Miracle-dz/UniFAR-FTUs huggingface dataset:
A procedure for optimal pulse selection strategy in radar imaging systems

Commonly ISAR/SAR images can be considered as sparse. Therefore they can be obtained from a reduced set of measurements (pulses) by using Compressive Sensing reconstruction techniques. In this paper we analyze influence of the pulse selection strategy to the uniqueness of the obtained radar image from a reduced set of pulses. A simple algorithm for optimal pulse selection strategy is proposed. It is shown that the proposed method can significantly increase sparsity limit (maximal number of target points in a radar image) when the reconstruction uniqueness can be guaranteed.
gpALIGNER: A Fast Algorithm for Global Pairwise Alignment of DNA Sequences

Bioinformatics, through the sequencing of the full genomes for many species, is increasingly relying on efficient global alignment tools exhibiting both high sensitivity and specificity. Many computational algorithms have been applied for solving the sequence alignment problem. Dynamic programming, statistical methods, approximation and heuristic algorithms are the most common methods applied to this problem. We introduce gpALIGNER, a fast pairwise DNA-DNA global alignment algorithm. gpALIGNER uses similar score schema with DIALIGN-T to produce the final alignment. It also uses the concept of "spaced seeds" to determine locally aligned subsequences which construct semi-global alignment as the preliminaries of global alignment computation. This enables gpALIGNER to have the precision provided by the DIALIGN-T algorithm in considerably less time and space complexities. We performed benchmarking of our approach based on numerous datasets from standard benchmarking databases and real sequences of NCBI database where gpALIGNER performed three times faster than DIALIGN-T. gpALIGNER is a new alternative for having sensitivity and selectivity of DIALIGN-T but with less computational cost.
Messy tabletops: clearing up the occlusion problem

When introducing interactive tabletops into the home and office, lack of space will often mean that these devices play two roles: interactive display and a place for putting things. Clutter on the table surface may occlude information on the display, preventing the user from noticing it or interacting with it. We present a technique for dealing with clutter on tabletops which finds a suitable unoccluded area of the display in which to show content. We discuss the implementation of this technique and some design issues which arose during implementation.
Negative Regulation of NF-κB Signaling by PIAS1

ABSTRACT The NF-κB family of transcription factors is activated by a wide variety of signals to regulate a spectrum of cellular processes. The proper regulation of NF-κB activity is critical, since abnormal NF-κB signaling is associated with a number of human illnesses, such as chronic inflammatory diseases and cancer. We report here that PIAS1 (protein inhibitor of activated STAT1) is an important negative regulator of NF-κB. Upon cytokine stimulation, the p65 subunit of NF-κB translocates into the nucleus, where it interacts with PIAS1. The binding of PIAS1 to p65 inhibits cytokine-induced NF-κB-dependent gene activation. PIAS1 blocks the DNA binding activity of p65 both in vitro and in vivo. Consistently, chromatin immunoprecipitation assays indicate that the binding of p65 to the promoters of NF-κB-regulated genes is significantly enhanced in Pias1 −/− cells. Microarray analysis indicates that the removal of PIAS1 results in an increased expression of a subset of NF-κB-mediated genes in response to tumor necrosis factor alpha and lipopolysaccharide. Consistently, Pias1 null mice showed elevated proinflammatory cytokines. Our results identify PIAS1 as a novel negative regulator of NF-κB.
Spring for Vivaldi -- Orchestrating Hierarchical Network Coordinates

Network coordinates have become an important building block for efficient peer-to-peer applications. They allow the peers to determine those other peers to whom they are likely to experience a low round trip time. So they can preferably attach to these selected peers and thereby create efficient overlay structures. Despite the substantial interest in this topic, there is still room for improvement. In this paper we propose Hierarchical Vivaldi, a variant of the popular Vivaldi algorithm. It predicts the peers' embedding error so that it can optimize the peer selection process. We conducted extensive studies with several sets of Internet traffic traces. They compare our proposal to Vivaldi and its previously proposed optimizations. Our results show that Hierarchical Vivaldi can improve the accuracy of the RTT predictions by an order of magnitude.
Marketing Spending and Customer Lifetime Value for Firms with Limited Capacity

The concept of customer lifetime value (CLV) is widely used by marketing practitioners and academics in making decisions about customer acquisition and retention spending. The traditional view of CLV, however, assumes that the firm has the unlimited capacity to serve all its acquired and retained customers. In this paper we consider a firm with limited capacity and determine the role that CLV plays in its acquisition and retention spending decisions. We find that the optimal spending for the firm with limited capacity increases in capacity and decreases in the number of customers. This stands in contrast to the firm with unlimited capacity that spends the CLV-maximizing amount regardless of the number of other customers. Surprisingly, however, the firm with limited capacity may spend more than the CLV-maximizing amount, which happens when the time value of money is large. When the time value of money is small, the two spending levels are nearly identical when the firm is well under capacity, but as the firm grows and approaches its capacity limit, optimal acquisition and retention spending decrease to substantially lower, but still non-zero levels. We characterize optimal spending for the firm at capacity in closed form and discuss the role CLV plays in optimizing customer mix. We also examine customer selection decisions. We again find that CLV alone does not determine those decisions. In particular, we show the firm with limited capacity may prefer a customer with a lower CLV. Lastly, we consider capacity expansion. We show that the expansion decision is of the threshold type: intuitively, the firm should expand when it becomes large enough. Interestingly, however, the value of expansion is largest when the firm still has significant unutilized capacity. CLV again offers little guidance in determining this value – per unit value of expanded capacity is much larger than the CLV of the customer who will occupy it. We also observe an interesting behavior of spending during the timelag between the decision to expand and the period when the additional capacity becomes operational: in that timelag the firm drastically reduces spending on the low-value customers, while increasing the spending on the high-value ones in order to rebalance its customer mix in anticipation of additional capacity.
Exploring the Use of Sensorial LTP/LTD-Like Stimulation to Modulate Human Performance for Complex Visual Stimuli

Is it possible to passively induce visual learning/unlearning in humans for complex stimuli such as faces? We addressed this question in a series of behavioral studies using passive visual stimulation (flickering of faces at specific temporal frequencies) inspired by well-known synaptic mechanisms of learning: long-term potentiation (LTP) vs long-term depression (LTD). We administered a face identity change detection task before and after a passive stimulation protocol to test for potential changes in visual performance. First, with bilateral stimulation, subjects undergoing high-frequency LTP-like stimulation outperformed those submitted to low-frequency LTD-like stimulation despite equivalent baseline performance (exp. 1). Second, unilateral stimulation replicated the differential modulation of performance, but in a hemifield-specific way (exp. 2). Third, for both stimulation groups, a sudden temporary drop in performance on the stimulated side immediately after the stimulation, followed by progressive recovering, can suggest either ‘visual fatigue’ or ‘face adaptation’ effects due to the stimulation. Fourth, we tested the life-time of these modulatory effects, revealing they vanish after one hour delay (exp. 3). Fifth, a control study (exp. 4) using low-level visual stimuli also failed to show longer-term effects of sensory stimulation, despite reports of strong effects in the literature. Future studies should determine the necessary and sufficient conditions enabling robust long-term modulation of visual performance using this technique. This step is required to consider further use in fundamental research (e.g., to study neural circuits involved in selective visual processing) and potential educational or clinical applications (e.g., inhibiting socially-irrelevant aspects of face processing in autism).
Innovation as a driver of internationalization in the software industry

Innovation and internationalization are two important factors for growth. This study analyzes whether innovativeness has an effect on the internationalization of software firms, and if so, how strong this effect is. Innovation and internationalization have rarely been studied together, with research tending to focus more on the relationship between innovations and growth. However, internationalization is a key prerequisite for growth for companies operating in small domestic markets. This paper analyzes the innovativeness and internationalization of firms, using data from the Software Industry Survey conducted in Finland. Since the speed of firm growth and internationalization are dependent on the willingness to grow and the age of the firm, these variables are used as moderators in the analysis. Our analysis suggests that innovativeness does contribute to internationalization; however, the effect is significant only for younger firms during their expansion to new markets, after which they prioritize revenue growth.
GATE: an Architecture for Development of Robust HLT Applications

In this paper we present GATE, a framework and graphical development environment which enables users to develop and deploy language engineering components and resources in a robust fashion. The GATE architecture has enabled us not only to develop a number of successful applications for various language processing tasks (such as Information Extraction), but also to build and annotate corpora and carry out evaluations on the applications generated. The framework can be used to develop applications and resources in multiple languages, based on its thorough Unicode support.
The Evolution of Entrepreneurial Intention in Transition Environment: Toward an Entrepreneurial Self-Efficacy Based Model

Entrepreneurial development has been viewed as a driving force for the successful transition from planned to market economies. Based on social cognition and self-efficacy theory, this study proposes an entrepreneurial self-efficacy based model of entrepreneurial intention. The model presents the complex interaction among entrepreneurs' individual characteristics, transition environment, entrepreneurial self-efficacy and entrepreneurial intention. And it also shows that entrepreneurial self-efficacy has direct and mediating impact on entrepreneurial intention. This model contributes to better understand and have implications for entrepreneurial practice in countries undergoing the difficult process of transition to a market economy.
Digital Forensics: Emerging Trends and Analysis of Counter-Security Environment

Corporates and organizations across the globe are spending huge sums on information security as they are reporting an increase in security related incidents. The proliferation of cloud, social network and multiple mobile device usage is on one side represent an opportunity and benefits to the organisation and on other side have posed new challenges for those policing cybercrimes. Cybercriminals have devised more sophisticated and targeted methods/techniques to trap victim and breach security setups. The emergence of highly technical nature of digital crimes has created a new branch of science known as digital forensics. Digital Forensics is the field of forensics science that deals with digital crimes and crimes involving computers. This paper focuses on briefing of digital forensics, various phases of digital forensics, digital forensics tools and its comparisons, and emerging trends and issues in this fascinated area.
Time Series Prediction Using Grammar-directed Genetic Programming Methods

We use a modified Genetic Programming System to predict the values of the reduced set proposed as benchmark for the ’07 Neural Forecasting Contest. Genetic Programming is a well known method used in symbolic regression, and classification, based in the evolution of arithmetic expressions according to a fitness function. We introduce here a grammar into the Genetic system, to let us use conditional expressions inside the syntactic trees representing the solutions to the problem. Additionally, we employ GA-P methods to automatically obtain constants inside the expressions. Our results proof the known power of Genetic Programming as a tool for solving Symbolic Regression problems, as the obtained expressions fit acceptably the proposed series. For the predicted values, some of them seem promising while others present too flat behaviors.
A Comparison of V-Frog[C] to Physical Frog Dissection

The purpose of the present study was to examine and compare the effectiveness of virtual frog dissection using V-Frog© and physical frog dissection on learning, retention, and affect. Subjects were secondary students enrolled in year-long life science classes in a suburban high school (N=102). Virtual dissections were done with V-Frog©, a virtual reality software application that allows users to work with a virtual specimen that can be cut and explored in ways that are therefore unique for each individual user. The study employed a pretest, posttest, delayed posttest design using the pretest as a covariate in the analysis of the posttest and delayed posttest. Scores on a posttest administered immediately following treatment indicated that the virtual group learned more than the physical group (p<.001). Delayed posttest scores indicated there were no effects for treatment found. In the area of affect, survey results were fairly even between the two groups. Students did not show superior retention using V-Frog©. However, it should be noted that with no additional instructional cost, students could repeat the virtual dissection to improve retention. The results of the study indicated that the V-Frog© provides a viable alternative to physical dissection that produces effective learning outcomes and may be appealing to teachers and students for a number of practical and/or ethical reasons.
Analysis of the Evolution of Research Groups and Topics in the ISMIR Conference

We present an analysis of the topics and research groups that participated in the ISMIR conference over the last 15 years, based on its proceedings. While we ﬁrst investigate the topological changes of the co-authorship network as well as topics over time, we also identify groups of researchers, allowing us to investigate their evolution and topic dependence. Notably, we ﬁnd that large groups last longer if they actively alter their membership. Furthermore, such groups tend to cover a wider selection of topics, suggesting that a change of members as well as of research topics increases their adaptability. In turn, smaller groups show the opposite behavior, persisting longer if their membership is altered minimally and focus on a smaller set of topics. Finally, by analyzing the effect of group size and lifespan on research impact, we observed that papers penned by medium sized and long lasting groups tend to have a citation advantage.
The Assessment of Landscape Expressivity: A Free Choice Profiling Approach

In this paper we explore a relational understanding of landscape qualities. We asked three independent groups of human observers to assess the expressive qualities of a range of landscapes in the UK and in Spain, either by means of personal visits or from a projected digital image. We employed a Free Choice Profiling (FCP) methodology, in which observers generated their own descriptive terminologies and then used these to quantify perceived landscape qualities on visual analogue scales. Data were analysed using Generalised Procrustes Analysis, a multivariate statistical technique that does not rely on fixed variables to identify underlying dimensions of assessment. The three observer groups each showed significant agreement, and generated two main consensus dimensions that suggested landscape ‘health’ and ‘development in time’ as common perceived themes of landscape expressivity. We critically discuss these outcomes in context of the landscape assessment literature, and suggest ways forward for further development and research.
Exposing the behavioral gambit: the evolution of learning and decision rules

Behavioral ecologists have long been comfortable assuming that genetic architecture does not constrain which phenotypescan evolve (the "phenotypic gambit"). For flexible behavioral traits, however, solutions to adaptive problems are reached not only by genetic evolution but also by behavioral changes within an individual's lifetime, via psychological mechanisms such as learning. Standard optimality approaches ignore these mechanisms, implicitly assuming that they do not constrain the expression of adaptive behavior. This assumption, which we dub the behavioral gambit, is sometimes wrong: evolved psychological mechanisms can prevent animals from behaving optimally in specific situations. To understand the functional basis of behavior, we would do better by considering the underlying mechanisms, rather than the behavioral outcomes they produce, as the target of selection. This change of focus yields new, testable predictions about evolutionary equilibria, the development of behavior, and the properties of cognitive systems. Studies on the evolution of learning rules hint at the potential insights to be gained, but such mechanism-based approaches are underexploited. We highlight three future research priorities: (1) systematic theoretical analysis of the evolutionary properties of learning rules; (2) detailed empirical study of how animals learn in nonforaging contexts;and (3) analysis of individual differences in learning rules and their associated fitness consequences.
A Context-Based Approach Towards Content Processing of Electronic Documents

This paper introduces a text-theoretically founded view on content processing of electronic documents. A central aspect is the representation of the contextual embedding of texts. It provides a basis for modelling mechanisms of the dynamic development of information and access perspectives during the process of information communication and for the management of vage and incomplete information. The paper firstly indroduces a basic concept of text production and understanding (section 2). On this basis it develpos a text model with a four-layered text representation and text-external context bindings (section 3). It then describes the components of a text analysis process from robust parsing to deep semantic analysis. It explains the establishment of conceptual and thematic access perspectives (section 4 and 5). An outlook sketches an application scenario of using the representation described in text and information retrieval and machine translation (section 6).
Detection Using Correlation Bound and its Application to Raman Spectroscopy

A detection approach, detection with correlation bound (DCB), is introduced based on a linear mixture model. We use the upper bound of the correlation between the target and mixing components as the detection index, and derive the expression for this correlation bound using the observed data. The proposed method is more robust and provides better detection performance than the currently used supervised and unsupervised approaches in Raman spectroscopy. We also apply the correlation bound to independent component analysis (ICA) within the framework of constrained ICA (c-ICA), and show how it can help improve the detection performance of ICA. Simulation results are presented to demonstrate the effectiveness of the proposed method in Raman spectroscopy for detection of surface-deposited chemical agents
Abductive Inference for Interpretation of Metaphors

This paper presents a metaphor interpretation pipeline based on abductive inference. In this framework following (Hobbs, 1992) metaphor interpretation is modelled as a part of the general discourse processing problem, such that the overall discourse coherence is supported. We present an experimental evaluation of the proposed approach using linguistic data in English and Russian.
An Efficient Hardware Implementation of DVFS in Multi-core System with Wireless Network-on-Chip

Networks-on-Chip (NoC) have emerged as communication backbones for enabling high degree of integration in future many-core chips. Despite their advantages, the communication is multi-hop and causes high latency and power dissipation, especially in larger systems. Wireless Network-on-Chip (WNoC) significantly improves the latency over traditional wired NoCs for multi-core systems. But on-chip wireless interfaces (WIs) have their own power and area overhead. In this paper we design and implement a Dynamic Voltage Frequency Scaling (DVFS) technique and extend it to provide power gating to the WIs. This approach effectively reduces the energy consumption in multi core systems. A centralized controller with dual-band wireless transceiver implements per-core DVFS. The scheme ensures balanced workload and energy consumption of the chip and efficient power gating for the WIs. It helps to alleviate the power consumption up to 33.085 % for on-chip communications infrastructure with little overheads.
Manifold and metric in numerical solution of the quasi-static electromagnetic boundary value problems

Classical vector analysis is the predominant formalism used by engineers of computational electromagnetism, despite the fact that manifold as a theoretical concept has existed for a century. This paper discusses the benefits of manifolds over the traditional approach in practical problems of modelling. With a structural approach, it outlines the role and interdependence of coordinate systems, metric, constitutive equations, and fields, and relates them to practical problems of quasi-static computational electromagnetics: mesh generation, open-boundary problems, and electromagnetic-mechanical coupled problems involving motion and deformation. The proposed procedures also imply improvements to the flexibility of the modelling software.