nils-herrmann/clean_scirepeval huggingface dataset:
Patterns of species ranges, speciation, and extinction.

The exact nature of the relationship among species range sizes, speciation, and extinction events is not well understood. The factors that promote larger ranges, such as broad niche widths and high dispersal abilities, could increase the likelihood of encountering new habitats but also prevent local adaptation due to high gene flow. Similarly, low dispersal abilities or narrower niche widths could cause populations to be isolated, but such populations may lack advantageous mutations due to low population sizes. Here we present a large-scale, spatially explicit, individual-based model addressing the relationships between species ranges, speciation, and extinction. We followed the evolutionary dynamics of hundreds of thousands of diploid individuals for 200,000 generations. Individuals adapted to multiple resources and formed ecological species in a multidimensional trait space. These species varied in niche widths, and we observed the coexistence of generalists and specialists on a few resources. Our model shows that species ranges correlate with dispersal abilities but do not change with the strength of fitness trade-offs; however, high dispersal abilities and low resource utilization costs, which favored broad niche widths, have a strong negative effect on speciation rates. An unexpected result of our model is the strong effect of underlying resource distributions on speciation: in highly fragmented landscapes, speciation rates are reduced.
Electromyography in the four competitive swimming strokes: a systematic review.

The aim of this paper is to give an overview on 50 years of research in electromyography in the four competitive swimming strokes (crawl, breaststroke, butterfly, and backstroke). A systematic search of the existing literature was conducted using the combined keywords "swimming" and "EMG" on studies published before August 2013, in the electronic databases PubMed, ISI Web of Knowledge, SPORT discus, Academic Search Elite, Embase, CINAHL and Cochrane Library. The quality of each publication was assessed by two independent reviewers using a custom made checklist. Frequency of topics, muscles studied, swimming activities, populations, types of equipment and data treatment were determined from all selected papers and, when possible, results were compared and contrasted. In the first 20 years of EMG studies in swimming, most papers were published as congress proceedings. The methodological quality was low. Crawl stroke was most often studied. There was no standardized manner of defining swimming phases, normalizing the data or of presenting the results. Furthermore, the variability around the mean muscle activation patterns is large which makes it difficult to define a single pattern applicable to all swimmers in any activity examined.
AI for Dynamic Difficulty Adjustment in Games

Video Games are boring when they are too easy and frustrating when they are too hard. While most singleplayer games allow players to adjust basic difficulty (easy, medium, hard, insane), their overall level of challenge is often static in the face of individual player input. This lack of flexibility can lead to mismatches between player ability and overall game difficulty. In this paper, we explore the computational and design requirements for a dynamic difficulty adjustment system. We present a probabilistic method (drawn predominantly from Inventory Theory) for representing and reasoning about uncertainty in games. We describe the implementation of these techniques, and discuss how the resulting system can be applied to create flexible interactive experiences that adjust on the fly.  Video games are designed to generate engaging experiences: suspenseful horrors, whimsical amusements, fantastic adventures. But unlike films, books, or televised media  which often have similar experiential goals  video games are interactive. Players create meaning by interacting with the games internal systems. One such system is inventory  the stock of items that a player collects and carries throughout the game world. The relative abundance or scarcity of inventory items has a direct impact on the players experience. As such, games are explicitly designed to manipulate the exchange of resources between world and player.  2001] This network of producer-consumer relationships can be viewed as an economy  or more broadly, as a dynamic system [Castronova, 2000, Luenberger, 79]. 1 Inventory items for first-person shooters include health, weapons, ammunition and power-ups like shielding or temporary invincibility. 2 A surplus of ammunition affords experimentation and shoot first tactics, while limited access to recovery items (like health packs) will promote a more cautious approach to threatening situations. Game developers iteratively refine these systems based on play testing feedback  tweaking behaviors and settings until the game is balanced. While balancing, they often analyze systems intuitively by tracking specific identifiable patterns or types of dynamic activity. It is a difficult and time consuming process [Rollings and Adams, 2003]. While game balancing and tuning cant be automated, directed mathematical analysis can reveal deeper structures and relationships within a game system. With the right tools, researchers and developers can calculate relationships in less time, with greater accuracy. In this paper, we describe a first step towards such tools. Hamlet is a Dynamic Difficulty Adjustment (DDA) system built using Valves Half Life game engine. Using techniques drawn from Inventory Theory and Operations Research, Hamlet analyzes and adjust the supply and demand of game inventory in order to control overall game difficulty.
A Teaching System to Learn Programming: the Programmer's Learning Machine

The Programmer's Learning Machine (PLM) is an interactive exerciser for learning programming and algorithms. Using an integrated and graphical environment that provides a short feedback loop, it allows students to learn in a (semi)-autonomous way. This generic platform also enables teachers to create specific programming microworlds that match their teaching goals. This paper discusses our design goals and motivations, introduces the existing material and the proposed microworlds, and details the typical use cases from the student and teacher point of views.
Multiview Partitioning via Tensor Methods

Clustering by integrating multiview representations has become a crucial issue for knowledge discovery in heterogeneous environments. However, most prior approaches assume that the multiple representations share the same dimension, limiting their applicability to homogeneous environments. In this paper, we present a novel tensor-based framework for integrating heterogeneous multiview data in the context of spectral clustering. Our framework includes two novel formulations; that is multiview clustering based on the integration of the Frobenius-norm objective function (MC-FR-OI) and that based on matrix integration in the Frobenius-norm objective function (MC-FR-MI). We show that the solutions for both formulations can be computed by tensor decompositions. We evaluated our methods on synthetic data and two real-world data sets in comparison with baseline methods. Experimental results demonstrate that the proposed formulations are effective in integrating multiview data in heterogeneous environments.
Per-Flow Queue Management with Succinct Priority Indexing Structures for High Speed Packet Scheduling

Priority queues are essential building blocks for implementing advanced per-flow service disciplines and hierarchical quality-of-service at high-speed network links. Scalable priority queue implementation requires solutions to two fundamental problems. The first is to sort queue elements in real time at ever increasing line speeds (e.g., at OC-768 rates). The second is to store a huge number of packets (e.g., millions of packets). In this paper, we propose novel solutions by decomposing the problem into two parts, a succinct priority index (PI) in SRAM that can efficiently maintain a real-time sorting of priorities, coupled with a DRAM-based implementation of large packet buffers. In particular, we propose three related novel succinct PI data structures for implementing high-speed PIs: a PI, a counting priority index (CPI), and a pipelined counting priority index (pCPI). We show that all three structures can be very compactly implemented in SRAM using only ⊖(U) space, where U is the size of the universe required to implement the priority keys (time stamps). We also show that our proposed PI structures can be implemented very efficiently as well by leveraging hardware-optimized instructions that are readily available in modern 64-bit processors. The operations on the PI and CPI structures take ⊖(logW U) time complexity, where W is the processor word length (i.e., W = 64). Alternatively, operations on the pCPI structure take amortized constant time with only ⊖(logW U) pipeline stages (e.g., only four pipeline stages for U = 16 million). Finally, we show the application of our proposed PI structures for the scalable management of large packet buffers at line speeds. The pCPI structure can be implemented efficiently in high-performance network processing applications such as advanced per-flow scheduling with quality-of-service guarantee.
Data-Driven Decentralized Optimal Power Flow

The implementation of optimal power flow (OPF) methods to perform voltage and power flow regulation in electric networks is generally believed to require communication. We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn control policies for each DER to reconstruct and mimic the solution to a centralized OPF problem from solely locally available information. Collectively, all local controllers closely match the centralized OPF solution, providing near-optimal performance and satisfaction of system constraints. A rate distortion framework facilitates the analysis of how well the resulting fully decentralized control policies are able to reconstruct the OPF solution. Our methodology provides a natural extension to decide what buses a DER should communicate with to improve the reconstruction of its individual policy. The method is applied on both singleand three-phase test feeder networks using data from real loads and distributed generators. It provides a framework for Distribution System Operators to efficiently plan and operate the contributions of DERs to active distribution networks.
High School Classmates and College Success.

This paper uses administrative data from the University of Texas-Austin to examine whether the number of same high school classmates at college entry influences college achievement, measured by grade point average (GPA) and persistence. For each freshman cohort from 1993 through 2003 we calculate the number and ethnic makeup of college freshmen from each Texas high school. Empirical specifications include high school fixed effects to control for unobservable differences across schools that influence both college enrollment behavior and academic performance. Using an instrumental variables/fixed effects estimation strategy, we also evaluate whether "marginal" increases in the number of high school classmates influence college grades. Results show that students who arrive on campus with a larger number of high school classmates outperform their counterparts from smaller high school cohorts. Average effects of larger high school cohorts on college achievement are small, but a marginal increase in the number of same-race classmates raises GPA by 0.1 point.  provide suggestive evidence that minority academic benefits from larger high school cohorts are greater for minority compared with white students.
A hybrid approach to assessing data quality

Various techniques have been proposed to enable organizations to initiate procedures to assess and ultimately to improve the quality of their data. The utility of these assessment techniques (ATs) has been demonstrated in different organizational contexts. However, while some of the ATs are geared towards specific application areas and are often not suitable in different applications, others are more general and therefore do not always meet specific requirements. To address this problem we propose the Hybrid Approach to assessing data quality, which can generate usable ATs for specific requirements using the activities of existing ATs. A literature review and bottom-up analysis of the existing data quality (DQ) ATs was used to identify the different activities proposed by each AT. Based on example requirements from an asset management organization, the activities were combined using the Hybrid Approach in order to generate an AT which can be followed to assess an existing DQ problem. The Hybrid Approach demonstrates that it is possible to develop new ways of assessing DQ which leverage the best practices proposed by existing ATs by combining the activities dynamically.
Building robust geospatial web services for agricultural information extraction and sharing

Geospatial web service of agricultural information has a wide variety of consumers. An operational agricultural service will receive considerable requests and process a huge amount of datasets each day. To ensure the service quality, many strategies have to be taken during developing and deploying agricultural information services. This paper presents a set of methods to build robust geospatial web service for agricultural information extraction and sharing. The service is designed to serve the public and handle heavy-load requests for a long-lasting term with least maintenance. We have developed a web service to validate our approach. The service is used to serve more than 10 TB data product of agricultural drought. The performance is tested. The result shows that the service has an excellent response time and the use of system resources is stable. We have plugged the service into an operational system for global drought monitoring. The statistics and feedbacks show our approach is feasible and efficient in operational web systems.
Improved LabVIEW code generation

Before implementation into hardware signal processing algorithms are tested in simulation mode. LabVIEW provides highly convenient environment for simulation development and also tools for generation of simulation environment that can include simulation itself and collection of simulation data. Despite the fact these tools use LabVIEW for code generation, it is not easy to understand the principles of code generation and effectively develop simulation generators. This paper presents toolbox for improved LabVIEW code generation. The developed toolbox is based on standard LabVIEW code generation functions maximally simplifying the application and minimizing the necessary amount of tools for code generation. This paper consists of theoretical part about LabVIEW code generation methods, practical part about principles of LabVIEW code generation using scripting and a graphical presentation of improved LabVIEW code generation advantages. The presented graphical results show that the improved LabVIEW code generation is simpler (thus better) and more understandable for practical realization and the code generator is clearer and more comprehensible than the original one.
The relationship between physical activity, sedentary behaviour and psychological wellbeing among adolescents

Previous studies examining the relationship between physical activity levels and broad-based measures of psychological wellbeing in adolescents have been limited by not controlling for potentially confounding variables. The present study examined the relationship between adolescents’ self-reported physical activity level, sedentary behaviour and psychological wellbeing; while controlling for a broad range of sociodemographic, health and developmental factors. The study entailed a cross-sectional school-based survey in ten British towns. Two thousand six hundred and twenty three adolescents (aged 13–16 years) reported physical activity levels, patterns of sedentary behaviour (TV/computer/video usage) and completed the strengths and difficulties questionnaire (SDQ). Lower levels of self-reported physical activity and higher levels of sedentary behaviour showed graded associations with higher SDQ total difficulties scores, both for boys (P < 0.001) and girls (P < 0.02) after adjustment for age and town. Additional adjustment for social class, number of parents, predicted school examination results, body mass index, ethnicity, alcohol intake and smoking status had little effect on these findings. Low levels of self-reported physical activity are independently associated with diminished psychological wellbeing among adolescents. Longitudinal studies may provide further insights into the relationship between wellbeing and activity levels in this population. Ultimately, randomised controlled trials are needed to evaluate the effects of increasing physical activity on psychological wellbeing among adolescents.
A general map matching algorithm for transport telematics applications

This paper describes a map-matching algorithm designed to support the navigational functions of a real-time vehicle performance and emissions monitoring system currently under development, and other transport telematics applications. The algorithm is used together with the outputs of an extended Kalman filter formulation for the integration of GPS and dead reckoning data, and a spatial digital database of the road network, to provide continuous, accurate and reliable vehicle location on a given road segment. This is irrespective of the constraints of the operational environment, thus alleviating outage and accuracy problems associated with the use of stand-alone location sensors. The map-matching algorithm has been tested using real field data and has been found to be superior to existing algorithms, particularly in how it performs at road intersections.
Predicting actions to act predictably: Cooperative partial motion planning with maximum entropy models

This paper reports on a data-driven motion planning approach for interaction-aware, socially-compliant robot navigation among human agents. Autonomous mobile robots navigating in workspaces shared with human agents require motion planning techniques providing seamless integration and smooth navigation in such. Smooth integration in mixed scenarios calls for two abilities of the robot: predicting actions of others and acting predictably for them. The former requirement requests trainable models of agent behaviors in order to accurately forecast their actions in the future, taking into account their reaction on the robot's decisions. A human-like navigation style of the robot facilitates other agents-most likely not aware of the underlying planning technique applied-to predict the robot motion vice versa, resulting in smoother joint navigation. The approach presented in this paper is based on a feature-based maximum entropy model and is able to guide a robot in an unstructured, real-world environment. The model is trained to predict joint behavior of heterogeneous groups of agents from onboard data of a mobile platform. We evaluate the benefit of interaction-aware motion planning in a realistic public setting with a total distance traveled of over 4 km. Interestingly the motion models learned from human-human interaction did not hold for robot-human interaction, due to the high attention and interest of pedestrians in testing basic braking functionality of the robot.
A replica technique for wordline and sense control in low-power SRAM's

With the migration toward low supply voltages in low-power SRAM designs, threshold and supply voltage fluctuations will begin to have larger impacts on the speed and power specifications of SRAM’s. We present techniques based on replica circuits which minimize the effect of operating conditions’ variability on the speed and power. Replica memory cells and bitlines are used to create a reference signal whose delay tracks that of the bitlines. This signal is used to generate the sense clock with minimal slack time and control wordline pulsewidths to limit bitline swings. We implemented the circuits for two variants of the technique, one using bitline capacitance ratioing in a 1.2m 8-kbyte SRAM, and the other using cell current ratioing in a 0.35m 2-kbyte SRAM. Both the RAM’s were measured to operate over a wide range of supply voltages, with the latter dissipating 3.6 mW at 150 MHz at 1 V and 5.2 W at 980 kHz at 0.4 V.
Vacuum decay container closure integrity leak test method development and validation for a lyophilized product-package system.

A leak test performed according to ASTM F2338-09 Standard Test Method for Nondestructive Detection of Leaks in Packages by Vacuum Decay Method was developed and validated for container-closure integrity verification of a lyophilized product in a parenteral vial package system. This nondestructive leak test method is intended for use in manufacturing as an in-process package integrity check, and for testing product stored on stability in lieu of sterility tests. Method development and optimization challenge studies incorporated artificially defective packages representing a range of glass vial wall and sealing surface defects, as well as various elastomeric stopper defects. Method validation required 3 days of random-order replicate testing of a test sample population of negative-control, no-defect packages and positive-control, with-defect packages. Positive-control packages were prepared using vials each with a single hole laser-drilled through the glass vial wall. Hole creation and hole size certification was performed by Lenox Laser. Validation study results successfully demonstrated the vacuum decay leak test method's ability to accurately and reliably detect those packages with laser-drilled holes greater than or equal to approximately 5 μm in nominal diameter. All development and validation studies were performed at Whitehouse Analytical Laboratories in Whitehouse, NJ, under the direction of consultant Dana Guazzo of RxPax, LLC, using a VeriPac 455 Micro Leak Test System by Packaging Technologies & Inspection (Tuckahoe, NY). Bristol Myers Squibb (New Brunswick, NJ) fully subsidized all work.



A leak test performed according to ASTM F2338-09 Standard Test Method for Nondestructive Detection of Leaks in Packages by Vacuum Decay Method was developed and validated to detect defects in stoppered vial packages containing lyophilized product for injection. This nondestructive leak test method is intended for use in manufacturing as an in-process package integrity check, and for testing product stored on stability in lieu of sterility tests. Test method validation study results proved the method capable of detecting holes laser-drilled through the glass vial wall greater than or equal to 5 μm in nominal diameter. Total test time is less than 1 min per package. All method development and validation studies were performed at Whitehouse Analytical Laboratories in Whitehouse, NJ, under the direction of consultant Dana Guazzo of RxPax, LLC, using a VeriPac 455 Micro Leak Test System by Packaging Technologies & Inspection (Tuckahoe, NY). Bristol Myers Squibb (New Brunswick, NJ) fully subsidized all work.