Miracle-dz/UniFAR-FTUs huggingface dataset:
Taxonomy-based dissimilarity measures for profile identification in medical data

The lists of diagnostic codes which are usually recorded in the hospitals for health management and/or costs reimbursement purposes can represent a useful source of information in the analysis of the (dis)similarity between different patients, as long as appropriate measures exist to estimate this (dis)similarity. The aim of this paper is to analyze various measures obtained by using different ways of computing the information content corresponding to entities in a taxonomy and by aggregating different types of measures. The discriminative power of these measures is evaluated by analyzing their ability to explain existing groups in data. A case study based on medical records containing lists of ICD (International Classification of Diseases) codes is presented and the proposed dissimilarity measures are used to identify prototypes in groups of patients.
Molecular Systems Biology Peer Review Process File Negative Feedback Buffers Effects of Regulatory Variants Transaction Report: Negative Feedback Buffers Effects of Regulatory Variants Point by Point Response

(Note: With the exception of the correction of typographical or spelling errors that could be a source of ambiguity, letters and reports are not edited. The original formatting of letters and referee reports may not be reflected in this compilation.) Thank you again for submitting your work to Molecular Systems Biology. We have now heard back from the three referees who accepted to evaluate the study. As you will see, the referees find the topic of your study of potential interest and are supportive. They raise however a series of concerns and make suggestions for modifications, which we would ask you to carefully address in a revision of the present work. The recommendations provided by the reviewers are very clear in this regard and refer mainly to additional clarifications and the need to word some of the conclusions more carefully. The authors use an elegant and efficient experimental design using RNA-seq in a hybrid diploid yeast and pools of halpoid segregants to distinguish between genetic variants close ('local') to a gene that influence expression in cis and trans. This allows them to reach the conclusion that many genes regulate there own expression by feedback ('tho the mechanisms are obscure). They also re-analyse ribosome profiling data that was previously used to suggest buffering of changes in RNA expression via changes in translation. They find no evidence for this but rather show the previous result is likely due to a technical artefact in the data processing. Overall I found this is a very interesting study that systematically addresses a basic and important question in gene regulation genome-wide. Minor suggestions: In the analysis with respect to gene importance the authors (need to) control for expression level. It would be reassuring to see this elsewhere. e.g. is there a bias in the detection of local-trans or local-cis eQTLs depending upon expression levels in the pools and does this effect the results? Molecular mechanisms-(for good reason) the authors speculate little about possible molecular mechanisms. But it would be interesting to know a bit more about the properties/functions of the genes they do detect feedback for-what functional categories/age of proteins do they cover etc? Fig 1B-I don't understand the choice of example shown here. If I understand correctly in this example the example is not any of their classes in fig 1C (looks like the expression levels are swapped between the diploids and haploids? ) Abstract-delete the …
Projecting species’ vulnerability to climate change: Which uncertainty sources matter most and extrapolate best?

Abstract Species distribution models (SDMs) are commonly used to assess potential climate change impacts on biodiversity, but several critical methodological decisions are often made arbitrarily. We compare variability arising from these decisions to the uncertainty in future climate change itself. We also test whether certain choices offer improved skill for extrapolating to a changed climate and whether internal cross‐validation skill indicates extrapolative skill. We compared projected vulnerability for 29 wetland‐dependent bird species breeding in the climatically dynamic Prairie Pothole Region, USA. For each species we built 1,080 SDMs to represent a unique combination of: future climate, class of climate covariates, collinearity level, and thresholding procedure. We examined the variation in projected vulnerability attributed to each uncertainty source. To assess extrapolation skill under a changed climate, we compared model predictions with observations from historic drought years. Uncertainty in projected vulnerability was substantial, and the largest source was that of future climate change. Large uncertainty was also attributed to climate covariate class with hydrological covariates projecting half the range loss of bioclimatic covariates or other summaries of temperature and precipitation. We found that choices based on performance in cross‐validation improved skill in extrapolation. Qualitative rankings were also highly uncertain. Given uncertainty in projected vulnerability and resulting uncertainty in rankings used for conservation prioritization, a number of considerations appear critical for using bioclimatic SDMs to inform climate change mitigation strategies. Our results emphasize explicitly selecting climate summaries that most closely represent processes likely to underlie ecological response to climate change. For example, hydrological covariates projected substantially reduced vulnerability, highlighting the importance of considering whether water availability may be a more proximal driver than precipitation. However, because cross‐validation results were correlated with extrapolation results, the use of cross‐validation performance metrics to guide modeling choices where knowledge is limited was supported.
A novel EMG-driven state space model for the estimation of continuous joint movements

Electromyography (EMG) has been widely used as control commands for prosthesis, powered exoskeletons and rehabilitative robots. In this paper, an EMG-driven state space model is developed to estimate continuous joint angular displacement and velocity, demonstrated by elbow flexion/ extension. The model combines the Hill-based muscle model with the forward dynamics of joint movement, in which kinematic variables are expressed as a function of neural activation levels. EMG features including integral of absolute value and waveform length are then extracted, and two quadratic equations which associate the kinematic variables with EMG features are constructed to represent the measurement equation. The proposed model are verified by extensively experiments, where the angular movements of human elbow joint are estimated only using the EMG signals, and the estimations are compared with the IMU measurements to validate the accuracy. As a demonstration, a robotic arm is commanded to follow the human elbow movement estimated by the proposed model, which shows the possibility of EMG-based robotic assisted rehabilitation.
Algebraic Effects for Functional Programming (Type Directed Compilation of Row-typed Algebraic Effects)

Algebraic effect handlers, are recently gaining in popularity as a purely functional approach to modeling effects. In this article, we give an end-to-end overview of practical algebraic effects in the context of a compiled implementation in the Koka language. In particular, we show how algebraic effects generalize over common constructs like exception handling, state, iterators and async-await. We give an effective type inference algorithm based on extensible effect rows using scoped labels, and a direct operational semantics. Finally, we show an efficient compilation scheme to common runtime platforms (such as JavaScript, the JVM, or .NET) using a type directed selective CPS translation.
Stationary Density of Stochastic Search Processes

The optimization of a cost function which has a number of local minima is a relevant subject in many important fields. For instance, the determination of the weights of learning machines depends in general on the solution of global optimization tasks (Haykin, 1999). A feature shared by almost all of the most common deterministic and stochastic algorithms for continuous non – linear optimization is that their performance is strongly affected by their starting conditions. Depending on the algorithm, the correct selection of an initial point or set of points have direct consequences on the efficiency, or even on the possibility to find the global minima. Of course, adequate selection of seeds implies prior knowledge on the structure of the optimization task. In the absence of prior information, a natural choice is to draw seeds from a uniform density defined over the search space. Knowledge on the problem can be gained through the exploration of this space. In this contribution is presented a method to estimate probability densities that describe the asymptotic behavior of general stochastic search processes over continuously differentiable cost functions. The relevance of such densities is that they give a description of the residence times over the different regions of the search space, after an infinitely long exploration. The preferred regions are those which minimize the cost globally, which is reflected in the asymptotic densities. In first instance, the resulting densities can be used to draw populations of points that are consistent with the global properties of the associated optimization tasks. BACKGROUND
An Improved Congest Control Protocol Based on TCP Veno Protocol

In the traditional network, TCP protocol is used to realize the reliable transmission and network congest control to release the network congestion. In the wireless network, TCP Veno is better than traditional TCP. This paper proposes an improved congest control protocol based on the TCP Veno protocol to improve the network performance by means of bandwidth prediction and dynamic window size adjust(BPDW). And through the math model and simulator experiments, it proves that the BPDW improves the throughput of network and increase the network efficiency.
Artificial Neural Networks for Iris Recognition System: Comparisons between Different Models, Architectures and Algorithms

In this research, an iris recognition system was suggested based on five Artificial Neural Network (ANN) models separately: feed forward (FFBPNN), cascade forward (CFBPNN), function fitting (FitNet), pattern recognition (PatternNet) and learning vector quantization (LVQNet). For each ANN model, two architectures were constructed separately; 4 layers and 7 layers, each with different numbers of hidden layer units (5, 10 and 15). Ten different ANN optimization training algorithms (LM, BFG, BR, CGF, GD, GDM, GDA, GDX, OSS and RP) were used to train each model separately. Many experiments were conducted for each one of the five models. Each model used two different architectures, a different number of hidden layer neurons and ten different training algorithms. The performance results of the models were compared according to mean square error to identify the best ANN model. The results showed that the PatternNet model was the best model used. Finally, comparisons between the ten training algorithms were performed through training the PatternNet model. Comparison results showed that TrainLM was the best training algorithm for the iris recognition system.
Theoretical and Computational Fluid Dynamics Large-eddy Simulation of the Temporal Mixing Layer Using the Clark Model

The Clark model for the turbulent stress tensor in large-eddy simulation is investigated from a theoretical and computational point of view. In order to be applicable to compressible turbulent flows, the Clark model has been reformulated. Actual large-eddy simulation of a weakly compressible, turbulent, temporal mixing layer shows that the eddy-viscosity part of the original Clark model gives rise to an excessive dissipation of energy in the transitional regime. On the other hand, the model gives rise to instabilities if the eddy-viscosity part is omitted and only the "gradient" part is retained. A linear stability analysis of the Burgers equation supplemented with the Clark model is performed in order to clarify the nature of the instability. It is shown that the growth-rate of the instability is infinite in the inviscid limit and that sufficient (eddy-)viscosity can stabilize the model. A model which avoids both the excessive dissipation of the original Clark model as well as the instability of the "gradient" part, is obtained when the dynamic procedure is applied to the Clark model. Large-eddy simulation using this new dynamic Clark model is found to yield satisfactory results when compared with a filtered direct numerical simulation. Compared with the standard dynamic eddy-viscosity model, the dynamic Clark model yields more accurate predictions, whereas compared with the dynamic mixed model the new model provides equal accuracy at a lower computational effort.
Adaptive instantaneous frequency estimation of multi-component FM signals

Estimation of the instantaneous frequency (IF) of multicomponent FM signals with additive Gaussian noise is considered. A reduced interference distribution (RID) with high resolution is used for time-frequency representation of signals. If the IF law is a nonlinear function of time, the bias and variance of the estimate depend on the window length. The optimal window length for bias-variance tradeoff depends on the unknown IF law, hence calling for an adaptive algorithm with time-varying and data-driven window length to resolve this tradeoff. The adaptive estimation of multiple IF laws is based on tracking the component maxima in the time-frequency plane. Simulation results for multi-component signals are comparable with those obtained for mono-component signals when the components are well separated.
Effects of Growth Phase and Extracellular Slime on Photodynamic Inactivation of Gram-Positive Pathogenic Bacteria

ABSTRACT The emergence of antibiotic resistance among pathogenic bacteria has led to efforts to find alternative antimicrobial therapeutics to which bacteria will not be easily able to develop resistance. One of these may be the combination of nontoxic dyes (photosensitizers [PS]) and visible light, known as photodynamic therapy, and we have reported its use to treat localized infections in animal models. While it is known that gram-positive species are generally susceptible to photodynamic inactivation (PDI), the factors that govern variation in degrees of killing are unknown. We used isogenic pairs of wild-type and transposon mutants deficient in capsular polysaccharide and slime production generated from Staphylococcus epidermidis and Staphylococcus aureus to examine the effects of extracellular slime on susceptibility to PDI mediated by two cationic PS (a polylysine-chlorine6 conjugate, pL-ce6, and methylene blue [MB]) and an anionic molecule, free ce6, and subsequent exposure to 665-nm light at 0 to 40 J/cm2. Free ce6 gave more killing of mutant strains than wild type, despite the latter taking up more PS. Log-phase cultures were killed more than stationary-phase cultures, and this correlated with increased uptake. The cationic pL-ce6 and MB gave similar uptakes and killing despite a 50-fold difference in incubation concentration. Differences in susceptibility between strains and between growth phases observed with free ce6 largely disappeared with the cationic compounds despite significant differences in uptake. These data suggest that slime production and stationary phase can be obstacles against PDI for gram-positive bacteria but that these obstacles can be overcome by using cationic PS.
An argument for simple COMA

We present design details and some initial performance results of a novel scalable shared memory multiprocessor architecture. This architecture features the automatic data migration and replication capabilities of cache-only memory architecture (COMA) machines, without the accompanying hardware complexity. A software layer manages cache space allocation at a page-granularity-similarly to distributed virtual shared memory (DVSM) systems, leaving simpler hardware to maintain shared memory coherence at a cache line granularity. By reducing the hardware complexity, the machine cost and development time are reduced. We call the resulting hybrid hardware and software multiprocessor architecture Simple COMA. Preliminary results indicate that the performance of Simple COMA is comparable to that of more complex contemporary all hardware designs. <<ETX>>
Low complexity iterative MLSE equalization of M-QAM signals in extremely long Rayleigh fading channels

This work proposes a neural network based iterative Maximum Likelihood Sequence Estimation (MLSE) equalizer, able to equalize signals in M-arry Quadrature Amplitude Modulation (M-QAM) modulated systems in a mobile fading environment with extremely long channels. Its computational complexity is linear in the data block length and approximately independent of the channel memory length, whereas conventional equalization algorithms have computational complexity linear in the data block length but exponential in the channel memory length. Its performance is compared to the Viterbi MLSE equalizer for short channels and it is shown that the proposed equalizer has the ability to equalize M-QAM signals in systems with hundreds of memory elements, achieving matched filter bound performance with perfect channel state information (CSI) knowledge in uncoded systems. The proposed equalizer is evaluated in a frequency selective Rayleigh fading environment.
Hierarchical Multi-label Classification incorporating prior information for gene function prediction

This paper proposes an improved Hierarchical Multi-label Classification (HMC) method for solving the gene function prediction. The HMC task is transferred into a series of binary SVM classification tasks. By introducing the hierarchy constraint into learning procedures, two measures with incorporating prior information are implemented to improve the HMC performance. Firstly, for imbalanced functional classes, a hierarchical SMOTE is proposed as over-sampling preprocessing to improve the SVM learning performance. Secondly, an improved True Path Rule consistency approach is introduced to ensemble the results of binary probabilistic SVM classifications. It can improve the classification results and guarantee the hierarchy constraint of classes.
Mel-Frequency Cepstrum Coeffficients as Higher Order Statistics Representation to Characterize Speech Signal for Speaker Identification System in Noisy Environment Using Hidden Markov Model

Sound is an effective and efficient magnitude for biometric characterization. However, the sound is a phenomenon that is a fusion of multidimensional and influenced many aspects, such as speaker characteristics (articulator configuration, emotions, health, age, sex, dialect), languages, and the environment (background and transmission media), so that the system has been developed until now has not been able to work well in real situations. This is the background of this research. In this research, we investigate higher order statistics (HOS) and Mel-Frequency Cepstrum Coefficients (MFCC) as a feature extraction, and integrated with a Hidden Markov Model (HMM) as a classifier to get a more robust speaker identification system, especially for Gaussian Noise. Research carried out more focused on feature extraction part of the speaker identification system. In classifier process stage, we use the HMM. This is a technique that has been widely used in voice processing provides good results. At the beginning, we empirically showed the failure of conventional MFCC using power spectrum in noisy environment. Then proceed with reviewing the matter, and proposed HOS-based extraction techniques to overcome these problems. Next is an experiments to demonstrate the effectiveness of the proposed method. Data used in this study came from 10 people who say the phrase 'PUDHESA' as much as 80 times with different ways of utterance. In this research, we use signals that are spoken with different variations of pressure, duration, emotional, loud and weak. Figure 1 presents the forms of signals for different utterances of a speaker. In accordance with the focus of this research is to build models that are more robust to noise, then we add a Gaussian noise signal to each original signal with a signal-tonoise ratio (SNR) of 20 dB, 10 dB, 5 dB and 0 dB.
Extracting the multilingual terminology from a web-based encyclopedia

—Multilingual linguistic resources are usually constructed from parallel corpora, but since these corpora are available only for selected text domains and language pairs, the potential of other resources is being explored as well. This article seeks to explore and to exploit the idea of using multilingual web-based encyclopedias such as Wikipedia as comparable corpora for bilingual terminology extraction. We propose an approach to extract terms and their translations from different types of Wikipedia link information and data. The next step will be using a linguistic-based information to re-rank and filter the extracted term candidates in the target language. Preliminary evaluations using the combined statistics-based and linguistic-based approaches were applied on different pairs of languages including Japanese, French and English. These evaluations showed a real open improvement and a good quality of the extracted term candidates for building or enriching multilingual ontologies, dictionaries or feeding a cross-language information retrieval system with the related expansion terms of the source query.
Review on Cluster-head Election Mechanisms for Clustering Based Routing in Mobile Ad-hoc Network Ms

Wireless Ad hoc network is a set of wireless devices which move randomly and communicate with other node via radio signal. Ad-hoc networks may be logically represented as a set of clusters by grouping together nodes on the basis of different criteria such as 1-hop and k-hop that are in close boundary with one another. Clusters are formed by diffusing node identities along the wireless links. Different heuristics employ different policies to elect cluster heads. Several of these policies are biased in favour of some nodes. As a result, these nodes should greater responsibility and may deplete their energy faster, causing them to drop out of the network. Therefore, there is a need for load-balancing among cluster-heads to allow all nodes the opportunity to serve as a cluster-head. In this paper, different cluster head election mechanism are discussed in the one hop clustering approach.