography, lighting and displays. We present an optical relay system that allows mechanical or electronic color spectrum control and use it to modify a conventional camera and projector. We use a diffraction grating to disperse the rays into different colors, and introduce a mask (or LCD/DMD) in the optical path to modulate the spectrum. We analyze the tradeoffs and limitations of this design, and demonstrate its use in a camera, projector and light source. We propose applications such as adaptive color primaries, metamer detection, scene contrast enhancement, photographing fluorescent objects, and high dynamic range photography using spectrum modulation.
A Multilingual Multimedia Indian Sign Language Dictionary Tool

This paper presents a cross platform multilingual multimedia Indian Sign Language (ISL) dictionary building tool. ISL is a linguistically under-investigated language with no source of well documented electronic data. Research on ISL linguistics also gets hindered due to a lack of ISL knowledge and the unavailability of any educational tools. Our system can be used to associate signs corresponding to a given text. The current system also facilitates the phonological annotation of Indian signs in the form of HamNoSys structure. The generated HamNoSys string can be given as input to an avatar module to produce an animated sign representation.
An Algorithm for Drawing General Undirected Graphs Tomihisa Kamada and Satoru Kawai

Graphs (networks) are very common data structures which are handled in computers. Diagrams are widely used to represent the graph structures visually in many information systems. In order to automatically draw the diagrams which are, for example, state graphs, data-flow graphs, Petri nets, and entity-relationship diagrams, basic graph drawing algorithms are required. The state of the art in automatic drawing is surveyed comprehensively in [7,19]. There have been only a few algorithms for general undirected graphs. This paper presents a simple but successful algorithm for drawing undirected graphs and weighted graphs. The basic idea of our algorithm is as follows. We regard the desirable "geometric" (Euclidean) distance between two vertices in the drawing as the "graph theoretic" distance between them in the corresponding graph. We introduce a virtual dynamic system in which every two vertices are connected by a "spring" of such desirable length. Then, we regard the optimal layout of vertices as the state in which the total spring energy of the system is minimal. The "spring" idea for drawing general graphs was introduced in , and similar methods were used for drawing planar graphs with fixed boundary [2,20]. This paper brings a new significant result in graph drawing based on the spring model.
Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems

Stream-processing workloads and modern shared cluster environments exhibit high variability and unpredictability. Combined with the large parameter space and the diverse set of user SLOs, this makes modern streaming systems very challenging to statically configure and tune. To address these issues, in this paper we investigate a novel control-plane design, Chi, which supports continuous monitoring and feedback, and enables dynamic re-configuration. Chi leverages the key insight of embedding control-plane messages in the data-plane channels to achieve a low-latency and flexible control plane for stream-processing systems. Chi introduces a new reactive programming model and design mechanisms to asynchronously execute control policies, thus avoiding global synchronization. We show how this allows us to easily implement a wide spectrum of control policies targeting different use cases observed in production. Large-scale experiments using production workloads from a popular cloud provider demonstrate the flexibility and efficiency of our approach.  Luo Mai, Kai Zeng, Rahul Potharaju, Le Xu, Steve Suh, Shivaram Venkataraman, Paolo Costa, Terry Kim, Saravanan Muthukrishnan, Vamsi Kuppa, Sudheer Dhulipalla, Sriram Rao.  A Scalable and Programmable Control Plane for Distributed Stream Processing Systems. PVLDB, 11 (10): xxxx-yyyy, 2018.
Efficient 3D Object Segmentation from Densely Sampled Light Fields with Applications to 3D Reconstruction

Precise object segmentation in image data is a fundamental problem with various applications, including 3D object reconstruction. We present an efficient algorithm to automatically segment a static foreground object from highly cluttered background in light fields. A key insight and contribution of our article is that a significant increase of the available input data can enable the design of novel, highly efficient approaches. In particular, the central idea of our method is to exploit high spatio-angular sampling on the order of thousands of input frames, for example, captured as a hand-held video, such that new structures are revealed due to the increased coherence in the data. We first show how purely local gradient information contained in slices of such a dense light field can be combined with information about the camera trajectory to make efficient estimates of the foreground and background. These estimates are then propagated to textureless regions using edge-aware filtering in the epipolar volume. Finally, we enforce global consistency in a gathering step to derive a precise object segmentation in both 2D and 3D space, which captures fine geometric details even in very cluttered scenes. The design of each of these steps is motivated by efficiency and scalability, allowing us to handle large, real-world video datasets on a standard desktop computer. We demonstrate how the results of our method can be used for considerably improving the speed and quality of image-based 3D reconstruction algorithms, and we compare our results to state-of-the-art segmentation and multiview stereo methods.
DERI&UPM: Pushing Corpus Based Relatedness to Similarity: Shared Task System Description

In this paper, we describe our system submitted for the semantic textual similarity (STS) task at SemEval 2012. We implemented two approaches to calculate the degree of similarity between two sentences. First approach combines corpus-based semantic relatedness measure over the whole sentence with the knowledge-based semantic similarity scores obtained for the words falling under the same syntactic roles in both the sentences. We fed all these scores as features to machine learning models to obtain a single score giving the degree of similarity of the sentences. Linear Regression and Bagging models were used for this purpose. We used Explicit Semantic Analysis (ESA) as the corpus-based semantic relatedness measure. For the knowledgebased semantic similarity between words, a modified WordNet based Lin measure was used. Second approach uses a bipartite based method over the WordNet based Lin measure, without any modification. This paper shows a significant improvement in calculating the semantic similarity between sentences by the fusion of the knowledge-based similarity measure and the corpus-based relatedness measure against corpus based measure taken alone.
MIMO SAR OFDM Chirp Waveform Diversity Design With Random Matrix Modulation

Multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) has received much attention due to its interesting application potentials, but effective waveform diversity design is still a technical challenge. In a MIMO SAR, each antenna should transmit a unique waveform, orthogonal to the waveforms transmitted by other antennas. The waveforms should have a large time-bandwidth product, low cross-correlation interferences, and a low peak-average ratio. To reach these aims, this paper proposes an orthogonal frequency division multiplexing (OFDM) chirp waveform with random matrix modulation. The designed waveforms are time-delay and frequency-shift decorrelated. Referring to MIMO SAR high-resolution imaging, the proposed OFDM chirp waveform parameters are optimally designed, and their performances are analyzed through the ambiguity function and range-Doppler-based MIMO SAR imaging algorithm. Extensive and comparative simulation results show that the waveforms have the superiorities of high range resolution, constant time domain and almost constant frequency-domain modulus, large time-bandwidth product, low peak-average ratio, and low time-delay and frequency-shift correlation peaks. More importantly, this scheme can easily generate over three orthogonal waveforms with a large time-bandwidth product.
Real-Time Imaging With a 140 GHz Inverse Synthetic Aperture Radar

A summary of a 140 GHz inverse synthetic aperture radar (ISAR) is presented. High resolution and real time imaging are two key points in the design. Using the frequency-modulated continuous-wave (FMCW) radar technique with a 5 GHz bandwidth, 3 cm range resolution is achieved with assistance of software calibration compensating for nonlinearities of the LFM waveform. In cross-range region, synthetic aperture technique is adopted to improve imaging resolution. Real time imaging capability is enabled by using a hardware structure constructed with FPGA+CPU+GPU in signal processing unit. The final system was used to get ISAR image of a rotating target at 5 m. The 2-D (range and cross-range) imaging resolution is 3 cm × 3 cm with a real time image frame rate of 5 Hz.
Crohn's disease of the vulva in a 10-year-old girl.

Crohn's disease may involve all parts of the gastrointestinal tract and may often involve other organs as well. These non-intestinal affections are termed extraintestinal manifestations. Vulval involvement is an uncommon extraintestinal manifestation of Crohn's disease, and it is very rare in children. Patients with vulval CD typically present with erythema and edema of the labia majora, which progresses to extensive ulcer formation. Vulval Crohn's disease can appear before or after intestinal problems or it may occur simultaneously. We present a 10-year-old girl with intestinal Crohn's disease complicated with perianal skin tags and asymptomatic unilateral labial hypertrophy. The course of her lesion was independent of the intestinal disease and responded significantly to medical treatment including azathioprine and topical steroid. We emphasize that although vulval involvement in childhood is uncommon, Crohn's disease must be considered in the differential diagnosis of nontender, red, edematous lesions of the genital area.
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural network trained on ChestX-ray14, currently the largest publicly available chest Xray dataset, containing over 100,000 frontalview X-ray images with 14 diseases. Four practicing academic radiologists annotate a test set, on which we compare the performance of CheXNet to that of radiologists. We find that CheXNet exceeds average radiologist performance on the F1 metric. We extend CheXNet to detect all 14 diseases in ChestX-ray14 and achieve state of the art results on all 14 diseases.
Description of Luciola aquatilis sp. nov., a new aquatic firefly (Coleoptera: Lampyridae: Luciolinae) from Thailand

A new species of aquatic firefly belonging to Luciola Laporte is described and illustrated based on external morphology of both males and females, and the genitalia of males. Luciola aquatilis sp. nov., a common firefly in Thailand was formerly commonly misidentified as Luciola brahmina Bourgeois. Other Luciola species that resemble L. aquatilis are discussed, as well as past confusion concerning their taxonomic affinities.
Low power in-memory computing based on dual-mode SOT-MRAM

In this paper, we propose a novel Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) array design that could simultaneously work as non-volatile memory and implement a reconfigurable in-memory logic (AND, OR) without add-on logic circuits to memory chip as in traditional logic-in-memory designs. The computed logic output could be simply read out like a normal MRAM bit-cell using the shared memory peripheral circuits. Such intrinsic in-memory logic could be used to process data within memory to greatly reduce power-hungry and long distance data communication in conventional Von-Neumann computing systems. We further employ in-memory data encryption using Advanced Encryption Standard (AES) algorithm as a case study to demonstrate the efficiency of the proposed design. The device to architecture co-simulation results show that the proposed design can achieve 70.15% and 80.87% lower energy consumption compared to CMOS-ASIC and CMOL-AES implementations, respectively. It offers almost similar energy consumption as recent DW-AES implementation, but with 60.65% less area overhead.
An Efficient Algorithm of Frequent Itemsets Mining Based on MapReduce

Mainstream parallel algorithms for mining frequent itemsets (patterns) were designed by implementing FP-Growth or Apriori algorithms on MapReduce (MR) framework. Existing MR FP-Growth algorithms can not distribute data equally among nodes, and MR Apriori algorithms utilize multiple map/reduce procedures and generate too many key-value pairs with value of 1; these disadvantages hinder their performance. This paper proposes an algorithm FIMMR: it firstly mines local frequent itemsets for each data chunk as candidates, applies prune strategies to the candidates, and then identifies global frequent itemsets from candidates. Experimental results show that the time efficiency of FIMMR outperforms PFP and SPC significantly; and under small minimum support threshold, FIMMR can achieve one order of magnitude improvement than the other two algorithms; meanwhile, the speedup of FIMMR is also satisfactory.