Miracle-dz/UniFAR-FTUs huggingface dataset:
Welding Inspection using Novel Specularity Features and a One-class SVM

We present a framework for automatic inspection of welding seams based on specular reflections. Therefore, we introduce a novel feature set – called specularity features (SPECs) – describing statistical properties of specular reflections. For classification we use a one-class support-vector approach. The SPECs significantly outperform statistical geometric features and raw pixel intensities, since they capture more complex characteristics and depencies of shape and geometry. We obtain an error rate of 9%, which corresponds to the level of human performance.
O BRINCAR E O ESPAÇO POTENCIAL NO AMBIENTE VIRTUAL

This article analyzes the subjective impacts from new forms of interaction allowed by technological advances in the field of virtuality. Semi-structured interviews were conducted with six adults participants who had a character in online games with a complex virtual world. The methodology for the analysis of the interviews followed the thematic analysis of its content and winicottian psychoanalytic interpretations. It was found that creative activity inserted in these games allows a particular interaction between the subject and the character belonging to the virtual world, establishing itself as potency to constitute a potential space and the emergence of new processes of subjectivity.
Summary and discussion of: \Dropout Training as Adaptive

Multi-layered (i.e. deep) artificial neural networks have recently undergone a resurgence in popularity due to improved processing capabilities and the increasing availability of large datasets. Popular in the 1980’s and before, they were largely abandoned in favor of convex methods (such as support vector machines) that came with optimality guarantees and often gave better results in far less time. However, with the modern ubiquity of GPUs and distributed computing, the same methods that were once spurned for their computational intractability have become the de-facto standard for large-scale commercial applications in companies such as Google, Facebook, Baidu, etc. This almost universal adoption of deep learning (as it’s now called) is not without reason; variants of these methods have achieved state-of-the-art performance (often by a significant margin over other competing algorithms) on numerous tasks such as image classification, speech recognition, etc. While improved computational power has allowed these models to be trained in a moderate amount of time, their performance is tied to the quantity and quality of the available training data. Deep neural networks are often very large, often consisting upwards of millions (or even billions) of parameters whose values must all be learnt from data. Thus, like any statistical model, they are susceptible to overfitting and typically require huge quantities of labeled training examples. Furthermore, the optimization landscape for the network parameters is highly non-convex with many local optima. So even with sufficient training data, the performance of a trained model could still be poor. These problems have been addressed empirically by a surprisingly simple method referred to as dropout. First introduced by Geoffrey Hinton, dropout has become an important component in most modern deep learning implementations because it results in reduced overfitting and often reaches better local minima. Even so, very little is understood theoretically about why this should be the case. The paper Dropout Training as Adaptive Regularization is one of several recent papers that attempts to understand the role of dropout in training deep neural networks.
Linear Power Spectra in Cold + Hot Dark Matter Models: Analytical Approximations and Applications

This paper presents simple analytic approximations to the linear power spectra, linear growth rates, and rms mass fluctuations for both components in a family of cold + hot dark matter (CDM+HDM) models that are of current cosmological interest. The formulas are valid for a wide range of wave numbers, neutrino fractions, redshifts, and Hubble constants: k ≲ 10 h Mpc−1, 0.05 ≲ Ωv ≲ 0.3, 0 ≤ z ≤ 15, and 0.5 ≤ h ≤ 0.8. A new, redshift-dependent shape parameter, Γv = a1/2 Ωv h2, is introduced to simplify the multidimensional parameter space and to characterize the effect of massive neutrinos on the power spectrum. The physical origin of Γv lies in the neutrino free-streaming process, and the analytic approximations can be simplified to depend only on this variable and Ωv. Linear calculations with these power spectra as input are performed to compare the predictions of Ωv ≲ 0.3 models with observational constraints from the reconstructed linear power spectrum and cluster abundance. The usual assumption of an exact scale-invariant primordial power spectrum is relaxed to allow a spectral index of 0.8 ≲ n ≤ 1. It is found that a slight tilt of n = 0.9 (no tensor mode) or n = 0.95 (with tensor mode) in Ωv ~ 0.1–0.2 CDM + HDM models gives a power spectrum similar to that of an open CDM model with a shape parameter Γ = 0.25, providing good agreement with the power spectrum reconstructed by Peacock & Dodds and the observed cluster abundance at low redshifts. Late galaxy formation at high redshifts, however, will be a more severe problem in tilted models.
Ethnography considered useful: situating criticality

Increasingly the fields of Human Computer Interaction (HCI) and art are intersecting. Interactive artworks are being evaluated by HCI methods and artworks are being created that employ and repurpose technology for interactive environments. In this paper we steer a path between empirical and critical-theoretical traditions, and discuss HCI research and art works that also span this divide. We address concerns about 'new' ethnography raised by Crabtree et al. (2009) in "Ethnography Considered Harmful", a critical essay that positions ethnographic and critical-theoretical views at odds with each other. We propose a mediated view for understanding interactions within open-ended interactive artworks that values both perspectives as we navigate boundaries between art practice and HCI.
Transfer of Polarized Radiation in Strongly Magnetized Plasmas and Thermal Emission from Magnetars: Effect of Vacuum Polarization

We present a theoretical study of radiative transfer in strongly magnetized electron-ion plasmas, focusing on the effect of vacuum polarization due to quantum electrodynamics. This study is directly relevant to thermal radiation from the surfaces of highly magnetized neutron stars, which have been detected in recent years. Strong-field vacuum polarization modifies the photon propagation modes in the plasma and induces a "vacuum resonance" at which a polarized X-ray photon propagating outward in the neutron star atmosphere can convert from a low-opacity mode to a high-opacity mode and vice versa. The effectiveness of this mode conversion depends on the photon energy and the atmosphere density gradient. For a wide range of field strengths, B ≳ 7 × 1013 G, the vacuum resonance lies between the photospheres of the two photon modes, and the emergent radiation spectrum from the neutron star is significantly modified by the vacuum resonance. (For lower field strengths, only the polarization spectrum is affected.) Under certain conditions, which depend on the field strength, photon energy, and propagation direction, the vacuum resonance is accompanied by the phenomenon of mode collapse (at which the two photon modes become degenerate) and the breakdown of Faraday depolarization. Thus, the widely used description of radiative transfer based on photon modes is not adequate to treat the vacuum polarization effect rigorously. We study the evolution of polarized X-rays across the vacuum resonance and derive the transfer equation for the photon intensity matrix (Stokes parameters), taking into account the effect of birefringence of the plasma-vacuum medium, free-free absorption, and scatterings by electrons and ions.
Prostate cancer: the need for biomarkers and new therapeutic targets

Prostate cancer (PCa) incidence and mortality have decreased in recent years. Nonetheless, it remains one of the most prevalent cancers in men, being a disquieting cause of men’s death worldwide. Changes in many cell signaling pathways have a predominant role in the onset, development, and progression of the disease. These include prominent pathways involved in the growth, apoptosis, and angiogenesis of the normal prostate gland, such as androgen and estrogen signaling, and other growth factor signaling pathways. Understanding the foundations of PCa is leading to the discovery of key molecules that could be used to improve patient management. The ideal scenario would be to have a panel of molecules, preferably detectable in body fluids, that are specific and sensitive biomarkers for PCa. In the early stages, androgen deprivation is the gold standard therapy. However, as the cancer progresses, it eventually becomes independent of androgens, and hormonal therapy fails. For this reason, androgen-independent PCa is still a major therapeutic challenge. By disrupting specific protein interactions or manipulating the expression of some key molecules, it might be possible to regulate tumor growth and metastasis formation, avoiding the systemic side effects of current therapies. Clinical trials are already underway to assess the efficacy of molecules specially designed to target key proteins or protein interactions. In this review, we address that recent progress made towards understanding PCa development and the molecular pathways underlying this pathology. We also discuss relevant molecular markers for the management of PCa and new therapeutic challenges.
Assessing the Impact of Public Venture Capital Programs in the United Kingdom: Do Regional Characteristics Matter?

Over the last two decades, many governments around the world have implemented programs to mobilize venture capital (VC) in support of new and innovative firms. In this paper, we analyze whether and how the impact of publicly-supported VC funds varies between high-tech and low-tech regions, when compared to private VC funds. Our analyses on a sample of 898 VC-backed companies in the United Kingdom in the period 1998-2007 show for public VC funds: (a) a greater propensity to invest in early-stage and technology-based sectors, which is especially pronounced in low-tech regions; (b) a greater propensity toward staging and a greater ability to attract partners in syndication, especially in high-tech regions; (c) lower exit rates of investee companies, in terms of IPO/acquisition, as compared to private VC funds. Overall, our findings confirm that regional characteristics do matter if we want to more rigorously assess the effectiveness of public VC programs.
Spectrum of Calcific Aortic Valve Disease: Pathogenesis, Disease Progression, and Treatment Strategies

Received October 26, 2004; revision received January 13, 2005; accepted February 4, 2005. Calcific aortic valve disease is a slowly progressive disorder with a disease continuum that ranges from mild valve thickening without obstruction of blood flow, termed aortic sclerosis, to severe calcification with impaired leaflet motion, or aortic stenosis (Figure 1). In the past, this process was thought to be “degenerative” because of time-dependent wear-and-tear of the leaflets with passive calcium deposition. Now, there is compelling histopathologic and clinical data suggesting that calcific valve disease is an active disease process akin to atherosclerosis with lipoprotein deposition, chronic inflammation, and active leaflet calcification. The overlap in the clinical factors associated with calcific valve disease and atherosclerosis and the correlation between the severity of coronary artery and aortic valve calcification provide further support for a shared disease process. Figure 1. Gross specimen of minimally diseased aortic valve (left) and severely stenotic aortic valve (right). In the severely stenotic valve, there are prominent lipocalcific changes on aortic side of valve cusps (arrow), with sparing of commissures. ### Anatomy of Normal Aortic Valve The normal aortic valve comprises 3 layers. The ventricularis, on the ventricular side of the leaflet, is composed of elastin-rich fibers that are aligned in a radial direction, perpendicular to the leaflet margin. The fibrosa, on the aortic side of the leaflet, comprises primarily fibroblasts and collagen fibers arranged circumferentially, parallel to the leaflet margin. The spongiosa is a layer of loose connective tissue at the base of the leaflet, between the fibrosa and ventricularis, composed of fibroblasts, mesenchymal cells, and a mucopolysaccharide-rich matrix. These layers work in concert to provide tensile strength and pliability for decades of repetitive motion. ### Early Lesion of Aortic Sclerosis Histopathologic studies of aortic sclerosis show focal subendothelial plaquelike lesions on the aortic side of the leaflet that extend to the adjacent fibrosa layer. Similarities to …
Efficient secret sharing schemes achieving optimal information rate

One of the important problems in secret sharing schemes is to establish bounds on the size of the shares to be given to participants in secret sharing schemes. The other important problem in secret sharing schemes is to reduce the computational complexity in both secret distribution phase and secret reconstruction phase. In this paper, we design efficient threshold (n, k) secret sharing schemes to achieve both of the above goals. In particular, we show that if the secret size |s| is larger than max{1 + log2 n, n(n - k)/(n - 1)}, then ideal secret sharing schemes exist. In the efficient ideal secret sharing schemes that we will construct, only XOR-operations on binary strings are required (which is the best we could achieve). These schemes will have many applications both in practice and in theory. For example, they could be used to design very efficient verifiable secret sharing schemes which will have broad applications in secure multi-party computation and could be used to design efficient privacy preserving data storage in cloud systems.
Hybrid Intelligent Technique for Text Categorization

Text categorization is the task in which documents are classified into one or more of predefined categories based on their contents. This paper shows that the proposed system consists of three main steps: document representation, classifier construction and performance evaluation. In the first step, a set of pre-classified documents is provided. Input documents are initially pre-processed in order to be split into features and eliminate non-informative features. The remaining features are next weighted based on the frequency of each feature in that document and standardized by reducing a feature to its root using the stemming process. Due to the large number of features even after the non-informative features removal and the stemming process, the proposed system applies specific thresholds to extract distinct features which represent the input document. In the second step, the text categorization model (classifier) is built by learning the distinct features which represent all the pre-classified documents, this process can be achieved by using one of the supervised classification techniques that is called the rough set theory. The model uses a pair of precise concepts from the above theory that are called lower and upper approximations to classify any test document into one or more of main categories and sub-categories. In the final step, the performance of the proposed system is evaluated. It has achieved good results up to 96%, when applied to a number of test documents for each sub-category of main categories.
Diverse Topic Phrase Extraction through Latent Semantic Analysis

We propose a novel algorithm for extracting diverse topic phrases in order to provide summary for large corpora. Previous works often ignore the importance of diversity and thus extract phrases crowded on some hot topics while failing to cover other less obvious but important topics. We solve this problem through document re-weighting and phrase diversification by using latent semantic analysis (LSA). Experiments on various datasets show that our new algorithm can improve relevance as well as diversity over different topics for topic phrase extraction problems.
Oral Anticoagulant Therapy and Bleeding Events with Vitamin K Antagonists in Patients with Atrial Fibrillation in a Hungarian County Hospital

Background Vitamin K antagonists, despite their tight therapeutic spectrum and the fear of bleeding complications, were long the most important drugs used in anticoagulant therapy. The aim of this study was to evaluate the quality of anticoagulant therapy and its relation with bleedings in everyday clinical practice. Material/Methods We analyzed the data of 272 patients with non-valvular atrial fibrillation treated in our county hospital using retrospective data collection of the last 1008±384 days. The INR (International Normalized Ratio) values and the time in therapeutic range (TTR) were analyzed. We asked patients about bleeding complications and searched the medical records. Results The TTR proved to be 64% and there was no statistically significant difference between that of 252 (92.7%) patients taking acenocoumarol and 20 (7.3%) on warfarin. Analyzing various factors leading to TTR under 70%, we found that none of them have a significant impact. Significantly more bleeding events occurred in the first 3 months after the initiation of anticoagulant therapy and in patients with TTR under 70%, but the latter was not significant after adjustment for factors influencing bleeding (OR 1.607, CI 0.571–4.522, p=0.392). Conclusions Although the present study’s TTR values were similar to those found in the warfarin branch of various large-scale international trials and in real-life settings, further improvement of vitamin K antagonist therapy are necessary. As the possibilities for this are limited, we believe that the new type anticoagulant agents have a place in everyday clinical practice.
Inflating examples to obtain rules

A new machine learning system is presented in this article. It is called INNER and induces classification rules from a set of training examples. The process followed by this system starts with the random selection of a subset of examples that are iteratively inflated in order to cover the surroundings provided that they are inhabited by examples of the same class, thus becoming rules that will be applied by means of a partial matching mechanism. The rules so obtained can be seen as clusters of examples and represent clear evidence to support explanations about their future classifications and may be used to build intelligent advisors. The whole algorithm can be seen as a set of elastic transformations of examples and rules and produces concise, accurate rule sets, as is experimentally demonstrated in the final section of the article. © 2003 Wiley Periodicals, Inc.
Integrating spelling into spoken dialogue recognition

Recognition of spelled letter sequences is essential for many real-world applications which involve arbitrary names or addresses. Often the letter sequences carry the sentence's crucial information; therefore, it is important to correctly localize and recognize the spelled string. However, large vocabulary speech recognizers tend to perform poorly on spelled letters, especially if they have to deal with spontaneous speech. The research presented here aims at improving the recognition accuracy of spontaneous speech with embedded spelled-letter sequences. We propose methods to localize spelled-letter segments and reclassify them with a specialized letter recognizer. In: 4th European Conference on Speech Communication and Technology (EUROSPEECH '95), Madrid, Spain, 18 21 September 1995
Pore formation by human stefin B in its native and oligomeric states and the consequent amyloid induced toxicity

It is well documented that amyloid forming peptides and proteins interact with membranes and that this correlates with cytotoxicity. To introduce the theme we give a brief description of some amyloidogenic proteins and note their similarities with pore forming toxins (PFTs) and cell penetrating peptides. Human stefin B, a member of the family of cystatins, is an amyloidogenic protein in vitro. This review describes our studies of the interaction of stefin B oligomers and prefibrillar aggregates with model membranes leading to pore formation. We have studied the interaction between human stefin B and artificial membranes of various compositions. We also have prepared distinct sizes and morphologies of stefin B prefibrillar states and assessed their toxicity. Furthermore, we have measured electrical currents through pores formed by stefin B prefibrillar oligomers in a planar lipid bilayer setup. We finally discuss the possible functional and pathological significance of such pores formed by human stefin B.
Fast automated design of waveguide filters using aggressive space mapping with a new segmentation strategy and a hybrid optimization algorithm

Waveguide filters are key elements present in many microwave and millimeter-wave communication systems. In recent times, ever-increasing efforts are being devoted to the development of automated computer-aided design (CAD) tools of such devices. In this paper, a novel CAD tool based on modal analysis methods, which improves the efficiency and robustness of the classical aggressive space-mapping technique, is presented for those purposes. The use of a new segmentation strategy and the hybridization of a specific combination of several well-known optimization algorithms is proposed. The CAD tool has been successfully validated with the practical design of several H-plane coupled cavity filters in a rectangular waveguide for space and communication applications. A filter prototype for local multipoint distribution systems operating at Ka-band, and two tunable H-plane filters with tuning posts operating at 11 and 13 GHz, have been successfully designed, manufactured, and measured.
A study of Through-Silicon-Via impact on the 3D stacked IC layout

Through-Silicon-Via (TSV) is the enabling technology for the finegrained 3D integration of multiple dies into a single stack. These TSVs occupy non-negligible silicon area because of their sheer size. This significant silicon area occupied by the TSVs and the interconnections made to the TSVs greatly affect area, power, performance, and reliability of 3D IC layouts. Well-managed TSVs alleviate congestion, reduce wirelength, and improve performance, whereas excessive TSVs not only increase the die area, but also have negative impact on many design objectives. In this paper, we study the impact of TSV on various aspects of 3D layouts. We use GDSII layouts of 2D and 3D designs, and thoroughly compare the pros and cons of TSV usage. We propose a new force-directed 3D gate-level placement that efficiently handles TSVs. In addition, we present an algorithm that assigns TSVs to nets to complete routing that involves TSVs. This algorithm, together with our 3D placer, is integrated into a commercial P&R tool to generate fully validated GDSII layouts. Our experiments based on synthesized benchmarks indicate that our algorithms help generate GDSII layouts of 3D designs that are optimized in terms of area, wirelength, and metal layer count.