nils-herrmann/clean_scirepeval huggingface dataset:
Beep: 3D indoor positioning using audible sound

Rapid growth in the number of wireless enabled devices has led to an increased interest in location-aware applications. The backbone of such applications is provided by a location system. In this paper we present Beep, an indoor location system that senses audible sound. The use of audible sound makes our system cheap and easily deplorable to most existing roaming devices. Unlike positioning systems using ultrasound and infrared signals, Beep does not require the user to carry any kind of specialized hardware. Our system is based on standard 3D multilateration algorithms. However, the requirement of being able to locate existing devices, whose sound cards were not designed for high-precision signaling, introduces additional challenges to the location problem. This paper describes how those problems were solved and presents experimental results. Beep works with an accuracy of about 2 feet in more than 97% cases. The paper also describes a sensor deployment strategy that requires low sensor density and consequently low installation costs.
The absence of favorable aromatic interactions between beta-sheet peptides.

This paper asks whether interactions between phenylalanine (Phe) residues of the non-hydrogen-bonded cross-strand pairs of antiparallel beta-sheets are important and finds that they are not. Peptides 1a-d [o-BuO-C6H4CO-AA1-Orn(i-PrCO-Hao)-Phe-Ile-AA5-NHMe: 1a AA1, AA5 = Phe; 1b AA1, AA5 = Cha (cyclohexylalanine); 1c AA1 = Phe, AA5 = Cha; 1d AA1 = Cha, AA5 = Phe] provide a sensitive system for probing interactions between phenylalanine residues. These peptides form beta-sheet homodimers in organic solvents. When the homodimers of different peptides are mixed, they equilibrate to form heterodimers, as well as homodimers. The position of the equilibrium reflects the propensity of the first (AA1) and fifth (AA5) amino acids to interact within the non-hydrogen-bonded cross-strand pairs of beta-sheets. Mixing peptides 1a-d in all six possible binary combinations provides a measure of the relative propensities of Phe and Cha to pair. Analysis by 1H NMR spectroscopy of the equilibrium constants in CDCl3 solution reveals no significant preference for the formation of Phe-Phe pairs. The equilibria in all six experiments are essentially statistical (K approximately 4), and no (<0.1 kcal/mol) preference is seen for any pairing combination. A survey of Phe-Phe pairs in the Interchain beta-Sheet Database (http://www.igb.uci.edu/servers/icbs/) corroborates that little significant contact occurs between the aromatic rings in the non-hydrogen-bonded cross-strand pairs of antiparallel beta-sheets at the interface between polypeptide chains. Even though contacts between aromatic rings are favorable when they are of suitable geometry, the energetic price of achieving suitable geometries appears to offset the energetic benefits of such contacts in the current model system, as well as in proteins.
Polymodal Sensory Function of the Caenorhabditis elegans OCR-2 Channel Arises from Distinct Intrinsic Determinants within the Protein and Is Selectively Conserved in Mammalian TRPV Proteins

Caenorhabditis elegans OCR-2 (OSM-9 and capsaicin receptor-related) is a TRPV (vanilloid subfamily of transient receptor potential channel) protein that regulates serotonin (5-HT) biosynthesis in chemosensory neurons and also mediates olfactory and osmotic sensation. Here, we identify the molecular basis for the polymodal function of OCR-2 in its native cellular environment. We show that OCR-2 function in 5-HT production and osmotic sensing is governed by its N-terminal region upstream of the ankyrin repeats domain, but the diacetyl sensitivity is mediated by independent mechanisms. The ocr-2(yz5) mutation results in a glycine-to-glutamate substitution (G36E) within the N-terminal region. The G36E substitution causes dramatic downregulation of 5-HT synthesis in the ADF neurons, eliminates osmosensation mediated by the ASH neurons, but does not affect the response to the odorant diacetyl mediated by the AWA neurons. Conversely, wild-type sequence of the N-terminal segment confers osmotic sensitivity and upregulation of 5-HT production to a normally insensitive C. elegans homolog, OCR-4, but this chimeric channel does not respond to diacetyl stimuli. Furthermore, expression of either the mouse or human TRPV2 gene under the ocr-2 promoter can substantially restore 5-HT biosynthesis in ocr-2-null mutants but cannot improve the deficits in osmotic or olfactory sensation, suggesting that TRPV2 can substitute for the role of OCR-2 only in serotonergic neurons. Thus, different sensory functions of OCR-2 arise from separable intrinsic determinants, and specific functional properties of TRPV channel proteins may be selectively conserved across phyla.
Global gene expression profiling in Escherichia coli K12: effects of oxygen availability and ArcA.

The ArcAB two-component system of Escherichia coli regulates the aerobic/anaerobic expression of genes that encode respiratory proteins whose synthesis is coordinated during aerobic/anaerobic cell growth. A genomic study of E. coli was undertaken to identify other potential targets of oxygen and ArcA regulation. A group of 175 genes generated from this study and our previous study on oxygen regulation (Salmon, K., Hung, S. P., Mekjian, K., Baldi, P., Hatfield, G. W., and Gunsalus, R. P. (2003) -29855), called our gold standard gene set, have p values <0.00013 and a posterior probability of differential expression value of 0.99. These 175 genes clustered into eight expression patterns and represent genes involved in a large number of cell processes, including small molecule biosynthesis, macromolecular synthesis, and aerobic/anaerobic respiration and fermentation. In addition, 119 of these 175 genes were also identified in our previous study of the fnr allele. A MEME/weight matrix method was used to identify a new putative ArcA-binding site for all genes of the E. coli genome. 16 new sites were identified upstream of genes in our gold standard set. The strict statistical analyses that we have performed on our data allow us to predict that 1139 genes in the E. coli genome are regulated either directly or indirectly by the ArcA protein with a 99% confidence level.
U-MAC: a proactive and adaptive UWB medium access control protocol: Research Articles

Ultra wide band (UWB) technology has received increasing recognition in recent years for its potential applications beyond radar technology to communication networks. UWB is a spread spectrum technology that requires careful coordination among communicating nodes to jointly control link power and transmission rates. Here, we present ultra wide band MAC (U-MAC), an adaptive medium access control (MAC) protocol for UWB in which nodes periodically declare their current state, so that neighbors can proactively assign power and rate values for new links locally in order to optimize global network performance. Simulations comparing U-MAC to the reactive approach confirm that U-MAC lowers link setup latency and control overhead, doubles the throughput and adapts better to high network loads. Simulations also reveal that the basic form of U-MAC favors nodes that are closer to the receiver. As a result, we also introduce novel mechanisms that control the radius around a receiver within which nodes can have fair access to it. We show through simulations the effect of the mechanisms on the tradeoff between network throughput and fair access.
Modeling and optimization of ad hoc and sensor networks

Wireless ad hoc networks and sensor networks are emerging as new network classes that differ considerably from conventional wired or wireless networks. The distinguishing characteristic of ad hoc and sensor networks is the lack of deployed infrastructure, which poses the design challenge of distributing network management among the nodes. Nodes in ad hoc and sensor networks also have limited resources, so the distributed algorithms and protocols that manage network behavior must also enable fine-grained optimization at the resource-limited nodes. In addition, ad hoc and sensor networks have a wide range of potential applications, each of which requires custom performance guarantees, suggesting the need for a flexible methodology to optimize network behavior. This dissertation presents a flexible cross-layer framework for optimizing the performance of ad hoc and sensor networks that enables each node to greedily and locally optimize its behavior in order to achieve a global performance goal. The framework provides a customizable definition of node states and it enables the network designer to select the network mechanisms at any layer of the communication stack that require optimization. The framework's applicability and optimization potential are validated through three diverse case studies: (1) minimization of energy consumption in a sensor network test-bed using radio frequency signals; (2) maximization of throughput and fairness, and reduction of control overhead and link setup latency in an ultra wide band ad hoc network; and (3) maximization of battery lifetime and minimization of power consumption in an acoustic underwater sensor network. Each case study adopts a customized version of the framework by tailoring state definitions and by defining mechanisms that require optimization in accordance with the case study's performance requirements. In all cases, the framework yields significant improvement in network performance.
ICBS: a database of interactions between protein chains mediated by ?-sheet formation

Interchain beta-sheet (ICBS) interactions occur widely in protein quaternary structures, interactions between proteins and protein aggregation. These interactions play a central role in many biological processes and in diseases ranging from AIDS and cancer to anthrax and Alzheimer's.



We have created a comprehensive database of ICBS interactions that is updated on a weekly basis and allows entries to be sorted and searched by relevance and other criteria through a simple Web interface. We derive a simple ICBS index to quantify the relative contributions of the beta-ladders in the overall interchain interaction and compute first- and second-order statistics regarding amino acid composition and pairing at different relative positions in the beta-strands. Analysis of the database reveals a 15.8% prevalence of significant ICBS interactions, the majority of which involve the formation of antiparallel beta-sheets and many of which involve the formation of dimers and oligomers. The frequencies of amino acids in ICBS interfaces are similar to those in intrachain beta-sheet interfaces. A full range of non-covalent interactions between side chains complement the hydrogen-bonding interactions between the main chains. Polar amino acids pair preferentially with polar amino acids and non-polar amino acids pair preferentially with non-polar amino acids among antiparallel (i, j) pairs. We anticipate that the statistics and insights gained from the database will guide the development of agents that control interchain beta-sheet interactions and that the database will help identify new protein interactions and targets for these agents.


AVAILABILITY
The database is available at: http://www.igb.uci.edu/servers/icbs/
Large-Scale Prediction of Disulphide Bond Connectivity

The formation of disulphide bridges among cysteines is an important feature of protein structures. Here we develop new methods for the prediction of disulphide bond connectivity. We first build a large curated data set of proteins containing disulphide bridges and then use 2-Dimensional Recursive Neural Networks to predict bonding probabilities between cysteine pairs. These probabilities in turn lead to a weighted graph matching problem that can be addressed efficiently. We show how the method consistently achieves better results than previous approaches on the same validation data. In addition, the method can easily cope with chains with arbitrary numbers of bonded cysteines. Therefore, it overcomes one of the major limitations of previous approaches restricting predictions to chains containing no more than 10 oxidized cysteines. The method can be applied both to situations where the bonded state of each cysteine is known or unknown, in which case bonded state can be predicted with 85% precision and 90% recall. The method also yields an estimate for the total number of disulphide bridges in each chain.
A Framework for Modeling Sensor Networks

Recent technological advances are likely to increase sensor network development and deployment efforts. The expected growth in sensor network deployment requires efficient sensor network modeling techniques to facilitate initial programming of the sensor nodes and their eventual reprogramming once the network is deployed. The myriad of sensor network application scenarios, physical deployment media, communication technologies, and performance requirements complicates the efficient modeling of sensor networks. This paper proposes a framework for sensor network modeling based on general features identified through a careful analysis of existing sensor networks. This framework facilitates the modeling of new sensor networks by characterizing them according to these general features and providing a set of performance goals. The specification of each network’s performance requirements within this framework enables the appropriate selection of communication protocols. The discussion in this paper focuses on sensor network routing protocols, but the approach applies to any kind of communication protocols.
Protocol Features I E E E C O M M U N I C a T I O N S a Survey, Classification and Comparative Analysis of Medium Access Control Protocols for Ad Hoc Networks

Recent technological advances in wireless communications offer new opportunities and challenges for wireless ad hoc networking. In the absence of the fixed infrastructure that characterizes traditional wireless networks, control and management of wireless ad hoc networks must be distributed across the nodes, thus requiring carefully designed Medium Access Control (MAC) layer protocols. In this article we survey, classify, and analyze 34 MAC layer protocols for wireless ad hoc networks, ranging from industry standards to research proposals. Through this analysis, six key features emerge: (1) channel separation and access; (2) topology; (3) power; (4) transmission initiation; (5) traffic load and scalability; and (6) range. These features allow us to characterize and classify the protocols, to analyze the tradeoffs produced by different design decisions, and to assess the suitability of various design combinations for ad hoc network applications. The classification and the tradeoff analysis yield design guidelines for future wireless ad hoc network MAC layer protocols. A SURVEY, CLASSIFICATION AND COMPARATIVE ANALYSIS OF MEDIUM ACCESS CONTROL PROTOCOLS FOR AD HOC NETWORKS FIRST QUARTER 2004, VOLUME 6, NO. 1 www.comsoc.org/pubs/surveys
A survey, classification and comparative analysis of medium access control protocols for ad hoc networks

Recent technological advances in wireless communications offer new opportunities and challenges for wireless ad hoc networking. In the absence of the fixed infrastructure that characterizes traditional wireless networks, control and management of wireless ad hoc networks must be distributed across the nodes, thus requiring carefully designed medium access control (MAC) layer protocols. In this article we survey, classify, and analyze 34 MAC layer protocols for wireless ad hoc networks, ranging from industry standards to research proposals. Through this analysis, six key features emerge: (1) channel separation and access; (2) topology; (3) power; (4) transmission initiation; (5) traffic load and scalability; and (6) range. These features allow us to characterize and classify the protocols, to analyze the tradeoffs produced by different design decisions, and to assess the suitability of various design combinations for ad hoc network applications. The classification and the tradeoff analysis yield design guidelines for future wireless ad hoc network MAC layer protocols.
On the Use of Bayesian Methods for Evaluating Compartmental Neural Models

Computational modeling is being used increasingly in neuroscience. In deriving such models, inference issues such as model selection, model complexity, and model comparison must be addressed constantly. In this article we present briefly the Bayesian approach to inference. Under a simple set of commonsense axioms, there exists essentially a unique way of reasoning under uncertainty by assigning a degree of confidence to any hypothesis or model, given the available data and prior information. Such degrees of confidence must obey all the rules governing probabilities and can be updated accordingly as more data becomes available. While the Bayesian methodology can be applied to any type of model, as an example we outline its use for an important, and increasingly standard, class of models in computational neuroscience—compartmental models of single neurons. Inference issues are particularly relevant for these models: their parameter spaces are typically very large, neurophysiological and neuroanatomical data are still sparse, and probabilistic aspects are often ignored. As a tutorial, we demonstrate the Bayesian approach on a class of one-compartment models with varying numbers of conductances. We then apply Bayesian methods on a compartmental model of a real neuron to determine the optimal amount of noise to add to the model to give it a level of spike time variability comparable to that found in the real cell.
Battery Lifetime Estimation and Optimization for Underwater Sensor Networks

AbstractAcoustic technology has been established as the exclusive technology that provides robust underwatercommunications for military and civilian applications. One particular civilian application of interest is thedeployment of underwater acoustic sensor networks. The main challenges of deploying such a network are thecost and the limited battery resources of individual sensor nodes. Here, we provide a method that addresses thesechallenges by estimating the battery lifetime and power cost of shallow water networks, in terms of four independentparameters: (1) internode distance; (2) transmission frequency; (3) frequency of data updates; and (4) number ofnodes per cluster. Because transmission loss in water is dependent on both frequency and distance, we extend thegeneral method to exploit topology-dependent distance and frequency assignments. We use the method to estimatethe battery life for tree, chain, and grid topologies for various combinations of internode distance, frequency andcluster size in a shallow water setting. The estimation results reveal that topology-dependent assignments prolongbattery life of the tier-independent method by a factor of 1.05 to 131 for large networks. In the case of a linearnetwork deployed along a coastline with a target battery life of 100 days, topology-dependent assignments couldincrease the network range and aggregated sensor data of the topology-independent method by a factor of 3.5.Index TermsWireless sensor networks, location-dependent and sensitive, general systems theory, distributed systems,clustering, network topology, network communications, wireless communication, distributed networks.
An acoustic identification scheme for location systems

Many pervasive computing applications require location awareness in order to successfully integrate technology into our daily lives. A location system consists of a group of sensors that determine the position of a mobile user and provide the user with useful context-specific information. Locating the user requires signal exchanges between the user's mobile device and the sensors. This paper considers an acoustic-based location system for pervasive computing applications. The system is comprised of a set of microphones connected to a central server. Mobile users produce acoustic signals through standard speakers, which are already available in most mobile devices, to perform location operations through the system. We focus on the design of robust acoustic signals using multi-frequency symbols that serve both in locating and uniquely identifying the user. We conduct experiments at distances between 1 and 17 ft to explore the ability of the server to recognize and decode signals originated by different users in the same general area.
An Acoustic Identification Scheme for Location Systems

Many pervasive computing applications require location awareness in order to successfully integrate technology into our daily lives. A location system consists of a group of sensors that determine the position of a mobile user and provide the user with useful context-specific information. Locating the user requires signal exchanges between the user's mobile device and the sensors. This paper considers an acoustic-based location system for pervasive computing applications. The system is comprised of a set of microphones connected to a central server. Mobile users produce acoustic signals through standard speakers, which are already available in most mobile devices, to perform location operations through the system. We focus on the design of robust acoustic signals using multi-frequency symbols that serve both in locating and uniquely identifying the user. We conduct experiments at distances between 1 and 17 ft to explore the ability of the server to recognize and decode signals originated by different users in the same general area.
Machine Learning Methods for Computational Proteomics and Beyond

Predicting protein structure is a fundamental problem in biology, especially in the genomic era where over one third of newly discovered genes have unknown structure and function. Because sequence and structure data (hence training sets) continue to grow exponentially, this area is ideally suited for machine learning approaches. Neural networks, in particular, have had remarkable success and have led, for instance, to the construction of the best secondary structure predictors. We will provide an overview of our own work and the state-of-the-art for several structure prediction problem including: (1) prediction of protein secondary structures; (2) prediction of relative solvent accessibility; (3) prediction of contacts; (4) prediction of three-dimensional protein structures; (5) prediction of interchain beta-sheet quaternary structures; using machine learning methods. 
 
The methods we have developed are based on the theory of graphical models but use deterministic recursive neural networks to speed up learning. We will discuss their applicability to other problems and the lessons learnt for the design of complex neural architectures.
Extensive gene gain associated with adaptive evolution of poxviruses

Previous studies of genome evolution usually have involved one or two genomes and have thus been limited in their ability to detect the direction and rate of evolutionary change. Here, we use complete genome data from 20 poxvirus genomes to build a robust phylogeny of the Poxviridae and to study patterns of genome evolution. We show that, although there has been little gene order evolution, there are substantial differences between poxviruses in terms of genome content. Furthermore, we show that the rate of gene acquisition is not constant over time and that it has increased in the orthopox lineage (which includes smallpox and vaccinia). We also tested for positive selection on 204 groups of genes and show that a disproportionately high proportion of genes in the orthopox clade are under positive selection. The association of an increased rate of gene gain and positive selection is indicative of adaptive genome evolution. Many of the genes involved in these processes are likely to be associated with host–parasite coevolution.