Miracle-dz/UniFAR-FTUs huggingface dataset:
Contributions to the linear and nonlinear theory of the beam–plasma interaction

We focus our attention on some relevant aspects of the beam–plasma instability in order to refine some features of the linear and nonlinear dynamics. After a re-analysis of the Poisson equation and of the assumption dealing with the background plasma in the form of a linear dielectric, we study the non-perturbative properties of the linear dispersion relation, showing the necessity for a better characterization of the mode growth rate in those flat regions of the distribution function where the Landau formula is no longer predictive. We then upgrade the original $N$-body approach in O'Neil et al. (Phys. Fluids, vol. 14, 1971, pp. 1204–1212), in order to include a return current in the background plasma. This correction term is responsible for smaller saturation levels and growth rates of the Langmuir modes, as result of the energy density transferred to the plasma via the return current. Finally, we include friction effects, as those due to the collective influence of all the plasma charges on the motion of the beam particles. The resulting force induces a progressive resonance detuning, because particles are losing energy and decreasing their velocity. This friction phenomenon gives rise to a deformation of the distribution function, associated with a significant growth of the less energetic particle population. The merit of this work is to show how a fine analysis of the beam–plasma instability outlines a number of subtleties about the linear, intermediate and late dynamics which can be of relevance when such a system is addressed as a paradigm to describe relevant nonlinear wave–particle phenomena (Chen & Zonca, Rev. Mod. Phys., vol. 88, 2016, 015008).
Quintessential inflation in Palatini gravity

We study a model of quintessential inflation in the context of Palatini gravity. As a representative example, we consider the Peebles-Vilenkin model of quintessential inflation with a small non-minimal coupling to gravity, which is consistent with the most recent Planck measurements. At the end of inflation, the inflaton field passes through a tachyonic region and it leads to explosive particle production through the tachyonic preheating process. After preheating, the Universe becomes dominated by the kinetic energy of the inflaton and enters a period of kination. Eventually, the total energy density of the Universe becomes dominated by radiation, resulting in reheating. We find that the model predicts the reheating temperature values TRH∼𝒪(103 - 108) GeV, which is significantly above the temperature of Big Bang Nucleosynthesis. Following reheating, the inflaton field rolls down the quintessence potential until it freezes. Since the quintessence remains frozen until the present day, the residual potential energy density at this field value explains the observed dark energy density.
Research Regarding the Quality of Lambs Carcass Resulted from Tsigaia Purebred Compared with Hybrids of Tsigaia Ewes Crossed with Blanc du Massif Central Rams

The research aims to compare some qualitative characteristics of purebred Tsigaia lambs and Tsigaia crossed with Blanc du Massif Central lambs. The criteria assessed were: the chemical composition of purebred and hybrid meat, the live body weight, the slaughtering performance and, the weight of different carcass cuts. For almost all criteria chosen the hybrid individuals recorded better results.
What and why: the current situation and future prospects of “ivy sign” in moyamoya disease

“Ivy sign” is a special imaging manifestation of moyamoya disease (MMD), which shows continuous linear or punctate high intensity along the cortical sulci and subarachnoid space on magnetic resonance images. Ivy sign was reported to reflect the development of compensatory collaterals, and to be closely related to hemodynamic changes and clinical symptoms, and to indicate the postoperative prognosis, in MMD patients. It is a unique and critical marker for MMD. However, due to the lack of consistent criteria, such as definition, grading, and identification standards, ivy sign has not received much attention. We undertook a comprehensive literature search and summarized the current situation regarding ivy sign in MMD in terms of baseline characteristics, detection methods, definition, regional division and distribution patterns, grading criterions, incidence, related factors, the mechanism of ivy sign, and the effects of treatments. We also provided related concerns raised and future prospects relevant to studies about ivy sign in MMD.
Detection of microRNAs in circulating tumor cells

Tremendous efforts have been made to develop reliable methods for detection and characterization of circulating tumor cells (CTCs). Hence, currently available approaches enable enumeration of carcinoma-derived CTCs with high sensitivity and specificity as prerequisite for estimation of prognosis and therapeutic monitoring of cancer patients. Moreover, phenotypic characterization of CTCs regarding the expression of selected therapeutic targets has already been included in clinical decision making and first assay formats for genomic as well as transcriptomic and epigenetic analyses of CTCs up to the single cell level were established to complement quantification of CTCs. Comparably less efforts have been directed towards uncovering the expression of microRNAs (miRNAs) in CTCs. Increasing numbers of miRNAs which are small non-coding RNAs have been described to regulate gene expression by interfering with mRNAs at posttranscriptional level. Acting on a multitude of different transcripts, miRNAs are capable to influence various processes associated with tumor development and progression. While miRNA expression was comprehensively studied in tumor tissues and in plasma and serum of cancer patients by now, the current review summarizes results of several proof-of-concept studies aimed to analyze miRNA expression in CTCs. Investigation of CTCs is of utmost interest especially in the metastatic setting where tumor tissues are only rarely available. Although not yet ready as additional biomarker to characterize CTCs, CTC miRNA analysis holds great promise to improve knowledge of regulatory mechanisms involved in tumor cell dissemination and might provide insights into the cellular origin of circulating miRNAs.
Predicting Chemical Properties using Self-Attention Multi-task Learning based on SMILES Representation

In the computational prediction of chemical compound properties, molecular descriptors and fingerprints encoded to low dimensional vectors are used. The selection of proper molecular descriptors and fingerprints is both important and challenging as the performance of such models is highly dependent on descriptors. To overcome this challenge, natural language processing models that utilize simplified molecular input line-entry system as input were studied, and several transformer-variant models achieved superior results when compared with conventional methods. In this study, we explored the structural differences of the transformer-variant model and proposed a new self-attention based model. The representation learning performance of the self-attention module was evaluated in a multi-task learning environment using imbalanced chemical datasets. The experiment results showed that our model achieved competitive outcomes on several benchmark datasets. The source code of our experiment is available at https://github.com/arwhirang/sa-mtl and the dataset is available from the same URL.
Stronger Transformers for Neural Multi-Hop Question Generation

Prior work on automated question generation has almost exclusively focused on generating simple questions whose answers can be extracted from a single document. However, there is an increasing interest in developing systems that are capable of more complex multi-hop question generation, where answering the questions requires reasoning over multiple documents. In this work, we introduce a series of strong transformer models for multi-hop question generation, including a graph-augmented transformer that leverages relations between entities in the text. While prior work has emphasized the importance of graph-based models, we show that we can substantially outperform the state-of-the-art by 5 BLEU points using a standard transformer architecture. We further demonstrate that graph-based augmentations can provide complimentary improvements on top of this foundation. Interestingly, we find that several important factors--such as the inclusion of an auxiliary contrastive objective and data filtering could have larger impacts on performance. We hope that our stronger baselines and analysis provide a constructive foundation for future work in this area.
Batch Exploration With Examples for Scalable Robotic Reinforcement Learning

Learning from diverse offline datasets is a promising path towards learning general purpose robotic agents. However, a core challenge in this paradigm lies in collecting large amounts of meaningful data, while not depending on a human in the loop for data collection. One way to address this challenge is through task-agnostic exploration, where an agent attempts to explore without a task-specific reward function, and collect data that can be useful for any subsequent task. While these approaches have shown some promise in simple domains, they often struggle to explore the relevant regions of the state space in more challenging settings, such as vision-based robotic manipulation. This challenge stems from an objective that encourages exploring everything in a potentially vast state space. To mitigate this challenge, we propose to focus exploration on the important parts of the state space using weak human supervision. Concretely, we propose an exploration technique, Batch Exploration with Examples (BEE), that explores relevant regions of the state-space, guided by a modest number of human-provided images of important states. These human-provided images only need to be provided once at the beginning of data collection and can be acquired in a matter of minutes, allowing us to scalably collect diverse datasets, which can then be combined with any batch RL algorithm. We find that BEE is able to tackle challenging vision-based manipulation tasks both in simulation and on a real Franka Emika Panda robot, and observe that compared to task-agnostic and weakly-supervised exploration techniques, it (1) interacts more than twice as often with relevant objects, and (2) improves subsequent task performance when used in conjunction with offline RL.
OPTIMIZING ECOTILLING TECHNIQUE FOR TRACKING FIELD COCHLIOBOLUS SATIVUS POPULATION

Accurate and robust detection and quantification of fungi is essential for diagnosis, modeling and surveillance. Also, direct detection of fungi enables a deeper understanding of natural microbial communities, particularly as a great number of fungi are difficult or impossible to cultivate. Yet, many methods are not suitable for developing countries with varying levels of laboratory infrastructure. Optimized low cost method for screening Cochliobolus sativus, the causal agent of barley spot disease, was established using self-extracted nuclease and agarose gel electrophoresis. Gene-specific primers were designed from the whole fungal genome for use in the Ecotilling assays on 50 isolates collected over the past 20 years throughout the Syrian territory, representing distinct virulence profiles. Gene-specific primer pairs were used to optimize enzymatic mismatch cleavage and polymorphism discovery in three GlutSynth, Carp1 andXYL2 genes.Thirty-seven putative nucleotide polymorphisms were identified. The protocol is rapid, inexpensive and can robustly distinguish pathotypes in haploid species, such as fungi without high informatics load of DNA sequencing. In principle, this strategy can be widely applied to monitor the epidemics of a variety of plant pathogens.
Effectiveness of the Entomopathogenic Fungal Species Metarhizium anisopliae Strain NCAIM 362 Treatments against Soil Inhabiting Melolontha melolontha Larvae in Sweet Potato (Ipomoea batatas L.)

The effect of fungal entomopathogen M. anisopliae strain NCAIM 362 against M. melolontha larvae in sweet potato was tested under open field conditions when crop management included compost supply and soil cover (agro-foil or agro-textile). Additionally, the effect of M. anisopliae same strain against M. melolontha was compared with the effect of α-cypermethrin under greenhouse conditions. Soil microbial community using Illumina sequencing and soil biological activity were tested as possible parameter influencing M. anisopliae effect. According to the results, compost supply and textile cover may enhance the effectiveness of M. anisopliae under open field conditions, while no effect of fungal treatment was detected under greenhouse conditions. Even if soil parameters (chemical composition, bacterial, and biological activity) were identical, the effect of α-cypermethrin against M. melolontha larvae was significant: lower ratio of larval survival and less damaged tubers were detected after the chemical treatment. Our results suggest that M. anisopliae strain NCAIM 362 is not effective to control M. melolontha larvae, further pieces of research are needed to test other species of the Metarhizium genus to find an effective agent for sustainable pest control in sweet potato.
Self-learning Monte Carlo for non-Abelian gauge theory with dynamical fermions

In this paper, we develop the self-learning Monte-Carlo (SLMC) algorithm for non-abelian gauge theory with dynamical fermions in four dimensions to resolve the autocorrelation problem in lattice QCD. We perform simulations with the dynamical staggered fermions and plaquette gauge action by both in HMC and SLMC for zero and finite temperature to examine the validity of SLMC. We confirm that SLMC can reduce autocorrelation time in non-abelian gauge theory and reproduces results from HMC. For finite temperature runs, we confirm that SLMC reproduces correct results with HMC, including higher-order moments of the Polyakov loop and the chiral condensate. Besides, our finite temperature calculations indicate that four flavor QC${}_2$D with $\hat{m} = 0.5$ is likely in the crossover regime in the Colombia plot.
Correlation-aware Cooperative Multigroup Broadcast 360{\deg} Video Delivery Network: A Hierarchical Deep Reinforcement Learning Approach

With the stringent requirement of receiving video from unmanned aerial vehicle (UAV) from anywhere in the stadium of sports events and the significant-high per-cell throughput for video transmission to virtual reality (VR) users, a promising solution is a cell-free multi-group broadcast (CF-MB) network with cooperative reception and broadcast access points (AP). To explore the benefit of broadcasting user-correlated decode-dependent video resources to spatially correlated VR users, the network should dynamically schedule the video and cluster APs into virtual cells for a different group of VR users with overlapped video requests. By decomposition the problem into scheduling and association sub-problems, we first introduce the conventional non-learning-based scheduling and association algorithms, and a centralized deep reinforcement learning (DRL) association approach based on the rainbow agent with a convolutional neural network (CNN) to generate decisions from observation. To reduce its complexity, we then decompose the association problem into multiple sub-problems, resulting in a networked-distributed Partially Observable Markov decision process (ND-POMDP). To solve it, we propose a multi-agent deep DRL algorithm. To jointly solve the coupled association and scheduling problems, we further develop a hierarchical federated DRL algorithm with scheduler as meta-controller, and association as the controller. Our simulation results shown that our CF-MB network can effectively handle real-time video transmission from UAVs to VR users. Our proposed learning architectures is effective and scalable for a high-dimensional cooperative association problem with increasing APs and VR users. Also, our proposed algorithms outperform non-learning based methods with significant performance improvement.
Conditional Independence by Typing

A central goal of probabilistic programming languages (PPLs) is to separate modelling from inference. However, this goal is hard to achieve in practice. Users are often forced to re-write their models to improve efficiency of inference or meet restrictions imposed by the PPL. Conditional independence (CI) relationships among parameters are a crucial aspect of probabilistic models that capture a qualitative summary of the specified model and can facilitate more efficient inference. We present an information flow type system for probabilistic programming that captures conditional independence (CI) relationships and show that, for a well-typed program in our system, the distribution it implements is guaranteed to have certain CI-relationships. Further, by using type inference, we can statically deduce which CI-properties are present in a specified model. As a practical application, we consider the problem of how to perform inference on models with mixed discrete and continuous parameters. Inference on such models is challenging in many existing PPLs, but can be improved through a workaround, where the discrete parameters are used implicitly, at the expense of manual model re-writing. We present a source-to-source semantics-preserving transformation, which uses our CI-type system to automate this workaround by eliminating the discrete parameters from a probabilistic program. The resulting program can be seen as a hybrid inference algorithm on the original program, where continuous parameters can be drawn using efficient gradient-based inference methods, while the discrete parameters are inferred using variable elimination. We implement our CI-type system and its example application in SlicStan: a compositional variant of Stan.1
Symbolic Self-triggered Control of Continuous-time Non-deterministic Systems without Stability Assumptions for 2-LTL Specifications

We propose a symbolic self-triggered controller synthesis procedure for non-deterministic continuous-time nonlinear systems without stability assumptions. The goal is to compute a controller that satisfies two objectives. The first objective is represented as a specification in a fragment of LTL, which we call 2-LTL. The second one is an energy objective, in the sense that control inputs are issued only when necessary, which saves energy. To this end, we first quantise the state and input spaces, and then translate the controller synthesis problem to the computation of a winning strategy in a mean-payoff parity game. We illustrate the feasibility of our method on the example of a navigating nonholonomic robot.
Intracellular accommodation of rhizobia in legume host cell: the fine-tuning of the endomembrane system

The symbiosis of legumes with rhizobia leads to the formation of root nodules. Rhizobia which are hosted inside specialized infected cells are surrounded by hostderived membranes, forming symbiosomes. Although it is known that symbiosome formation involves proliferation of membranes and changing of host cell architecture the mechanisms involved in these processes remain largely uncovered. In this thesis, I studied in more detail the adaptation of the endomembrane system of infected cells to intracellular rhizobia. I have shown that in the first cell layer of the nitrogen-fixing zone, the vacuole of the infected cells shrinks, creating space for the expanding symbiosomes. Here the expression of homotypic fusion and vacuole protein sorting complex (HOPS) genes VPS11 and VPS39 are switched off, whereas tonoplast proteins, like the vacuolar aquaporin TIP1g, are targeted to the symbiosome membrane. These observations suggest that tonoplast-targeted traffic in infected cells is altered. This retargeting is essential for the maturation of symbiosomes. Accommodation of intracellular rhizobia requires also the reorganization of the actin cytoskeleton. I have shown that during symbiosome development the symbiosomes become surrounded by a dense actin network and in this way, the actin configuration in infected cells is changed markedly. The actin nucleating factor ARP3 is operational in the rearrangement of actin around the symbiosome. It is known that the plasma membrane is inelastic; its capacity to stretch is only around 1-3%. Exocytosis of new membrane material is therefore involved in changes in the size of the membrane surface and in repair of damaged membrane loci. Membrane tension may create a vector for the fusion of membrane vesicles. To test this, the localization of proteins from the group of synaptotamin calcium sensors involved in membrane fusion, was studied. I have shown that the Medicago synaptotamins, MtSyt2 and MtSyt3, are localised on protrusions of the host plasma membrane created by expanding rhizobia (infection threads, cell wall-free unwalled droplets). Hence, at these sites of contact between symbionts membrane tension may create a vector for exocytosis. It is known that the host cell wall is modified during the development of infected cells. This process is mediated by the exocytotic pathway employing vesicle-associated membrane proteins (VAMPs) from the VAMP721 family. Previously it was shown in Medicago nodules, that cell-wall free interface membrane formation during bacterial release is dependent on these proteins. I have shown that the pectin modifying enzyme pectate lyase is delivered to the site of bacterial release in soybean nodules by VAMP721-positive vesicles. My study uncovered new mechanisms involved in the adaptation of host cells to intracellular rhizobia: defunctionalization of the vacuole, actin cytoskeleton rearrangement and the retargeting of host cell proteins to the interface membrane.
mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer

The recent “Text-to-Text Transfer Transformer” (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We detail the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. We also describe a simple technique to prevent “accidental translation” in the zero-shot setting, where a generative model chooses to (partially) translate its prediction into the wrong language. All of the code and model checkpoints used in this work are publicly available.
A Novel Convolutional Neural Network Model to Remove Muscle Artifacts from EEG

The recorded electroencephalography (EEG) signals are usually contaminated by many artifacts. In recent years, deep learning models have been used for denoising of electroencephalography (EEG) data and provided comparable performance with that of traditional techniques. However, the performance of the existing networks in electromyograph (EMG) artifact removal was limited and suffered from the over-fitting problem. Here we introduce a novel convolutional neural network (CNN) with gradually ascending feature dimensions and downsampling in time series for removing muscle artifacts in EEG data. Compared with other types of convolutional networks, this model largely eliminates the over-fitting and significantly outperforms four benchmark networks in EEGdenoiseNet. Our study suggested that the deep network architecture might help avoid overfitting and better remove EMG artifacts in EEG.