Miracle-dz/UniFAR-FTUs huggingface dataset:
Stretchable patterns on elastomeric substrates

Compression-induced buckling instability of metal thin films on a compliant base result in surface wrinkles. A stiff thin film, perfectly bonded to an infinitely deep pre-stretched dielectric elastomer (DE) substrate, is considered. Linear perturbation analysis reveals the critical strain in the layer at the onset of wrinkling. A fully coupled Finite-Element (FE) scheme, based on large deformation electroelasticity, where domains of the mechanical and electrical problems coincide, is developed to model the elastomer. The procedure can handle nearly incompressible materials, which require careful considerations. The thin film is modeled as a beam. The numerical technique is used to study the post-buckling behavior upon relaxation of the pre-strained substrate. The FE domain is a unit cell of twice the characteristic wavelength. Assuming that there is no wrinkling-induced delamination at the film/substrate interface, the numerical analyses overview various tunable surface configurations distinctly different from the sinusoidal shape. The several topographic options broaden the functional horizon of surface instability patterns. Again, corrugation is essential to provide a degree of electrode compliance when the elastomer elongates in the lateral direction due to an applied electric potential. Simulations show the response of DE for the different surface profiles and also demonstrate the failure mechanisms that limit the actuation.
Two Steps Forward and One Step Sideways: The Propagation of Relativistic Jets in Realistic Binary Neutron Star Merger Ejecta

The association of GRB170817A with GW170817 has confirmed the long-standing hypothesis that binary neutron star (BNS) mergers are the progenitors of at least some short gamma-ray bursts (SGRBs). This connection has ushered in an era in which broadband observations of SGRBs, together with measurements of the time delay between the gravitational waves and the electromagnetic radiation, allow for probing the properties of the emitting outflow and its engine to an unprecedented detail. Because the structure of the radiating outflow is molded by the interaction of a relativistic jet with the binary ejecta, it is of paramount importance to study the system in a realistic setting. Here we present a three-dimensional hydrodynamic simulation of a relativistic jet propagating in the ejecta of a BNS merger, which were computed with a general relativistic magnetohydrodynamic simulation. We find that the jet’s centroid oscillates around the axis of the system, due to inhomogeneities encountered in the propagation. These oscillations allow the jet to find the path of least resistance and travel faster than an identical jet in smooth ejecta. In our setup the breakout time is ∼0.6 s, which is comparable to the expected central engine duration in SGRBs and possibly a non-negligible fraction of the total delay between the gravitational and gamma-ray signals. Our simulation also shows that energy is carried in roughly equal amounts by the jet and by the cocoon, and that about 20% of the injected energy is transferred to the ejecta via mechanical work.
Translatotron 2: Robust direct speech-to-speech translation

We present Translatotron 2 , a neural direct speech-to-speech translation model that can be trained end-to-end. Translatotron 2 consists of a speech encoder, a phoneme decoder, a mel-spectrogram synthesizer, and an attention module that connects all the previous three components. Experimental results suggest that Translatotron 2 outperforms the original Translatotron by a large margin in terms of translation quality and predicted speech naturalness, and drastically improves the robustness of the predicted speech by mitigating over-generation, such as babbling or long pause. We also propose a new method for retaining the source speaker’s voice in the translated speech. The trained model is restricted to retain the source speaker’s voice, and unlike the original Translatotron, it is not able to generate speech in a different speaker’s voice, making the model more robust for production deployment, by mitigating potential misuse for creating spooﬁng audio artifacts. When the new method is used together with a simple concatenation-based data augmentation, the trained Translatotron 2 model is able to retain each speaker’s voice for input with speaker turns.
Intrinsic and extrinsic gravitational flexions

The topic of this paper is a generalisation of the linear model for intrinsic alignments of galaxies to intrinsic flexions: In this model, third moments of the brightness distribution reflect distortions of elliptical galaxies caused by third derivatives of the gravitational potential, or, equivalently, gradients of the tidal gravitational fields. With this extension of the linear model mediating between the brightness distribution and the tidal gravitational fields and with a quantification of the shape of the galaxy at third order provided by the HOLICs-formalism, we are able to compute angular spectra of intrinsic flexions and the cross-spectra with weak lensing flexions. Spectra for intrinsic flexions are typically an order of magnitude smaller than lensing flexions, exactly as in the case of intrinsic ellipticity in comparison to weak shear. We find a negative cross correlation between intrinsic and extrinsic gravitational flexions, too, complementing the analogous correlation between intrinsic and extrinsic ellipticity. After discussing the physical details of the alignment model to provide intrinsic flexions and their scaling properties, we quantify the observability of the intrinsic and extrinsic flexions and estimate with the Fisher-formalism how well the alignment parameter can be determined from a Euclid-like weak lensing survey. Intrinsic flexions are very difficult to measure and yield appreciable signals only with highly optimistic parameter choices and noise levels, while being basically undetectable for more realistisc flexion measurement errors.
Influence of a Set of Variables on a Boolean Function

The influence of a variable is an important concept in the analysis of Boolean functions. The more general notion of influence of a set of variables on a Boolean function has four separate definitions in the literature. In the present work, we introduce a new definition of influence of a set of variables which is based on the auto-correlation function and develop its basic theory. Among the new results that we obtain are generalisations of the Poincar\'e inequality and the edge expansion property of the influence of a single variable. Further, we obtain new characterisations of resilient and bent functions using the notion of influence. We show that the previous definition of influence due to Fischer et. al. (2002) and Blais (2009) is half the value of the auto-correlation based influence that we introduce. Regarding the other prior notions of influence, we make a detailed study of these and show that each of these definitions do not satisfy one or more desirable properties that a notion of influence may be expected to satisfy.
CVEfixes: automated collection of vulnerabilities and their fixes from open-source software

Data-driven research on the automated discovery and repair of security vulnerabilities in source code requires comprehensive datasets of real-life vulnerable code and their fixes. To assist in such research, we propose a method to automatically collect and curate a comprehensive vulnerability dataset from Common Vulnerabilities and Exposures (CVE) records in the National Vulnerability Database (NVD). We implement our approach in a fully automated dataset collection tool and share an initial release of the resulting vulnerability dataset named CVEfixes. The CVEfixes collection tool automatically fetches all available CVE records from the NVD, gathers the vulnerable code and corresponding fixes from associated open-source repositories, and organizes the collected information in a relational database. Moreover, the dataset is enriched with meta-data such as programming language, and detailed code and security metrics at five levels of abstraction. The collection can easily be repeated to keep up-to-date with newly discovered or patched vulnerabilities. The initial release of CVEfixes spans all published CVEs up to 9 June 2021, covering 5365 CVE records for 1754 open-source projects that were addressed in a total of 5495 vulnerability fixing commits. CVEfixes supports various types of data-driven software security research, such as vulnerability prediction, vulnerability classification, vulnerability severity prediction, analysis of vulnerability-related code changes, and automated vulnerability repair.
DeepCC: Bridging the Gap Between Congestion Control and Applications via Multiobjective Optimization

The increasingly complicated and diverse applications have distinct network performance demands, e.g., some desire high throughput while others require low latency. Traditional congestion controls (CC) have no perception of these demands. Consequently, literatures have explored the objective-specific algorithms, which are based on either offline training or online learning, to adapt to certain application demands. However, once generated, such algorithms are tailored to a specific performance objective function. Newly emerged performance demands in a changeable network environment require either expensive retraining (in the case of offline training), or manually redesigning a new objective function (in the case of online learning). To address this problem, we propose a novel architecture, DeepCC. It generates a CC agent that is generically applicable to a wide range of application requirements and network conditions. The key idea of DeepCC is to leverage both offline deep reinforcement learning and online fine-tuning. In the offline phase, instead of training towards a specific objective function, DeepCC trains its deep neural network model using multi-objective optimization. With the trained model, DeepCC offers near Pareto optimal policies w.r. t different user-specified trade-offs between throughput, delay, and loss rate without any redesigning or retraining. In addition, a quick online fine-tuning phase further helps DeepCC achieve the application-specific demands under dynamic network conditions. The simulation and real-world experiments show that DeepCC outperforms state-of-the-art schemes in a wide range of settings. DeepCC gains a higher target completion ratio of application requirements up to 67.4% than that of other schemes, even in an untrained environment.
PICASO: Permutation-Invariant Cascaded Attentional Set Operator

Set-input deep networks have recently drawn much interest in computer vision and machine learning. This is in part due to the increasing number of important tasks such as meta-learning, clustering, and anomaly detection that are defined on set inputs. These networks must take an arbitrary number of input samples and produce the output invariant to the input set permutation. Several algorithms have been recently developed to address this urgent need. Our paper analyzes these algorithms using both synthetic and real-world datasets, and shows that they are not effective in dealing with common data variations such as image translation or viewpoint change. To address this limitation, we propose a permutation-invariant cascaded attentional set operator (PICASO). The gist of PICASO is a cascade of multihead attention blocks with dynamic templates. The proposed operator is a stand-alone module that can be adapted and extended to serve different machine learning tasks. We demonstrate the utilities of PICASO in four diverse scenarios: (i) clustering, (ii) image classification under novel viewpoints, (iii) image anomaly detection, and (iv) state prediction. PICASO increases the SmallNORB image classification accuracy with novel viewpoints by about 10% points. For set anomaly detection on CelebA dataset, our model improves the areas under ROC and PR curves dataset by about 22% and 10%, respectively. For the state prediction on CLEVR dataset, it improves the AP by about 40%.
Darmok and Jalad at Tanagra: A Dataset and Model for English-to-Tamarian Translation

Tamarian, a ﬁctional language introduced in the Star Trek episode Darmok , communicates meaning through utterances of metaphorical references, such as “Darmok and Jalad at Tanagra” instead of “We should work together. ” This work assembles a Tamarian-English dictionary of utterances from the original episode and several follow-on novels, and uses this to construct a parallel corpus of 456 English-Tamarian utterances. A machine translation system based on a large language model (T5) is trained using this parallel corpus, and is shown to produce an accuracy of 76% when translating from English to Tamarian on known utterances.
Input Agnostic Deep Learning for Alzheimer’s Disease Classification Using Multimodal MRI Images

Alzheimer’s disease (AD) is a progressive brain disorder that causes memory and functional impairments. The advances in machine learning and publicly available medical datasets initiated multiple studies in AD diagnosis. In this work, we utilize a multi-modal deep learning approach in classifying normal cognition, mild cognitive impairment and AD classes on the basis of structural MRI and diffusion tensor imaging (DTI) scans from the OASIS-3 dataset. In addition to a conventional multi-modal network, we also present an input agnostic architecture that allows diagnosis with either sMRI or DTI scan, which distinguishes our method from previous multi-modal machine learning-based methods. The results show that the input agnostic model achieves 0.96 accuracy when both structural MRI and DTI scans are provided as inputs.
Inverse Problem of Nonlinear Schrödinger Equation as Learning of Convolutional Neural Network

In this work, we use an explainable convolutional neural network (NLS-Net) to solve an inverse problem of the nonlinear Schr\"odinger equation, which is widely used in fiber-optic communications. The landscape and minimizers of the non-convex loss function of the learning problem are studied empirically. It provides a guidance for choosing hyper-parameters of the method. The estimation error of the optimal solution is discussed in terms of expressive power of the NLS-Net and data. Besides, we compare the performance of several training algorithms that are popular in deep learning. It is shown that one can obtain a relatively accurate estimate of the considered parameters using the proposed method. The study provides a natural framework of solving inverse problems of nonlinear partial differential equations with deep learning.
Look Ahead ORAM: Obfuscating Addresses in Recommendation Model Training

— In the cloud computing era, data privacy is a critical concern. Memory accesses patterns can leak private information. This data leak is particularly challenging for deep learning recommendation models, where data associated with a user is used to train a model. Recommendation models use embedding tables to map categorical data (embedding table indices) to large vector space, which is easier for recommendation systems to learn. Categorical data is directly linked to a user’s private interaction with a social media platform, such as the news articles read, ads clicked. Thus, just knowing the embedding indices accessed can compromise users’ privacy. Oblivious RAM (ORAM) [4] and its enhancements [15] [18] are proposed solutions to prevent memory accesses patterns from leaking information. ORAM solutions hide access patterns by fetching multiple data blocks per each demand fetch and then shufﬂing the location of blocks after each access. In this paper, we propose a new PathORAM architecture designed to protect users’ input privacy when training recommendation models. Look Ahead ORAM exploits the fact that during training, embedding table indices that are going to be accessed in a future batch are known beforehand. Look Ahead ORAM preprocesses future training samples to identify indices that will co-occur and groups these accesses into a large superblock. Look Ahead ORAM performs the ”same-path” assignment by grouping multiple data blocks into superblocks. Accessing a superblock will require fewer fetched data blocks than accessing all data blocks without grouping them as superblocks. Effectively, Look Ahead ORAM reduces the number of reads/writes per access. Look Ahead ORAM also introduces a fat-tree structure for PathORAM, i.e. a tree with variable bucket size. Look Ahead ORAM achieves 2x speedup compared to PathORAM and reduces the bandwidth requirement by 3.15x while providing the same security as PathORAM.
Multimodal Reward Shaping for Efficient Exploration in Reinforcement Learning

Maintaining the long-term exploration capability of the agent remains one of the critical challenges in deep reinforcement learning. A representative solution is to leverage reward shaping to provide intrinsic rewards for the agent to encourage exploration. However, most existing methods suffer from vanishing intrinsic rewards, which cannot provide sustainable exploration incentives. Moreover, they rely heavily on complex models and additional memory to record learning procedures, resulting in high computational complexity and low robustness. To tackle this problem, entropy-based methods are proposed to evaluate the global exploration performance, encouraging the agent to visit the state space more equitably. However, the sample complexity of estimating the state visitation entropy is prohibitive when handling environments with high-dimensional observations. In this paper, we introduce a novel metric entitled Jain's fairness index (JFI) to replace the entropy regularizer, which solves the exploration problem from a brand new perspective. In sharp contrast to the entropy regularizer, JFI is more computable and robust and can be easily applied generalized into arbitrary tasks. Furthermore, we leverage a variational auto-encoder (VAE) model to capture the life-long novelty of states, which is combined with the global JFI score to form multimodal intrinsic rewards. Finally, extensive simulation results demonstrate that our multimodal reward shaping (MMRS) method can achieve higher performance than other benchmark schemes.
Structured Stochastic Gradient MCMC

Stochastic gradient Markov Chain Monte Carlo (SGMCMC) is considered the gold standard for Bayesian inference in large-scale models, such as Bayesian neural networks. Since practitioners face speed versus accuracy tradeoffs in these models, variational inference (VI) is often the preferable option. Unfortunately, VI makes strong assumptions on both the factorization and functional form of the posterior. In this work, we propose a new non-parametric variational approximation that makes no assumptions about the approximate posterior's functional form and allows practitioners to specify the exact dependencies the algorithm should respect or break. The approach relies on a new Langevin-type algorithm that operates on a modified energy function, where parts of the latent variables are averaged over samples from earlier iterations of the Markov chain. This way, statistical dependencies can be broken in a controlled way, allowing the chain to mix faster. This scheme can be further modified in a"dropout"manner, leading to even more scalability. We test our scheme for ResNet-20 on CIFAR-10, SVHN, and FMNIST. In all cases, we find improvements in convergence speed and/or final accuracy compared to SG-MCMC and VI.
Fully Automated Machine Learning Pipeline for Echocardiogram Segmentation

Nowadays, cardiac diagnosis largely depends on left ventricular function assessment. With the help of the segmentation deep learning model, the assessment of the left ventricle becomes more accessible and accurate. However, deep learning technique still faces two main obstacles: the difficulty in acquiring sufficient training data and time consuming in developing quality models. In the ordinary data acquisition process, the dataset was selected randomly from a large pool of unlabeled images for labeling, leading to massive labor time to annotate those images. Besides that, hand-designed model development is strenuous and also costly. This paper introduces a pipeline that relies on Active Learning to ease the labeling work and utilizes Neural Architecture Search's idea to design the adequate deep learning model automatically. We called this Fully automated machine learning pipeline for echocardiogram segmentation. The experiment results show that our method obtained the same IOU accuracy with only two-fifths of the original training dataset, and the searched model got the same accuracy as the hand-designed model given the same training dataset.
Singular Metrics with Negative Scalar Curvature

Motivated by the work of Li and Mantoulidis, we study singular metrics which are uniformly Euclidean $(L^\infty)$ on a compact manifold $M^n$ ($n\ge 3$) with negative Yamabe invariant $\sigma(M)$. It is well-known that if $g$ is a smooth metric on $M$ with unit volume and with scalar curvature $R(g)\ge \sigma(M)$, then $g$ is Einstein. We show, in all dimensions, the same is true for metrics with edge singularities with cone angles $\leq 2\pi$ along codimension-2 submanifolds. We also show in three dimension, if the Yamabe invariant of connected sum of two copies of $M$ attains its minimum, then the same is true for $L^\infty$ metrics with isolated point singularities.
Amino acids, polyamines, and nitric oxide synthesis in the ovine conceptus

Amino Acids, Polyamines, and Nitric Oxide Synthesis in the Ovine Conceptus. (May 2004) Hyuk Jung Kwon, B.S., Konkuk University Co-Chairs of Advisory Committee: Dr. Thomas E. Spencer Dr. Guoyao Wu The objective of this study was to determine concentrations of amino acids and polyamines as well as nitric oxide (NO) and polyamine synthesis in the ovine conceptus (embryo/fetal and associated placental membrane). Ewes were hysterectomized on Days 30, 40, 60, 80, 100, 120, or 140 of gestation to obtain allantoic and amniotic fluids, intercotyledonary placenta, placentomes and uterine endometrium for the analyses. Alanine, citrulline plus glutamine accounted for about 80% of total α-amino acids in allantoic fluid during early gestation. Serine (16.5 mM) contributed about 60% of total α-amino acids in allantoic fluid on Day 140 of gestation. Maximal ornithine decarboxylase (ODC) and arginase activities and highest rates of polyamine and NO synthesis occured in all tissues on Day 40 of gestation. In ovine allantoic and amniotic fluids, polyamines were most abundant during early (Days 40-60) and late (Days 100-140) gestation, respectively. Activity of guanosine 5’-triphosphatecyclohydrolase I (GTP-CH), and concentrations of NOS cofactors, tetrahydrobiopterin (BH4) and NADPH (nicotinamide adenine dinucleotide), peaked on Day 40 of gestation in placental and endometrial tissues. In these tissues, NO synthesis was positively correlated with total NOS activity, GTP-CH activity, and concentrations of BH4 and NADPH. The physiological significance of these changes was manifested by undernutrition-induced intrauterine growth retardation (IUGR). Maternal undernutrition (50% of National Research Council nutrient requirements) reduced concentrations of total α-amino acids in fetal
Advances on methane steam reforming to produce hydrogen through membrane reactors technology: A review

ABSTRACT Methane steam reforming is the most common industrial process used for almost the 50% of the world’s hydrogen production. Commonly, this reaction is performed in fixed bed reactors and several stages are needed for separating hydrogen with the desired purity. The membrane reactors represent a valid alternative to the fixed bed reactors, by combining the reforming reaction for producing hydrogen and its separation in only one stage. This article deals with the recent progress on methane steam reforming reaction, giving a short overview on catalysts utilization as well as on the fundamentals of membrane reactors, also summarizing the relevant advancements in this field.