Miracle-dz/UniFAR-FTUs huggingface dataset:
The Actin-Related Protein BAF53 Is Essential for Chromosomal Subdomain Integrity

A chromosome territory is composed of chromosomal subdomains. The internal structure of chromosomal subdomains provides a structural framework for many genomic activities such as replication and DNA repair, and thus is key to determining the basis of their mechanisms. However, the internal structure and regulating proteins of a chromosomal subdomain remains elusive. Previously, we showed that the chromosome territory expanded after BAF53 knockdown. Because the integrity of chromosomal subdomains is a deciding factor of the volume of a chromosome territory, we examined here the effect of BAF53 knockdown on chromosomal subdomains. We found that BAF53 knockdown led to the disintegration of histone H2B-GFP-visualized chromosomal subdomains and BrdU-labeled replication foci. In addition, the size of DNA loops measured by the maximum fluorescent halo technique increased and became irregular after BAF53 knockdown, indicating DNA loops were released from the residual nuclear structure. These data can be accounted for by the model that BAF53 is prerequisite for maintaining the structural integrity of chromosomal subdomains.
Low quiescent current variable output digital controlled voltage regulator

In this paper, a digital controlled push-pull linear voltage regulator is proposed. The designed regulator can provide a variable output voltage ranging from 0.5V to 1V in steps of 0.1V. It can supply a maximum of 100mA for each output level. A time interleaving control technique is also presented to enhance the output performance. By using UMC 65nm standard CMOS technology, the ripple of the output voltage when the load or output level changes is smaller than 10% of each specific output level. The current efficiency is 99.9% with only 126µA quiescent current.
Toward the Reverse Engineering of UML Sequence Diagrams for Distributed Java Software

This paper proposes a methodology and instrumentation infrastructure toward the reverse engineering of UML (Unified Modeling Language) sequence diagrams from dynamic analysis. One motivation is, of course, to help people understand the behavior of systems with no (complete) documentation. However, such reverse-engineered dynamic models can also be used for quality assurance purposes. They can, for example, be compared with design sequence diagrams and the conformance of the implementation to the design can thus be verified. Furthermore, discrepancies can also suggest failures in meeting the specifications. Due to size constraints, this paper focuses on the distribution aspects of the methodology we propose. We formally define our approach using metamodels and consistency rules. The instrumentation is based on aspect-oriented programming in order to alleviate the effort overhead usually associated with source code instrumentation. A case study is discussed to demonstrate the applicability of the approach on a concrete example
Cooperative Collision Avoidance between Multiple Robots Based on Bézier Curves

In this paper a new cooperative collision-avoidance method for multiple nonholonomic robots with constraints and known start and goal velocities based on Bernstein-Bezier curves is presented. In the simulation example the velocities of the mobile robots are constrained and the start and the goal velocity are defined for each robot. This means that the proposed method can be used as subroutine in a huge path-planning problem in the real time, in a way to split the whole path in smaller partial paths. The reference path of each robot from the start pose to the goal pose, is obtained by minimizing the penalty function, which takes into account the sum of all the paths subjected to the distances between the robots, which should be bigger than the minimal distance defined as the safety distance, and subjected to the velocities which should be lower than the maximal allowed velocities of each robot. When the reference paths are defined one of the trajectory tracking control algorithms should be used to define the control. The simulation results of the path planning algorithm and some future work ideas are discussed.
An STS-Based Map of the Human Genome

A physical map has been constructed of the human genome containing 15,086 sequence-tagged sites (STSs), with an average spacing of 199 kilobases. The project involved assembly of a radiation hybrid map of the human genome containing 6193 loci and incorporated a genetic linkage map of the human genome containing 5264 loci. This information was combined with the results of STS-content screening of 10,850 loci against a yeast artificial chromosome library to produce an integrated map, anchored by the radiation hybrid and genetic maps. The map provides radiation hybrid coverage of 99 percent and physical coverage of 94 percent of the human genome. The map also represents an early step in an international project to generate a transcript map of the human genome, with more than 3235 expressed sequences localized. The STSs in the map provide a scaffold for initiating large-scale sequencing of the human genome.
Discovery of a Second L Subdwarf in the Two Micron All Sky Survey

I report the discovery of the second L subdwarf identified in the Two Micron All Sky Survey, 2MASS J16262034+3925190. This high proper motion object (μ = 1.″27 ± 0.″03 yr-1) exhibits near-infrared spectral features indicative of a subsolar metallicity L dwarf, including strong metal hydride and H2O absorption bands, pressure-broadened alkali lines, and blue near-infrared colors caused by enhanced collision-induced H2 absorption. This object is of later type than any of the known M subdwarfs but does not appear to be as cool as the apparently late-type sdL 2MASS 0532+8246. The radial velocity (Vrad = -260 ± 35 km s-1) and estimated tangential velocity (Vtan ≈ 90-210 km s-1) of 2MASS 1626+3925 indicate membership in the Galactic halo, and this source is likely near or below the hydrogen-burning minimum mass for a metal-poor star. L subdwarfs such as 2MASS 1626+3925 are useful probes of gas and condensate chemistry in low-temperature stellar and brown dwarf atmospheres, but more examples are needed to study these objects as a population as well as to define a rigorous classification scheme.
The affect of fuzzification on neural networks intrusion detection system

Intrusion detection (ID) is an interesting approach that could be used to improve the security of network systems. IDS detects suspected patterns of network traffic on the remaining open parts through monitoring user activities (runtime gathering of data from system operations), and the subsequent analysis of these activities. The purpose of this work is to contribute ideas of finding a solution to detect attacks (Intrusion) through building artificial detection system using feedforward neural networks to detect attacks with low false negative rate (which is the most important point), and low false positive rate. To do so, two feedforward neural networks architectures (one for non fuzzified data, the other for fuzzified data) are suggested, and their behaviors in detecting the attacks are studied. In this research, the suggested IDS not only has the ability to distinguish if the access is normal or attack, but also capable of distinguishing the attack type.
Coalition Agents Experiment : Multiagent Cooperation in International Coalitions

increases confusion in the opponent’s mind. This process is command-led, with human decision making primary and technology playing a secondary role. Shared understanding and information superiority are key enablers in this process and are fundamental to network-centric warfare (www.dodccrp.org). In addressing interoperability requirements, we must also address data security, control over semitrusted software from other coalition partners, and the resulting system’s robustness—for example, its ability to withstand denial-of-service attacks. However, good decisions depend on good data, which can become a problem as mission complexity increases. Military coalitions—large-scale, multifaceted, multinational, virtual organizations—often must be rapidly created and changed as circumstances shift. In addition to integrating single-service and joint capabilities into a coherent force, coalition operations must rapidly configure foreign or legacy systems into a cohesive whole. Yet current coalition operations often suffer from data overload, information starvation, laborintensive information collection and coordination, and stand-alone stovepipe command systems that use incompatible data formats. This leads to a horrendous technical integration task and offers commanders only scattered snapshots of the battlespace. In the inevitable absence of preexisting coordinated systems, we must take a rapid, flexible, on-the-fly approach that permits assembling capabilities at runtime. We believe agent-based computing (described in the sidebar, “Software Agent Technology”) offers a promising new approach to effective coalition operations because it embraces the coalition environment’s open, heterogeneous, diverse, dispersed nature. This article describes how software agents acting on behalf of human users enable military commanders to act decisively in cyberspace and thus contribute to “cyberspace superiority,” a critical component of warfare in the information age.1 We focus here on the rapid integration of agents and legacy systems to improve interoperability and support human situational awareness and decision making, rather than, for example, sophisticated teamwork and planning between agents.2
Genus: Making Generics Object-Oriented, Expressive, and Lightweight

The support for generic programming in modern object-oriented programming languages is awkward and lacks desirable expressive power. We introduce an expressive genericity mechanism that adds expressive power and strengthens static checking, while remaining lightweight and simple in common use cases. Like type classes and concepts, the mechanism allows existing types to model type constraints retroactively. For expressive power, we expose models as named constructs that can be defined and selected explicitly to witness constraints; in common uses of genericity, however, types implicitly witness constraints without additional programmer effort. Models are integrated into the object-oriented style, with features like model generics, model-dependent types, model enrichment, model multimethods, constraint entailment, model inheritance, and existential quantification further extending expressive power in an object-oriented setting. We introduce the new genericity features and show that common generic programming idioms, including current generic libraries, can be expressed more precisely and concisely. The static semantics of the mechanism and a proof of a key decidability property are provided.
A New Efficient Algorithm for Detecting Communities in Complex Networks

In this paper, we propose a new efficient algorithm which makes a good thing out of the betweenness centrality and the local information to detect community structures in complex networks. When being tested on some typical real world networks, our algorithm demonstrates excellent community partition results and very fast processing performance, much faster than the existing classical community detecting algorithms.
Modeling and analysis of nodal behavior in delay tolerant wireless sensor networks

In wireless sensor networks (WSNs), nodes are often scheduled to alternate between a working mode and a sleeping mode from the energy efficiency point of view. When delay is tolerable, it is not necessary to preserve network connectivity during activity (working or sleeping) scheduling, in order to enable more sensors to be switched to sleeping mode and thus more energy savings. In this paper we model and analyze nodal behavior in such delay-tolerant WSNs (DT-WSNs). We derive the maximum hop count that a routing path is allowed to have in order not to violate a given sensor-to-sink delay constraint, along with extensive simulation results. This research should provide a guidance for designing future DT-WSN routing protocols.
Multi-objective Differential Evolution with Helper Functions for Constrained Optimization

Solving constrained optimization problems by multi-objective evolutionary algorithms has scored tremendous achievements in the last decade. Standard multi-objective schemes usually aim at minimizing the objective function and also the degree of constraint violation simultaneously. This paper proposes a new multi-objective method for solving constrained optimization problems. The new method keeps two standard objectives: the original objective function and the sum of degrees of constraint violation. But besides them, four more objectives are added. One is based on the feasible rule. The other three come from the penalty functions. This paper conducts an initial experimental study on thirteen benchmark functions. A simplified version of CMODE is applied to solving multi-objective optimization problems. Our initial experimental results confirm our expectation that adding more helper functions could be useful. The performance of SMODE with more helper functions (four or six) is better than that with only two helper functions.
Worm plot: a simple diagnostic device for modelling growth reference curves

The worm plot visualizes differences between two distributions, conditional on the values of a covariate. Though the worm plot is a general diagnostic tool for the analysis of residuals, this paper focuses on an application in constructing growth reference curves, where the covariate of interest is age. The LMS model of Cole and Green is used to construct reference curves in the Fourth Dutch Growth Study 1997. If the model fits, the measurements in the reference sample follow a standard normal distribution on all ages after a suitably chosen Box–Cox transformation. The coefficients of this transformation are modelled as smooth age‐dependent parameter curves for the median, variation and skewness, respectively. The major modelling task is to choose the appropriate amount of smoothness of each parameter curve. The worm plot assesses the age‐conditional normality of the transformed data under a variety of LMS models. The fit of each parameter curve is closely related to particular features in the worm plot, namely its offset, slope and curvature. Application of the worm plot to the Dutch growth data resulted in satisfactory reference curves for a variety of anthropometric measures. It was found that the LMS method generally models the age‐conditional mean and skewness better than the age‐related deviation and kurtosis. Copyright © 2001 John Wiley & Sons, Ltd.
The NTNU Concatenative Speech Synthesizer

This paper describes NTNU’s entry for the Blizzard Challenge 2010. Our system is a conceptually simple variation of an HMM-based unit selection system, which uses diphones as the basic unit and employs a combined selection of units and their join points. The evaluation results of the Blizzard Challenge 2010 show that the system performs well when compared with the other systems.
The Role of End-to-End Congestion Control in Networks with Fairness-Enforcing Routers

Traditionally, capacity on the Internet has been allocated between competing flows through a distributed fairness calculation implemented by end-to-end congestion control protocols like TCP. Increasingly, however, network operators are deploying fair queuing and other forms of router-based enforcement mechanisms to prevent greedy or misbehaving end points from consuming more than their fair share of the network’s capacity. In environments where fairness is enforced by the network itself, it seems worthwhile to reconsider the role of the congestion control protocol. In particular, we ask if it might be both safe and sensible in the long term for self-interested senders to send at rates that exceed the capacity of the network. Through simulation, we identify and quantify the source of inefficiency in this regime, which we term zombie packets. Surprisingly, we show that such aggressive mechanisms are not only tenable in a wide variety of network structures, but, combined with effective use of erasure coding, they can avoid creating zombie packets and achieve throughputs that approach optimal.
2 General issues in assessing model adequacy

Environmental models are simplified representations of complex physical systems. The implementation of any such model, as a computer simulator, involves further simplifications and approximations. The value of the resulting simulator, in giving scientific and practical insights into the functioning of the corresponding physical system, depends both on the nature and degree of these simplifications and also on the objectives for which the model is to be used. This chapter provides an introduction to some basic general techniques for assessing the adequacy of a computer model for its intended purpose. There are many ways to approach this question. We will take the view that the aim of the model is to provide some, necessarily partial, information about the behaviour of the system, and we will consider the model adequate for an intended task if the information that is provided by the simulator is sufficient to allow us to carry out this task. We would usually prefer precise forecasts of system behaviour but we may often be able to tolerate probabilistic forecasts, provided that we are able to quantify the level of uncertainty with which these forecasts should be interpreted, and to confirm that this uncertainty is not so large as to prevent us from achieving our objectives. This will, inevitably, be a pragmatic judgement. Therefore, in our account, we will outline some basic methods for assessing the degree of uncertainty that it would be reasonable to associate with model outcomes. It is beyond the scope of this account to produce precise quantifications of predictive uncertainty, as such analysis requires rather more technical machinery than we have space to describe. Instead, we offer some basic tools for making order of magnitude quantifications for such uncertainties, which should indicate whether the limitations of the model are likely to render it unfit for the task at hand. This is by no means a complete account, even for our stated goal, as such analysis is strongly dependent both on the scientific context and also on the size and complexity of the model. In the next section, we outline the general methods that we suggest and, in the following sections, we illustrate how the methods can be used in practice, by applying them to a rainfall runoff model.
Knowledge and Information Systems REGULAR

In this work we introduce the new problem of finding time series discords. Time series discords are subsequences of longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. While discords have many uses for data mining, they are particularly attractive as anomaly detectors because they only require one intuitive parameter (the length of the subsequence) unlike most anomaly detection algorithms that typically require many parameters. While the brute force algorithm to discover time series discords is quadratic in the length of the time series, we show a simple algorithm that is three to four orders of magnitude faster than brute force, while guaranteed to produce identical results. We evaluate our work with a comprehensive set of experiments on diverse data sources including electrocardiograms, space telemetry, respiration physiology, anthropological and video datasets.
Causal Discovery for Manufacturing Domains

Yield and quality improvement is of paramount importance to any manufacturing company. One of the ways of improving yield is through discovery of the root causal factors affecting yield. We propose the use of data-driven interpretable causal models to identify key factors affecting yield. We focus on factors that are measured in different stages of production and testing in the manufacturing cycle of a product. We apply causal structure learning techniques on real data collected from this line. Specifically, the goal of this work is to learn interpretable causal models from observational data produced by manufacturing lines. Emphasis has been given to the interpretability of the models to make them actionable in the field of manufacturing. We highlight the challenges presented by assembly line data and propose ways to alleviate them.We also identify unique characteristics of data originating from assembly lines and how to leverage them in order to improve causal discovery. Standard evaluation techniques for causal structure learning shows that the learned causal models seem to closely represent the underlying latent causal relationship between different factors in the production process. These results were also validated by manufacturing domain experts who found them promising. This work demonstrates how data mining and knowledge discovery can be used for root cause analysis in the domain of manufacturing and connected industry.
The Impact of Public Library Use on Reading, Television, and Academic Outcomes

Do individuals engage in beneficial activities, like recreational reading, if the necessary materials are easily accessible and relatively inexpensive? I investigate this issue by estimating how much reading time increases as a result of public library use. To address the endogeneity of library use I use an IV approach where the instrument is a household's distance to their closest public library. Using data from the Current Population Survey, American Time Use Survey, and National Household Education Survey, I find that library use increases the amount of time an individual spends reading by approximately 27 min on an average day. Moreover, it increases the amount of time parents spend reading to/with young children by 14 min. This increase in reading is more than offset by a 59 min decrease in time spent watching television, and there is no significant change in time spent on other activities. For children in school, library use positively impacts homework completion rates. A simple cost-benefit exercise highlights the potential application of these results for local governments who fund these libraries.
Aspects of an XML-Based Phraseology Database Application

In this paper we discuss technical aspects of a phraseology database application that is being developed in the scope of the Ephras project. Thereby we give a special attention to the data modeling perspective of such an application. We argue that the phraseology data is simply a particular kind of semi-structured data. Therefore, this data should be represented and managed by technologies that are speciﬁcally targeted at management of semi-structured data. Currently, the most prominent of such technologies is eXtensible Markup Language technology. Thus, in the remainder of the paper we discuss diﬀerent implications of this technology on the application architecture and its implementation.
The Future Is User-Led: The Path towards Widespread Produsage

In the emerging social software, ‘Web2.0’ environment, the production of ideas takes place in a collaborative, participatory mode which breaks down the boundaries between producers and consumers and instead enables all participants to be users as much as producers of information and knowledge, or what can be described as produsers. These produsers engage not in a traditional form of content production, but are instead involved in produsage – the collaborative and continuous building and extending of existing content in pursuit of further improvement. This paper examines the overall characteristics of produsers and produsage, and identifies key questions for the produsage model.