nils-herrmann/clean_scirepeval huggingface dataset:
Indicator selection for daily equity trading with recurrent reinforcement learning

Recurrent reinforcement learning (RRL), a machine learning technique, is very successful in training high frequency trading systems. When trading analysis of RRL is done with lower frequency financial data, e.g. daily stock prices, the decrease of autocorrelation in prices may lead to a decrease in trading profit. In this paper, we propose a RRL trading system which utilizes the price information, jointly with the indicators from technical analysis, fundamental analysis and econometric analysis, to produce long/short signals for daily trading. In the proposed trading system, we use a genetic algorithm as a pre-screening tool to search suitable indicators for RRL trading. Moreover, we modify the original RRL parameter update scheme in the literature for out-of-sample trading. Empirical studies are conducted based on data sets of 238 S&P stocks. It is found that the trading performance concerning the out-of sample daily Sharpe ratios turns better: the number of companies with a positive and significant Sharpe ratio increases after feeding the selected indicators jointly with prices information into the RRL system.
A Rank Minimization Heuristic with Application to Minimum Order System Approximation

Several problems arising in control system analysis and design, such as reduced order controller synthesis, involve minimizing the rank of a matrix variable subject to linear matrix inequality (LMI) constraints. Except in some special cases, solving this rank minimization problem (globally) is very difficult. One simple and surprisingly effective heuristic, applicable when the matrix variable is symmetric and positive semidefinite, is to minimize its trace in place of its rank. This results in a semidefinite program (SDP) which can be efficiently solved. In this paper we describe a generalization of the trace heuristic that applies to general nonsymmetric, even non-square, matrices, and reduces to the trace heuristic when the matrix is positive semidefinite. The heuristic is to replace the (nonconvex) rank objective with the sum of the singular values of the matrix, which is the dual of the spectral norm. We show that this problem can be reduced to an SDP, hence efficiently solved. To motivate the heuristic, we show that the dual spectral norm is the convex envelope of the rank on the set of matrices with norm less than one. We demonstrate the method on the problem of minimum order system approximation.
Sketch of Big Data Real-Time Analytics Model

Big Data has drawn huge attention from researchers in information sciences, decision makers in governments and enterprises. However, there is a lot of potential and highly useful value hidden in the huge volume of data. Data is the new oil, but unlike oil data can be refined further to create even more value. Therefore, a new scientific paradigm is born as data-intensive scientific discovery, also known as Big Data. The growth volume of real-time data requires new techniques and technologies to discover insight value. In this paper we introduce the Big Data real-time analytics model as a new technique. We discuss and compare several Big Data technologies for real-time processing along with various challenges and issues in adapting Big Data. Realtime Big Data analysis based on cloud computing approach is our future research direction.
Effect of superstrate height on gain of MSA fed Fabry Perot Cavity antenna

This article highlights the effect of superstrate height on gain of MSA fed Fabry Perot Cavity (FPC) antenna. The FPC antenna structure consists of a microstrip antenna, which feeds an array of square parasitic metal patches fabricated on a FR4 superstrate. These patches are good reflector of microwave frequency and therefore are used to enhance the reflection coefficient and the gain. Field amplitude and phase depend on dimensions and spacing between parasitic patches. Antenna gain can be further increased by increasing the superstrate height. However, differential gain improvement decreases with superstrate height. High gain is achieved by placing superstrate layer at about integral multiple of half wavelength above the ground plane. Structures are designed, fabricated and tested. Measured VSWR is less than 2 over 5.725–5.875 GHz, ISM band. The analysed structures can be packaged inside an application platform.
Dielectric properties estimation of normal and malignant skin tissues at millimeter-wave frequencies using effective medium theory

Millimeter-wave reflectometry can be used as a non-invasive method for early detection of skin cancers. Less penetration and higher resolution are only some reasons to use mm-waves for skin cancer diagnosis. The dielectric parameters data of human skin at mm-wave frequencies are not widely addressed in the literature due to the expensive instruments and the complicated structure of human skin. Available dielectric properties of cancerous tissues in the literature are for other tissues rather than skin and are restricted to the frequencies below 20 GHz. In this paper, dielectric properties of healthy and malignant skin tissues were estimated in the range of 20-100 GHz by applying the effective medium theory. A stratified skin model and some skin tumors were presented. The possibility of mm-wave reflectometry for detection of skin cancers has also been studied. The results of this paper can be used as a theoretical basis for taking further steps in skin cancer research.
Robust analysis of feature spaces: color image segmentation

A general technique for the recovery of signi cant image features is presented. The technique is based on the mean shift algorithm, a simple nonparametric procedure for estimating density gradients. Drawbacks of the current methods (including robust clustering) are avoided. Feature space of any nature can be processed, and as an example, color image segmentation is discussed. The segmentation is completely autonomous, only its class is chosen by the user. Thus, the same program can produce a high quality edge image, or provide, by extracting all the signi cant colors, a preprocessor for content-based query systems. A 512 512 color image is analyzed in less than 10 seconds on a standard workstation. Gray level images are handled as color images having only the lightness coordinate.
Neighborhood playgrounds, fast food restaurants, and crime: relationships to overweight in low-income preschool children.

We examined the relationship between overweight in preschool children and three environmental factors--the proximity of the children's residences to playgrounds and to fast food restaurants and the safety of the children's neighborhoods. We hypothesized that children who lived farther from playgrounds, closer to fast food restaurants, and in unsafe neighborhoods were more likely to be overweight.



This was a cross-sectional study of 7,020 low-income children, 36 through 59 months of age living in Cincinnati, OH. Overweight was defined as a measured body mass index > or =95th percentile. The distance between each child's residence and the nearest public playground and fast food restaurant was determined with geographic information systems. Neighborhood safety was defined by the number of police-reported crimes per 1,000 residents per year in each of 46 city neighborhoods.



Overall, 9.2% of the children were overweight, 76% black, and 23% white. The mean (+/- SD) distances from a child's home to the nearest playground and fast food restaurant were 0.31 (+/- 0.22) and 0.70 (+/- 0.38) miles, respectively. There was no association between child overweight and proximity to playgrounds, proximity to fast food restaurants, or level of neighborhood crime. The association between child overweight and playground proximity did not differ by neighborhood crime level.



Within a population of urban low-income preschoolers, overweight was not associated with proximity to playgrounds and fast food restaurants or with the level of neighborhood crime.
Bayesian Unsupervised Topic Segmentation

This paper describes a novel Bayesian approach to unsupervised topic segmentation. Unsupervised systems for this task are driven by lexical cohesion: the tendency of wellformed segments to induce a compact and consistent lexical distribution. We show that lexical cohesion can be placed in a Bayesian context by modeling the words in each topic segment as draws from a multinomial language model associated with the segment; maximizing the observation likelihood in such a model yields a lexically-cohesive segmentation. This contrasts with previous approaches, which relied on hand-crafted cohesion metrics. The Bayesian framework provides a principled way to incorporate additional features such as cue phrases, a powerful indicator of discourse structure that has not been previously used in unsupervised segmentation systems. Our model yields consistent improvements over an array of state-of-the-art systems on both text and speech datasets. We also show that both an entropy-based analysis and a well-known previous technique can be derived as special cases of the Bayesian
Automated Static Code Analysis for Classifying Android Applications Using Machine Learning

In this paper we apply Machine Learning (ML) techniques on static features that are extracted from Android's application files for the classification of the files. Features are extracted from Android’s Java byte-code (i.e.,.dex files) and other file types such as XML-files. Our evaluation focused on classifying two types of Android applications: tools and games. Successful differentiation between games and tools is expected to provide positive indication about the ability of such methods to learn and model Android benign applications and potentially detect malware files. The results of an evaluation, performed using a test collection comprising 2,285 Android. apk files, indicate that features, extracted statically from. apk files, coupled with ML classification algorithms can provide good indication about the nature of an Android application without running the application, and may assist in detecting malicious applications. This method can be used for rapid examination of Android. apks and informing of suspicious applications.
Efficacy and safety of oral magnesium supplementation in the treatment of depression in the elderly with type 2 diabetes: a randomized, equivalent trial.

To evaluate the efficacy and safety of oral magnesium supplementation, with magnesium chloride (MgCl2), in the treatment of newly diagnosed depression in the elderly with type 2 diabetes and hypomagnesemia. Twenty-three elderly patients with type 2 diabetes and hypomagnesemia were enrolled and randomly allocated to receive either 50 mL of MgCl2 5% solution equivalent to 450 mg of elemental magnesium or Imipramine 50 mg daily during 12 weeks. Widowhood or divorce in the last six months, alcoholism, degenerative illnesses of the nervous central system, recent diagnosis of diabetes, previous or current treatment with antidepressants, chronic diarrhea, use of diuretics, and reduced renal function were exclusion criteria. Hypomagnesemia was defined by serum magnesium levels < 1.8 mg/dL and depression by Yasavage and Brink score > or = 11 points. The primary trial end point was the improvement of depression symptoms. At baseline, there were no differences by age (69 +/- 5.9 and 66.4 +/- 6.1 years, p = 0.39), duration of diabetes (11.8 +/- 7.9 and 8.6 +/- 5.7 years, p = 0.33), serum magnesium levels (1.3 +/- 0.04 and 1.4 +/- 0.04 mg/dL, p = 0.09), and Yasavage and Brink Score (17.9 +/- 3.9 and 16.1 +/- 4.5 point, p = 0.34) in the groups with MgCl2 and imipramine, respectively. At end of follow-up, there were no significant differences in the Yasavage and Brink score (11.4 +/- 3.8 and 10.9 +/- 4.3, p = 0.27) between the groups in study; whereas serum magnesium levels were significantly higher in the group with MgCl2 (2.1 +/- 0.08 mg/dL) than in the subjects with imipramine (1.5 +/- 0.07 mg/dL), p < 0.0005. In conclusion, MgCl2 is as effective in the treatment of depressed elderly type 2 diabetics with hypomagnesemia as imipramine 50 mg daily.
A Peridynamic Perspective on Spring-Mass Fracture

The application of spring-mass systems to the animation of brittle fracture is revisited. The motivation arises from the recent popularity of peridynamics in the computational physics community. Peridynamic systems can be regarded as spring-mass systems with two specific properties. First, spring forces are based on a simple strain metric, thereby decoupling spring stiffness from spring length. Second, masses are connected using a distance-based criterion. The relatively large radius of influence typically leads to a few hundred springs for every mass point. Spring-mass systems with these properties are shown to be simple to implement, trivially parallelized, and well-suited to animating brittle fracture.
Black Hat Visualization

People lie, mislead, and bullshit in a myriad of ways. Visualizations, as a form of communication, are no exception to these tendencies. Yet, the language we use to describe how people can use visualizations to mislead can be relatively sparse. For instance, one can be “lying with vis” or using “deceptive visualizations.” In this paper, we use the language of computer security to expand the space of ways that unscrupulous people (black hats) can manipulate visualizations for nefarious ends. In addition to forms of deception well-covered in the visualization literature, we also focus on visualizations which have fidelity to the underlying data (and so may not be considered deceptive in the ordinary use of the term in visualization), but still have negative impact on how data are perceived. We encourage designers to think defensively and comprehensively about how their visual designs can result in data being misinterpreted.
Opportunistic Flooding in Low-Duty-Cycle Wireless Sensor Networks with Unreliable Links

Intended for network-wide dissemination of commands, configurations and code binaries, flooding has been investigated extensively in wireless networks. However, little work has yet been done on low-duty-cycle wireless sensor networks in which nodes stay asleep most of time and wake up asynchronously. In this type of network, a broadcasting packet is rarely received by multiple nodes simultaneously, a unique constraining feature that makes existing solutions unsuitable. Combined with unreliable links, flooding in low-duty-cycle networks is a new challenging issue.
 In this paper, we introduce Opportunistic Flooding, a novel design tailored for low-duty-cycle networks with unreliable wireless links and predetermined working schedules. The key idea is to make probabilistic forwarding decisions at a sender based on the delay distribution of next-hop nodes. Only opportunistically early packets are forwarded using links outside the energy optimal tree to reduce the flooding delay and redundancy in transmission. To improve performance further, we propose a forwarder selection method to alleviate the hidden terminal problem and a link-quality-based backoff method to resolve simultaneous forwarding operations. We evaluate Opportunistic Flooding with extensive simulation and a test-bed implementation consisting of 30 MicaZ nodes. Evaluation shows our design is close to the optimal performance achievable by oracle flooding designs. Compared with improved traditional flooding, our design achieves significantly shorter flooding delay while consuming only 20% ~ 60% of the transmission energy in various low-duty-cycle network settings.
Experimental characterization of Bowden cable friction

This paper presents a systematic method for experimental characterization of Bowden cable friction. A novel tension measurement method using a motion capture system and a spring is introduced. With the tension measurement method, the effects of nine variables on friction are investigated. Experimental results show that i) a combination of 7×19 FEP-coated stainless steel cable and double-sheaths has the highest force transmission efficiency; ii) smaller coefficient of friction material, smaller cable moving speed, shorter cable length, longer clamp distance, stiffer cable/sheath all help to increase the force transmission efficiency; and iii) the static friction pretension ratio only decreases as the coefficient of friction of cable/sheath decreases or as cable/sheath stiffness increases. We have generated guidelines for the Bowden cable performance which may help robotics researchers in choosing materials for the Bowden cables and designing control systems for actuation.
Design and Implementation of a Fast Dynamic Packet Filter

This paper presents Swift, a packet filter for high-performance packet capture on commercial off-the-shelf hardware. The key features of the Swift include: 1) extremely lowfilter update latency for dynamic packet filtering, and 2) gigabits-per-second high-speed packet processing. Based on complex instruction set computer (CISC) instruction set architecture (ISA), Swift achieves the former with an instruction set design that avoids the need for compilation and security checking, and the latter by mainly utilizing single instruction, multiple data (SIMD). We implement Swift in the Linux 2.6 kernel for both i386 and ×86-64 architectures and extensively evaluate its dynamic and static filtering performance on multiple machines with different hardware setups. We compare Swift to BPF (the BSD packet filter)--the de facto standard for packet filtering in modern operating systems--and hand-coded optimized C filters that are used for demonstrating possible performance gains. For dynamic filtering tasks, Swift is at least three orders of magnitude faster than BPF in terms of filter update latency. For static filtering tasks, Swift outperforms BPF up to three times in terms of packet processing speed and achieves much closer performance to the optimized C filters. We also show that Swift can harness the processing power of hardware SIMD instructions by virtue of its SIMD-capable instruction set.
Identifying Analogies Across Domains

Identifying analogies across domains without supervision is an important task for artificial intelligence. Recent advances in cross domain image mapping have concentrated on translating images across domains. Although the progress made is impressive, the visual fidelity many times does not suffice for identifying the matching sample from the other domain. In this paper, we tackle this very task of finding exact analogies between datasets i.e. for every image from domain A find an analogous image in domain B. We present a matching-by-synthesis approach: AN-GAN, and show that it outperforms current techniques. We further show that the cross-domain mapping task can be broken into two parts: domain alignment and learning the mapping function. The tasks can be iteratively solved, and as the alignment is improved, the unsupervised translation function reaches quality comparable to full supervision.
Self-inflicted explosive death by intra-oral detonation of a firecracker: a case report.

Self-inflicted explosive deaths due to detonation of fireworks are rare. In this case report, a peculiar case of an elderly male who discharged a firecracker inside his mouth, resulting in fatal blast induced craniofacial injuries, is described. There is paucity of published data describing fireworks-related suicidal and/or non-suicidal deaths. Even scantier data is present specifically describing fireworks-related blast induced neurotrauma and the mechanism(s) of injury involved in such cases. This case report emphasizes the severe damage that a commercially available explosive, the so-called "Gorilla Bomb", can cause, and raises questions about the relative ease of its acquisition.
Scaling Human-Object Interaction Recognition Through Zero-Shot Learning

Recognizing human object interactions (HOI) is an important part of distinguishing the rich variety of human action in the visual world. While recent progress has been made in improving HOI recognition in the fully supervised setting, the space of possible human-object interactions is large and it is impractical to obtain labeled training data for all interactions of interest. In this work, we tackle the challenge of scaling HOI recognition to the long tail of categories through a zero-shot learning approach. We introduce a factorized model for HOI detection that disentangles reasoning on verbs and objects, and at test-time can therefore produce detections for novel verb-object pairs. We present experiments on the recently introduced large-scale HICODET dataset, and show that our model is able to both perform comparably to state-of-the-art in fully-supervised HOI detection, while simultaneously achieving effective zeroshot detection of new HOI categories.
Real time heart rate variability assessment from Android smartphone camera photoplethysmography: Postural and device influences

The aim of this paper is to present a smartphone based system for real-time pulse-to-pulse (PP) interval time series acquisition by frame-to-frame camera image processing. The developed smartphone application acquires image frames from built-in rear-camera at the maximum available rate (30 Hz) and the smartphone GPU has been used by Renderscript API for high performance frame-by-frame image acquisition and computing in order to obtain PPG signal and PP interval time series. The relative error of mean heart rate is negligible. In addition, measurement posture and the employed smartphone model influences on the beat-to-beat error measurement of heart rate and HRV indices have been analyzed. Then, the standard deviation of the beat-to-beat error (SDE) was 7.81 ± 3.81 ms in the worst case. Furthermore, in supine measurement posture, significant device influence on the SDE has been found and the SDE is lower with Samsung S5 than Motorola X. This study can be applied to analyze the reliability of different smartphone models for HRV assessment from real-time Android camera frames processing.
Hippocampal Replay Is Not a Simple Function of Experience

Replay of behavioral sequences in the hippocampus during sharp wave ripple complexes (SWRs) provides a potential mechanism for memory consolidation and the learning of knowledge structures. Current hypotheses imply that replay should straightforwardly reflect recent experience. However, we find these hypotheses to be incompatible with the content of replay on a task with two distinct behavioral sequences (A and B). We observed forward and backward replay of B even when rats had been performing A for >10 min. Furthermore, replay of nonlocal sequence B occurred more often when B was infrequently experienced. Neither forward nor backward sequences preferentially represented highly experienced trajectories within a session. Additionally, we observed the construction of never-experienced novel-path sequences. These observations challenge the idea that sequence activation during SWRs is a simple replay of recent experience. Instead, replay reflected all physically available trajectories within the environment, suggesting a potential role in active learning and maintenance of the cognitive map.
AMOEBA: HIERARCHICAL CLUSTERING BASED ON SPATIAL PROXIMITY USING DELAUNATY DIAGRAM

Exploratory data analysis is increasingly more necessary as larger spatial data is managed in electro-magnetic media. We propose an exploratory method that reveals a robust clustering hierarchy. Our approach uses the Delaunay diagram to incorporate spatial proximity. It does not require any prior knowledge about the data set, nor does it require parameters from the user. Multi-level clusters are successfully discovered by this new method in only O(nlogn) time, where n is the size of the data set. The efficiency of our method allows us to construct and display a new type of tree graph that facilitates understanding of the complex hierarchy of clusters. We show that clustering methods that adopt a raster-like or vector-like representation of proximity are not appropriate for spatial clustering. We illustrate the robustness of our method with an experimental evaluation with synthetic data sets as well as real data sets.