Miracle-dz/UniFAR-FTUs huggingface dataset:
Pparα and fatty acid oxidation coordinate hepatic transcriptional architecture

Fasting requires tight coordination between the metabolism and transcriptional output of hepatocytes to maintain systemic glucose and lipid homeostasis. Genetically-defined deficits in hepatic fatty acid oxidation result in dramatic fasting-induced hepatocyte lipid accumulation and induction of genes for oxidative metabolism, thereby providing a mouse model to interrogate the mechanisms by which the liver senses and transcriptionally responds to fluctuations in lipid levels. While fatty acid oxidation is required for a rise in acetyl-CoA and subsequent lysine acetylation following a fast, changes in histone acetylation (total, H3K9ac, and H3K27ac) associated with transcription do not require fatty acid oxidation. Instead, excess fatty acids prompt induction of lipid catabolic genes largely via ligand-activated Pparα. We observe that active enhancers in fasting mice are enriched for Pparα binding motifs, and that inhibition of hepatic fatty acid oxidation results in elevated enhancer priming and acetylation proximal to Pparα binding sites within regulatory elements largely associated with genes in lipid metabolism. Also, a greater number of Pparα-associated H3K27ac signal changes occur at active enhancers compared to promoters, suggesting a genomic mechanism for Pparα to tune target gene expression levels. Overall, these data demonstrate the requirement for Pparα activation in maintaining transcriptionally permissive hepatic genomic architecture particularly when fatty acid oxidation is limiting. HIGHLIGHTS Fasting-induced transcription and histone acetylation are largely independent of acetyl-CoA concentration. Deficits in fatty acid oxidation prompt epigenetic changes and Pparα-sensitive transcription. Fasting prompts enhancer priming and acetylation proximal to Pparα binding sites independent of Pparα. Patterns of Pparα target genes can be distinguished by epigenetic marks at promoters and enhancers.
Proteomic Signatures of Microbial Adaptation to the Highest UV-Irradiation on Earth: Lessons from a Soil Actinobacterium

In the Puna region, the total solar irradiation and the UV incidence is the highest on Earth, thus, restraining the physiology of individual microorganisms and the composition of microbial communities. UV-resistance of microbial strains thriving in High-Altitude Andean Lakes was demonstrated and their mechanisms were partially characterized by genomic analysis, biochemical and physiological assays. In this work, we present the molecular events involved in the adaptive response of the model HAAL extremophilic actinobacterium Nesterenkonia sp. Act20 under artificial UV-B radiation, herein called as UV-resistome. Proteomic profiles of cultures exposed to different UV-experimental conditions showed that the leading systems for adaptation to the UV-challenge in-vitro are DNA repair and antioxidant mechanisms.
Protein-Protein Interaction Site Prediction Based on Attention Mechanism and Convolutional Neural Networks

Proteins usually perform their cellular functions by interacting with other proteins. Accurate identification of protein-protein interaction sites (PPIs) from sequence is import for designing new drugs and developing novel therapeutics. A lot of computational models for PPIs prediction have been developed because experimental methods are slow and expensive. Most models employ a sliding window approach in which local neighbors are concatenated to present a target residue. However, those neighbors are not been distinguished by pairwise information between a neighbor and the target. In this study, we propose a novel PPIs prediction model AttCNNPPISP, which combines attention mechanism and convolutional neural networks (CNNs). The attention mechanism dynamically captures the pairwise correlation of each neighbor-target pair within a sliding window, and therefore makes a better understanding of the local environment of target residue. And then, CNNs take the local representation as input to make prediction. Experiments are employed on several public benchmark datasets. Compared with the state-of-the-art models, AttCNNPPISP significantly improves the prediction performance. Also, the experimental results demonstrate that the attention mechanism is effective in terms of constructing comprehensive context information of target residue.
Age-related differences in encoding-retrieval similarity and their relationship to false memory

Typical aging is associated with increases in false memory rates among older adults. Such errors are frequently associated with differential neural activity during encoding and retrieval in older compared to younger adults within visual cortices, hippocampus, and front-parietal regions. It remains unknown how pattern similarity reductions relate to false memories in healthy aging. Using encoding-retrieval similarity (ERS) analyses in a sample of younger and older adults, we examined how the similarity of neural patterns between memory phases associated with target and lure objects was impacted by age and contributed to false memory rates. Single-item ERS for targets and lures was reduced by age throughout much of the ventral visual stream and the posterior hippocampus. Furthermore, ERS associated with perceptual lures within the visual stream maintained differential relationships with false memory. Finally, a global ERS metric accounted for age deficits in single-item ERS, but did not contribute to false memory rates. These findings highlight the contribution of age-related reductions in ERS across multiple representational levels to false memories in healthy aging.
How Does Music-Message Congruency in Online Video Advertisements Enhance Consumers’ Behavior Intentions? A Serial Multiple Mediator Model

This study provides new insights into the influence of music-message congruency on behavior intentions. A serial multiple mediation model in which the relationship between music-message congruency and behavior intentions is sequentially mediated by arousal and brand trust was constructed and tested. A total of 261 valid questionnaires were obtained after participants watched the online video advertisements. PROCESS macro for SPSS was then used to test the serial mediation hypotheses. The results showed that: (1) music-message congruency has a positive effect on behavior intentions; (2) brand trust plays a partial mediating role between music-message congruency and behavior intentions, and (3) arousal and brand trust mediate the relationship between music-message congruency and behavior intentions sequentially.
Federated Learning under Arbitrary Communication Patterns

Federated Learning is a distributed learning setting where the goal is to train a centralized model with training data distributed over a large number of heterogeneous clients, each with unreliable and relatively slow network connections. A common optimization approach used in federated learning is based on the idea of local SGD : each client runs some number of SGD steps locally and then the updated local models are averaged to form the updated global model on the coordinating server. In this paper, we investigate the performance of an asynchronous version of local SGD wherein the clients can communicate with the server at arbitrary time intervals. Our main result shows that for smooth strongly convex and smooth nonconvex functions we achieve convergence rates that match the synchronous version that requires all clients to communicate simultaneously.
Improving Gradient Regularization using Complex-Valued Neural Networks

Gradient regularization is a neural network defense technique that requires no prior knowledge of an adversarial attack and that brings only limited increase in training computational complexity. A form of complex-valued neural network (CVNN) is proposed to improve the performance of gradient regularization on classiﬁcation tasks of real-valued input in adversarial settings. The activation derivatives of each layer of the CVNN are dependent on the combination of inputs to the layer, and locally stable representations can be learned for inputs the network is trained on. Furthermore, the properties of the CVNN parameter derivatives resist decrease of performance on the standard objective that is caused by competition with the gradient regularization objective. Experimental results show that the performance of gradient regularized CVNN surpasses that of real-valued neural networks with comparable storage and computational complexity. Moreover, gradient regularized complex-valued networks exhibit robust performance approaching that of real-valued networks trained with multi-step adversarial training.
Teaching Programming in Secondary Schools: Stepping and Stumbling Stones

Programming is introduced in secondary education in a growing number of subjects. This results in an increasing number of teachers teaching programming in their classes, often without proper training. Learning programming might be complicated, even more so is teaching it. In this context, there is a need to understand teachers’ perspectives on teaching programming. This paper aims to identify challenges that teachers in secondary schools face and might negatively impact their teaching, i.e., stumbling stones, as well as elements that promote teaching and give motivation, i.e., stepping stones. The paper is based on the analysis of reflection notes delivered by in-service teachers attending a university-level course on teaching programming. The teachers compile the reflection notes after they complete their final project. Projects are centred around the definition of teaching plans to be tried out in class. The reflection notes of 173 students are analysed to identify issues related to: programming; teaching programming; recurrent didactic issues; and external challenges. The analysis is then summarised in a set of stumbling and stepping stones. For example, time is identified as one of the main stumbling stones by teachers. On the other side, motivation is one of the central stepping stones that we can identify in the data, often connected to the excitement of teaching something that was not previously taught in schools or that teachers perceive as highly relevant for society and the future job market. Implications for teacher training are also identified.
Double-Win Quant: Aggressively Winning Robustness of Quantized Deep Neural Networks via Random Precision Training and Inference

Quantization is promising in enabling powerful yet complex deep neural networks (DNNs) to be deployed into resource constrained platforms. However, quantized DNNs are vulnerable to adversarial attacks unless being equipped with sophisticated techniques, leading to a dilemma of struggling between DNNs’ efﬁciency and robustness. In this work, we demonstrate a new perspective regarding quantization’s role in DNNs’ robustness, advocating that quantization can be leveraged to largely boost DNNs’ robustness, and propose a framework dubbed Double-Win Quant that can boost the robustness of quantized DNNs over their full precision counterparts by a large margin. Speciﬁcally, we for the ﬁrst time identify that when an adversarially trained model is quantized to different precisions in a post-training manner, the associated adversarial attacks transfer poorly between different precisions. Leveraging this intriguing observation, we further develop Double-Win Quant integrating random precision inference and training to further reduce and utilize the poor adversarial transferability, enabling an aggressive “win-win” in terms of DNNs’ robustness and efﬁciency. Extensive experiments and ablation studies consistently validate Double-Win Quant’s effectiveness and advantages over state-of-the-art (SOTA) adversarial training meth-ods across various attacks/models/datasets. Our codes are available at: https://github.com/RICE-EIC/Double-Win-Quant.
One Pass Late Fusion Multi-view Clustering

Existing late fusion multi-view clustering (LFMVC) optimally integrates a group of pre-speciﬁed base partition matrices to learn a consensus one. It is then taken as the input of the widely used k-means to generate the cluster labels. As observed, the learning of the consensus partition matrix and the generation of cluster labels are separately done. These two procedures lack necessary negotiation and can not best serve for each other, which may adversely affect the clustering performance. To address this issue, we propose to unify the aforementioned two learning procedures into a single optimization, in which the consensus partition matrix can better serve for the generation of cluster labels, and the latter is able to guide the learning of the former. To optimize the resultant optimization problem, we develop a four-step alternate algorithm with proved convergence. We theoretically analyze the clustering generalization error of the proposed algorithm on unseen data. Comprehensive experiments on multiple benchmark datasets demonstrate the superiority of our algorithm in terms of both clustering accuracy and computational efﬁciency. It is expected that the simplicity and effectiveness of our algorithm will make it a good option to be considered for practical multi-view clustering applications.
Model-Targeted Poisoning Attacks with Provable Convergence

In a poisoning attack, an adversary who controls a small fraction of the training data attempts to select that data, so a model is induced that misbehaves in a particular way. We consider poisoning attacks against convex machine learning models and propose an efficient poisoning attack designed to induce a model specified by the adversary. Unlike previous model-targeted poisoning attacks, our attack comes with provable convergence to any attainable target model. We also provide a lower bound on the minimum number of poisoning points needed to achieve a given target model. Our method uses online convex optimization and finds poisoning points incrementally. This provides more flexibility than previous attacks which require an a priori assumption about the number of poisoning points. Our attack is the first modeltargeted poisoning attack that provides provable convergence for convex models. In our experiments, it either exceeds or matches state-of-the-art attacks in terms of attack success rate and distance to the target model.
Brexit and Power in the Council of the European Union

The exit of the United Kingdom from the European Union has had profound economic and political effects. Here, we look at a particular aspect, the power distribution in the Council of the European Union. Using the Shapley–Shubik power index, we calculate the member states’ powers with and without the United Kingdom and update earlier power forecasts using the Eurostat’s latest population projections. There is a remarkably sharp relation between population size and the change in power: Brexit increases the largest members’ powers while decreasing the smallest ones’ powers.
Representation Subspace Distance for Domain Adaptation Regression

Regression, as a counterpart to classiﬁcation, is a major paradigm with a wide range of applications. Domain adaptation regression extends it by generalizing a regressor from a labeled source domain to an unlabeled target domain. Existing domain adaptation regression methods have achieved positive results limited only to the shallow regime. A question arises: Why learning invariant representations in the deep regime less pronounced? A key ﬁnding of this paper is that classiﬁcation is robust to feature scaling but regression is not, and aligning the distributions of deep representations will alter feature scale and impede domain adaptation regression. Based on this ﬁnding, we propose to close the domain gap through orthogonal bases of the representation spaces, which are free from feature scaling. Inspired by Riemannian geometry of Grassmann manifold, we deﬁne a geometrical distance over representation subspaces and learn deep transferable representations by minimizing it. To avoid breaking the geometrical properties of deep representations, we further introduce the bases mismatch penalization to match the ordering of orthogonal bases across representation sub-spaces. Our method is evaluated on three domain adaptation regression benchmarks, two of which are constructed in this paper. Our method outperforms the state-of-the-art methods signiﬁcantly, forming early positive results in the deep regime.
SagaNet: A Small Sample Gated Network for Pediatric Cancer Diagnosis

The scarcity of available samples and the high annotation cost of medical data cause a bottleneck in many digital diagnosis tasks based on deep learning. This problem is especially severe in pediatric tumor tasks, due to the small population base of children and high sample diversity caused by the high metastasis rate of related tumors. Targeted research on pediatric tumors is urgently needed but lacks sufﬁcient attention. In this work, we pro-pose a novel model to solve the diagnosis task of small round blue cell tumors (SRBCTs). To solve the problem of high noise and high diversity in the small sample scenario, the model is constrained to pay attention to the valid areas in the pathological image with a masking mechanism, and a length-aware loss is proposed to improve the tolerance to feature diversity. We evaluate this framework on a challenging small sample SRBCTs dataset, whose classiﬁcation is difﬁcult even for professional pathologists. The proposed model shows the best performance compared with state-of-the-art deep models and generalization on another pathological dataset, which illustrates the potentiality of deep learning applications in difﬁcult small sample medical tasks.
Network-Coding-based Jamming With Triple Transmission Time Slots: A Method To Secure Transmission In An Extreme Case of Source-Wiretapping and Unshared Jamming Signal

This article resolves an extreme case in physical layer security: an eavesdropper, located near to a source, can spy on the jamming-seed if it is just cryptographically shared. The direct link between the source and destination is even unavailable. The system is proposed to operate in triple transmission phases (timeslots). In the first phase, jamming signal is proposed to carry a random binary network-coding-based jamming (NCJ) message, transmitted by an active jammer. As NCJ cannot be just cryptographically protected, we propose a solution of using physical layer security to secure this message. As a result, a network-coding method can be employed in which NCJ acts as a key to protect the source message from this extreme case of wiretapping. The spatial diversities in both jamming and legitimate transmission is fully exploited to overcome this challenge with high performance. Analysis and simulation of the outage performance and comparison with current methods are provided to validate the performance of the proposed method. Received on 04 June 2021; accepted on 21 June 2021; published on 23 June 2021
Testing DNN-based Autonomous Driving Systems under Critical Environmental Conditions

Due to the increasing usage of Deep Neural Network (DNN) based autonomous driving systems (ADS) where erroneous or unexpected behaviours can lead to catastrophic accidents, testing such systems is of growing importance. Existing approaches often just focus on ﬁnding erroneous behaviours and have not thoroughly studied the impact of environmental conditions. In this paper, we propose to test DNN-based ADS under different environmental conditions to identify the critical ones, that is, the environmental conditions under which the ADS are more prone to errors. To tackle the problem of the space of environmental conditions being extremely large, we present a novel approach named T ACTIC that employs the search-based method to identify critical environmental conditions generated by an image-to-image translation model. Large-scale experiments show that T ACTIC can effectively identify critical environmental conditions and produce realistic testing images, and meanwhile, reveal more erroneous behaviours compared to existing approaches.
Unsupervised Representation Learning via Neural Activation Coding

We present neural activation coding (NAC) as a novel approach for learning deep representations from unlabeled data for downstream applications. We argue that the deep encoder should maximize its nonlinear expressivity on the data for downstream predictors to take full advantage of its representation power. To this end, NAC maximizes the mutual information between activation patterns of the encoder and the data over a noisy communication channel. We show that learning for a noise-robust activation code increases the number of distinct linear regions of ReLU encoders, hence the maximum nonlinear expressivity. More interestingly, NAC learns both continuous and discrete representations of data, which we respectively evaluate on two downstream tasks: (i) linear classification on CIFAR-10 and ImageNet-1K and (ii) nearest neighbor retrieval on CIFAR-10 and FLICKR-25K. Empirical results show that NAC attains better or comparable performance on both tasks over recent baselines including SimCLR and DistillHash. In addition, NAC pretraining provides significant benefits to the training of deep generative models. Our code is available at https://github.com/yookoon/nac.
Training Recurrent Neural Networks via Forward Propagation Through Time

Back-propagation through time (BPTT) has been widely used for training Recurrent Neural Networks (RNNs). BPTT updates RNN parameters on an instance by back-propagating the error in time over the entire sequence length, and as a result, leads to poor trainability due to the well-known gradient explosion/decay phenomena. While a number of prior works have proposed to mitigate vanishing/explosion effect through careful RNN architecture design, these RNN variants still train with BPTT. We propose a novel forward-propagation algorithm, FPTT , where at each time, for an instance, we update RNN parameters by optimizing an instantaneous risk function. Our proposed risk is a regularization penalty at time t that evolves dynamically based on previously observed losses, and allows for RNN parameter updates to converge to a stationary solution of the empirical RNN objective. We consider both sequence-to-sequence as well as terminal loss problems. Empirically FPTT outperforms BPTT on a number of well-known benchmark tasks, thus enabling architectures like LSTMs to solve long range dependencies problems.
Best Model Identification: A Rested Bandit Formulation

We introduce and analyze a best arm identiﬁcation problem in the rested bandit setting, wherein arms are themselves learning algorithms whose expected losses decrease with the number of times the arm has been played. The shape of the expected loss functions is similar across arms, and is assumed to be available up to unknown parameters that have to be learned on the ﬂy. We deﬁne a novel notion of regret for this problem, where we compare to the policy that always plays the arm having the smallest expected loss at the end of the game. We analyze an arm elimination algo-rithm whose regret vanishes as the time horizon increases. The actual rate of convergence depends in a detailed way on the postulated functional form of the expected losses. We complement our analysis with lower bounds, indicating strengths and limitations of the proposed solution.