Miracle-dz/UniFAR-FTUs huggingface dataset:
Stress hormone norepinephrine incites resistance of oral cancer cells to chemotherapy.

This study investigated whether norepinephrine and epinephrine interfere in the response of head and neck squamous cell carcinoma (SCC) cell lines to cisplatin and explored the mechanisms of chemoresistance. Head and neck squamous cell carcinoma-derived cell lines SCC-9, Cal27, SCC-25, and FaDu were stimulated with norepinephrine or epinephrine and treated with the inhibitory concentration of cisplatin for 24 hours. As for ADRB inhibition, cells were treated with propranolol. The results showed that, when combined with norepinephrine, cisplatin effectiveness against SCC-9 and Cal27 but not SCC-25 and FaDu cells were notably reduced. Epinephrine did not affect the response of the cells to cisplatin. Further experiments were performed with the responsive SCC-9 and SCC-25 cell lines and the hormone norepinephrine. Time-course assay showed that stimulation of oral SCC cells with norepinephrine decreased the cleavage of caspase-3 and expression of MDR-1 but only transiently affected ABCG2 expression. The expression of cleaved caspase-3 and bcl-2 were, respectively, decreased and increased by the combination of norepinephrine and cisplatin in SCC-9 and Cal27 cells. Norepinephrine-induced resistance was reverted by previous treatment with propranolol. Expressions of ABCG2, and p-Akt but not of MDR-1, were enhanced by norepinephrine plus cisplatin when compared to cisplatin only in both cell lines. Migratory activity of oral SCC cells challenged with cisplatin was not affected by norepinephrine. These findings reveal for the first time that stress hormone norepinephrine induce resistance of oral cancer cells to cisplatin in vitro through the ADRB/Akt/ABCG2 pathway, pumping the drug out of the cell and inhibiting apoptosis.
Role is in the eye of the beholder—the multiple functions of the antibacterial compound tropodithietic acid produced by marine Rhodobacteraceae

Abstract Many microbial secondary metabolites have been studied for decades primarily because of their antimicrobial properties. However, several of these metabolites also possess nonantimicrobial functions, both influencing the physiology of the producer and their ecological neighbors. An example of a versatile bacterial secondary metabolite with multiple functions is the tropone derivative tropodithietic acid (TDA). TDA is a broad-spectrum antimicrobial compound produced by several members of the Rhodobacteraceae family, a major marine bacterial lineage, within the genera Phaeobacter, Tritonibacter, and Pseudovibrio. The production of TDA is governed by the mode of growth and influenced by the availability of nutrient sources. The antibacterial effect of TDA is caused by disruption of the proton motive force of target microorganisms and, potentially, by its iron-chelating properties. TDA also acts as a signaling molecule, affecting gene expression in other bacteria, and altering phenotypic traits such as motility, biofilm formation, and antibiotic production in the producer. In microbial communities, TDA-producing bacteria cause a reduction of the relative abundance of closely related species and some fast-growing heterotrophic bacteria. Here, we summarize the current understanding of the chemical ecology of TDA, including the environmental niches of TDA-producing bacteria, and the molecular mechanisms governing the function and regulation of TDA.
Fragmented ARES: Dynamic Storage for Large Objects

Data availability is one of the most important features in distributed storage systems, made possible by data replication. Nowadays data are generated rapidly and the goal to develop efficient, scalable and reliable storage systems has become one of the major challenges for high performance computing. In this work, we develop a dynamic, robust and strongly consistent distributed storage implementation suitable for handling large objects (such as files). We do so by integrating an Adaptive, Reconfigurable, Atomic Storage framework, called ARES, with a distributed file system, called COBFS, which relies on a block fragmentation technique to handle large objects. With the addition of ARES, we also enable the use of an erasure-coded algorithm to further split our data and to potentially improve storage efficiency at the replica servers and operation latency. To put the practicality of our outcomes at test, we conduct an in-depth experimental evaluation on the Emulab and AWS EC2 testbeds, illustrating the benefits of our approaches, as well as other interesting tradeoffs.
You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration

Promising results have been achieved recently in category-level manipulation that generalizes across object instances. Nevertheless, it often requires expensive real-world data collection and manual specification of semantic keypoints for each object category and task. Additionally, coarse keypoint predictions and ignoring intermediate action sequences hinder adoption in complex manipulation tasks beyond pick-and-place. This work proposes a novel, category-level manipulation framework that leverages an object-centric, category-level representation and model-free 6 DoF motion tracking. The canonical object representation is learned solely in simulation and then used to parse a category-level, task trajectory from a single demonstration video. The demonstration is reprojected to a target trajectory tailored to a novel object via the canonical representation. During execution, the manipulation horizon is decomposed into longrange, collision-free motion and last-inch manipulation. For the latter part, a category-level behavior cloning (CatBC) method leverages motion tracking to perform closed-loop control. CatBC follows the target trajectory, projected from the demonstration and anchored to a dynamically selected category-level coordinate frame. The frame is automatically selected along the manipulation horizon by a local attention mechanism. This framework allows to teach different manipulation strategies by solely providing a single demonstration, without complicated manual programming. Extensive experiments demonstrate its efficacy in a range of challenging industrial tasks in highprecision assembly, which involve learning complex, long-horizon policies. The process exhibits robustness against uncertainty due to dynamics as well as generalization across object instances and scene configurations. The supplementary video is available at https://www.youtube. com/watch? v=WAr8ZY3mYyw
Higher regularity of homeomorphisms in the Hartman-Grobman theorem for semilinear evolution equations

Hein and Prüss [20] presented a version of Hartman-Grobman type C0 linearization result for semilinear hyperbolic evolution equations. They showed that the linearising map (homeomorphism) H and its inverse G = H−1 are Hölder continuous. An important question: is it possible to improve the regularity of the homeomorphismsH? In the present paper, we first formulate the result that the homeomorphisms H in the HartmanGrobman theorem is Lipchitzian, but the inverse G is merely Hölder continuous. We also give a generalized local linearization result in this paper. Finally, some applications end the paper. As pointed out by Backes et al. [1], even if the diffeomorphism F is C∞, the conjugacy (homeomorphism) can fail to be locally Lipschitz. The homeomorphisms This paper was jointly supported from the National Natural Science Foundation of China under Grant (No. 11931016 and 11671176) and Grant Fondecyt 1170466. Corresponding author. Y-H. Xia, xiadoc@outlook.com;yhxia@zjnu.cn. Address: College of Mathematics and Computer Science, Zhejiang Normal University, 321004, Jinhua, China 1 are in general only locally Hölder continuous. In fact,it is proved that in all the previous works on Hartman-Grobaman theorem in C0 linearization, the homeomorphisms are Hölder continuous. However, by establishing two effective dichotomy integral inequalities, we prove that the conjugacy is Lipchitzian, but the inverse is Hölder continuous. keywords: Hartman-Grobman theorem; semilinear evolution equations; homeomorphisms; linearization
Zeroth-Order Actor-Critic

The recent advanced evolution-based zeroth-order optimization methods and the policy gradient-based first-order methods are two promising alternatives to solve reinforcement learning (RL) problems with complementary advantages. The former methods work with arbitrary policies, drive state-dependent and temporally-extended exploration, possess robustness-seeking property, but suffer from high sample complexity, while the latter methods are more sample efficient but are restricted to differentiable policies and the learned policies are less robust. To address these issues, we propose a novel Zeroth-Order Actor-Critic algorithm (ZOAC), which unifies these two methods into an on-policy actor-critic architecture to preserve the advantages from both. ZOAC conducts rollouts collection with timestep-wise perturbation in parameter space, first-order policy evaluation (PEV) and zeroth-order policy improvement (PIM) alternately in each iteration. We extensively evaluate our proposed method on a wide range of challenging continuous control benchmarks using different types of policies, where ZOAC outperforms zeroth-order and first-order baseline algorithms.
SPDY: Accurate Pruning with Speedup Guarantees

The recent focus on the efficiency of deep neural networks (DNNs) has led to significant work on model compression approaches, of which weight pruning is one of the most popular. At the same time, there is rapidly-growing computational support for efficiently executing the unstructured-sparse models obtained via pruning. Yet, most existing pruning methods minimize just the number of remaining weights, i.e. the size of the model, rather than optimizing for inference time. We address this gap by introducing SPDY, a new compression method which automatically determines layer-wise sparsity targets achieving a desired inference speedup on a given system, while minimizing accuracy loss. SPDY is composed of two new techniques: the first is an efficient dynamic programming algorithm for solving the speedup-constrained layer-wise compression problem assuming a set of given layer-wise sensitivity scores; the second is a local search procedure for determining accurate layer-wise sensitivity scores. Experiments across popular vision and language models show that SPDY guarantees speedups while recovering higher accuracy relative to existing strategies, both for one-shot and gradual pruning scenarios, and is compatible with most existing pruning approaches. We also extend our approach to the recently-proposed task of pruning with very little data, where we achieve the best known accuracy recovery when pruning to the GPU-supported 2:4 sparsity pattern.
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms

Motivated by online recommendation systems, we propose the problem of finding the optimal policy in multitask contextual bandits when a small fraction $\alpha<1/2$ of tasks (users) are arbitrary and adversarial. The remaining fraction of good users share the same instance of contextual bandits with $S$ contexts and $A$ actions (items). Naturally, whether a user is good or adversarial is not known in advance. The goal is to robustly learn the policy that maximizes rewards for good users with as few user interactions as possible. Without adversarial users, established results in collaborative filtering show that $O(1/\epsilon^2)$ per-user interactions suffice to learn a good policy, precisely because information can be shared across users. This parallelization gain is fundamentally altered by the presence of adversarial users: unless there are super-polynomial number of users, we show a lower bound of $\tilde{\Omega}(\min(S,A) \cdot \alpha^2 / \epsilon^2)$ {\it per-user} interactions to learn an $\epsilon$-optimal policy for the good users. We then show we can achieve an $\tilde{O}(\min(S,A)\cdot \alpha/\epsilon^2)$ upper-bound, by employing efficient robust mean estimators for both uni-variate and high-dimensional random variables. We also show that this can be improved depending on the distributions of contexts.
NeuralTailor

The fields of SocialVR, performance capture, and virtual try-on are often faced with a need to faithfully reproduce real garments in the virtual world. One critical task is the disentanglement of the intrinsic garment shape from deformations due to fabric properties, physical forces, and contact with the body. We propose to use a garment sewing pattern, a realistic and compact garment descriptor, to facilitate the intrinsic garment shape estimation. Another major challenge is a high diversity of shapes and designs in the domain. The most common approach for Deep Learning on 3D garments is to build specialized models for individual garments or garment types. We argue that building a unified model for various garment designs has the benefit of generalization to novel garment types, hence covering a larger design domain than individual models would. We introduce NeuralTailor, a novel architecture based on point-level attention for set regression with variable cardinality, and apply it to the task of reconstructing 2D garment sewing patterns from the 3D point cloud garment models. Our experiments show that NeuralTailor successfully reconstructs sewing patterns and generalizes to garment types with pattern topologies unseen during training.
Can Adversarial Training Be Manipulated By Non-Robust Features?

Adversarial training, originally designed to resist test-time adversarial examples, has shown to be promising in mitigating training-time availability attacks. This defense ability, however, is challenged in this paper. We identify a novel threat model named stability attack, which aims to hinder robust availability by slightly manipulating the training data. Under this threat, we show that adversarial training using a conventional defense budget $\epsilon$ provably fails to provide test robustness in a simple statistical setting, where the non-robust features of the training data can be reinforced by $\epsilon$-bounded perturbation. Further, we analyze the necessity of enlarging the defense budget to counter stability attacks. Finally, comprehensive experiments demonstrate that stability attacks are harmful on benchmark datasets, and thus the adaptive defense is necessary to maintain robustness. Our code is available at https://github.com/TLMichael/Hypocritical-Perturbation.
The Geometry of Robust Value Functions

The space of value functions is a fundamental concept in reinforcement learning. Characterizing its geometric properties may provide insights for optimization and representation. Existing works mainly focus on the value space for Markov Decision Processes (MDPs). In this paper, we study the geometry of the robust value space for the more general Robust MDPs (RMDPs) set-ting, where transition uncertainties are considered. Speciﬁcally, since we ﬁnd it hard to directly adapt prior approaches to RMDPs, we start with revisiting the non-robust case, and introduce a new perspective that enables us to characterize both the non-robust and robust value space in a similar fashion. The key of this perspective is to decompose the value space, in a state-wise manner, into unions of hypersurfaces. Through our analysis, we show that the robust value space is determined by a set of conic hypersurfaces , each of which contains the robust values of all policies that agree on one state. Furthermore, we ﬁnd that taking only extreme points in the uncertainty set is sufﬁcient to determine the robust value space. Finally, we discuss some other aspects about the robust value space, including its non-convexity and policy agreement on multiple states.
Estrazione di energia dalle onde tramite boe a moto verticale : modellizzazione e controllo

Lo scopo della presente trattazione e quello di studiare un possibile sistema per convertire il movimento oscillatorio delle onde in energia elettrica. Nella prima parte della tesi sara esposto lo studio di un modello matematico che possa rappresentare nel modo piu accurato possibile il sistema di conversione, che sara definito con l’acronimo WEC (Wave Energy Converter), questo sara il punto di partenza per la creazione di un modello da implementare nel simulatore numerico Matlab/Simlulink. Nella seconda parte della tesi invece si indagheranno le tecniche di controllo per massimizzare l’energia estraibile, seguendo due filoni diversi di studio. Il primo e piu classico approccio si basera sull’analisi dei risultati che si ottengono assumendo un profilo del mare regolare (sinusoidale); mentre il secondo studio cerchera di indagare come il profilo del mare reale (che si presenta come fortemente irregolare) possa influire sull’estrazione di potenza rispetto al primo caso teorico. In fase di conclusione della tesi invece, si descrivera brevemente la parte di elettronica e di controllo necessaria al funzionamento del generatore elettrico.
A Brief Overview of Physics-inspired Metaheuristic Optimization Techniques

Metaheuristic algorithms are methods devised to efficiently solve computationally challenging optimization problems. Researchers have taken inspiration from various natural and physical processes alike to formulate meta-heuristics that have successfully provided near-optimal or optimal solutions to several engineering tasks. This chapter focuses on meta-heuristic algorithms modelled upon non-linear physical phenomena having a concrete optimization paradigm, having shown formidable exploration and exploitation abilities for such optimization problems. Specifically, this chapter focuses on several popular physics-based metaheuristics as well as describing the underlying unique physical processes associated with each algorithm.
Steady-State Error Compensation in Reference Tracking and Disturbance Rejection Problems for Reinforcement Learning-Based Control

Reinforcement learning (RL) is a promising, upcoming topic in automatic control applications. Where classical control approaches require a priori system knowledge, data-driven control approaches like RL allow a model-free controller design procedure, rendering them emergent techniques for systems with changing plant structures and varying parameters. While it was already shown in various applications that the transient control behavior for complex systems can be sufficiently handled by RL, the challenge of non-vanishing steady-state control errors remains, which arises from the usage of control policy approximations and finite training times. To overcome this issue, an integral action state augmentation (IASA) for actor-critic-based RL controllers is introduced that mimics an integrating feedback, which is inspired by the delta-input formulation within model predictive control. This augmentation does not require any expert knowledge, leaving the approach model free. As a result, the RL controller learns how to suppress steady-state control deviations much more effectively. Two exemplary applications from the domain of electrical energy engineering validate the benefit of the developed method both for reference tracking and disturbance rejection. In comparison to a standard deep deterministic policy gradient (DDPG) setup, the suggested IASA extension allows to reduce the steady-state error by up to 52 $\%$ within the considered validation scenarios.
Perspective on Code Submission and Automated Evaluation Platforms for University Teaching

We present a perspective on platforms for code submission and automated evaluation in the context of university teaching. Due to the COVID-19 pandemic, such platforms have become an essential asset for remote courses and a reasonable standard for structured code submission concerning increasing numbers of students in computer sciences. Utilizing automated code evaluation techniques exhibits notable positive impacts for both students and teachers in terms of quality and scalability. We identified relevant technical and non-technical requirements for such platforms in terms of practical applicability and secure code submission environments. Furthermore, a survey among students was conducted to obtain empirical data on general perception. We conclude that submission and automated evaluation involves continuous maintenance yet lowers the required workload for teachers and provides better evaluation transparency for students.
Novel machine learning approaches for improving the reproducibility and reliability of functional and effective connectivity from functional MRI

Objective. New measures of human brain connectivity are needed to address gaps in the existing measures and facilitate the study of brain function, cognitive capacity, and identify early markers of human disease. Traditional approaches to measure functional connectivity (FC) between pairs of brain regions in functional MRI, such as correlation and partial correlation, fail to capture nonlinear aspects in the regional associations. We propose a new machine learning based measure of FC ( ML.FC ) which efficiently captures linear and nonlinear aspects. Approach. To capture directed information flow between brain regions, effective connectivity (EC) metrics, including dynamic causal modeling and structural equation modeling have been used. However, these methods are impractical to compute across the many regions of the whole brain. Therefore, we propose two new EC measures. The first, a machine learning based measure of effective connectivity ( ML.EC ), measures nonlinear aspects across the entire brain. The second, Structurally Projected Granger Causality ( SP.GC ) adapts Granger Causal connectivity to efficiently characterize and regularize the whole brain EC connectome to respect underlying biological structural connectivity. The proposed measures are compared to traditional measures in terms of reproducibility and the ability to predict individual traits in order to demonstrate these measures’ internal validity. We use four repeat scans of the same individuals from the Human Connectome Project and measure the ability of the measures to predict individual subject physiologic and cognitive traits. Main results. The proposed new FC measure of ML.FC attains high reproducibility (mean intra-subject R 2 of 0.44), while the proposed EC measure of SP.GC attains the highest predictive power (mean R 2 across prediction tasks of 0.66). Significance. The proposed methods are highly suitable for achieving high reproducibility and predictiveness and demonstrate their strong potential for future neuroimaging studies.
Exact Decomposition of Joint Low Rankness and Local Smoothness Plus Sparse Matrices

It is known that the decomposition in low-rank and sparse matrices (L+S for short) can be achieved by several Robust PCA techniques. Besides the low rankness, the local smoothness (LSS) is a vitally essential prior for many real-world matrix data such as hyperspectral images and surveillance videos, which makes such matrices have low-rankness and local smoothness property at the same time. This poses an interesting question: Can we make a matrix decomposition in terms of L&LSS +S form exactly? To address this issue, we propose in this paper a new RPCA model based on three-dimensional correlated total variation regularization (3DCTV-RPCA for short) by fully exploiting and encoding the prior expression underlying such joint low-rank and local smoothness matrices. Specifically, using a modification of Golfing scheme, we prove that under some mild assumptions, the proposed 3DCTV-RPCA model can decompose both components exactly, which should be the first theoretical guarantee among all such related methods combining low rankness and local smoothness. In addition, by utilizing Fast Fourier Transform (FFT), we propose an efficient ADMM algorithm with a solid convergence guarantee for solving the resulting optimization problem. Finally, a series of experiments on both simulations and real applications are carried out to demonstrate the general validity of the proposed 3DCTV-RPCA model.