Miracle-dz/UniFAR-FTUs huggingface dataset:
Wegener′s granulomatosis: A diagnostic dilemma for otologists

Sir, A 26‐year‐old female, software engineer by profession, presented with 1‐month history of right earache and persistent headache with fever. The otological examination was suggestive of right middle ear effusion,and audiological examination showed right sided severe mixed hearing loss. Patient had also developed progressive involvement of left ear with impairment of hearing. Nasal endoscopy was normal. Routine blood investigations and serum biochemical evaluation were normal. Patient underwent regular charting of erythrocyte sedimentation rate (ESR), hemogram, and liver and renal function test. All blood investigations were normal except ESR, which was persistently high. Comprehensive autoimmunological workup was performed including antineutrophilic cytoplasmic antibodies (ANCA), RH factor, antinuclear antibody, anti‐ds‐DNA, C3, immunoglobulin ‐G, A, M, cryoglobulin, which was normal. The myringotomy and ventilation tube insertion was tried but had failed to halt the disease. Right mastoid exploration was hence performed considering the diagnosis of intractable otitis media. Granulation tissue was found in middle ear cleft, which on histopathology revealed inflammatory granulation tissue and was not helpful in attaining an accurate diagnosis. In view of high suspicion of an autoimmune disorder, further workup was planned. The final diagnosis of this case was clinched by high‐resolution computed tomography (CT) chest finding [Figure 1], suggestive of nodule in the right lung and confirmed by CT guided fine‐needle aspiration cytology to be a case of Wegeners granulomatosis (WG). The patient was initiated on cyclophosohamide therapy along with steroids. Patient at 21⁄2 year of follow‐up is asymptomatic with very significant improvement of hearing loss allowing her to lead a normal professional and social life.
Système coopératif radiofréquence de positionnement latéral d'un véhicule sur la chaussée

La recherche menee durant cette these a comme objectif de developper un systeme de positionnement lateral d’un vehicule sur sa voie de circulation afin d’alerter le conducteur en cas de sortie involontaire de sa voie ou de fournir a un systeme de conduite autonome la position laterale du vehicule. Le systeme developpe est base sur une interaction de type hyperfrequence entre un module d’emission-reception embarque dans le vehicule et des transpondeurs passifs integres dans la chaussee entre chaque voies de circulation. Ce systeme fonctionne dans la bande UHF. Les transpondeurs passifs contiennent chacun une antenne connectee a deux resonateurs a onde acoustique de surface ayant des coefficients de qualite eleves a leur frequence de resonance. Chaque transpondeur recoit l’onde emise par le module hyperfrequence puis la reemet en lui ajoutant une signature specifique venant des resonateurs. Le module hyperfrequence embarque dans le vehicule contient des antennes d’emission et de reception ainsi qu’une chaine de transmission et d’acquisition. Il fonctionne autour des deux frequences centrales des resonateurs. Un algorithme de traitement du signal recu permet de determiner la distance laterale entre le vehicule et les transpondeurs en utilisant les dephasages d’arrive du signal du transpondeur apres l’avoir extrait du signal global recu. Le systeme a ete experimente en milieu exterieur et il presente une erreur d’estimation de la distance laterale d’environ 4cm.
Auditing Pointwise Reliability Subsequent to Training

To use machine learning in high stakes applications (e.g. medicine), we need tools for building confidence in the system and evaluating whether it is reliable. Methods to improve model reliability are often applied at train time (e.g. using Bayesian inference to obtain uncertainty estimates). An alternative is to audit a fixed model subsequent to training. In this paper, we describe resampling uncertainty estimation (RUE), an algorithm to audit the pointwise reliability of predictions. Intuitively, RUE estimates the amount that a single prediction would change if the model had been fit on different training data drawn from the same distribution by using the gradient and Hessian of the model's loss on training data. Experimentally, we show that RUE more effectively detects inaccurate predictions than existing tools for auditing reliability subsequent to training. We also show that RUE can create predictive distributions that are competitive with state-of-the-art methods like Monte Carlo dropout, probabilistic backpropagation, and deep ensembles, but does not depend on specific algorithms at train-time like these methods do.
The Stochastic Complexity of Principal Component Analysis

PCA (principal component analysis) and its variants are ubiquitous techniques for matrix dimension reduction and reduced-dimension latent-factor extraction. For an arbitrary matrix, they cannot, on their own, determine the size of the reduced dimension, but rather must be given this as an input. NML (normalized maximum likelihood) is a universal implementation of the Minimal Description Length principle, which gives an objective compression-based criterion for model selection. This work applies NML to PCA. A direct attempt to do so would involve the distributions of singular values of random matrices, which is difficult. A reduction to linear regression with a noisy unitary covariate matrix, however, allows finding closed-form bounds on the NML of PCA.
Base-pair mismatch can destabilize small DNA loops through cooperative kinking

Base pair mismatch can relieve mechanical stress in highly strained DNA molecules, but how it affects their kinetic stability is not known. Using single-molecule Fluorescence Resonance Energy Transfer (FRET), we measured the lifetimes of tightly bent DNA loops with and without base pair mismatch. Surprisingly, for loops captured by stackable sticky ends, the mismatch decreased the loop lifetime despite reducing the overall bending stress, and the decrease was largest when the mismatch was placed at the DNA midpoint. These findings show that base pair mismatch transfers bending stress to the opposite side of the loop through an allosteric mechanism known as cooperative kinking. Based on this mechanism, we present a three-state model that explains the apparent dichotomy between thermodynamic and kinetic stability of DNA loops.
Average SEP-Optimal Precoding for Correlated Massive MIMO With ZF Detection: An Asymptotic Analysis

This paper investigates the symbol error probability (SEP) of point-to-point massive multiple-input multiple-output (MIMO) systems using equally likely PAM, PSK, and square QAM signallings in the presence of transmitter correlation. The receiver has perfect knowledge of the channel coefficients, while the transmitter only knows first- and second-order channel statistics. With a zero-forcing (ZF) detector implemented at the receiver side, we design and derive closed-form expressions of the optimal precoders at the transmitter that minimizes the average SEP over channel statistics for various modulation schemes. We then unveil some nice structures on the resulting minimum average SEP expressions, which naturally motivate us to explore the use of two useful mathematical tools to systematically study their asymptotic behaviors. The first tool is the Szegö’s theorem on large Hermitian Toeplitz matrices and the second tool is the well-known limit: $\lim _{x\to \infty }(1+1/x)^{x}=e$ . The application of these two tools enables us to attain very simple expressions of the SEP limits as the number of the transmitter antennas goes to infinity. A major advantage of our asymptotic analysis is that the asymptotic SEP converges to the true SEP when the number of antennas is moderately large. As such, the obtained expressions can serve as an effective SEP approximations for massive MIMO systems even when the number of antennas is not very large. For the widely used exponential correlation model, we derive closed-form expressions for the SEP limits of both optimally precoded and uniformly precoded systems. Extensive simulations are provided to demonstrate the effectiveness of our asymptotic analysis and compare the performance limit of optimally precoded and uniformly precoded systems.
High Performance GNR Power Gating for Low-Voltage CMOS Circuits

A robust power gating design using Graphene Nano-Ribbon Field Effect Transistors (GNRFET) is proposed using 16nm technology. The Power Gating (PG) structure is composed of GNRFET as a power switch and MOS power gated module. The proposed structure resolves the main drawbacks of the traditional PG design from the point of view increasing the propagation delay and wake-up time in low voltage regions. GNRFET/MOSFET Conjunction (GMC) is employed to build various structures of PG, GMCPG-SS and GMCPG-NS. In addition to exploiting it to build two multi-mode PG structures. Circuit analysis for CMOS power gated logic modules ISCAS85 benchmark of 16nm technology is used to evaluate the performance of the proposed GNR power switch is compared to the traditional MOS one. Leakage power, wake-up time and power delay product are used as performance circuit parameters for the evaluation.
Computing the k-coverage of a wireless network

Coverage is one of the main quality of service of a wireless network. k-coverage, that is to be covered simultaneously by k network nodes, is synonym of reliability and numerous applications such as multiple site MIMO features, or handovers. We introduce here a new algorithm for computing the k-coverage of a wireless network. Our method is based on the observation that k-coverage can be interpreted as k layers of 1-coverage, or simply coverage. We use simplicial homology to compute the network's topology and a reduction algorithm to indentify the layers of 1-coverage. We provide figures and simulation results to illustrate our algorithm.
openCoT: The opensource Cloud of Things platform

In order to address the complexity and extensiveness of technology, Cloud Computing is utilized with four main service models. The most recent service model, function-as-a-service, enables developers to develop their application in a function-based structure and then deploy it to the Cloud. Using an optimum elastic auto-scaling, the performance of executing an application over FaaS Cloud, overcomes the extra overhead and reduces the total cost. However, researchers need a simple and well-documented FaaS Cloud manager in order to implement their proposed Auto-scaling algorithms. In this paper, we represent the openCoT platform and explain its building blocks and details. Experimental results show that executing a function (invoking and passing arguments) and returning the result using openCoT takes 21 ms over a remote connection. The source code of openCoT is available in the GitHub repository of the project (\code{www.github.com/adanayi/opencot}) for public usage.
A Field-Size Independent Code Construction for Groupcast Index Coding Problems

The length of an optimal scalar linear index code of a groupcast index coding problem is equal to the minrank of its side-information hypergraph. The side-information hyper-graph becomes a side-information graph for a special class of groupcast index coding problems known as unicast index coding problems. The number of computations required to find the minrank of a side-information graph depends on the number of edges present in the side-information graph. In this paper, we define the notion of minrank-critical edges in a side-information graph and derive some properties of minrank, which identifies minrank-non-critical edges. Using these properties we present a method to reduce the number of computations required to compute minrank. Apart from this, we give a heuristic method to compute minrank. Also, we give an heuristic algorithm to find a clique cover of the side-information graph by using some binary operations on the adjacency matrix of the side-information graph. We also give a method to convert a groupcast index coding problem into a single unicast index coding problem. Combining all these results, we give a method to construct index codes (with not necessarily optimal length) for groupcast index coding problems. The construction technique is independent of field size and hence can be used to construct index codes over the binary field. In some cases the constructed index codes are better than the best known in the literature both in terms of the length of the code and the minimum field size required.
Energy Efficiency Optimization of Generalized Spatial Modulation with Sub-connected Hybrid Precoding

Energy efficiency (EE) optimization of millimeter wave (mm-Wave) massive multiple-input multiple-output (MI-MO) systems is emerging as an important challenge for the fifth generation (5G) mobile communication systems. However, the power of radio frequency (RF) chains increases sharply due to the high carrier frequency in mm-Wave massive MIMO systems. To overcome this issue, a new energy efficiency optimization solution is proposed based on the structure of the generalized spatial modulation (GSM) and sub-connected hybrid precoding (HP). Moreover, the computation power of mm-Wave massive MIMO systems is considered for optimizing the EE. Simulation results indicate that the EE of the GSM-HP scheme outperforms the full digital precoding (FDP) scheme in the mm-Wave massive MIMO scene, and 88% computation power can be saved by the proposed GSM-HP scheme.
An algorithm for the dynamic inference of hidden Markov models (DIHMM)

The DIHMM algorithm performs a robust estimation of the HMM topology and parameters. It allows a better control of the speech variability within each state of the HMM, yielding enhanced estimates. The DIHMM parameters (number of states, structure of the Gaussian mixture density functions, transition matrix) are obtained from the training data via probabilistic grammatical inference techniques welded in a Viterbi-like training framework. Experimental results on various databases indicate a global improvement of the recognition rates in adverse environments; the results averaged on three databases show an increase of 12.8% on raw data and 2.4% when using NSS (nonlinear spectral subtraction). <<ETX>>
New data fusion and tracking approaches in multiple targets/distributed sensors network contexts

The problem of target tracking from distributed sensors in a cluttered environment is addressed. The author and P. Forster have introduced a new and original approach in target tracking and target motion analysis. Two variants of this method have been developed, one using the Bayesian probabilities theory, and one using the theory of evidence. These variants and their specificities with regard to classic tracking methods are recalled, with some extensions to the previous work. Special attention is given to the comparison of these two variants, and to difficult cases such as multiple maneuvering targets or crossing targets. <<ETX>>
Highly-constrained neural networks with application to visual inspection of machined parts

The authors investigate techniques for embedding domain specific spatial invariances into highly constrained neural networks. This information is used to reduce drastically the number of weights which have to be determined during the learning phase, thus allowing application of artificial neural networks to problems characterized by a relatively small number of available examples. As an application of the proposed technique, the problem of optical inspection of machined parts is studied. More specifically, the performance of a network created according to this strategy which accepts images of the parts under inspection at its input and issues at its output a flag which states whether the part is defective, is characterized. The results obtained so far show that such a classifier provides a potentially relevant approach for the quality control of metallic objects since it offers at the same time accuracy and short software development time. <<ETX>>
Subband or cepstral domain filtering for recognition of Lombard and channel-distorted speech

High-pass or band-pass filtering of log subband energies has been shown to improve the robustness of automatic speech recognition to convolutional channel distortions. The authors compare several such filters and apply them in the PLP cepstral domain as well as the log subband domain. They evaluate the robustness of these techniques to Lombard-style test speech with additive noise and their ability to cancel channel effects. They explicitly examine the interactions of such high-pass or band-pass filters with cepstral time derivatives (which are themselves high-pass functions). Conclusions are drawn about factors (e.g., log subband vs cepstral domain, high-pass vs band-pass filter characteristics, and use of time derivatives) which determine the success of these filtering approaches for speaker-independent speech recognition in distorted-channel and noisy-Lombard conditions. <<ETX>>
Probabilistic image modeling via neural networks

The authors propose a neural network approach for the estimation of the local conditional distributions of textured images. They use these distributions to generate a probability distribution on the entire image. The proposed approach overcomes many of the difficulties encountered when using Markov random field (MRF) approaches. In particular, the approach does not require the trial-and-error choice of clique functions or the subsequent estimation of clique parameters. Simulation results show that the images synthesized using neural network modeling produced desired textures more consistently than MRF/Gibbs distribution based methods. <<ETX>>
Computer Vision and Image Understanding

Multi-planar buildings and man-made structures are characterized by a profusion of parallel and orthogonal lines. In this paper, using orthogonal line-pairs as the primary feature, we describe an automatic algorithm to recover 3D structure of a multi-planar scene from a single image. First, we show how the presence of such regular angles can be used for 2D recti ﬁ cation of an image of a plane to a fronto-parallel view. Next, by exploiting this ability to rectify scene planes, we propose an automatic Single-View Reconstruction (SVR) method, assuming there are enough orthogonal line-pairs available on each plane. This angle regularity is only imposed on physically intersecting line-pairs, making it a local constraint. Furthermore, we also describe a novel algo-rithm to automatically segment planes within a scene, and discover their extents and adjacency relationships, using only orthogonal line-pairs. Unlike earlier literature, our approach does not make restrictive assumptions about the orientation of the planes or the camera view, and works for both indoor and outdoor scenes. Results are shown on challenging images which would be di ﬃ cult to reconstruct for existing automatic SVR algorithms.
Phonetic recognition in a segment-based HMM

The author describes a segment-based HMM (hidden Markov model) recognizer and presents phonetic recognition results achieved with the system. As opposed to a conventional frame-based HMM, measurements in such a system are made on variable-duration segments. The key experimental result is that inclusion of measurements made beyond segment boundaries improves phonetic recognition performance significantly. On a set of nine male test speakers from the VOYAGER corpus, the system obtained a phonetic recognition accuracy of 59% (95% confidence interval of 53-65%) on a 39-class phonetic recognition task. Although little attempt was made to optimize system parameters, this result is competitive with existing systems of comparable complexity. <<ETX>>
A hybrid parallel M-D FFT algorithm without interprocessor communication

To reduce or eliminate the interprocessor communications, I. Gertner, R. Tolimieri, and their colleagues proposed an M-D fast Fourier transform (FFT) algorithm, called the reduced transform algorithm (RTA). In the present work, the idea of RTA is extended to the M-D Cooley-Tukey (C-T) FFT algorithm and M-D Good-Thomas (G-T) prime factor algorithm. A new implementation strategy for these algorithms that requires no interprocessor communication is discussed. A hybrid algorithm which combines the C-T or G-T algorithm with RTA is also described. <<ETX>>
Characterization and recognition of radar targets using multiscale edges

A method for characterizing radar signatures using the wavelet transform is developed based on the principle of scattering centers. Scattering features represented as multiscale edges can be identified based on their Lipshitz regularity coefficients. The extracted features are directly related to target geometry and can be used for target identification. The denoising algorithm developed by S. Mallet el al. (IEE Trans vol. PAMI-14, no.7, p.710-32, July 1992) was also implemented, and the extracted wavelet-based features were used for target classification. The target analysis and recognition technique was tested using experimentally measured radar signals. <<ETX>>
Improvements in connected digit recognition using linear discriminant analysis and mixture densities

Four methods were used to reduce the error rate of a continuous-density hidden Markov-model-based speech recognizer on the TI/NIST connected-digits recognition task. Energy thresholding sets a lower limit on the energy in each frequency channel to suppress spurious distortion accumulation caused by random noise. This led to an improvement in error rate by 15%. Spectrum normalization was used to compensate for across-speaker variations, resulting in an additional improvement by 20%. The acoustic resolution was increased up to 32 component densities per mixture. Each doubling of the number of component densities yielded a reduction in error rate by roughly 20%. Linear discriminant analysis was used for improved feature selection. A single class-independent transformation matrix was applied to a large input vector consisting of several adjacent frames, resulting in an improvement by 20% for high acoustic resolution. The final string error rate was 0.84%. <<ETX>>
INDEPENDENT SPEAKER IDENTIFICATION USING FUZZY MATHEMATICAL ALGORITHM

In this paper, the different effectiveness in representing the individnal features of speakers between Line Spectrum Pair Preqnencies(LSP) and Cepstrum(CEP) derived from linear prediction analysis is demonstrated. We select LSP parameters as speaker’s feature and explore the ways for solving the problem of speaker recognition. The Fuzzy mathematical algorithm is introduced in this study, using Fuzzy statistical method to build up membership function as speaker’s template, and using the maximum value of membership function as deciding criterion. The random combination of isolated digit from 0 to 9 are specified as identification utterences. The system has been evaluate on a database of isolated digit utterence of 42 speakers, 20 males and 22 females, all are university students. For each speaker, the training utterance is approximate 36 sec. The correct identification rate is approximately 99.7% for the random combination of five isolated digits.
Tree-based unit selection for English speech synthesis

In concatenative synthesis for English, scarcity of speech data for many contexts is a serious problem. The authors propose a novel unit selection scheme using a decision-tree-based clustering method that combines acoustic and linguistic knowledge with statistical modeling. This approach makes it possible not only to find a trainable and consistent set of generalized allophonic models but also to achieve some local optimality with respect to the limited training data. To evaluate the validity of this algorithm, regression tree generation has been carried out for both vowels and consonants from 200 phonetically balanced sentences read by a female speaker. Experimental results show that regression trees offer a promising solution for the data scarcity problem. <<ETX>>