Miracle-dz/UniFAR-FTUs huggingface dataset:
OF THE THESIS A Pathogen Independent T-cell Intrinsic role for IRF3

IRF3 has been recognized as a crucial transcription factor for the activation of innate immune responses. Recent studies have implicated IRF3 in regulating IL17A production of the CD4+ subset, Th17. Previous results using in vivo models of autoimmune disorders suggest this regulation is necessary for optimal disease induction. Using in vitro culture methods, we further demonstrated the relevance of IRF3 to mouse Th17 cells by co-culturing murine Antigen Presenting cells with purified murine naïve CD4 T cells under Th17 polarizing conditions. We show that IRF3 is required in both Antigen Presenting Cell and CD4 T cells to elicit proper Th17 differentiation independently of antigen presentation. Lastly, we validated an existing Th17 culture method that facilitated Th17 differentiation to better correlate IRF3 with IL17A in Th17 subsets and provide a robust cytokine cocktail formulation to pinpoint in Th17 cell yields in WT vs. IRF3KO cells. Our results demonstrate the multi-faceted role of IRF3 in regulating the immune response within cell subsets and further establish its T-cell intrinsic role.
PHYSICAL REVIEW B 101 , 045116 ( 2020 ) First-principles study of transport in WO

We investigate the transport properties of tungsten trioxide (WO3) from first principles. The full k dependence of the scattering rate is included, and electron mobilities as a function of doping are calculated using Boltzmann transport theory. Our predicted room-temperature mobility is as high as 485 cm2 V−1 s−1 at 1020 cm−3 doping, which makes WO3 a promising material for device applications. We elucidate why the mobility is so much higher in WO3 compared to the prototype perovskite oxide SrTiO3 based on an analysis of the electronic and vibronic structure.
Quantitative Proteomics Reveals That the Specific Methyltransferases Txr1p and Ezl2p Differentially Affect the Mono-, Di- and Trimethylation States of Histone H3 Lysine 27

Nuclear DNA in eukaryotic cells is assembled into the hierarchical chromatin structure via a process that is dynamically affected by the combinatorial set of post-trans-lational modifications (PTMs) of histones in a dynamic manner responsive to physiological and environmental changes. The precise quantification of these complex modifications is challenging. Here we present a robust MS-based quantitative proteomics method for studying histone PTMs using 15 N metabolically labeled histones as the internal reference. Using this approach, we identified Tetrahymena trithorax related 1 (Txr1p) as a histone methyltransferase in Tetrahymena thermophila and characterized the relationships of the Txr1p and Ezl2p methyltransferases to histone H3 modification. We identified 32 PTMs in more than 60 tryptic peptides from histone H3 of the ciliate model organism Tetrahymena thermophila , and we quantified them (average coefficient of variation: 13%). We examined perturbations to histone modification patterns in two knockout strains of SET-domain-containing histone methyltransferases (HMT). Knockout of TXR1 led to progressively decreased mono-, di-, and tri-methyla-tion of H3K27 and apparent reduced monomethylation of H3K36 in vivo . In contrast, EZL2 knockout resulted in dramatic reductions in both di- and tri-methylation of H3K27 in vivo , whereas the levels of monomethylation of H3K27 increased significantly. This buildup of mono-methyl H3K27 is consistent with its role as a substrate for Ezl2p. These results were validated via immunoblotting using modification site-specific antibodies. Taken to-gether, our studies define Txr1p as an H3K27 monomethyl-ation-specific HMT that facilitates the buildup of H3K27 di- and trimethylation by the canonical H3K27-specific HMT, Ezl2p. Our studies also delineate some of the inter-dependences between various H3 modifications, as compensatory increases in monomethylation at H3K4, H3K23, and H3K56 were also observed for both TXR1 and ELZ2 mutants.
Hypochlorite-modiﬁed albumin colocalizes with RAGE in the artery wall and promotes MCP-1 expression via the RAGE-Erk1/2 MAP-kinase pathway

Signal transduction via the endothelial receptor for advanced glycation end products (RAGE) plays a key role in vascular inﬂammation. Recent obser-vations have shown that the myeloperoxidase-H 2 O 2 chloride system of activated phagocytes is highly up-regulated under inﬂammatory conditions where hypochlorous acid (HOCl) is formed as the major oxidant. Albumin, an in vivo carrier for myeloperoxidase is highly vulnerable to oxidation and a major representative of circulating advanced oxidized proteins during inﬂammatory diseases. Immunohistochemical studies performed in the present study revealed marked colocalization of HOCl-modiﬁed epitopes with RAGE and albumin in sections of human atheroma, mainly at the endothelial lining. We show that albumin modiﬁed with physiologically relevant concentrations of HOCl, added as reagent or generated by the myelo-peroxidase-H 2 O 2 -chloride system, is a high afﬁnity ligand for Albumin, modiﬁed by HOCl in the absence of free amino acids/carbohydrates/lipids to exclude formation of AGE-like structures, induced a rapid, RAGE-dependent activation of extracellular sig-nal-regulated kinase 1/2 and up-regulation of the proinﬂammatory mediator monocyte chemoattractant pro-tein-1. Cellular activation could be blocked either by a speciﬁc polyclonal anti-RAGE IgG and/or a speciﬁc mitogen-activated protein-kinase kinase inhibitor. The present study demonstrates that HOCl-modiﬁed albumin acts as a ligand for RAGE and promotes RAGE-mediated inﬂammatory complications. —Marsche, albumin colocalizes with RAGE in the artery wall and promotes MCP-1 expression via the RAGE-Erk1/2 MAP-kinase pathway. transcript expression by HOCl-albumin. Control HEK and RAGE-HEK cells were incubated for 3 h with indicated concentrations of albumin (Alb) and HOCl-Alb (oxidant:protein molar ratio of 100:1) in the absence or presence of polyclonal anti-sRAGE IgG (200 (cid:1) g/ml) or the speciﬁc MAP-kinase kinase inhibitor PD 98059 (25 (cid:1) M). MCP-1 transcripts were determined by RT-PCR as described in the Methods section. Results are the average of duplicates of one representative experiment out of three, and error bars represent the ranges of the measurements. MCP-1 expression of nontreated RAGE-HEK cells were used as controls.
A COMPARATIVE RESEARCH TO EVALUATE MVA (MANUAL VACUUM ASPIRATION) AND MEDICAL TREATMENT TO MANAGE MISSED MISCARRIAGE (FIRST TRIMESTER)

: Objective: The objective of this research is to evaluate and contrast the medical treatment and MVA (Manual Vacuum Aspiration) for managing the miscarriage in the 1 st trimester. Material and Methods: This comparative research was conducted at Sir Ganga Ram Hospital, Lahore (August 2017 to March 2018). This research consists of a total of 92 cases of patients having a gestation age of less than twelve (12) weeks of missed abortion in the first trimester. Two groups were made of the patients: group A was treated with MVA whereas group B was treated medically and their effectiveness was evaluated. Results: The patients’ mean age was (29.77 ± 6.786) years in which group A patients’ mean age was (30.61 ± 6.754) years while group B patients’ mean age was (28.93 ± 6.787) years. The gestational mean age was (5.87 ± 3.592) weeks in which group A gestational mean age was (6.09 ± 3.699) weeks while group B gestational mean age was (5.65 ± 3.510) weeks. In group A patients, the effectiveness of treatment was 91.30% (42 out of 46 patients) while in the group B patients, the effectiveness of the treatment was 69.57% (32 out of 46 patients). The rate of effectiveness of cure for group A was considerably greater than Group B i.e., 91.30% as compared to 69.57%, having (p=0.009). Conclusion: The outcome of this research proved that MVA is a far more useful modality of treatment than medical treatment i.e., misoprostol intravaginally, as the rate of effectiveness of cure was considerably greater for the group of MVA than the group which was treated medically and it was especially true in the group of patients with older ages.
Polynomial energy decay rate of a 2D Piezoelectric beam with magnetic effect on a rectangular domain without geometric conditions

In this paper, we investigate the stability of coupled equations modelling a 2D piezoelectric beam with magnetic eﬀect with only one local viscous damping on a rectangular domain without geometric condi- tions. We prove that the energy of the system decays polynomially with the rate t − 1
Deep Learning-based approaches for automatic detection of shell nouns and evaluation on WikiText-2

In some areas, such as Cognitive Linguistics, researchers are still using traditional techniques based on manual rules and patterns. Since the definition of shell noun is rather subjective and there are many exceptions, this time-consuming work had to be done by hand in the past when Deep Learning techniques were not mature enough. With the increasing number of networked languages, these rules are becoming less useful. However, there is a better alternative now. With the development of Deep Learning, pre-trained language models have provided a good technical basis for Natural Language Processing. Automated processes based on Deep Learning approaches are more in line with modern needs. This paper collaborates across borders to propose two Neural Network models for the automatic detection of shell nouns and experiment on the WikiText-2 dataset. The proposed approaches not only allow the entire process to be automated, but the precision has reached 94% even on completely unseen articles, comparable to that of human annotators. This shows that the performance and generalization ability of the model is good enough to be used for research purposes. Many new nouns are found that fit the definition of shell noun very well. All discovered shell nouns as well as pre-trained models and code are available on GitHub.
Learning Task Automata for Reinforcement Learning using Hidden Markov Models

Training reinforcement learning (RL) agents using scalar reward signals is often infeasible when an environment has sparse and non-Markovian rewards. Moreover, handcrafting these reward functions before training is prone to misspecification, especially when the environment's dynamics are only partially known. This paper proposes a novel pipeline for learning non-Markovian task specifications as succinct finite-state `task automata' from episodes of agent experience within unknown environments. We leverage two key algorithmic insights. First, we learn a product MDP, a model composed of the specification's automaton and the environment's MDP (both initially unknown), by treating the product MDP as a partially observable MDP and using the well-known Baum-Welch algorithm for learning hidden Markov models. Second, we propose a novel method for distilling the task automaton (assumed to be a deterministic finite automaton) from the learnt product MDP. Our learnt task automaton enables the decomposition of a task into its constituent sub-tasks, which improves the rate at which an RL agent can later synthesise an optimal policy. It also provides an interpretable encoding of high-level environmental and task features, so a human can readily verify that the agent has learnt coherent tasks with no misspecifications. In addition, we take steps towards ensuring that the learnt automaton is environment-agnostic, making it well-suited for use in transfer learning. Finally, we provide experimental results compared with two baselines to illustrate our algorithm's performance in different environments and tasks.
Convergence of random-weight sequential Monte Carlo methods

We investigate the properties of a sequential Monte Carlo method where the particle weight that appears in the algorithm is estimated by a positive, unbiased estimator. We present broadly-applicable convergence results, including a central limit theorem, and we discuss their relevance for applications in statistical physics. Using these results, we show that the resampling step reduces the impact of the randomness of the weights on the asymptotic variance of the estimator. In addition, we explore the limits of convergence of the sequential Monte Carlo method, with a focus on almost sure convergence. We construct an example algorithm where we can prove convergence in probability, but which does not converge almost surely, even in the non-random-weight case.
An analysis of load-balancing algorithms on edge-Markovian evolving graphs

Analysis of algorithms on time-varying networks (often called evolving graphs) is a modern challenge in theoretical computer science. The edge-Markovian is a relatively simple and comprehensive model of evolving graphs: every pair of vertices which is not a current edge independently becomes an edge with probability $p$ at each time-step, as well as every edge disappears with probability $q$. Clearly, the edge-Markovian graph changes its shape depending on the current shape, and the dependency refuses some useful techniques for an independent sequence of random graphs which often behaves similarly to a static random graph. It motivates this paper to develop a new technique for analysis of algorithms on edge-Markovian evolving graphs. Specifically speaking, this paper is concerned with load-balancing, which is a popular subject in distributed computing, and we analyze the so-called random matching algorithms, which is a standard scheme for load-balancing. We prove that major random matching algorithms achieve nearly optimal load balance in $O(r \log (\Delta n))$ steps on edge-Markovian evolving graphs, where $r = \max\{p/(1-q), (1-q)/p\}$, $n$ is the number of vertices (i.e., processors) and $\Delta$ denotes the initial gap of loads unbalance. We remark that the independent sequences of random graphs correspond to $r=1$. To avoid the difficulty of an analysis caused by a complex correlation with the history of an execution, we develop a simple proof technique based on history-independent bounds. As far as we know, this is the first theoretical analysis of load-balancing on randomly evolving graphs, not only for the edge-Markovian but also for the independent sequences of random graphs.
Quantum Phase Reduction for Continuous Measurement

We develop a general framework of phase reduction theory for quantum nonlinear oscillators. Employing quantum trajectory theory, we deﬁne the limit cycle trajectory and the phase space according to a stochastic Schr¨odinger equation. Unlike classical dynamics, because a perturbation, which is applied to quantum states, is represented by a unitary transformation, phase response curves are calculated with respect to generators of Lie algebra. In the classical limit, we show that the proposed approach reproduces the conventional phase reduction theory. The derived phase equation numerically demonstrates that quantum noise desynchronizes the quantum van der Pol oscillators.
A Deterministic Adjoint-Based Semi-Analytical Algorithm for Fast Response Change Computations in Proton Therapy

Abstract In this paper we propose a solution to the need for a fast particle transport algorithm in Online Adaptive Proton Therapy capable of cheaply, but accurately computing the changes in patient dose metrics as a result of changes in the system parameters. We obtain the proton phase-space density through the product of the numerical solution to the one-dimensional Fokker-Planck equation and the analytical solution to the Fermi-Eyges equation. Moreover, a corresponding adjoint system was derived and solved for the adjoint flux. The proton phase-space density together with the adjoint flux and the metric (chosen as the energy deposited by the beam in a variable region of interest) allowed assessing the accuracy of our algorithm to different perturbation ranges in the system parameters and regions of interest. The algorithm achieved negligible errors ( ) for small Hounsfield unit (HU) perturbation ranges (–40 HU to 40 HU) and small to moderate errors (3% to 17%) – in line with the well-known limitation of adjoint approaches – for large perturbation ranges (–400 HU to 400 HU) in the case of most clinical interest where the region of interest surrounds the Bragg peak. Given these results coupled with the capability of further improving the timing performance it can be concluded that our algorithm presents a viable solution for the specific purpose of Online Adaptive Proton Therapy.
Attractors. Then and now

This survey is dedicated to the 100th anniversary of Mark Iosifovich Vishik and is based on a number of mini-courses taught by the author at the University of Surrey (UK) and Lanzhou University (China). It discusses the classical and modern results of the theory of attractors for dissipative PDEs, including attractors for autonomous and non-autonomous equations, dynamical systems in general topological spaces, various types of trajectory, pullback and random attractors, exponential attractors, determining functionals and inertial manifolds, as well as the dimension theory for the classes of attractors mentioned above. The theoretical results are illustrated by a number of clarifying examples and counterexamples. Bibliography: 248 titles.
Global Linear and Local Superlinear Convergence of IRLS for Non-Smooth Robust Regression

We advance both the theory and practice of robust $\ell_p$-quasinorm regression for $p \in (0,1]$ by using novel variants of iteratively reweighted least-squares (IRLS) to solve the underlying non-smooth problem. In the convex case, $p=1$, we prove that this IRLS variant converges globally at a linear rate under a mild, deterministic condition on the feature matrix called the \textit{stable range space property}. In the non-convex case, $p\in(0,1)$, we prove that under a similar condition, IRLS converges locally to the global minimizer at a superlinear rate of order $2-p$; the rate becomes quadratic as $p\to 0$. We showcase the proposed methods in three applications: real phase retrieval, regression without correspondences, and robust face restoration. The results show that (1) IRLS can handle a larger number of outliers than other methods, (2) it is faster than competing methods at the same level of accuracy, (3) it restores a sparsely corrupted face image with satisfactory visual quality. https://github.com/liangzu/IRLS-NeurIPS2022
Learning Rate Perturbation: A Generic Plugin of Learning Rate Schedule towards Flatter Local Minima

Learning rate is one of the most important hyper-parameters that has significant influence for neural network training. Learning rate schedules are widely used in real practice to adjust the learning rate according to pre-defined schedules for the fast convergence and good generalization. However, existing learning rate schedules are all heuristic algorithms and lack theoretical support. Therefore, people usually choose the learning rate schedules through multiple ad-hoc trial, and the obtained learning rate schedules are sub-optimal. To boost the performance of the obtained sub-optimal learning rate schedule, we propose a generic learning rate schedule plugin, called LEArning Rate Perturbation (LEAP), which can be applied to various learning rate schedules to improve the model training by introducing a certain perturbation to the learning rate. We found that, with such simple yet effective strategy, training processing exponentially favors flat minima rather than sharp minima with guaranteed convergence, which leads to better generalization ability. In addition, we conduct extensive experiments which show that training with LEAP can improve the performance of various deep learning models on diverse datasets using various learning rate schedules (including constant learning rate).
Efficient Activation Quantization via Adaptive Rounding Border for Post-Training Quantization

Post-training quantization (PTQ) attracts increasing attention due to its convenience in deploying quantized neural networks. Rounding, the primary source of quantization error, is optimized only for model weights, while activations still use the rounding-to-nearest operation. In this work, for the ﬁrst time, we demonstrate that well-chosen rounding schemes for activations can improve the ﬁnal accuracy. To deal with the challenge of the dynamicity of the activation rounding scheme, we adaptively adjust the rounding border through a simple function to generate rounding schemes at the inference stage. The border function covers the impact of weight errors, activation errors, and propagated errors to eliminate the bias of the element-wise error, which further beneﬁts model accuracy. We also make the border aware of global errors to better ﬁt different arriving activations. Finally, we propose the AQuant framework to learn the border function. Extensive experiments show that AQuant achieves noticeable improvements with negligible overhead compared with state-of-the-art works and pushes the accuracy of ResNet-18 up to 60.31% under the 2-bit weight and activation post-training quantization.
Prevent: An Unsupervised Approach to Predict Software Failures in Production

This paper presents Prevent, a fully unsupervised approach to predict and localize failures in distributed enterprise applications. Software failures in production are unavoidable. Predicting failures and locating failing components online are the first steps to proactively manage faults in production. Many techniques predict failures from anomalous combinations of system metrics with supervised, weakly supervised, and semi-supervised learning models. Supervised approaches require large sets of labelled data not commonly available in large enterprise applications, and address failure types that can be either captured with predefined rules or observed while training supervised models. Prevent integrates the core ingredients of unsupervised approaches into a novel fully unsupervised approach to predict failures and localize failing resources. The results of experimenting with Prevent on a commercially-compliant distributed cloud system indicate that Prevent provides more stable, reliable and timely predictions than supervised learning approaches, without requiring the often impractical training with labeled data.
CMOS-based area-and-power-efficient neuron and synapse circuits for time-domain analog spiking neural networks

Conventional neural structures tend to communicate through analog quantities, such as currents or voltages; however, as CMOS devices shrink and supply voltages decrease, the dynamic range of voltage/current-domain analog circuits becomes narrower, the available margin becomes smaller, and noise immunity decreases. More than that, the use of operational amplifiers (op-amps) and continuous-time or clocked comparators in conventional designs leads to high energy consumption and large chip area, which would be detrimental to building spiking neural networks. In view of this, we propose a neural structure for generating and transmitting time-domain signals, including a neuron module, a synapse module, and two weight modules. The proposed neural structure is driven by a leakage current of MOS transistors and uses an inverter-based comparator to realize a firing function, thus providing higher energy and area efficiency compared to conventional designs. The proposed neural structure is fabricated using a TSMC 65 nm CMOS technology. The proposed neuron and synapse occupy the area of 127 and 231 [Formula: see text], respectively, while achieving millisecond time constants. Actual chip measurements show that the proposed structure implements the temporal signal communication function with millisecond time constants, which is a critical step toward hardware reservoir computing for human–computer interaction. Simulation results of the spiking neural network for reservoir computing with the behavioral model of the proposed neural structure demonstrates the learning function.