Miracle-dz/UniFAR-FTUs huggingface dataset:
A Lagrangian Formulation of Neural Networks

In Part I of this work we showed how a tradeoff between measures of neural net cost (of operation) and functionality (efiicacity) could be used to derive the dynamics of the net, and in particular, optimize thereby a class of objective functions. IIere we extend that methodology; a I,agrangian formulation and greedy variation to treat more ramified problenls. We introduce a notion of clocking and a class of clocked objective functions to CIO this. A kincl of switching dynamics occurs which is suitable for many applications. This notational clocking calculus makes for time-scaled computational techniques employing a “focus of attention” (similar to saccading, foveation, and covert attention in biological vision). Experiments dealing with applications are referenced.
Correcting Spelling Errors by Modelling Their Causes

This paper accounts for a new technique of correcting isolated words in typed texts. A language-dependent set of string substitutions reflects the surface form of errors that result from vocabulary incompetence, misspellings, or mistypings. Candidate corrections are formed by applying the substitutions to text words absent from the computer lexicon. A minimal acyclic deterministic finite automaton storing the lexicon allows quick rejection of nonsense corrections, while costs associated with the substitutions serve to rank the remaining ones. A comparison of the correction lists generated by several spellcheckers for two corpora of English spelling errors shows that our technique suggests the right words more accurately than the others.
Computational filter-aperture approach for single-view multi-focusing

Most of the focusing techniques need to estimate depth information for ensuring that the object of interest is at an appropriate distance for full frontal focus. Computational cameras which can variably focus different regions of the scene with large depth of field have been proposed. In this paper we propose a full auto-focusing algorithm using computational camera without involving any digital image restoration methods and just one input. The proposed computational camera uses multiple filter apertures corresponding to each color channel which can acquire three shifted views of a scene in the RGB color planes. We can make any region focused by appropriately shifting each color channel to be aligned. Depth map estimation is carried out to extract different regions from these channel shifted images which is later fused to produce the final image without any focal blur. Experimental results show performance and feasibility of the proposed algorithm for auto-focusing images with one or more differently out-of-focused objects.
Simplifying deep neural networks for neuromorphic architectures

Deep learning using deep neural networks is taking machine intelligence to the next level in computer vision, speech recognition, natural language processing, etc. Brain-like hardware platforms for the brain-inspired computational models are being studied, but none of such platforms deals with the huge size of practical deep neural networks. This paper presents two techniques, factorization and pruning, that not only compress the models but also maintain the form of the models for the execution on neuromorphic architectures. We also propose a novel method to combine the two techniques. The proposed method shows significant improvements in reducing the number of model parameters over standalone use of each method while maintaining the performance. Our experimental results show that the proposed method can achieve 31 x reduction rate without loss of accuracy for the largest layer of AlexNet.
Seasonality in Iranian Fruit and Vegetable Dietary Intake

Materials and Methods: We analyzed the existing data of the latest Iranian household food pattern study, collected by three consecutive 24 hours recalls. Totally 7158 Iranian households were selected by Iranian statistic center by a systematic cluster random sampling method. Calculation of energy and nutrients consumptions were made by an access program designed using Iranian food composition table and Modified by USDA and database. Cooking coefficient has been considered for vitamin C. Data were expressed as mean and SE. Intakes during different seasons were compared by one-way ANOVA test. A Newman-Keuls post hoc analysis was used to locate statistical significance in seasons, when an ANOVA was significant. Results: The highest consumption of vegetables was seen in summer (289 ± 3.4 g/day) while the lowest was in winter (224 ± 3.2 g/day) (P < 0.1). Fruit consumption showed a more dramatic variation, with a peak in summer (263 ± 6/1 g/day) (P < 0.1) and the lowest level in spring (143 ± 5.0). Energy intake showed a significant but not sharp variation (P < 0.01). Vitamin C had a dramatically significant variation (P < 0.01), with the lowest in the spring. Seasonal variations were not significant in vitamin A intake. Conclusions: Due to seasonal variations in fruit and vegetable dietary intakes, the interventional and case-control studies should consider seasonality. Educational and price controlling programs may control such observed seasonality in the intake of fruit and vegetables.
Context-based coding of Sequitur generated grammar for improved text compression

Sequitur is an algorithm for deriving phrase hierarchy in text or semi-structured text source. First, it represents the hierarchy in the form of context-free grammar and then expresses the same by a sequence of symbols, where a symbol represents either an input alphabet or a subsequence of symbols appeared before; in this case, the symbol represents a rule head and the subsequence represents its right hand side of the rule. To compress the input, Sequitur encodes each symbol separately, i.e., it does not take preceding symbols into account. This article describes use of partial matching for developing a context-based coder for Sequitur. More specifically, if the preceding symbol is a rule, our encoder codes the next symbol in the context of the last alphabet of the preceding rule. Our coding method makes two changes to the representation of the first and second occurrences of rules. Experimental results show that context-based coding can reduce the file size additionally by upto 7% on average.
A Hierarchical Intrusion Detection System in Wireless Sensor Networks

Summary Wireless sensor networks (WSNs) often consist of tiny devices with limited energy, computational power, transmission range, and memory. WSNs offer a variety of potential means to monitor environments. However, WSNs are vulnerable to various attacks because these networks are often deployed in open and unprotected environments. Therefore, security design is an important aspect of WSNs. In this paper, we describe various threats to WSN and then examine existing approaches to identify these threats. Finally, we propose an intrusion detection mechanism based on these existing approaches to identifying threats.
A low phase noise low dc power quadrature voltage-controlled oscillator using a 0.18-µm CMOS process

This paper describes a low phase noise low dc power quadrature voltage controlled oscillator (QVCO) with a reflection-type IQ modulator. The QVCO using a standard bulk 0.18-µm CMOS process achieves a low phase noise of −115 dBc/Hz at 1-MHz offset frequency, a figure of merit (FOM) of − 191 dBc/Hz, and a core dc power consumption of 5.8 mW with a supply voltage of 1.8 V. The tuning frequency of the QVCO is from 13.4 to 14.4 GHz. The amplitude and phase errors of the QVCO are within 0.5 dB and 3°, respectively. To the best of the authors' knowledge, this work demonstrates the best FOM among all the reported Si-based QVCOs above 10 GHz.
Lossless Medical Image Compression using Redundancy Analysis

††† Summary We studied two image characteristics, the smoothness and the similarity, which give rise to local and global redundancy in image representation. The smoothness means that the gray level values within a given block vary gradually rather than abruptly. The similarity means that any patterns in an image repeat itself anywhere in the rest of the image. In this sense, we proposed a lossless medical image compression scheme which exploits both types of redundancy. The proposed method segments the image into variable block size(VBS) and encodes them depending on characteristics of the blocks. The proposed compression schemes works better 10~40[%] than other compression scheme such as the Huffman, the arithmetic, the Lempel-Ziv, HINT(Hierarchical Interpolation), JPEG-LS(Lossless Scheme of JPEG) and lossless JPEG2000.
Interactive Task Planning through Multiple Abstraction: Application to Assistant Robotics

Assistant robotics has become an emergent field within the robotic and artificial intelligence communities ([1],[2]). The main characteristic of assistant robots is that they are designed to serve non-expert people within their environment. They must plan and act efficiently to accomplish tasks specified in a human-like manner, i.e. "take this envelope to Peter’s office". Thus, an assistant robot must manage a symbolic model of its environment -its world modelthat involves human concepts. A human-inspired world representation can be used to endow assistant robots with that mentioned capability. It is stated in literature that humans widely use the mechanism of abstraction ([3],[6],[8]). Abstraction allows humans to work with abstract concepts that group other, more particular concepts. For instance, when somebody refers to "my office" she/he is talking about an abstract concept, dropping unnecessary details like door, wall, table, chair, cabinet, etc. A robot managing a world model that includes human concepts allows users to specify tasks in a human-like manner. Furthermore, the user can also interact with the robot during the planning process since the robot reports an understandable plan to the human. The work presented in this paper is intended to jointly cover human interaction and task planning efficiency, by using a hierarchical model of the environment. For our purposes, a multihierarchical world model called Multi-AH-graph ([6],[7]) has been implemented with two hierarchies of abstraction: the task planning hierarchy devoted to efficient task planning and the cognitive hierarchy engaged in human communication. The use of these two hierarchies requires a translation process to transform concepts from one hierarchy to the other, which is addressed in this paper. The paper is structured as follows. Section 2 briefly reviews the Multi-AH-graph model. Section 3 is devoted to describe the human interaction mechanism in robot task planning and its application to a real robotic application. Finally, some conclusions and future work are outlined.
Penalized partial least squares for multi-label data

Multi-label learning has attracted an increasing attention from many domains, because of its great potential applications. Although many learning methods have been witnessed, two major challenges are still not handled very well. They are the correlations and the high dimensionality of data. In this paper, we exploit the inherent property of the multi-label data and propose an effective sparse multi-label learning algorithm. Specifically, it handles the high-dimensional multi-label data by using a regularized partial least squares discriminant analysis with a l1-norm penalty. Consequently, the proposed method can not only capture the label correlations effectively, but also perform the operation of dimensionality reduction at the same time. The experimental results conducted on eight public data sets show that our method is promising and outperformed the state-of-the-art multi-label classifiers in most cases.
Identifying Common Research Interest through Matching of Ontological Research Profiles

— In this paper, we present an ontology-based methodology for identifying common interest among researchers. The methodology uses an ontology building algorithm to build researchers’ ontological research profiles from publication keywords. Then an ontology matching algorithm is used to find similarity between research profiles. Our ontology matching also considers location of the ontological terms within the two profiles that are matched. That is, the terms that are located near the bottom of the ontologies should indicate specialization of researchers, and more attention should be paid to matching of such terms than to the matched terms that are closer to the top of the ontologies. Particularly, this paper experiments with cross-field collaboration through matching of research profiles from different fields, and reports an evaluation using an ontology matching benchmark and a performance evaluation of the methodology. We consider that the methodology is useful as it can quantify similarity of research interest as well as give practical similarity results.
Implementing Dynamic Code Assembly for Client-Based Query Processing

Advanced database management systems (DBMS) are faced with increasingly complex query-processing tasks due to more powerful data models and query languages. These systems are usually conceived for client/server environments, with most application-oriented processing being done in the main-memory buffer at the client side. For performance reasons, it is indispensable to exploit the buffer contents when optimizing queries. As the buffer contents is only known at run time, advanced DBMS should support run-time optimization and offer concepts guaranteeing flexibility and efficiency of the subsequent phase of code generation. This paper addresses these issues. Our approach, dynamic code assembly, is based on a strictly orthogonal specification and implementation of the concepts involved in code generation, allowing to dynamically assemble executables from precompiled pieces of code, thus reducing explicit code generation to a minimum. We discuss our considerations and implementation referring to the KBMS KRISYS, although our ideas are generally applicable to advanced DBMS requiring run-time optimization and consequently flexible and efficient generation of executable code at run time.
Pairwise Linear Regression Classification for Image Set Retrieval

This paper proposes the pairwise linear regression classification (PLRC) for image set retrieval. In PLRC, we first define a new concept of the unrelated subspace and introduce two strategies to constitute the unrelated subspace. In order to increase the information of maximizing the query set and the unrelated image set, we introduce a combination metric for two new classifiers based on two constitution strategies of the unrelated subspace. Extensive experiments on six well-known databases prove that the performance of PLRC is better than that of DLRC and several state-of-theart classifiers for different vision recognition tasks: clusterbased face recognition, video-based face recognition, object recognition and action recognition.
Stable cell fate changes in marrow cells induced by lung-derived microvesicles

Background: Interest has been generated in the capacity of cellular-derived microvesicles to alter the fate of different target cells. Lung, liver, heart and brain-derived vesicles can alter the genetic phenotype of murine marrow cells; however, the stability of such changes and the mechanism of these changes remain unclear. In the present work, we show that lung-derived microvesicles (LDMV) alter the transcriptome and proteome of target marrow cells initially by mRNA and regulator(s) of transcription transfer, but that long term phenotype change is due solely to transfer of a transcriptional regulator with target cell. Methods/results: In vivo studies: Whole bone marrow cells (WBM) were co-cultured with LDMV (both isolated from male C57BL/6 mice) or cultured alone (control). One week later, cultured WBM was transplanted into lethally-irradiated female C57BL/6 mice. Recipient mice were sacrificed 6 weeks later and WBM, spleens and livers were examined for the presence of lung-specific gene expression, including surfactants A, B, C and D, aquaporin-5, and clara cell specific protein, via real-time RT-PCR. Immunohistochemistry was also performed on lungs to determine the number of transplanted marrow-derived (Y chromosome+) type II pneumocytes (prosurfactant C+). Mice transplanted with LDMV co-cultured WBM expressed pulmonary epithelial cell genes in the cells of their bone marrow, livers and spleens and over fivefold more transplanted marrow-derived Y+/prosurfactant C+cells could be found in their lungs (vs. control mice). In vitro studies: WBM (from mice or rats) was cultured with or without LDMV (from mice or rats) for 1 week then washed and cultured alone. WBM was harvested at 2-week intervals for real-time RT-PCR analysis, using species-specific surfactant primers, and for Western Blot analysis. Proteomic and microRNA microarray analyses were also performed on cells. LDMV co-cultured WBM maintained expression of pulmonary epithelial cell genes and proteins for up to 12 weeks in culture. Surfactant produced at later time points was specific only to the species of the marrow cell in culture indicating de novo mRNA transcription. These findings, in addition to the altered protein and microRNA profiles of LDMV co-cultured WBM, support a stable transcriptional mechanism for these changes. Conclusions: These data indicate that microvesicle alteration of cell fate is robust and long-term and represents an important new aspect of cellular biology.
ACE: an aggressive classifier ensemble with error detection, correction and cleansing

Learning from noisy data is a challenging and reality issue for real-world data mining applications. Common practices include data cleansing, error detection and classifier ensembling. The essential goal is to reduce noise impacts and enhance the learners built from the noise corrupted data, so as to benefit further data mining procedures. In this paper, we present a novel framework that unifies error detection, correction and data cleansing to build an aggressive classifier ensemble for effective learning from noisy data. Being aggressive, the classifier ensemble is built from the data that has been preprocessed by the data cleansing and correcting techniques. Experimental comparisons will demonstrate that such an aggressive classifier ensemble is superior to the model built from the original noisy data, and is more reliable in enhancing the learning theory extracted from noisy data sources, in comparison with simple data correction or cleansing efforts
The Road to Riches? A Model of the Cognitive Processes and Inflection Points Underpinning Entrepreneurial Action

Studies of entrepreneurial action often distinguish between different phases such as opportunity identification, evaluation, and exploitation. Yet, the richness of past contributions masks the absence of an integral framework to organize, in a theoretically consistent ensemble, the different kinds of cognitive processes that underpin entrepreneurial action. In this chapter, we draw from research on human action and cognition to offer an integrative model of the cognitive processes that foster entrepreneurial action. By presenting a more specific articulation of when, how, and why different cognitive processes operate, we provide theorists and empiricists with a more complete picture of how entrepreneurs’ thinking evolves from the emergence of an opportunity idea to the initiation of concrete entrepreneurial acts. In addition, our framework draws attention to cognitive inflection points that entrepreneurs must navigate in their journey toward entrepreneurship. By explicitly locating these inflection points and specifying the changes in mental processing that occurs at each point, we highlight that for entrepreneurial action to ensue, entrepreneurs must shift from one type of cognitive processing to another. Along this line, our model draws attention to the entire set of cognitive “skills” entrepreneurs must master for successful completion of each phase and successful transitions between phases.
On the Primary Ionization Mechanism(s) in Matrix-Assisted Laser Desorption Ionization

A mechanism is proposed for the first step of ionization occurring in matrix-assisted laser desorption ionization, leading to protonated and deprotonated matrix (Ma) molecules ([Ma + H]+ and [Ma − H]− ions). It is based on observation that in solid state, for carboxyl-containing MALDI matrices, the molecules form strong hydrogen bonds and their carboxylic groups can act as both donors and acceptors. This behavior leads to stable dimeric structures. The laser irradiation leads to the cleavage of these hydrogen bonds, and theoretical calculations show that both [Ma + H]+ and [Ma − H]− ions can be formed through a two-photon absorption process. Alternatively, by the absorption of one photon only, a heterodissociation of one of the O–H bonds can lead to a stable structure containing both cationic and anionic sites. This structure could be considered an intermediate that, through the absorption of a further photon, leads to the formation of matrix ions. Some experiments have been performed to evaluate the role of thermal ionization and indicate that its effect is negligible. Some differences have been observed for different matrices in the formation of analyte molecule (M) ion [M + H]+, [M − H]−, M+•, and [M − 2H]-•, and they have been explained in terms of ionization energies, pKa values, and thermodynamic stability.
A Limited Resource Model of Self-Control and the Relevant Studies

Self-control is vitally related with reasonable and harmonious living order.In the last decade,research about self-control indicated that self-control strength is similar to the energy of muscle.For overcoming the conflict of limited resources with the infinite demand,people tend to use the invaluable self-control recourses selectively.Expectancy and priming can lower the effect of self-control depletion to some extent,and exercises can higher the strength of self-control effectively.The most important aspect of the future research is to develop a direct measure of self-control recourses,and establish the conviction of the limited self-control strength.
Scrum Practice Mitigation of Global Software Development Coordination Challenges: A Distinctive Advantage?

Global software development is a major trend in software engineering. Practitioners are increasingly trying Agile methods in distributed projects to tap into the benefits experienced by co-located teams. This paper considers the issue by examining whether Scrum practices, used in four global software development projects to leverage the benefits of Agile methods over traditional software engineering methods, provided any distinctive advantage in mitigating coordination challenges. Four temporal, geographical and socio-cultural distance-based coordination challenges and seven scrum practices are identified from the literature. The cases are analyzed for evidence of use of the Scrum practices to mitigate each challenge and whether the mitigation mechanisms employed relate to any distinctive characteristics of the Scrum method. While some mechanisms used were common to other/ traditional methods, it was found that Scrum offers a distinctive advantage in mitigating geographical and socio-cultural but not temporal distance-based GSD coordination challenges. Implications are discussed.