Miracle-dz/UniFAR-FTUs huggingface dataset:
Slightly different metabolomic profiles are associated with high or low weight duck foie gras

Understanding the evolution of fatty liver metabolism of ducks is a recurrent issue for researchers and industry. Indeed, the increase in weight during the overfeeding period leads to an important change in the liver metabolism. However, liver weight is highly variable at the end of overfeeding within a batch of animals reared, force-fed and slaughtered in the same way. For this study, we performed a proton nuclear magnetic resonance (1H-NMR) analysis on two classes of fatty liver samples, called low-weight liver (weight between 550 and 599 g) and high-weight liver (weight above 700 g). The aim of this study was to identify the differences in metabolism between two classes of liver weight (low and high). Firstly, the results showed that increased liver weight is associated with higher glucose uptake leading to greater lipid synthesis. Secondly, this increase is probably also due to a decline in the level of export of triglycerides and cholesterol from the liver by maintaining them at high hepatic concentration levels. Finally, the increase in liver weight could lead to a significant decrease in the efficiency of aerobic energy metabolism associated with a significant increase in the level of oxidative stress.
Asynchronous and coherent dynamics in balanced excitatory-inhibitory populations

Dynamic excitatory-inhibitory (E-I) balance is a paradigmatic mechanism invoked to explain the irregular low firing activity observed in the cortex. However, we will show that the E-I balance can be at the origin of other regimes observable in the brain. The analysis is performed by combining extensive simulations of sparse E-I networks composed of N spiking neurons with analytical investigations of low dimensional neural mass models. The bifurcation diagrams, derived for the neural mass model, allow to classify the possible asynchronous and coherent behaviours emerging in balanced E-I networks with structural heterogeneity for any finite in-degree K. In the limit N >> K >> 1 both supra and sub-threshold balanced asynchronous regimes can be observed in our system. Due to the heterogeneity the asynchronous states are characterized by the splitting of the neurons in three groups: silent, fluctuation and mean driven. These features are consistent with experimental observations reported for heterogeneous neural circuits. The coherent rhythms observed in our system can range from periodic and quasi-periodic collective oscillations (COs) to coherent chaos. These rhythms are characterized by regular or irregular temporal fluctuations joined to spatial coherence somehow similar to coherent fluctuations observed in the cortex over multiple spatial scales. The COs can emerge due to two different mechanisms. A first mechanism similar to the pyramidal-interneuron gamma (PING) one, usually invoked for the emergence of γ-oscillations. The second mechanism is intimately related to the presence of current fluctuations, which sustain COs characterized by an essentially simultaneous bursting of the two populations. We observe period-doubling cascades involving the PING-like COs finally leading to the appearance of coherent chaos. Fluctuation driven COs are usually observable in our system as quasi-periodic collective motions characterized by two incommensurate frequencies. However, for sufficiently strong current fluctuations we report a novel mechanism of frequency locking among collective rhythms promoted by these intrinsic fluctuations. Our analysis suggest that despite PING-like or fluctuation driven COS are observable for any finite in-degree K, in the limit N >> K >> 1 these solutions finally result in two coexisting balanced regimes: an asynchronous and a fully synchronized one.
Barley guanine nucleotide exchange factor HvGEF14 is an activator of the susceptibility factor HvRACB and supports host cell entry by Blumeria graminis f. sp. hordei

In barley (Hordeum vulgare), the function of ROPs appears central to polar cell development and the interaction outcome with parasitic fungi but little is known about ROP activation. Guanine nucleotide exchange factors (GEFs) facilitate the exchange of GDP with GTP and thereby turn ROPs into a signalling-activated ROP-GTP state. Plants possess a unique class of GEFs harbouring a plant specific ROP nucleotide exchanger domain (PRONE). We performed phylogenetic analyses and annotated barley PRONE GEFs. HvGEF14 is expressed in leaf epidermal tissue and downregulated after inoculation with Blumeria graminis f.sp. hordei. Protein-protein interaction assays indicate that HvGEF14 and the type I barley ROP protein HvRACB can interact in yeast and in planta. Overexpression of HvGEF14 further recruited the ROP-GTP downstream interactor HvRIC171 to the cell periphery and let to interaction of HvRACB with an extended CRIB (Cdc42/Rac Interactive Binding motif) peptide of HvRIC171 in a similar manner as constitutively activated HvRACB. Finally, the over expression of HvGEF14 led to enhanced susceptibility to fungal entry while HvGEF14 RNAi provoked a trend to more penetration resistance. HvGEF14 might therefore play a role in the activation of HvRACB in barley epidermal cells, which is crucial for fungal penetration success. Highlight The activated GTPase HvRACB is a susceptibility factor of barley in response to Blumeria graminis f.sp. hordei. The newly discovered guanine nucleotide exchange factor HvGEF14 is a HvRACB activator.
Probing the stability of the SpCas9-DNA complex after cleavage

CRISPR-Cas9 is a ribonucleoprotein complex that sequence-specifically binds and cleaves double-stranded DNA. Wildtype Cas9 as well as its nickase and cleavage-incompetent mutants have been used in various biological techniques due to their versatility and programmable specificity. Cas9 has been shown to bind very stably to DNA even after cleavage of the individual DNA strands, inhibiting further turnovers and considerably slowing down in-vivo repair processes. This poses an obstacle in genome editing applications. Here, we employed single-molecule magnetic tweezers to investigate the binding stability of different S. pyogenes Cas9 variants after cleavage by challenging them with supercoiling. We find that different release mechanisms occur depending on which DNA strand is cleaved. After non-target strand cleavage, supercoils are immediately but slowly released by swiveling of the non-target strand around the DNA with friction. Consequently, Cas9 and its non-target strand nicking mutant stay stably bound to the DNA for many hours even at elevated torsional stress. After target-strand cleavage, supercoils are only removed after the collapse of the R-loop. We identified several states with different stabilities of the R-loop. Most importantly, we find that the post-cleavage state of Cas9 exhibits a higher stability compared to the pre-cleavage state. This suggests that Cas9 has evolved to remain tightly bound to its cut target.
A Penalty Alternating Direction Method of Multipliers for Convex Composite Optimization Over Decentralized Networks

Consider the problem of minimizing a sum of convex composite functions over a decentralized network, where each agent in the network holds a private function consisting of a smooth part and a nonsmooth part, and it can only exchange information with its neighbors during the optimization process. One approach to tackling this problem is to study its penalized approximation. Although such an approximation becomes more accurate as the penalty parameter becomes smaller, it is well known that the penalized objective will also become more ill-conditioned, thereby causing the popular proximal gradient descent method to slow down substantially. To break this accuracy-speed tradeoff, we propose to solve the penalized approximation with the alternating direction method of multipliers (ADMM). We also exploit the composite structure of the private functions by linearizing the smooth parts and handling the nonsmooth parts with proximal operators, which allows us to further reduce the computational costs. The proposed penalty ADMM (abbreviated as PAD) is proven to be sublinearly convergent when the private functions are convex, and linearly convergent when in addition the smooth parts are strongly convex. We present numerical results to corroborate the theoretical analyses and to further demonstrate the advantages of PAD over existing state-of-the-art algorithms such as DL-ADMM, PG-EXTRA, and NIDS.
A Wireless System for inner Temperature Measurement of High Speed Electric Motors

The design of an electric motor requires, in addition to electromagnetic modeling, also a specific thermal analysis. The search of the maximum performance with minimum dimensions leads to a rise in internal temperature. Above certain limits, this can lead to loss of performance or even to irreversible damage, such as the demagnetization of the magnets or stator windings deterioration. Therefore, it is important to be able to accurately estimate the temperatures at critical points. This can be done with the help of models and simulation tools. However, modeling may be misleading if it is not supported by experimental evidence. Considering that an error in the estimation of temperatures may have non-reversible consequences, it is very important to validate the models by comparing numerical estimates with data obtained from an experimental campaign. In this case, it is necessary to measure the temperatures in the various internal points of the motor: both in the stator part and in the rotor part.The measurement system introduced in this paper involves the use of 24 type-K thermocouples for temperature measurements up to 250◦C with a resolution of 0.25◦C and sampling frequency equal to 1Hz. The system consists in two circular-shaped PCB with a diameter of 46.8mm, one placed on the stator and one on the rotor.
A Novel Resource Reliability-Aware Infrastructure Manager for Containerized Network Functions

A major challenge to the anticipated large scale deployment of virtual network functions (VNFs) at the network edge (NE) is the ability to efficiently allocate and manage its scarce resources to meet these functions workload fluctuations. In this paper, we describe a novel containerized infrastructure manager (cIM) that extends current managers, such as Kubernetes, with the necessary building blocks to provide an accurate resource allocation service to containerized VNFs at scale. The proposed cIM treats the containerized VNF components (cNFCs), as atomic special purpose functions that can be rapidly deployed to form complex network services. The main component of the proposed cIM, the resource reservation manager (RRM), employs concepts of risk pooling in the insurance industry to accurately reserve the needed resources for the hosting containers and meet anticipated cNFCs demand fluctuation. The reserved quota of re-sources ensures the desired availability level of the cNFCs without over-provisioning the scarce resources of the NE. Experimental results demonstrate that our proposed cIM significantly improve the performance of the cNFCs and guarantees their availability with minimal resource reservation.
Multi-attribute decision making using interval-valued pythagorean fuzzy set and differential evolutionary algorithm

This paper presents a multi-attribute decision-making approach based on interval-valued pythagorean fuzzy set and differential evolutionary algorithm. Interval-valued pythagorean fuzzy set has always been effective in representing real world complex problems due to its large scope of bound compared to fuzzy sets. Differential evolutionary algorithm is the most widely and successfully used optimization algorithms for obtaining optimal results. In this paper, initially, we use an accuracy function to transform the input decision matrix represented by interval-valued pythagorean fuzzy values into a transformed decision matrix. Next, for the corresponding attributes, we obtain the optimal weight vector using differential evolutionary algorithm and transformed decision matrix. We apply interval-valued pythagorean fuzzy weighted geometric operator to compute the aggregated interval-valued pythagorean fuzzy values for the various alternatives. Subsequently, we find the final accuracy values of the aggregated interval-valued pythagorean fuzzy values to decide the ranking of the alternatives. Moreover, we perform the said multi-attribute decision-making approach using particle swarm optimization replacing differential evolutionary algorithm. The proposed approaches are explained using numerical examples. Finally, the results of the differential evolutionary based approach have been compared with the existing approaches and particle swarm optimization based approach, where differential evolutionary based decision-making approach is found to perform better in imprecise environments.
Opportunistic Attribute Caching: Improving the Efficiency of ABAC in Fog-Based IoT Networks

The performance of Attribute-Based Access Control is negatively affected by communications over the network between the policy decision point and policy information point for each attribute request. Attribute caching is a standard solution to mitigate this problem. However, due to the dynamic nature of attributes, the cost to keep them refreshed increases for each new attribute replica. This paper presents a method that predicts each request and anticipate the positioning of the attributes closer to the requester exploring the tradeoff between the cost of creating a new replica versus updating a replica. The proposed method follows a two-way approach, where it deals with the current attribute requests and their estimates based on user mobility and the best positions for the attributes. Through trace-driven simulations, considering traces from a large university campus, the method shows a reduction of up to 80% in the number of hops to get the needed attributes at negligible refreshment cost.
NVBitFI: Dynamic Fault Injection for GPUs

GPUs have found wide acceptance in domains such as high-performance computing and autonomous vehicles, which require fast processing of large amounts of data along with provisions for reliability, availability, and safety. A key component of these dependability characteristics is the propagation of errors and their eventual effect on system outputs. In addition to analytical and simulation models, fault injection is an important technique that can evaluate the effect of errors on a complete computing system running the full software stack. However, the complexity of modern GPU systems and workloads challenges existing fault injection tools. Some tools require the recompilation of source code that may not be available, struggle to handle dynamic libraries, lack support for modern GPUs, or add unacceptable performance overheads. We introduce the NVBitFI tool for fault injection into GPU programs. In contrast with existing tools, NVBitFI performs instrumentation of code dynamically and selectively to instrument the minimal set of target dynamic kernels; as it requires no access to source code, NVBitFI provides improvements in performance and usability. The NVBitFI tool is publicly available for download and use at https://github.com/NVlabs/nvbitfi.
Auto-creation of Android Malware Family Tree

Android malware has been a growing threat. For an effective countermeasure against Android malware, we need to not only detect the malware at a certain point in time but also analyze its time-series changes of malware, taking into account that the family of Android malware will increase in number over time. In this paper, we propose a new method for automatically creating a "family tree" of Android malware that can represent how the newly detected Android malware is related to existing Android malware and its families, and how they have changed over time. Our evaluation using 24,474 actual Android malware APKs shows that our proposed family tree is able to accurately represent time-series changes between malware families.
Performance Study of Random Access NOMA with Truncated Channel Inversion Power Control

In this paper, we analytically study the performance of non-orthogonal multiple access (NOMA) transmissions in a random access network with truncated channel inversion power control. Specifically, in a slotted ALOHA network in support of NOMA transmissions, a wireless device randomly selects the transmission power with a certain probability, using channel inversion power control yet subject to the upper bound of the transmission power. Taking into consideration the stochastic nature of wireless fading channels, we first quantify two network areas such that devices in different areas have various choices of transmission powers for NOMA transmissions. An analytical model is developed to analyze the successful transmission probability and throughput of wireless devices located in different areas. Based on the analysis, two optimization problems are formulated to maximize the network throughput and the minimum throughput of wireless devices by tuning the transmission probabilities of each device. To solve the formulated combinatorial optimization problems, two heuristic algorithms are proposed. Extensive simulations are conducted to validate the analysis, and verify the efficiency of the proposed algorithm to attain the maximum network throughput and max-min fairness.
OCTOPOCS: Automatic Verification of Propagated Vulnerable Code Using Reformed Proofs of Concept

Addressing vulnerability propagation has become a major issue in software ecosystems. Existing approaches hold the promise of detecting widespread vulnerabilities but cannot be applied to verify effectively whether propagated vulnerable code still poses threats. We present OCTOPOCS, which uses a reformed Proof-of-Concept (PoC), to verify whether a vulnerability is propagated. Using context-aware taint analysis, OCTOPOCS extracts crash primitives (the parts used in the shared code area between the original vulnerable software and propagated software) from the original PoC. OCTOPOCS then utilizes directed symbolic execution to generate guiding inputs that direct the execution of the propagated software from the entry point to the shared code area. Thereafter, OCTOPOCS creates a new PoC by combining crash primitives and guiding inputs. It finally verifies the propagated vulnerability using the created PoC. We evaluated OCTOPOCS with 15 real-world C and C++ vulnerable software pairs, with results showing that OCTOPOCS successfully verified 14 propagated vulnerabilities.
Feature Selection Evaluation towards a Lightweight Deep Learning DDoS Detector

Today’s networks and services undoubtedly require a high level of protection from cyber threats and attacks. State-of-the-art solutions that implement Machine Learning (ML) have shown to improve the accuracy and confidence in threat detection compared to previous approaches, making it suitable for detecting today’s sophisticated attacks such as Distributed Denial of Service (DDoS). However, in real-world deployments, input data streams take large bandwidth and processing capacity, especially for Deep Learning (DL) solutions that require extensive input data. On the other hand, deployment environments usually have limited bandwidth and computing resources, such as in the Internet of Things (IoT). Thus, a lightweight detection solution that satisfies such constraints is needed. In this paper, we utilize a feature reduction approach for our DL-based DDoS detector based on the Analysis of Variance (ANOVA), which is used to identify important data features and reduce the data inputs needed for detection. Our result shows that we can reduce the data input needed by up to 84.21% while only reducing 0.1% detection accuracy. We also provide a detailed analysis of the characteristics of DDoS attacks using ANOVA and compared our work with recent DL-based DDoS detection systems to demonstrate that our results are comparable to existing approaches.
GCRINT: Network Traffic Imputation Using Graph Convolutional Recurrent Neural Network

Missing values appear in most multivariate time series, especially in the monitored network traffic data due to high measurement cost and unavoidable loss. In the networking fields, missing data prevents advanced analysis and downgrades downstream applications such as traffic engineering and anomaly detection. Despite the great potential, existing imputation approaches based on tensor decomposition and deep learning techniques have shown limitations in addressing missing values of traffic data due to its dynamic behavior. In this paper, we propose Graph Convolutional Recurrent Neural Network for Imputing Network Traffic (GCRINT), a combination between Recurrent Neural Network (RNN) and Graph Convolutional Neural Network, for filling the missing values of network traffic data. We use a bidirectional Long Short-Term Memory network and Graph Neural Network to efficiently learn the spatial-temporal correlations in partially observed data. We conducted extensive experiments to evaluate our model by using two different datasets and various missing scenarios. The experiment results show that GCRINT achieves significantly low imputation errors and reduces the error by 35% compared to the state-of-the-art methods. GCRINT also helps to obtain a stable performance in the traffic engineering problem.
Do Explicit Alignments Robustly Improve Massively Multilingual Encoders?

Multilingual BERT (mBERT), XLM-RoBERTa (XLMR) and other unsupervised multilingual encoders can effectively learn cross-lingual representation. Explicit alignment objectives based on bitexts like Europarl or MultiUN have been shown to further improve these representations. However, word-level alignments are often suboptimal and such bitexts are unavailable for many languages. In this paper, we propose a new contrastive alignment objective that can better utilize such signal, and examine whether these previous alignment methods can be adapted to noisier sources of aligned data: a randomly sampled 1 million pair subset of the OPUS collection. Additionally, rather than report results on a single dataset with a single model run, we report the mean and standard derivation of multiple runs with different seeds, on four datasets and tasks. Our more extensive analysis finds that, while our new objective outperforms previous work, overall these methods do not improve performance with a more robust evaluation framework. Furthermore, the gains from using a better underlying model eclipse any benefits from alignment training. These negative results dictate more care in evaluating these methods and suggest limitations in applying explicit alignment objectives.
Progress in Crystal Growth and Characterization of Materials

1. IntroductionNanocrystalsareaspecialclassofnanoparticleswithimprovedcrystallinity.Oftenbesidesthedesiredproperties of nanoparticles related with quantum effects and high speciﬁc surface, other importantproperties suffer a very important damage. This is the case of the magnetic properties. Magnetic nano-crystals (>10 nm) combine the nanometric dimension and therefore the superparamagnetic behaviorwithmagneticpropertiessuchassaturationmagnetizationclosetothebulkvalue.Inthisreviewwewilldescribe the preparation of magnetic nanocrystals and their performance in selected biomedical appli-cations that are expected to be strongly dependent on the nanoparticle magnetic response.2. Preparation of magnetic nanocrystalsHigh-quality magnetite nanocrystals up to 20 nm can be obtained by thermal decomposition oforganic iron precursors in the presence of a surfactant [1e3]. These materials need a complex