nils-herrmann/clean_scirepeval huggingface dataset:
Model-free Trajectory Optimization for Reinforcement Learning

Many of the recent Trajectory Optimization algorithms alternate between local approximation of the dynamics and conservative policy update. However, linearly approximating the dynamics in order to derive the new policy can bias the update and prevent convergence to the optimal policy. In this article, we propose a new model-free algorithm that backpropagates a local quadratic time-dependent Q-Function, allowing the derivation of the policy update in closed form. Our policy update ensures exact KL-constraint satisfaction without simplifying assumptions on the system dynamics demonstrating improved performance in comparison to related Trajectory Optimization algorithms linearizing the dynamics.
ExpIt-OOS: Towards Learning from Planning in Imperfect Information Games

The current state of the art in playing many important perfect information games, including Chess and Go, combines planning and deep reinforcement learning with self-play. We extend this approach to imperfect information games and present ExIt-OOS, a novel approach to playing imperfect information games within the Expert Iteration framework and inspired by AlphaZero. We use Online Outcome Sampling, an online search algorithm for imperfect information games in place of MCTS. While training online, our neural strategy is used to improve the accuracy of playouts in OOS, allowing a learning and planning feedback loop for imperfect information games.
Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series

This article proposes a Bayesian nonparametric method for forecasting, imputation, and clustering in sparsely observed, multivariate time series data. The method is appropriate for jointly modeling hundreds of time series with widely varying, non-stationary dynamics. Given a collection of N time series, the Bayesian model first partitions them into independent clusters using a Chinese restaurant process prior. Within a cluster, all time series are modeled jointly using a novel “temporally-reweighted” extension of the Chinese restaurant process mixture. Markov chain Monte Carlo techniques are used to obtain samples from the posterior distribution, which are then used to form predictive inferences. We apply the technique to challenging forecasting and imputation tasks using seasonal flu data from the US Center for Disease Control and Prevention, demonstrating superior forecasting accuracy and competitive imputation accuracy as compared to multiple widely used baselines. We further show that the model discovers interpretable clusters in datasets with hundreds of time series, using macroeconomic data from the Gapminder Foundation.
Learning to Align from Scratch

Unsupervised joint alignment of images has been demonstrated to improve performance on recognition tasks such as face verification. Such alignment reduces undesired variability due to factors such as pose, while only requiring weak supervision in the form of poorly aligned examples. However, prior work on unsupervised alignment of complex, real-world images has required the careful selection of feature representation based on hand-crafted image descriptors, in order to achieve an appropriate, smooth optimization landscape. In this paper, we instead propose a novel combination of unsupervised joint alignment with unsupervised feature learning. Specifically, we incorporate deep learning into the congealing alignment framework. Through deep learning, we obtain features that can represent the image at differing resolutions based on network depth, and that are tuned to the statistics of the specific data being aligned. In addition, we modify the learning algorithm for the restricted Boltzmann machine by incorporating a group sparsity penalty, leading to a topographic organization of the learned filters and improving subsequent alignment results. We apply our method to the Labeled Faces in the Wild database (LFW). Using the aligned images produced by our proposed unsupervised algorithm, we achieve higher accuracy in face verification compared to prior work in both unsupervised and supervised alignment. We also match the accuracy for the best available commercial method.
SOI CMOS tunable capacitors for RF antenna aperture tuning

This paper provides a detailed analysis of a SOI CMOS tunable capacitor for antenna tuning. Design expressions for a switched capacitor network are given and quality factor of the whole network is expressed as a function of design parameters. Application to antenna aperture tuning is described by combining a 130 nm SOI CMOS tunable capacitor with a printed notch antenna. The proposed tunable multiband antenna can be tuned from 420 MHz to 790 MHz, with an associated radiation efficiency in the 33-73% range.
Recognition of Image-Orientation-Based Iris Spoofing

This paper presents a solution to automatically recognize the correct left/right and upright/upside-down orientation of iris images. This solution can be used to counter spoofing attacks directed to generate fake identities by rotating an iris image or the iris sensor during the acquisition. Two approaches are compared on the same data, using the same evaluation protocol: 1) feature engineering, using hand-crafted features classified by a support vector machine (SVM) and 2) feature learning, using data-driven features learned and classified by a convolutional neural network (CNN). A data set of 20 750 iris images, acquired for 103 subjects using four sensors, was used for development. An additional subject-disjoint data set of 1,939 images, from 32 additional subjects, was used for testing purposes. Both same-sensor and cross-sensor tests were carried out to investigate how the classification approaches generalize to unknown hardware. The SVM-based approach achieved an average correct classification rate above 95% (89%) for recognition of left/right (upright/upside-down) orientation when tested on subject-disjoint data and camera-disjoint data, and 99% (97%) if the images were acquired by the same sensor. The CNN-based approach performed better for same-sensor experiments, and presented slightly worse generalization capabilities to unknown sensors when compared with the SVM. We are not aware of any other papers on the automatic recognition of upright/upside-down orientation of iris images, or studying both hand-crafted and data-driven features in same-sensor and cross-sensor subject-disjoint experiments. The data sets used in this paper, along with random splits of the data used in cross-validation, are being made available.
Improve immunogenicity of DNA vaccine against foot-and-mouth disease virus: Role of intron and probably viral 3C protease as biological adjuvants

Foot-and-mouth disease (FMD) is one of the most important diseases with heavy economic losses. The causative agent of the disease is a virus, named as FMD virus, belonging to the picornavirus family. There is no treatment for the disease and vaccination is the main control strategy. Several vaccination methods have been introduced against FMD including DNA vaccines. In this study, two genetic constructs, which were defined by absence and presence of an intron, were tested for their ability to induce the anti-FMD virus responses in mouse. Both constructs encoded a fusion protein consisting of viral (P12A and 3C) and EGFP proteins under the control of CMV promoter. The protein expression was studied in the COS-7 cells transfected with the plasmids by detecting EGFP protein. Cell death was induced in the cells expressing the P12A3C-EGFP, but not the EGFP, protein. This might be explained by the protease activity of the 3C protein which cleaved critical proteins of the host cells. Mice injected with the intron-containing plasmid induced 16-fold higher antibody level than the intronless plasmid. In addition, serum neutralization antibodies were only induced in the mice injected with intron-containing plasmid. In conclusion, the use of intron might be a useful strategy for enhancing antibody responses by DNA vaccines. Moreover, cell death inducing activity of the 3C protein might suggest applying it along with DNA vaccines to improve immunogenicity.
Geolocation Prediction in Social Media Data by Finding Location Indicative Words

Geolocation prediction is vital to geospatial applications like localised search and local event detection. Predominately, social media geolocation models are based on full text data, including common words with no geospatial dimension (e.g. today) and noisy strings (tmrw), potentially hampering prediction and leading to slower/more memory-intensive models. In this paper, we focus on finding location indicative words (LIWs) via feature selection, and establishing whether the reduced feature set boosts geolocation accuracy. Our results show that an information gain ratiobased approach surpasses other methods at LIW selection, outperforming state-of-the-art geolocation prediction methods by 10.6% in accuracy and reducing the mean and median of prediction error distance by 45km and 209km, respectively, on a public dataset. We further formulate notions of prediction confidence, and demonstrate that performance is even higher in cases where our model is more confident, striking a trade-off between accuracy and coverage. Finally, the identified LIWs reveal regional language differences, which could be potentially useful for lexicographers.
Giant Magnetoresistance Sensors: A Review on Structures and Non-Destructive Eddy Current Testing Applications

Non-destructive eddy current testing (ECT) is widely used to examine structural defects in ferromagnetic pipe in the oil and gas industry. Implementation of giant magnetoresistance (GMR) sensors as magnetic field sensors to detect the changes of magnetic field continuity have increased the sensitivity of eddy current techniques in detecting the material defect profile. However, not many researchers have described in detail the structure and issues of GMR sensors and their application in eddy current techniques for nondestructive testing. This paper will describe the implementation of GMR sensors in non-destructive testing eddy current testing. The first part of this paper will describe the structure and principles of GMR sensors. The second part outlines the principles and types of eddy current testing probe that have been studied and developed by previous researchers. The influence of various parameters on the GMR measurement and a factor affecting in eddy current testing will be described in detail in the third part of this paper. Finally, this paper will discuss the limitations of coil probe and compensation techniques that researchers have applied in eddy current testing probes. A comprehensive review of previous studies on the application of GMR sensors in non-destructive eddy current testing also be given at the end of this paper.
The involvement of primary motor cortex in mental rotation revealed by transcranial magnetic stimulation.

We used single-pulse transcranial magnetic stimulation of the left primary hand motor cortex and motor evoked potentials of the contralateral right abductor pollicis brevis to probe motor cortex excitability during a standard mental rotation task. Based on previous findings we tested the following hypotheses. (i) Is the hand motor cortex activated more strongly during mental rotation than during reading aloud or reading silently? The latter tasks have been shown to increase motor cortex excitability substantially in recent studies. (ii) Is the recruitment of the motor cortex for mental rotation specific for the judgement of rotated but not for nonrotated Shepard & Metzler figures? Surprisingly, motor cortex activation was higher during mental rotation than during verbal tasks. Moreover, we found strong motor cortex excitability during the mental rotation task but significantly weaker excitability during judgements of nonrotated figures. Hence, this study shows that the primary hand motor area is generally involved in mental rotation processes. These findings are discussed in the context of current theories of mental rotation, and a likely mechanism for the global excitability increase in the primary motor cortex during mental rotation is proposed.
Seed Crystal Homogeneity Controls Lateral and Vertical Heteroepitaxy of Monolayer MoS2 and WS2.

Heteroepitaxy between transition-metal dichalcogenide (TMDC) monolayers can fabricate atomically thin semiconductor heterojunctions without interfacial contamination, which are essential for next-generation electronics and optoelectronics. Here we report a controllable two-step chemical vapor deposition (CVD) process for lateral and vertical heteroepitaxy between monolayer WS2 and MoS2 on a c-cut sapphire substrate. Lateral and vertical heteroepitaxy can be selectively achieved by carefully controlling the growth of MoS2 monolayers that are used as two-dimensional (2D) seed crystals. Using hydrogen as a carrier gas, we synthesize ultraclean MoS2 monolayers, which enable lateral heteroepitaxial growth of monolayer WS2 from the MoS2 edges to create atomically coherent and sharp in-plane WS2/MoS2 heterojunctions. When no hydrogen is used, we obtain MoS2 monolayers decorated with small particles along the edges, inducing vertical heteroepitaxial growth of monolayer WS2 on top of the MoS2 to form vertical WS2/MoS2 heterojunctions. Our lateral and vertical atomic layer heteroepitaxy steered by seed defect engineering opens up a new route toward atomically controlled fabrication of 2D heterojunction architectures.
Simulation a shunt active power filter using MATLAB/Simulink

Along with increasing demand on improving power quality, the most popular technique that has been used is Active Power Filter (APF); this is because APF can easily eliminate unwanted harmonics, improve power factor and overcome voltage sags. This paper will discuss and analyze the simulation result for a three-phase shunt active power filter using MATLAB/Simulink program. This simulation will implement a non-linear load and compensate line current harmonics under balance and unbalance load. As a result of the simulation, it is found that an active power filter is the better way to reduce the total harmonic distortion (THD) which is required by quality standards IEEE-519.
Novel anti-B-cell maturation antigen antibody-drug conjugate (GSK2857916) selectively induces killing of multiple myeloma.

B-cell maturation antigen (BCMA), highly expressed on malignant plasma cells in human multiple myeloma (MM), has not been effectively targeted with therapeutic monoclonal antibodies. We here show that BCMA is universally expressed on the MM cell surface and determine specific anti-MM activity of J6M0-mcMMAF (GSK2857916), a novel humanized and afucosylated antagonistic anti-BCMA antibody-drug conjugate via a noncleavable linker. J6M0-mcMMAF specifically blocks cell growth via G2/M arrest and induces caspase 3-dependent apoptosis in MM cells, alone and in coculture with bone marrow stromal cells or various effector cells. It strongly inhibits colony formation by MM cells while sparing surrounding BCMA-negative normal cells. J6M0-mcMMAF significantly induces effector cell-mediated lysis against allogeneic or autologous patient MM cells, with increased potency and efficacy compared with the wild-type J6M0 without Fc enhancement. The antibody-dependent cell-mediated cytotoxicity and apoptotic activity of J6M0-mcMMAF is further enhanced by lenalidomide. Importantly, J6M0-mcMMAF rapidly eliminates myeloma cells in subcutaneous and disseminated mouse models, and mice remain tumor-free up to 3.5 months. Furthermore, J6M0-mcMMAF recruits macrophages and mediates antibody-dependent cellular phagocytosis of MM cells. Together, these results demonstrate that GSK2857916 has potent and selective anti-MM activities via multiple cytotoxic mechanisms, providing a promising next-generation immunotherapeutic in this cancer.
Low-overhead memory leak detection using adaptive statistical profiling

Sampling has been successfully used to identify performance optimization opportunities. We would like to apply similar techniques to check program correctness. Unfortunately, sampling provides poor coverage of infrequently executed code, where bugs often lurk. We describe an adaptive profiling scheme that addresses this by sampling executions of code segments at a rate inversely proportional to their execution frequency. To validate our ideas, we have implemented SWAT, a novel memory leak detection tool. SWAT traces program allocations/ frees to construct a heap model and uses our adaptive profiling infrastructure to monitor loads/stores to these objects with low overhead. SWAT reports 'stale' objects that have not been accessed for a 'long' time as leaks. This allows it to find all leaks that manifest during the current program execution. Since SWAT has low runtime overhead (‹5%), and low space overhead (‹10% in most cases and often less than 5%), it can be used to track leaks in production code that take days to manifest. In addition to identifying the allocations that leak memory, SWAT exposes where the program last accessed the leaked data, which facilitates debugging and fixing the leak. SWAT has been used by several product groups at Microsoft for the past 18 months and has proved effective at detecting leaks with a low false positive rate (‹10%).
Unconscious intuition or conscious analysis ? Critical questions for the Deliberation-Without-Attention paradigm

The Deliberation without Attention (DWA) effect refers to apparent improvements in decision-making following a period of distraction. It has been presented as evidence for beneficial unconscious cognitive processes. We identify two major concerns with this claim: first, as these demonstrations typically involve subjective preferences, the effects of distraction cannot be objectively assessed as beneficial; second, there is no direct evidence that the DWA manipulation promotes unconscious decision processes. We describe two tasks based on the DWA paradigm in which we found no evidence that the distraction manipulation led to decision processes that are subjectively unconscious, nor that it reduced the influence of presentation order upon performance. Crucially, we found that a lack of awareness of decision process was associated with poorer performance, both in terms of subjective preference measures used in traditional DWA paradigm and in an equivalent task where performance can be objectively assessed. Therefore, we argue that reliance on conscious memory itself can explain the data. Thus the DWA paradigm is not an adequate method of assessing beneficial unconscious thought.
A Feature Subset Selection Method based on Conditional Mutual Information and Ant Colony Optimization

Feature subset selection is one of the key problems in the area of pattern recognition and machine learning. Feature subset selection refers to the problem of selecting only those features that are useful in predicting a target concept i. e. class. Most of the data acquired through different sources are not particularly screened for any specific task e. g. classification, clustering, anomaly detection, etc. When this data is fed to a learning algorithm, its results deteriorate. The proposed method is a pure filter based feature subset selection technique that incurs less computational cost and highly efficient in terms of classification accuracy. Moreover, along with high accuracy the proposed method requires less number of features in most of the cases. In the proposed method the issue of feature ranking and threshold value selection is addressed. The proposed method adaptively selects number of features as per the worth of an individual feature in the dataset. An extensive experimentation is performed, comprised of a number of benchmark datasets over three well known classification algorithms. Empirical results endorse efficiency and effectiveness of the proposed method.
Intramuscular fat in lamb muscle and the impact of selection for improved carcass lean meat yield.

Intramuscular fat percentage (IMF%) has been shown to have a positive influence on the eating quality of red meat. Selection of Australian lambs for increased lean tissue and reduced carcass fatness using Australian Sheep Breeding Values has been shown to decrease IMF% of the Muscularis longissimus lumborum. The impact this selection has on the IMF% of other muscle depots is unknown. This study examined IMF% in five different muscles from 400 lambs (M. longissimus lumborum, Muscularis semimembranosus, Muscularis semitendinosus, Muscularis supraspinatus, Muscularis infraspinatus). The sires of these lambs had a broad range in carcass breeding values for post-weaning weight, eye muscle depth and fat depth over the 12th rib (c-site fat depth). Results showed IMF% to be highest in the M. supraspinatus (4.87 ± 0.1, P<0.01) and lowest in the M. semimembranosus (3.58 ± 0.1, P<0.01). Hot carcass weight was positively associated with IMF% of all muscles. Selection for decreasing c-site fat depth reduced IMF% in the M. longissimus lumborum, M. semimembranosus and M. semitendinosus. Higher breeding values for post-weaning weight and eye muscle depth increased and decreased IMF%, respectively, but only in the lambs born as multiples and raised as singles. For each per cent increase in lean meat yield percentage (LMY%), there was a reduction in IMF% of 0.16 in all five muscles examined. Given the drive within the lamb industry to improve LMY%, our results indicate the importance of continued monitoring of IMF% throughout the different carcass regions, given its importance for eating quality.
Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks

This paper presents a convolutional neural network (CNN) based approach for fault diagnosis of rotating machinery. The proposed approach incorporates sensor fusion by taking advantage of the CNN structure to achieve higher and more robust diagnosis accuracy. Both temporal and spatial information of the raw data from multiple sensors is considered during the training process of the CNN. Representative features can be extracted automatically from the raw signals. It avoids manual feature extraction or selection, which relies heavily on prior knowledge of specific machinery and fault types. The effectiveness of the developed method is evaluated by using datasets from two types of typical rotating machinery, roller bearings, and gearboxes. Compared with traditional approaches using manual feature extraction, the results show the superior diagnosis performance of the proposed method. The present approach can be extended to fault diagnosis of other machinery with various types of sensors due to its end to end feature learning capability.