Miracle-dz/UniFAR-FTUs huggingface dataset:
Infrared laser light further improves bone healing when associated with bone morphogenetic proteins and guided bone regeneration: an in vivo study in a rodent model.

OBJECTIVE This study assessed histologically the effect of laser photobiomodulation on the repair of surgical defects created in the femurs of Wistar rats treated or not treated with bone morphogenetic proteins (BMPs) and organic bovine bone graft. BACKGROUND DATA This paper is part of an ongoing series of works in which biomaterials and/or guided bone regeneration (GBR) are used in association with laser photobiomodulation. Several previous reports from our group have shown that the use of laser photobiomodulation improves the treatment of bone defects. MATERIALS AND METHODS Forty-eight adult male Wistar rats were divided into four randomized groups: group 1 (controls, n = 12); group 2 (laser photobiomodulation, n = 12); group 3 (BMPs + organic bovine bone graft + GBR, n = 12); and group 4 (BMPs + organic bovine bone graft + GBR + laser photobiomodulation, n = 12). The irradiated groups received seven irradiations every 48 h, the first immediately after the surgical procedure. Laser photobiomodulation (830 nm, 40 mW, CW, phi approximately 0.6 mm) consisted of a total of 16 J/cm2 per session at four points (4 J/cm2 each) equally spaced around the periphery of the defect. The animals were sacrificed after 15, 21, and 30 d, and the specimens were routinely embedded in wax and stained with hematoxylin and eosin and Sirius red stains and analyzed under light microscopy. RESULTS The results showed histological evidence of increased deposition of collagen fibers (at 15 and 21 d), as well as an increased amount of well-organized bone trabeculi at the end of the experimental period (30 d) in irradiated animals compared to non-irradiated controls. CONCLUSION We concluded that the use of laser photobiomodulation in association with BMPs, organic bovine bone grafts, and GBR increases the positive biomodulating effects of laser energy.
SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images

In this paper, we present a large-scale dataset and establish a baseline for prohibited item discovery in Security Inspection X-ray images. Our dataset, named SIXray, consists of 1,059,231 X-ray images, in which 6 classes of 8,929 prohibited items are manually annotated. It raises a brand new challenge of overlapping image data, meanwhile shares the same properties with existing datasets, including complex yet meaningless contexts and class imbalance. We propose an approach named class-balanced hierarchical refinement (CHR) to deal with these difficulties. CHR assumes that each input image is sampled from a mixture distribution, and that deep networks require an iterative process to infer image contents accurately. To accelerate, we insert reversed connections to different network backbones, delivering high-level visual cues to assist mid-level features. In addition, a class-balanced loss function is designed to maximally alleviate the noise introduced by easy negative samples. We evaluate CHR on SIXray with different ratios of positive/negative samples. Compared to the baselines, CHR enjoys a better ability of discriminating objects especially using mid-level features, which offers the possibility of using a weakly-supervised approach towards accurate object localization. In particular, the advantage of CHR is more significant in the scenarios with fewer positive training samples, which demonstrates its potential application in real-world security inspection.
Nash Equilibria on (Un)Stable Networks

In response to a change, individuals may choose to follow the responses of their friends or, alternatively, to change their friends. To model these decisions, consider a game where players choose their behaviors and friendships. In equilibrium, players internalize the need for consensus in forming friendships and choose their optimal strategies on subsets of k players—a form of bounded rationality. The k‐player consensual dynamic delivers a probabilistic ranking of a game's equilibria, and via a varying k, facilitates estimation of such games. Applying the model to adolescents' smoking suggests that: (a) the response of the friendship network to changes in tobacco price amplifies the intended effect of price changes on smoking, (b) racial desegregation of high schools decreases the overall smoking prevalence, (c) peer effect complementarities are substantially stronger between smokers compared to between nonsmokers.
Euclidean and Hermitian Hulls of MDS Codes and Their Applications to EAQECCs

In this paper, we construct several classes of maximum distance separable (MDS) codes via generalized Reed-Solomon (GRS) codes and extended GRS codes, where we can determine the dimensions of their Euclidean hulls or Hermitian hulls. It turns out that the dimensions of Euclidean hulls or Hermitian hulls of the codes in our constructions can take all or almost all possible values. As a consequence, we can apply our results to entanglement-assisted quantum error-correcting codes (EAQECCs) and obtain several new families of MDS EAQECCs with flexible parameters. The required number of maximally entangled states of these MDS EAQECCs can take all or almost all possible values. Moreover, several new classes of q-ary MDS EAQECCs of length $ {n} > {q}+1$ are also obtained.
SiCloPe: Silhouette-Based Clothed People

We introduce a new silhouette-based representation for modeling clothed human bodies using deep generative models. Our method can reconstruct a complete and textured 3D model of a person wearing clothes from a single input picture. Inspired by the visual hull algorithm, our implicit representation uses 2D silhouettes and 3D joints of a body pose to describe the immense shape complexity and variations of clothed people. Given a segmented 2D silhouette of a person and its inferred 3D joints from the input picture, we first synthesize consistent silhouettes from novel view points around the subject. The synthesized silhouettes which are the most consistent with the input segmentation are fed into a deep visual hull algorithm for robust 3D shape prediction. We then infer the texture of the subject's back view using the frontal image and segmentation mask as input to a conditional generative adversarial network. Our experiments demonstrate that our silhouette-based model is an effective representation and the appearance of the back view can be predicted reliably using an image-to-image translation network. While classic methods based on parametric models often fail for single-view images of subjects with challenging clothing, our approach can still produce successful results, which are comparable to those obtained from multi-view input.
A CNN adapted to time series for the classification of Supernovae

Cosmologists are facing the problem of the analysis of a huge quantity of data when observing the sky. The methods used in cosmology are, for the most of them, relying on astrophysical models, and thus, for the classification, they usually use a machine learning approach in two-steps, which consists in, first, extracting features, and second, using a classifier. In this paper, we are specifically studying the supernovae phenomenon and especially the binary classification "I.a supernovae versus not-I.a supernovae". We present two Convolutional Neural Networks (CNNs) defeating the current state-of-the-art. The first one is adapted to time series and thus to the treatment of supernovae light-curves. The second one is based on a Siamese CNN and is suited to the nature of data, i.e. their sparsity and their weak quantity (small learning database).
Protein , malnutrition and wasting disorders CKD autophagy activation and skeletal muscle atrophy — a preliminary study of mitophagy and in fl ammation

Background/Objectives Long-lived proteins and organelles, such as mitochondria and the sarcoplasmic reticulum, are degraded by autophagy. However, the specific role of autophagy in chronic kidney disease (CKD) muscle atrophy is still undefined. Subjects/Methods This was a cross-sectional study with 20 subjects and 11 controls. Autophagy induction was studied in human skeletal muscle biopsies from CKD patients and controls by comparing the cross-sectional areas of muscle fibers, protein, and mRNA expression of autophagy-related genes and the appearance of autophagosomes. Results The cross-sectional area of muscle fibers was decreased in CKD patients as compared with the control group. CKD was associated with activated autophagy and mitophagy, as measured by the elevated mRNA and protein expression of BNIP3, (microtubule-associated proteins 1 A/1B light chain 3, also MAP1LC3) LC3, p62, PINK1, and PARKIN in the skeletal muscle and isolated mitochondria of the CKD group. Electron microscopy and immunohistofluorescence analysis showed mitochondrial engulfment by autophagosomes. Mitophagy was further demonstrated by the colocalization of LC3 and p62 puncta with the mitochondrial outer membrane protein TOM20. In addition, degradative FOXO3 (Forkhead box O3) was activated and synthetic mTOR (mammalian target of rapamycin) was inhibited, whereas the upstream mediators VPS34 (class III PI3-kinase) and AKT (protein kinase B, PKB) were activated in CKD patients. Conclusions Hyperactive autophagy and mitophagy may play important roles in CKD muscle atrophy. Autophagy was activated by FOXO3 translational factors in the skeletal muscle tissues of CKD patients, which maybe a new way of intervention for CKD muscle atrophy.
Potent pairing: ensemble of long short-term memory networks and support vector machine for chemical-protein relation extraction

Abstract Biomedical researchers regularly discover new interactions between chemical compounds/drugs and genes/proteins, and report them in research literature. Having knowledge about these interactions is crucially important in many research areas such as precision medicine and drug discovery. The BioCreative VI Task 5 (CHEMPROT) challenge promotes the development and evaluation of computer systems that can automatically recognize and extract statements of such interactions from biomedical literature. We participated in this challenge with a Support Vector Machine (SVM) system and a deep learning-based system (ST-ANN), and achieved an F-score of 60.99 for the task. After the shared task, we have significantly improved the performance of the ST-ANN system. Additionally, we have developed a new deep learning-based system (I-ANN) that considerably outperforms the ST-ANN system. Both ST-ANN and I-ANN systems are centered around training an ensemble of artificial neural networks and utilizing different bidirectional Long Short-Term Memory (LSTM) chains for representing the shortest dependency path and/or the full sentence. By combining the predictions of the SVM and the I-ANN systems, we achieved an F-score of 63.10 for the task, improving our previous F-score by 2.11 percentage points. Our systems are fully open-source and publicly available. We highlight that the systems we present in this study are not applicable only to the BioCreative VI Task 5, but can be effortlessly re-trained to extract any types of relations of interest, with no modifications of the source code required, if a manually annotated corpus is provided as training data in a specific file format.
Fast algorithms for close-to-Toeplitz-plus-Hankel systems of equation

The authors extend the low-displacement rank definition of close-to-Toeplitz (CT) matrices to close-to-Toeplitz-plus-Hankel (CTPH) matrices and develop fast algorithms for solving CTPH systems of equations. A matrix is defined as CTPH if it is the sum of a CT matrix and a second CT matrix postmultiplied by an exchange matrix; an equivalent definition in terms of UV rank is also given. This definition is motivated by the application of the algorithms to two-sided prediction in which both past and future time-series values are used to estimate the present value. <<ETX>>
Simplified Kalman estimation of fading mobile radio channels: high performance at LMS computational load

Low-complexity algorithms for channel estimation in Rayleigh fading environments are presented. The channel estimators are presumed to operate in conjunction with a Viterbi decoder, or an equalizer. The algorithms are based on simplified internal modeling of time-invariant channel coefficients and approximation of a Kalman estimator. A novel averaging approach is used to replace the online update of the Riccati equation with a constant matrix. The associated Kalman gain is expressed in an analytical form. Compared with RLS (recursive least squares) tracking, both a significantly lower bit error rate and a much lower computational complexity are achieved. <<ETX>>
Trigger-based language models: a maximum entropy approach

Ongoing efforts at adaptive statistical language modeling are described. To extract information from the document history, trigger pairs are used as the basic information-bearing elements. To combine statistical evidence from multiple triggers, the principle of maximum entropy (ME) is used. To combine the trigger-based model with the static model, the latter is absorbed into the ME formalism. Given consistent statistical evidence, a unique ME solution is guaranteed to exist, and an iterative algorithm exists which is guaranteed to converge to it. Among the advantages of this approach are its simplicity, generality, and incremental nature. Among its disadvantages are its computational requirements. The model described here was trained on five million words of Wall Street Journal text. It used some 40000 unigram constraints, 200000 bigram constraints, 200000 trigram constraints, and 60000 trigger constraints. After 13 iterations, it produced a language model whose perplexity was 12% lower than that of a conventional trigram, as measured on independent data. <<ETX>>
Chaotic signals and systems for communications

The authors propose and demonstrate two approaches to communications based on synchronized chaotic signals and systems. In the first approach, a chaotic masking signal is added at the transmitter and regenerated and subtracted at the receiver. The second approach utilizes modulation of the coefficients of the chaotic system in the transmitter and corresponding detection of synchronization error in the receiver to transmit binary-valued bit streams. Both approaches are demonstrated using a transmitter circuit with dynamics that are governed by the chaotic Lorenz system. A synchronizing receiver circuit which exploits the ideas of synchronized chaotic systems is used for signal recovery. <<ETX>>
Robust vector quantization in spectral coding

A mapping of a binary block code is used to generate the reconstruction vectors of a vector quantizer. The aim is to secure channel robustness while allowing for efficient design, storage, and handling of the vector quantizer. The general procedure is exemplified by the task of spectral coding for speech transmission. Using an LSP (line spectrum pair)-representation for the spectrum, it is demonstrated that short block codes are feasible, thus allowing for compact storage of the code-book. These short block codes combine low quantization noise in clear channel conditions with robustness to bit errors introduced by a noisy channel. A reduced length code offers the possibility of obtaining a vector quantizer. The block code structure of the code vector generation may also be exploited for efficient search of the code-book. <<ETX>>
Real-time neural computation of the noise subspace for the MUSIC algorithm

A neural network approach to computing in real time the noise subspace for the MUSIC bearing estimation algorithm is proposed. The authors show analytically and by simulation results that the proposed neural network is guaranteed to provide the solution arbitrarily close to the accurate noise subspace during an elapsed time of only a few characteristic time constants of the circuit. The key features of this proposed computational approach are asynchronous parallel processing, continuous-time dynamics, and a high-speed computational compatibility. <<ETX>>
Large-vocabulary dictation using SRI's DECIPHER speech recognition system: progressive search techniques

The authors describe a technique called progressive search which is useful for developing and implementing speech recognition systems with high computational requirements. The scheme iteratively uses more and more complex recognition schemes, where each iteration constrains the speech space of the next. An algorithm called the forward-backward word-life algorithm is described. It can generate a word lattice in a progressive search that would be used as a language model embedded in a succeeding recognition pass to reduce computation requirements. It is shown that speed-ups of more than an order of magnitude are achievable with only minor costs in accuracy. <<ETX>>
Exact reconstruction analysis/synthesis filter banks with time-varying filters

The authors examine some of the analysis/synthesis issues associated with FIR (finite impulse response) time-varying filter banks where the filter bank coefficients are allowed to change in response to the input signal. Several issues are identified as being important for realizing performance gains from time-varying filter banks in image coding applications. These issues relate to the behavior of the filters as transition from one set of filter banks to another occurs. Lattice structure formulations for the time-varying filter bank problems are introduced and discussed in terms of their properties and transition characteristics. <<ETX>>
Keyword-spotting using SRI's DECIPHER large-vocabulary speech-recognition system

The application of the speaker-independent large-vocabulary CSR (continuous speech recognition) system DECIPHER to the keyword-spotting task is described. A transcription is generated for the incoming spontaneous speech by using a CSR system, and any keywords that occur in the transcription are hypothesized. It is shown that the use of improved models of nonkeyword speech with a CSR system can yield significantly improved keyword spotting performance. The algorithm for computing the score of a keyword combines information from acoustics, language, and duration. One key limitation of this approach is that keywords are only hypothesized if they are included in the Viterbi backtrace. This does not allow the system builder to operate effectively at high false alarm levels if desired. Other algorithms are being considered for hypothesizing good score keywords that are on high scoring paths. An algorithm for smoothing language model probabilities was also introduced. This algorithm combines small task-specific language model training data with large task-independent language training data, and provided a 14% reduction in test set perplexity. <<ETX>>
A delta -operator recursive gradient algorithm for adaptive signal processing

The authors investigate how the delta operator can be used in adaptive signal processing and its numerical advantages in identifying LDLF (lightly damped low-frequency) poles. A delta recursive gradient algorithm is developed, and its local convergence speed is analyzed. Computer simulations show drastic improvement in convergence speed over the q-operator-based algorithms for LDLF poles. <<ETX>>
Phoneme HMMs constrained by frame correlations

Phoneme HMMs (hidden Markov models) that use correlations between two frames are proposed. The proposed technique constrains the output probability distributions of speaker-independent HMMs so that they are suitable for the input speaker. The speaker-dependent BC (bigram-constrained)-HMMs and speaker-independent BC-HMMs are generated from the conventional speaker-independent HMMs by combining the VQ (vector quantization)-code bigram (discrete case and tied-mixture case) or the conditional Gaussian density function (continuous case). The new models were evaluated by 23-phoneme recognition in continuous speech. In the speaker-dependent BC-HMMs, which use the speaker-dependent bigram created by 50 additional sentences of the test speaker, the best recognition accuracy of 74.8% was obtained by the tied-mixture type BC-HMMs. In the speaker-independent BC-HMMs, the best recognition accuracy of 67.5% was obtained by the continuous type BC-HMMs. <<ETX>>
PENERAPAN MODEL PEMBELAJARAN KOOPERATIF LEARNING TIPE NUMBERED HEAD TOGETHER (NHT) UNTUK MENINGKATKAN AKTIVITAS DAN HASIL BELAJAR SISTEM PERSAMAAN LINEAR DUA VARIABEL (SPLDV) DI KELAS VIII SMP NEGERI 1 SEI RAMPAH TAHUN AJARAN 2013/2014

Penelitian ini bertujuan untuk mengetahui peningkatan aktivitas dan hasil belajar matematika siswa dengan menggunakan model pembelajaran kooperatif tipe Numbered Head Together (NHT) pada materi sistem persamaan linear dua variabel (SPLDV) di kelas VIII SMP Negeri 1 Sei Rampah tahun ajaran 2013/2014. Jenis penelitian ini adalah Penelitian Tindakan Kelas (Classroom Action Research). Subjek penelitian ini adalah siswa/siswi kelas VIII-1 dan VIII-3 di SMP Negeri 1 Sei Rampah yang masing-masing berjumlah 35 orang. Objek penelitian ini adalah meningkatkan aktivitas dan hasil belajar siswa pada pada materi SPLDV melalui penerapan model pembelajaran kooperatif tipe Numbered Head Together (NHT). Prosedur dalam penelitian ini dimulai dari pelaksanaan siklus I dikelas VIII-3 dan dilanjutkan dengan siklus II di kelas VIII-1 untuk melihat peningkatan aktivtas dan hasil belajar siswa pada materi sistem persamaan linear dua variabel (SPLDV). Peningkatan aktivitas belajar siswa diketahui dari hasil pengamatan oleh observer pada saat proses pembelajaran berlangsung. Pada siklus I di kelas VIII-3 rata-rata aktivitas belajar siswa 58,29%. Pada siklus II di kelas VIII-1 rata-rata aktivitas belajar siswa sebesar 78,29%. Hal ini menunjukkan bahwa aktivitas belajar siswa mengalami peningkatan dari siklus I ke siklus II. Berdasarkan hasil analisis data pada siklus I di kelas VIII-3 sebanyak 35 orang siswa, terdapat 14 siswa (40%) telah mencapai ketuntasan belajar sedangkan 21 siswa (60%) belum tuntas dengan rata-rata kelas 58,77. Pada siklus II di kelas VIII-1 sebanyak 35 orang siswa, terdapat 30 siswa (85,71%) telah mencapai ketuntasan belajar sedangkan 5 siswa (14,29%) belum tuntas dengan rata-rata kelas 71,83. Tingkat penguasaan siswa dari tes belajar II mencapai kriteria sedang, maka pembelajaran ini dikatakan telah tercapai. Dari tindakan, pengematan dan analisis yang dilakukan dapat disimpulkan bahwa model pembelajaran kooperatif learning tipe numbered head together (NHT) dapat meningkatkan aktivitas dan hasil belajar sistem persamaan linear dua variabel.
Uncovering the digital "x" phenomena in the IS field: A text analysis approach

As digital computers permeate an increasing number of activities, the term digital is associated with more processes, entities, and objects (e.g., digital economy, digital natives). This trend reflects in Information Systems research, with “digital” appearing before existing research concepts (e.g., digital infrastructure), often substituting for “IT”. In this study, we contribute to the delineation between digital “x” and IT “x” research by investigating the degree of usage of the terms and uncovering the most widespread digital “x” constructs. Using text analysis techniques, we can analyze a broad set of journals and a long time period. Early results, based on 17 years of MIS Quarterly publications, confirm the increasing use of the term digital. Conversely, digitize and digitalize, the two main forms of the term digital, are rarely used and do not show any significant increase in usage over time. While preliminary and not concluding, our results confirm the increasing popularity of the term digital. However, a limited number of digital “x” terms appear to connote novel constructs and the bulk of the research does not address the socio-technical process of digitalization that is central to the IS discipline. We discuss the implications of our early results and provide a research agenda.