Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of stiffness feedback for hard nodule identification on a phantom silicone model

Haptic information in robotic surgery can significantly improve clinical outcomes and help detect hard soft-tissue inclusions that indicate potential abnormalities. Visual representation of tissue stiffness information is a cost-effective technique. Meanwhile, direct force feedback, although considerably more expensive than visual representation, is an intuitive method of conveying information regarding tissue stiffness to surgeons. In this study, real-time visual stiffness feedback by sliding indentation palpation is proposed, validated, and compared with force feedback involving human subjects. In an experimental tele-manipulation environment, a dynamically updated color map depicting the stiffness of probed soft tissue is presented via a graphical interface. The force feedback is provided, aided by a master haptic device. The haptic device uses data acquired from an F/T sensor attached to the end-effector of a tele-manipulated robot. Hard nodule detection performance is evaluated for 2 modes (force feedback and visual stiffness feedback) of stiffness feedback on an artificial organ containing buried stiff nodules. From this artificial organ, a virtual-environment tissue model is generated based on sliding indentation measurements. Employing this virtual-environment tissue model, we compare the performance of human participants in distinguishing differently sized hard nodules by force feedback and visual stiffness feedback. Results indicate that the proposed distributed visual representation of tissue stiffness can be used effectively for hard nodule identification. The representation can also be used as a sufficient substitute for force feedback in tissue palpation.
Black funnels and droplets from the AdS C-metrics

We recently argued that the dynamics of strongly coupled field theories in black hole backgrounds is related via the AdS/CFT correspondence to two new classes of AdS black hole solutions: black funnels and black droplets suspended above a second disconnected horizon. The funnel solutions are dual to black holes coupling strongly to a field theory plasma. In contrast, the droplet solutions describe black holes coupling only weakly. We continue our investigation of these solutions and construct a wide variety of examples from the AdS C-metric in four bulk spacetime dimensions. The solutions we find are dual to field theories on spatially compact universes with Killing horizons.
Thylacinus cynocephalus The mitochondrial genome sequence of the Tasmanian tiger ( Material Supplemental

Material Supplemental http://genome.cshlp. org/content/suppl/2009/01/14/gr.082628. 108.DC1. html References http://genome.cshlp. org/content/19/2/213.full. html#ref-list-1 This article cites 23 articles, 14 of which can be accessed free at: Open Access Freely available online through the Genome Research open access option. service Email alerting click here top right corner of the article or Receive free email alerts when new articles cite this article sign up in the box at the
Regional Innovation Efficiency Study Based on Super-Efficiency Evaluation Model -- Technological Innovation Efficiency Analysis of 11 Provinces and Cities in Eastern China

The imbalance of regional economic development in China is increasingly intensifying in recent years. The gap of regional technological innovation efficiency is the important appearance and deep reason of the regional economic development imbalance. C2R model of DEA and super-efficiency model are used to calculate the technological innovation efficiency of eleven provinces and cities in eastern China. The results show that super-efficiency model can evaluate and rank all decision making units sufficiently. Hainan, Shanghai and Beijing etc. 7 provinces and cities are DEA effective and Fujian, Shandong, Liaoning and Hebei are DEA non-effective. The low resource utilization is the main reason of DEA non-availability. Inputs and outputs should be coordinated to improve technological innovation efficiency.
Concept learning for image and video retrieval: The inverse random under sampling approach

A typical concept-detection problem is characterised by greatly disproportionate sizes of the populations of training samples in the concept and anti-concept classes. In many cases, the population of anti-concept (negative) examples outnumber the concept examples. In this paper, an inverse random under sampling method is proposed to solve this imbalance problem. By the proposed method of inverse under sampling of the anti-concept class we can construct a large number of concept detectors which in the fusion stage facilitate a fine control of both false negative rates and false positive rates. In this method the main emphasis in learning the discriminant functions is on the concept class, leading to an almost perfect separation of the two classes for each detector. The proposed methodology is applied to commonly-used video and image collection benchmarks: Mediamill and Scene datasets. The results indicate significant performance gains. For some concepts, the improvement in the average precision is by several orders of magnitude, and the mean average precision is 12% and 17% better for Mediamill and Scene datasets respectively when compared with conventionally trained logistic regression classifier.
Collaborative embedded systems development: survey of state of the practice

This paper describes the results of a survey about the problems of and solutions for collaborative SW development. The survey was done through several interviews of companies doing collaborative development and also through a literature search to find already published experiences and solutions. As a result, we found that the literature focuses on solutions for more general issues like communication and team building, and industrial problems are related to specific engineering tasks. Mapping and practical examples of general solutions to specific tasks are needed to support collaborative software development
OPT: organization and process together

The role of humans in the software development process must be studied in the context of their organizational configuration. The organizational structure within which the process executes has a profound effect on its outcome. The communication and interaction problems associated with human involvement in development cannot be addressed by process improvement alone. The solution must include organizational improvement as well. Because organizational factors are complex, their analysis is a non-trivial task. Methods are needed for improving both organizations and processes, as well as the relationships between them. The aim of the OPT approach is the improvement of software development through improvement of organizational structures and processes. The approach is an iterative improvement method. The steps include modeling the relationship between the organization and the process, measuring various properties of this relationship, and choosing organizational and process changes to be implemented.
Genome compression using normalized maximum likelihood models for constrained Markov sources

The paper presents exact and implementable solutions to the problem of universal coding of approximate repeats by using the normalized maximum likelihood model for the class of Markov sources of first order, incorporating constraints which are standard in the context of fast searching similarities over full genomes. A coding scheme combining universal codes for memoryless sources and for sources with memory is then presented. The results when compressing the full human genome show that the combined scheme is able to provide slight improvements over the existing state of the art. As a side result, interesting pairs of sequences may be found, which are highly similar by the new NML model for Markov sources, but have a lower similarity score when evaluated with the NML for memoryless sources.
An Improved SDA Based Defect Prediction Framework for Both Within-Project and Cross-Project Class-Imbalance Problems

Background. Solving the class-imbalance problem of within-project software defect prediction (SDP) is an important research topic. Although some class-imbalance learning methods have been presented, there exists room for improvement. For cross-project SDP, we found that the class-imbalanced source usually leads to misclassification of defective instances. However, only one work has paid attention to this cross-project class-imbalance problem. Objective. We aim to provide effective solutions for both within-project and cross-project class-imbalance problems. Method. Subclass discriminant analysis (SDA), an effective feature learning method, is introduced to solve the problems. It can learn features with more powerful classification ability from original metrics. For within-project prediction, we improve SDA for achieving balanced subclasses and propose the improved SDA (ISDA) approach. For cross-project prediction, we employ the semi-supervised transfer component analysis (SSTCA) method to make the distributions of source and target data consistent, and propose the SSTCA+ISDA prediction approach. Results. Extensive experiments on four widely used datasets indicate that: 1) ISDA-based solution performs better than other state-of-the-art methods for within-project class-imbalance problem; 2) SSTCA+ISDA proposed for cross-project class-imbalance problem significantly outperforms related methods. Conclusion. Within-project and cross-project class-imbalance problems greatly affect prediction performance, and we provide a unified and effective prediction framework for both problems.
Application of Gene Expression Programming to Event Selection in High Energy Physics

Gene Expression Programming is a new evolutionary algorithm found to be very efficient for solving benchmark problems from computer science. The algorithm was successfully tested for event selection in high energy physics for the KS π π decay process. This paper presents an extended version of this analysis, as well as a comparison of its results with those obtained with an Artificial Neural Network and a Boosted Decision Trees method. All three methods produced selection functions which allowed good signal and background separation for the problem investigated, with the classification accuracies higher than 95%.
Behaviour-Based Attack Detection and Classification in Cyber Physical Systems Using Machine Learning

Cyber-physical systems (CPS) are often network integrated to enable remote management, monitoring, and reporting. Such integration has made them vulnerable to cyber attacks originating from an untrusted network (e.g., the internet). Once an attacker breaches the network security, he could corrupt operations of the system in question, which may in turn lead to catastrophes. Hence there is a critical need to detect intrusions into mission-critical CPS. Signature based detection may not work well for CPS, whose complexity may preclude any succinct signatures that we will need. Specification based detection requires accurate definitions of system behaviour that similarly can be hard to obtain, due to the CPS's complexity and dynamics, as well as inaccuracies and incompleteness of design documents or operation manuals. Formal models, to be tractable, are often oversimplified, in which case they will not support effective detection. In this paper, we study a behaviour-based machine learning (ML) approach for the intrusion detection. Whereas prior unsupervised ML methods have suffered from high missed detection or false-positive rates, we use a high-fidelity CPS testbed, which replicates all main physical and control components of a modern water treatment facility, to generate systematic training data for a supervised method. The method does not only detect the occurrence of a cyber attack at the physical process layer, but it also identifies the specific type of the attack. Its detection is fast and robust to noise. Furthermore, its adaptive system model can learn quickly to match dynamics of the CPS and its operating environment. It exhibits a low false positive (FP) rate, yet high precision and recall.
Collection and analysis of a Japanese-English emphasized speech corpora

Speech-to-speech (S2S) translation [10] is gradually starting to break down the language barrier, bringing opportunities for people to understand each other using different languages. However, one of the limitations of current S2S systems that they usually do not translate the paralinguistic information included in the input speech. Among the various types of paralinguistic information, we focus on emphasis, a type of information that is used to convey the focus of the sentence, emotion of the speaker, or other high level information useful for communication. This paper describes the collection of an Japanese-English emphasized speech corpora that can be used in the study of how emphasis is expressed across languages. We constructed 2 corpora, one containing digit strings and one with a utterances from a conversational setting. The speakers who can speak both Japanese and English were selected for the recording. 500 parallel digit strings for the digit corpus and 2030 parallel sentences for the conversation corpus were collected. The corpora may be used to analyze emphasis of one language or between languages, or develop emphasized speech translation systems.
Low-Rank Coding with b-Matching Constraint for Semi-Supervised Classification

Graph based semi-supervised learning (GSSL) plays an important role in machine learning systems. The most crucial step in GSSL is graph construction. Although several interesting graph construction methods have been proposed in recent years, how to construct an effective graph is still an open problem. In this paper, we develop a novel approach to constructing graph, which is based on low-rank coding and b-matching constraint. By virtue of recent advances in low-rank subspace recovery theory, compact encoding using low-rank representation coefficients allows us to obtain a robust similarity metric between all pairs of samples. Meanwhile, the b-matching constraint helps in obtaining a sparse and balanced graph, which benefits label propagation in GSSL. We build a joint optimization model to learn low-rank codes and balanced graph simultaneously. After using a graph re-weighting strategy, we present a semi-supervised learning algorithm by incorporating our sparse and balanced graph with Gaussian harmonic function (GHF). Experimental results on the Extended YaleB, PIE, ORL and USPS databases demonstrate that our graph outperforms several state-of-the-art graphs, especially when the labeled samples are very scarce.
Convex Optimization Project Report : Mode Seeking with Convex Shift and Interactive Learning

We give a brief introduction of nonparametric density estim ation and the family of mode seeking methods under this frame work. Regarding the limit of current methods, we propose several i mprovements. Specifically, we look into a mode seeking metho d called the Mean Shift, whose paper was published on IEEE Trans. Pattern Anal. Mach. Intell. 2002 and is the major reference paper we are targeting to improve. The paper is highly cited and there ave been numerous works later discussing potential improv ements and extensions over the original method. Our proposed metho d, however, differs from previous works. The major contribu tion is basically two fold: 1. We show that for mean shift with an Epan echnikov kernel, each kernel shift can actually be formulat ed in a convex form. By doing this we further generalize it for a numb er of convex metrics e.g., KL divergence and Jeffrey diverg ence where it is difficult or impossible to clearly define a ”mean” f or mode seeking. 2. We also introduce interactive learning t o image segmentation which is a specific application of mode seeking algorithms. By adopting interactive segmentation through convex learning, better segmentation results can be achieved over segmentation with traditional mode seeking algorithms. I. I NTRODUCTION Nonparametric density estimation provides a versatile too l f r feature space analysis such as clustering and local max ima detection. The rationale behind, as pointed out by Comanici u et al., is that ”feature space can be regarded as the empiric al probability density function(pdf) of the represented para meter” [1]. Given a set of independent and identically distributed (i.i.d.) data{x1,x2, . ,xN} drawn from some distribution with an unknown density, nonpa rametric density estimation seeks to approximate the probability density function p(x). Instead of representing p(x) by a single parametric model or a mixture model, the method finds a small number of nearest (or most simi lar) training instances and interpolate from them, which ca n be represented as follows:
Foundations of cognitive psychology

1Introduction How doesmemory work? How doweunderstandlanguage, andproduce itsothat others canu nderstand? How dow eperceiveour environment? How dow ei nfer from patterns oflightorsoundthe presence ofobjects inour environment,andtheir properties? How dowereason,andsolveproblems? How dowethink? Thesea resomeofthe foundationalq uestions thatcognitivepsychology examines. Theyarefoundationalpartly becauseeach concerns the natureofabasic psychologicalability,abilitiesthatweoftentakefor granted, yetwhich arevitalto our normal,healthyfunctioningandarekeytoour understandingofwhatitmeans to be human. Andtheyaref oundationalp artly becausetheyarei mportant for psychologyasawhole, andnot just cognitivepsychology. For instance, how canwe hopeto understandcompletely the behaviour ofemployeesinanorganization unless wefi rst understandtheirp erceptions andmemories,a ndh ow theyr eason and attempt to solveproblems? How canweunderstandthe wayinwhich peopleinteract to shapeoneanother'sopinions if wedonot understandhow peopleunderstandand process language, andhow theymakejudgements? Throughout thisbook,the various authors tackletheseandotherquestions,and show you how much ofthesef oundations cognitivepsychologists haveso far uncovered.The book begins withanexploration ofperceptualprocesses,movestoa discussion ofcategorization andlanguage, through to memory,andthentothinking processes. The last part ofthe book isdevoted to widerissues:to topicsthathave beenthoughttopresent achallenge to cognitivepsychology–such asconsciousness andemotion –andto someofthe themesandtheoreticalquestions which pervade the cognitiveapproach. Inthischapter,wetry to answerthe question 'Whatiscognitivepsychology? ' and, ins odoing, outlinesomeofthe foundationalassumptions thatcognitive psychologists tendto make, asw ell ass omeofthe reasons whyitiss uch an important andf ascinatingsubject–not least the factt hatitr aisesm any deepand important questions concerningthe mind.We considersomeofthe issuesthathave attracted andcontinueto attractthe interest ofcognitivepsychologists,andsomeof the assumptions theym akei no rdert odevelop models andtheories. We also considert he cognitivea pproach ingeneralandthe kindso fe xplanation cognitive psychologists favour.We touch upon the relations betweencognitivepsychology andothers ub-disciplineso fpsychology,andthosebetweencognitivepsychology andotherdisciplines(such asphilosophy,computing, andlinguistics). Therearemany substantialissuesthatweonly touch on –itisnot easy to define the relationshipbetweent wo academic disciplines,f or example–a ndso weonly hopeto conveysomethingoftheirflavour here.Our aiminthischapteristherefore merely to introduce cognitivepsychology,to explainsomeofits keydistinguishing features,a ndto uncovers omeofthe many broad issuesl yingb eneathi ts surface. You will
Molecular cloning and characterization of TK1111, a cupin-type phosphoglucose isomerase from Thermococcus kodakarensis

Aim: To characterize, TK1111, a cupin-type phosphoglucose isomerase from a hyperthermophilic archaeon Thermococcus kodakarensis. Methods: The genome of T. kodakarensis was searched in order to find an open reading frame annotated as glucose-6-phosphate isomerase. The structural gene was cloned, expressed in Escherichia coli and the purified gene product was characterized. Effect of metal ions on the restoration of enzyme activity was examined. Results: Genome search of T. kodakarensis revealed the presence of an open reading frame, TK1111, annotated as glucose-6-phosphate isomerase. TK1111 exhibited high homology with archaeal PGIs related to the cupin-superfamily instead of well-known family of PGI enzymes. The gene encoding TK1111 consisted of 567 nucleotides corresponding to a polypeptide of 189 amino acids. The structural gene was cloned, expressed in Escherichia coli and the purified gene product was characterized. Recombinant TK1111 displayed highest activity (43 U/mg) at 90 °C and pH 6.0. To our knowledge this is the only PGI from cupin-superfamily exhibiting highest activity in the acidic pH. TK1111 catalyzed the conversion of fructose 6-phosphate to glucose 6-phosphate following Michaelis-Menten kinetics with a Km and Vmax values of 2.2 mM and 12.5 mmol min -1 mg-1, respectively at 37 °C. Addition of Zn2+ ions in the assay mixture remarkably enhanced the enzyme activity in contrast to the PGI from Thermococcus litoralis, another member of the same genus exhibiting 75% identity to TK1111. The enzyme activity was completely diminished when TK1111 was heated at 70 °C in the presence of EDTA. Complete restoration of the enzyme activity was observed when TK1111 was incubated at room temperature in the presence of Zn2+. Conclusion: TK1111 from T. kodakaraensis is a novel phosphoglucose isomerase that belongs to cupintype isomerases. To our knowledge this is the only PGI from cupin-family exhibiting highest activity in the acidic pH. Enzyme activity was remarkably enhanced in the presence of Zn2+ in contrast to other members of the family.
Focused and casual interactions: allowing users to vary their level of engagement

We describe the focused-casual continuum, a framework for describing interaction techniques according to the degree to which they allow users to adapt how much attention and effort they choose to invest in an interaction conditioned on their current situation. Casual interactions are particularly appropriate in scenarios where full engagement with devices is frowned upon socially, is unsafe, physically challenging or too mentally taxing. Novel sensing approaches which go beyond direct touch enable wider use of casual interactions, which will often be 'around device' interactions. We consider the degree to which previous commercial products and research prototypes can be considered as fitting the focused-casual framework, and describe the properties using control theoretic concepts. In an experimental study we observe that users naturally apply more precise and more highly engaged interaction techniques when faced with a more challenging task and use more relaxed gestures in easier tasks.
Network Analysis for Wikipedia

Network analysis is a quantitative methodology for studying properties related to connectivity and distances in graphs, with diverse applications like citation indexing and information retrieval on the Web. The hyperlinked structure of Wikipedia and the ongoing, incremental editing process behind it make it an interesting and unexplored target domain for network analysis techniques. In this paper we apply two relevance metrics, HITS and PageRank, to the whole set of English Wikipedia entries, in order to gain some preliminary insights on the macro-structure of the organization of the corpus, and on some cultural biases related to speciﬁc topics