Miracle-dz/UniFAR-FTUs huggingface dataset:
Frequency of Reduced Vancomycin Susceptibility among Clinical Staphylococcus aureus Isolated in Ahvaz Iran

Introduction: One of the most important agents in hospital-acquired infections is Staphylococcus aureus. Treatment of methicillin-resistant S. aureus (MRSA) infections with decreased susceptibility to vancomycin has recently been more difficult. The aim of this study was to evaluate the possible presence of vancomycin intermediate S. aureus (VISA) and vancomycin- resistant S. aureus (VRSA) and also to determine the frequency of MRSA in clinical specimens. M e thods: In this study, 195 S. aureus isolates were collected from the patients were examined. All of the isolates were identified using standard biochemical tests. Susceptibility of S. aureus isolates against 10 antibiotics was detected by disk diffusion method and was followed by E-test and vancomycin screen agar methods. Minimum inhibitory concentration (MIC) of vancomycin was determined according to the CLSI guidelines. Also, detection of mecA gene was performed by PCR and finally, the results were compared. Results: All of the isolates were susceptible to vancomycin (i.e. MIC range of vancomycin was between 0.25-2 µg/ml). Out of 195 S. aureus isolates, 99 isolates (50.8%) were resistant to methicillin, and mecA gene was detected in 96 isolates. These results also showed that the highest and lowest resistance rate of isolates was to penicillin (96.9%) and chloramphenicol (0%), respectively. Conclusion: Our findings showed that vancomycin can still be used as a valuable drug for treatment of S. aureus infections in our region. However, periodic evaluation of vancomycin MIC of S. aureus isolates is critical for monitoring MRSA and preventing the spread of VISA or VRSA among patients.
TIME CHANGES OF CONTRAST THRESHOLDS DURING AUTOMATIC PERIMETRY

Contrast thresholds were continuously recorded in six points of the visual field through a repetitive up‐and‐down staircase method using the automatic perimeter developed by Heijl & Krakau (1975b). The uninterrupted sessions lasted about 30 min. Nineteen patients with a verified diagnosis of glaucoma, or in whom glaucoma was suspected, and twelve healthy normal subjects were tested.
Generalized Zero-Shot Learning via VAE-Conditioned Generative Flow

Generalized zero-shot learning (GZSL) aims to recognize both seen and unseen classes by transferring knowledge from semantic descriptions to visual representations. Recent generative methods formulate GZSL as a missing data problem, which mainly adopts GANs or VAEs to generate visual features for unseen classes. However, GANs often suffer from instability, and VAEs can only optimize the lower bound on the log-likelihood of observed data. To overcome the above limitations, we resort to generative flows, a family of generative models with the advantage of accurate likelihood estimation. More specifically, we propose a conditional version of generative flows for GZSL, i.e., VAE-Conditioned Generative Flow (VAE-cFlow). By using VAE, the semantic descriptions are firstly encoded into tractable latent distributions, conditioned on that the generative flow optimizes the exact log-likelihood of the observed visual features. We ensure the conditional latent distribution to be both semantic meaningful and inter-class discriminative by i) adopting the VAE reconstruction objective, ii) releasing the zero-mean constraint in VAE posterior regularization, and iii) adding a classification regularization on the latent variables. Our method achieves state-of-the-art GZSL results on five well-known benchmark datasets, especially for the significant improvement in the large-scale setting. Code is released at this https URL
String shoving effects on jets in p-p collisions

Di-jet observables are excellent probes to study the effect of jets in dense systems. Interacting Lund strings will affect jet observables and suggests a new common mechanism responsible for jet modification in p-A and A-A. In this proceeding, we present our new implementation of the string shoving mechanism in PYTHIA8 which lets us study the effects on jet observables in p-p and nuclear collisions. We also present preliminary results showing the effects in hadron-jet correlation studies.
The Computational Capacity of Memristor Reservoirs

Reservoir computing is a machine learning paradigm in which a high-dimensional dynamical system, or \emph{reservoir}, is used to approximate and perform predictions on time series data. Its simple training procedure allows for very large reservoirs that can provide powerful computational capabilities. The scale, speed and power-usage characteristics of reservoir computing could be enhanced by constructing reservoirs out of electronic circuits, but this requires a precise understanding of how such circuits process and store information. We analyze the feasibility and optimal design of such reservoirs by considering the equations of motion of circuits that include both linear elements (resistors, inductors, and capacitors) and nonlinear memory elements (called memristors). This complements previous studies, which have examined such systems through simulation and experiment. We provide analytic results regarding the fundamental feasibility of such reservoirs, and give a systematic characterization of their computational properties, examining the types of input-output relationships that may be approximated. This allows us to design reservoirs with optimal properties in terms of their ability to reconstruct a certain signal (or functions thereof). In particular, by introducing measures of the total linear and nonlinear computational capacities of the reservoir, we are able to design electronic circuits whose total computation capacity scales linearly with the system size. Comparison with conventional echo state reservoirs show that these electronic reservoirs can match or exceed their performance in a form that may be directly implemented in hardware.
GIF: Generative Interpretable Faces

Photo-realistic visualization and animation of expressive human faces have been a long standing challenge. 3D face modeling methods provide parametric control but generates unrealistic images, on the other hand, generative 2D models like GANs (Generative Adversarial Networks) output photo-realistic face images, but lack explicit control. Recent methods gain partial control, either by attempting to disentangle different factors in an unsupervised manner, or by adding control post hoc to a pre-trained model. Unconditional GANs, however, may entangle factors that are hard to undo later. We condition our generative model on pre-defined control parameters to encourage disentanglement in the generation process. Specifically, we condition StyleGAN2 on FLAME, a generative 3D face model. While conditioning on FLAME parameters yields unsatisfactory results, we find that conditioning on rendered FLAME geometry and photometric details works well. This gives us a generative 2D face model named GIF (Generative Interpretable Faces) that offers FLAME’s parametric control. Here, interpretable refers to the semantic meaning of different parameters. Given FLAME parameters for shape, pose, expressions, parameters for appearance, lighting, and an additional style vector, GIF outputs photo-realistic face images. We perform an AMT based perceptual study to quantitatively and qualitatively evaluate how well GIF follows its conditioning. The code, data, and trained model are publicly available for research purposes at http://gif.is. tue.mpg. de.
The Extremal Number of Tight Cycles

A tight cycle in an $r$-uniform hypergraph $\mathcal{H}$ is a sequence of $\ell \geq r+1$ vertices $x_1,. ,x_{\ell }$ such that all $r$-tuples $\{x_{i},x_{i+1},. ,x_{i+r-1}\}$ (with subscripts modulo $\ell $) are edges of $\mathcal{H}$. An old problem of V. Sós, also posed independently by J. Verstraëte, asks for the maximum number of edges in an $r$-uniform hypergraph on $n$ vertices, which has no tight cycle. Although this is a very basic question, until recently, no good upper bounds were known for this problem for $r\geq 3$. Here we prove that the answer is at most $n^{r-1+o(1)}$. This is tight up to the $o(1)$ error term, and it was shown recently by B. Janzer that this error term is indeed needed. Our proof is based on finding robust expanders in the line-graph of $\mathcal{H}$ together with certain density increment type arguments.
Church's thesis and related axioms in Coq's type theory

"Church's thesis" ($\mathsf{CT}$) as an axiom in constructive logic states that every total function of type $\mathbb{N} \to \mathbb{N}$ is computable, i.e. definable in a model of computation. $\mathsf{CT}$ is inconsistent in both classical mathematics and in Brouwer's intuitionism since it contradicts Weak Konig's Lemma and the fan theorem, respectively. Recently, $\mathsf{CT}$ was proved consistent for (univalent) constructive type theory. Since neither Weak Konig's Lemma nor the fan theorem are a consequence of just logical axioms or just choice-like axioms assumed in constructive logic, it seems likely that $\mathsf{CT}$ is only inconsistent with a combination of classical logic and choice axioms. We study consequences of $\mathsf{CT}$ and its relation to several classes of axioms in Coq's type theory, a constructive type theory with a universe of propositions which does neither prove classical logical axioms nor strong choice axioms. We thereby provide a partial answer to the question which axioms may preserve computational intuitions inherent to type theory, and which certainly do not. The paper can also be read as a broad survey of axioms in type theory, with all results mechanised in the Coq proof assistant.
On the convergence of an improved and adaptive kinetic simulated annealing

Inspired by the work of [Fang et al. An improved annealing method and its large-time behaviour. Stochastic Process. Appl. (1997), Volume 71 Issue 1 Page 55-74. ], who propose an improved simulated annealing algorithm based on a variant of overdamped Langevin diffusion with state-dependent diffusion coefficient, we cast this idea in the kinetic setting and develop an improved kinetic simulated annealing (IKSA) method for minimizing a target function $U$. To analyze its convergence, we utilize the framework recently introduced by [Monmarche. Hypocoercivity in metastable settings and kinetic simulated annealing. Probab. Theory Related Fields (2018), Volume 172 Page 1215-1248. ] for the case of kinetic simulated annealing (KSA). The core idea of IKSA rests on introducing a parameter $c > \inf U$, which de facto modifies the optimization landscape and clips the critical height in IKSA at a maximum of $c - \inf U$. Consequently IKSA enjoys improved convergence with faster logarithmic cooling than KSA. To tune the parameter $c$, we propose an adaptive method that we call IAKSA which utilizes the running minimum generated by the algorithm on the fly, thus avoiding the need to manually adjust $c$ for better performance. We present positive numerical results on some standard global optimization benchmark functions that verify the improved convergence of IAKSA over other Langevin-based annealing methods.
Irreducibility of integer-valued polynomials I

Abstract Let be an arbitrary subset of a unique factorization domain R and be the field of fractions of R. The ring of integer-valued polynomials over S is the set This article is an effort to study the irreducibility of integer-valued polynomials over arbitrary subsets of a unique factorization domain. We give a method to construct special kinds of sequences, which we call d-sequences. We then use these sequences to obtain a criteria for the irreducibility of the polynomials in In some special cases, we explicitly construct these sequences and use these sequences to check the irreducibility of some polynomials in At the end, we suggest a generalization of our results to an arbitrary subset of a Dedekind domain.
Morpho-Molecular Characterization and Virulence Determination ofEntomopathogenic Fungal Isolates Native to Indian Subcontinent

Background: The house fly, Musca domestica L. is a major pest of medical and veterinary significance which due to their ubiquitous lifestyle and broad food preference achieves a very high density in favorable climatic conditions of Indian subcontinent. This has led to several instances of severe health condition among human and animal population, calling attention towards M. domestica control through existing and innovative methods. Use of biological control agent, such as entomopathogenic fungi (ENPF) for M. domestica control has shown potential, and need to be further explored for the newer isolates suited to particular environment for greater efficacy. The present study attempted the isolation and identification of native ENPF strains suitable for M. domestica control. Method and findings: Five strains of ENPF were isolated from soil samples, which on preliminary identification of morphological, microscopic and spore attributes were determined to be isolates of Beauveria species. The isolates showed wide variation in pathogenicity against M. domestica, with 72.7%-100% and 36.7%-72.3% mortality against adults and larvae life stage, respectively. Isolate ‘1’ depicting maximum insecticidal activity against M. domestica was selected for molecular identification using 18s-rRNA, and found to be Beauveria bassia HQ917687. The molecular analysis using random amplified polymorphic DNA (RAPD) showed isolate ‘2’ to be most similar to isolate ‘3’, while isolate ‘1’ and ‘5’ showed maximum variation between them. RAPD result was in conformity with various other properties of isolates; such as biomass, spore yield, hydrophobicity and insecticidal activity. Conclusion: The variations in activity obtained through pathogenicity assessment of fungal isolates in this study reflected the strain diversity of Beauveria isolates. The results also signified the importance of strain selection for effective control measures as well as for further consideration of commercial aspects.
Image Super-Resolution using Explicit Perceptual Loss

This paper proposes an explicit way to optimize the super-resolution network for generating visually pleasing images. The previous approaches use several loss functions which is hard to interpret and has the implicit relationships to improve the perceptual score. We show how to exploit the machine learning based model which is directly trained to provide the perceptual score on generated images. It is believed that these models can be used to optimizes the super-resolution network which is easier to interpret. We further analyze the characteristic of the existing loss and our proposed explicit perceptual loss for better interpretation. The experimental results show the explicit approach has a higher perceptual score than other approaches. Finally, we demonstrate the relation of explicit perceptual loss and visually pleasing images using subjective evaluation.
Determination of $\alpha_{S}$ beyond NNLO using event shape moments

We consider a method for determining the QCD strong coupling constant using fits of perturbative predictions for event shape moments to data collected at the LEP, PETRA, PEP and TRISTAN colliders. To obtain highest accuracy predictions we use a combination of perturbative ${\cal{O}}(\alpha_{S}^{3})$ calculations and estimations of the ${\cal{O}}(\alpha_{S}^{4})$ perturbative coefficients from data. We account for non-perturbative effects using modern Monte Carlo event generators and analytic hadronization models.
Optimal Solution Analysis and Decentralized Mechanisms for Peer-to-Peer Energy Markets

This paper studies the optimal clearing problem for prosumers in peer-to-peer (P2P) energy markets. It is proved that if no trade weights are enforced and the communication structure between successfully traded peers is connected, then the optimal clearing price and total traded powers in P2P market are the same with that in the pool-based market. However, if such communication structure is unconnected, then the P2P market is clustered into smaller P2P markets. If the trade weights are imposed, then the derived P2P market solutions can be significantly changed. Next, a novel decentralized optimization approach is proposed to derive a trading mechanism for P2P markets, based on the alternating direction method of multipliers (ADMM) which naturally fits into the bidirectional trading in P2P energy systems and converges reasonably fast. Analytical formulas of variable updates reveal insightful relations for each pair of prosumers on their individually traded prices and powers with their total traded powers. Further, based on those formulas, decentralized learning schemes for tuning parameters of prosumers cost functions are proposed to attain successful trading with total traded power amount as desired. Case studies on a synthetic system and the IEEE European Low Voltage Test Feeder are then carried out to verify the proposed approaches.
Birational geometry of the Mukai system of rank two and genus two

Using the techniques of Bayer--Macri, we determine the walls in the movable cone of the Mukai system of rank two for a general K3 surface $S$ of genus two. We study the (essentially unique) birational map to $S^{[5]}$ and decompose it into a sequence of flops. We give an interpretation of the exceptional loci in terms of Brill--Noether loci.
Towards Evaluating Exploratory Model Building Process with AutoML Systems

The use of Automated Machine Learning (AutoML) systems are highly open-ended and exploratory. While rigorously evaluating how end-users interact with AutoML is crucial, establishing a robust evaluation methodology for such exploratory systems is challenging. First, AutoML is complex, including multiple sub-components that support a variety of sub-tasks for synthesizing ML pipelines, such as data preparation, problem specification, and model generation, making it difficult to yield insights that tell us which components were successful or not. Second, because the usage pattern of AutoML is highly exploratory, it is not possible to rely solely on widely used task efficiency and effectiveness metrics as success metrics. To tackle the challenges in evaluation, we propose an evaluation methodology that (1) guides AutoML builders to divide their AutoML system into multiple sub-system components, and (2) helps them reason about each component through visualization of end-users' behavioral patterns and attitudinal data. We conducted a study to understand when, how, why, and applying our methodology can help builders to better understand their systems and end-users. We recruited 3 teams of professional AutoML builders. The teams prepared their own systems and let 41 end-users use the systems. Using our methodology, we visualized end-users' behavioral and attitudinal data and distributed the results to the teams. We analyzed the results in two directions: what types of novel insights the AutoML builders learned from end-users, and (2) how the evaluation methodology helped the builders to understand workflows and the effectiveness of their systems. Our findings suggest new insights explaining future design opportunities in the AutoML domain as well as how using our methodology helped the builders to determine insights and let them draw concrete directions for improving their systems.
When Costly Punishment Becomes Evolutionarily Beneficial

Cooperation in evolutionary biology means paying a cost, c, to enjoy benefits, b. A defector is one who does not pay any cost but enjoys the benefits of cooperators. Human societies, especially, have evolved a strategy to discourage defection, punishment. Costly punishment is a type of punishment where an agent in a biological network or some cooperative scheme pays a cost to ensure another agent incurs some cost. A previous study shows how parameters like diversity in neighbors across games, density of connectivity, and costly punishment influence the evolution of cooperation in non-regular networks. In this study, evolution in regular networks due to the influence of costly punishment is also considered, specifically spatial lattices. This study compares observations between non-regular and regular networks as these parameters change and brings a clearer understanding of interactions that occur in these networks. The models, results and analysis bring a new understanding to game theory and punishment. The results show that costly punishment never arises as a sole evolutionary strategy. However, in evolutionary games where costly punishers could evolve more favorable strategies, the initial presence of costly punishers would bring about high average payoffs in all types of regular networks and heterogeneous networks. In regular networks, every node has the same degree, k. Although punishment is conventionally thought to be anti-progressive, in the presence of diversity in neighbors, it magnifies the payoff of a group for all heterogeneous networks. In regular networks however, diversity in neighbors is not required for costly punishment to boost average payoff. This suggests an answer to the question on why costly punishment has been favored by natural selection, which is particularly obvious in human populations.
Introductory computations in the cohomology of arithmetic groups

This paper describes an approach to computer aided calculations in the cohomology of arithmetic groups. It complements existing literature on the topic by emphasizing homotopies and perturbation techniques, rather than cellular subdivision, as the tools for implementing on a computer topological constructions that fail to preserve cellular structures Furthermore, it focuses on calculating integral cohomology rather than just rational cohomology or cohomology at large primes. In particular, the paper describes and fully implements algorithms for computing Hecke operators on the integral cuspidal cohomology of congruence subgroups $\Gamma$ of $SL_2(\mathbb Z)$, and then partially implements versions of the algorithms for the special linear group $SL_2({\cal O}_d)$ over various rings of quadratic integers ${\cal O}_d$. The approach is also relevant for computations on congruence subgroups of $SL_m({\cal O}_d)$, $m\ge 2$.
Railgun: streaming windows for mission critical systems

Some mission critical systems, such as fraud detection, require accurate, real-time metrics over long time windows on applications that demand high throughputs and low latencies. As these applications need to run "forever", cope with large and spiky data loads, they further require to be run in a distributed setting. Unsurprisingly, we are unaware of any distributed streaming system that provides all those properties. Instead, existing systems take large simplifications, such as implementing sliding windows as a fixed set of partially overlapping windows, jeopardizing metric accuracy (violating financial regulator rules) or latency (breaching service agreements). In this paper, we propose Railgun, a fault-tolerant, elastic, and distributed streaming system supporting real-time sliding windows for scenarios requiring high loads and millisecond-level latencies. We benchmarked an initial prototype of Railgun using real data, showing significant lower latency than Flink, and low memory usage, independent of window size.
Multimodal Aggregation Approach for Memory Vision-Voice Indoor Navigation with Meta-Learning

Vision and voice are two vital keys for agents’ interaction and learning. In this paper, we present a novel indoor navigation model called Memory Vision-Voice Indoor Navigation (MVV-IN), which receives voice commands and analyzes multimodal information of visual observation in order to enhance robots’ environment understanding. We make use of single RGB images taken by a rst-view monocular camera. We also apply a self-attention mechanism to keep the agent focusing on key areas. Memory is important for the agent to avoid repeating certain tasks unnecessarily and in order for it to adapt adequately to new scenes, therefore, we make use of meta-learning. We have experimented with various functional features extracted from visual observation. Comparative experiments prove that our methods outperform state-of-the-art baselines.