Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges in Procedural Multimodal Machine Comprehension: A Novel Way To Benchmark

We focus on Multimodal Machine Reading Comprehension (M3C) where a model is expected to answer questions based on given passage (or context), and the context and the questions can be in different modalities. Previous works such as RecipeQA have proposed datasets and cloze-style tasks for evaluation. However, we identify three critical biases stemming from the question-answer generation process and memorization capabilities of large deep models. These biases makes it easier for a model to overfit by relying on spurious correlations or naive data patterns. We propose a systematic framework to address these biases through three Control-Knobs that enable us to generate a test bed of datasets of progressive difficulty levels. We believe that our benchmark (referred to as Meta- RecipeQA) will provide, for the first time, a fine grained estimate of a model’s generalization capabilities. We also propose a general M3C model that is used to realize several prior SOTA models and motivate a novel hierarchical transformer based reasoning network (HTRN). We perform a detailed evaluation of these models with different language and visual features on our benchmark. We observe a consistent improvement with HTRN over SOTA (~ 18% in Visual Cloze task and ~ 13% in average over all the tasks). We also observe a drop in performance across all the models when testing on RecipeQA and proposed Meta–RecipeQA (e.g. 83.6% versus 67.1% for HTRN), which shows that the proposed dataset is relatively less biased. We conclude by highlighting the impact of the control knobs with some quantitative results.
Simple Dialogue System with AUDITED

We devise a multimodal conversation system for dialogue utterances composed of text, image or both modalities. We leverage Auxiliary UnsuperviseD vIsual and TExtual Data (AUDITED). To improve the performance of text-based task, we utilize translations of target sentences from English to French to form the assisted supervision. For the image-based task, we employ the DeepFashion dataset in which we seek nearest neighbor images of positive and negative target images of the MMD data. These nearest neighbors form the nearest neighbor embedding providing an external context for target images. We form two methods to create neighbor embedding vectors, namely Neighbor Embedding by Hard Assignment (NEHA) and Neighbor Embedding by Soft Assignment (NESA) which generate context subspaces per target image. Subsequently, these subspaces are learnt by our pipeline as a context for the target data. We also propose a discriminator which switches between the image- and text-based tasks. We show improvements over baselines on the large-scale Multimodal Dialogue Dataset (MMD) and SIMMC.
Polynomial-Time Sum-of-Squares Can Robustly Estimate Mean and Covariance of Gaussians Optimally

In this work, we revisit the problem of estimating the mean and covariance of an unknown $d$-dimensional Gaussian distribution in the presence of an $\varepsilon$-fraction of adversarial outliers. The pioneering work of [DKK+16] gave a polynomial time algorithm for this task with optimal $\tilde{O}(\varepsilon)$ error using $n = \textrm{poly}(d, 1/\varepsilon)$ samples. On the other hand, [KS17b] introduced a general framework for robust moment estimation via a canonical sum-of-squares relaxation that succeeds for the more general class of certifiably subgaussian and certifiably hypercontractive [BK20] distributions. When specialized to Gaussians, this algorithm obtains the same $\tilde{O}(\varepsilon)$ error guarantee as [DKK+16] but incurs a super-polynomial sample complexity ($n = d^{O(\log(1/\varepsilon)}$) and running time ($n^{O(\log(1/\varepsilon))}$). This cost appears inherent to their analysis as it relies only on sum-of-squares certificates of upper bounds on directional moments while the analysis in [DKK+16] relies on lower bounds on directional moments inferred from algebraic relationships between moments of Gaussian distributions. We give a new, simple analysis of the same canonical sum-of-squares relaxation used in [KS17b, BK20] and show that for Gaussian distributions, their algorithm achieves the same error, sample complexity and running time guarantees as of the specialized algorithm in [DKK+16]. Our key innovation is a new argument that allows using moment lower bounds without having sum-of-squares certificates for them. We believe that our proof technique will likely be useful in developing further robust estimation algorithms.
Soft Lattice Modules That Behave Independently and Collectively

Natural systems integrate the work of many sub-units (cells) toward a large-scale unified goal (morphological and behavioral), which can counteract the effects of unexpected experiences, damage, or simply changes in tasks demands. In this letter, we exploit the opportunities presented by soft, modular, and tensegrity robots to introduce soft lattice modules that parallel the sub-units seen in biological systems. The soft lattice modules are comprised of 3D printed plastic “skeletons,” linear contracting shape memory alloy spring actuators, and permanent magnets that enable adhesion between modules. The soft lattice modules are capable of independent locomotion, and can also join with other modules to achieve collective, self-assembled, larger scale tasks such as collective locomotion and moving an object across the surface of the lattice assembly. This work represents a preliminary step toward soft modular systems capable of independent and collective behaviors, and provide a platform for future studies on distributed control.
MIGS: Meta Image Generation from Scene Graphs

Generation of images from scene graphs is a promising direction towards explicit scene generation and manipulation. However, the images generated from the scene graphs lack quality, which in part comes due to high difficulty and diversity in the data. We propose MIGS (Meta Image Generation from Scene Graphs), a meta-learning based approach for few-shot image generation from graphs that enables adapting the model to different scenes and increases the image quality by training on diverse sets of tasks. By sampling the data in a task-driven fashion, we train the generator using meta-learning on different sets of tasks that are categorized based on the scene attributes. Our results show that using this meta-learning approach for the generation of images from scene graphs achieves state-of-the-art performance in terms of image quality and capturing the semantic relationships in the scene. Project Website: https://migs2021.github. io/
Variational Wasserstein Barycenters with C-cyclical Monotonicity Regularization

Wasserstein barycenter, built on the theory of Optimal Transport (OT), provides a powerful framework to aggregate probability distributions, and it has increasingly attracted great attention within the machine learning community. However, it is often intractable to precisely compute, especially for high dimensional and continuous settings. To alleviate this problem, we develop a novel regularization by using the fact that c-cyclical monotonicity is often necessary and sufficient conditions for optimality in OT problems, and incorporate it into the dual formulation of Wasserstein barycenters. For efficient computations, we adopt a variational distribution as the approximation of the true continuous barycenter, so as to frame the Wasserstein barycenters problem as an optimization problem with respect to variational parameters. Upon those ideas, we propose a novel end-to-end continuous approximation method, namely Variational Wasserstein Barycenters with c-Cyclical Monotonicity Regularization (VWB-CMR), given sample access to the input distributions. We show theoretical convergence analysis and demonstrate the superior performance of VWB-CMR on synthetic data and real applications of subset posterior aggregation.
Rethinking Generalization Performance of Surgical Phase Recognition with Expert-Generated Annotations

As the area of application of deep neural networks expands to areas requiring expertise, e.g., in medicine and law, more exquisite annotation processes for expert knowledge training are required. In particular, it is difficult to guarantee generalization performance in the clinical field in the case of expert knowledge training where opinions may differ even among experts on annotations. To raise the issue of the annotation generation process for expertise training of CNNs, we verified the annotations for surgical phase recognition of laparoscopic cholecystectomy and subtotal gastrectomy for gastric cancer. We produce calibrated annotations for the seven phases of cholecystectomy by analyzing the discrepancies of previously annotated labels and by discussing the criteria of surgical phases. For gastrectomy for gastric cancer has more complex twenty-one surgical phases, we generate consensus annotation by the revision process with five specialists. By training the CNN-based surgical phase recognition networks with revised annotations, we achieved improved generalization performance over models trained with original annotation under the same cross-validation settings. We showed that the expertise data annotation pipeline for deep neural networks should be more rigorous based on the type of problem to apply clinical field.
Merging Two Cultures: Deep and Statistical Learning

Our goal is to provide a review of deep learning methods which provide insight into structured high‐dimensional data. Merging the two cultures of algorithmic and statistical learning sheds light on model construction and improved prediction and inference, leveraging the duality and trade‐off between the two. Prediction, interpolation, and uncertainty quantification can be achieved using probabilistic methods at the output layer of the model. Rather than using shallow additive architectures common to most statistical models, deep learning uses layers of semi‐affine input transformations to provide a predictive rule. Applying these layers of transformations leads to a set of attributes (or, features) to which probabilistic statistical methods can be applied. Thus, the best of both worlds can be achieved: scalable prediction rules fortified with uncertainty quantification where sparse regularization finds the features. We review the duality between shallow and wide models such as principal components regression, and partial least squares and deep but skinny architectures such as autoencoders, multilayer perceptrons, convolutional neural net, and recurrent neural net. The connection with data transformations is of practical importance for finding good network architectures. By incorporating probabilistic components at the output level, the predictive uncertainty is allowed. We illustrate this idea by comparing plain Gaussian processes (GP) with partial least squares + Gaussian process (PLS + GP) and deep learning + Gaussian process (DL + GP). This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Deep Learning
STANDARDIZATION AND COMPARATIVE EVALUATION OF AYURVEDIC POLYHERBAL GHRITA FORMULATION WITH MODERN EXTRACTION TECHNIQUE FOR EXTRACTION EFFICIENCY USING REVERSED PHASE-HIGH-PERFORMANCE LIQUID CHROMATOGRAPHY

Objective: Sarasvata ghrita (SG) is a polyherbal formulation in Ayurvedic Indian medicinal system, in which ghee is the main ingredient used for extraction. Ghee is 100% lipid, thus its regular use is limited, and there is a lack of quality control profile of SG. Thus, the objective of the study is to develop quality control method for standardization of SG and to analyze manufacturing process of SG and an effective method of extraction to extract phytoconstituents from herbs used in SG to overcome the limitation of SG.Methods: SG was processed as per the traditional method, whereas ethanolic extract (EE) and hydroalcoholic extract (HAE) were obtained by the conventional method and lipid-based extract (LE) was prepared by modern extraction method. SG and all extracts were standardized using newly developed high-performance liquid chromatography (LC) with respect to bebeerine, piperine, 6-shogaol, I²-asarone, and chebulinic acid. All extracts were analyzed for pesticides, and heavy metal content by LC/mass spectrometry (MS/MS) and inductively coupled plasma/MS, respectively, screened for total polyphenols and flavonoids content, in vitro antioxidant potential, and for assessing its stability over time.Results: The better extraction was observed with maceration extraction using ethanol compared to ayurvedic method and LE method. All extracts were found to have a negligible amount of pesticide and heavy metals and found to be stable for 6 months under accelerated storage condition. Better polyphenols and flavonoid content and in vitro antioxidant potential were resulted in EE.Conclusion: EE showed a better potential in comparison with SG and LE.
Chemical and Enzymatic Probing of Viral RNAs: From Infancy to Maturity and Beyond

RNA molecules are key players in a variety of biological events, and this is particularly true for viral RNAs. To better understand the replication of those pathogens and try to block them, special attention has been paid to the structure of their RNAs. Methods to probe RNA structures have been developed since the 1960s; even if they have evolved over the years, they are still in use today and provide useful information on the folding of RNA molecules, including viral RNAs. The aim of this review is to offer a historical perspective on the structural probing methods used to decipher RNA structures before the development of the selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) methodology and to show how they have influenced the current probing techniques. Actually, these technological breakthroughs, which involved advanced detection methods, were made possible thanks to the development of next-generation sequencing (NGS) but also to the previous works accumulated in the field of structural RNA biology. Finally, we will also discuss how high-throughput SHAPE (hSHAPE) paved the way for the development of sophisticated RNA structural techniques.
A Reference-Sampling Based Calibration-Free Fractional-N PLL with a PI-Linked Sampling Clock Generator

Sampling-based PLLs have become a new research trend due to the possibility of removing the frequency divider (FDIV) from the feedback path, where the FDIV increases the contribution of in-band noise by the factor of dividing ratio square (N2). Between two possible sampling methods, sub-sampling and reference-sampling, the latter provides a relatively wide locking range, as the slower input reference signal is sampled with the faster VCO output signal. However, removal of FDIV makes the PLL not feasible to implement fractional-N operation based on varying divider ratios through random sequence generators, such as a Delta-Sigma-Modulator (DSM). To address the above design challenges, we propose a reference-sampling-based calibration-free fractional-N PLL (RSFPLL) with a phase-interpolator-linked sampling clock generator (PSCG). The proposed RSFPLL achieves fractional-N operations through phase-interpolator (PI)-based multi-phase generation instead of a typical frequency divider or digital-to-time converter (DTC). In addition, to alleviate the power burden arising from VCO-rated sampling, a flexible mask window generation method has been used that only passes a few sampling clocks near the point of interest. The prototype PLL system is designed with a 65 nm CMOS process with a chip size of 0.42 mm2. It achieves 322 fs rms jitter, −240.7 dB figure-of-merit (FoM), and −44.06 dBc fractional spurs with 8.17 mW power consumption.
Outbreak of a Systemic Form of Camelpox in a Dromedary Herd (Camelus dromedarius) in the United Arab Emirates

Camelpox virus (CMLV) is the causative agent of camelpox, which frequently occurs in the Old World camelids-rearing countries except for Australia. It has also been described in experimentally inoculated New World camelids. Camelpox outbreaks are often experienced shortly after the rainy season, which occurs twice a year on the Arabian Peninsula because of the increased density of the insect population, particularly mosquitos. A systemic form of camelpox outbreak in seven dromedary camels was diagnosed by histology, virus isolation, and PCR. A phylogenetic analysis using full length CMLV genomes of the isolated CMLV strains showed a single phylogenetic unit without any distinctive differences between them. The United Arab Emirates (UAE) isolate sequences showed phylogenetical relatedness with CMLV isolates from Israel with only minor sequence differences. Although the sequences of viruses from both countries were closely related, the disease manifestation was vastly different. Our study shows that the virulence is not only determined by genetic features of CMLV alone but may also depend on other factors such as unknown aspects of the host (e.g., age, overall fitness), management, and the environment.
A Framework for Analyzing Neighbor Discovery Protocols under Non-Ideal Conditions

Neighbor Discovery (ND) protocols are crucial to achieving the paradigm of interconnecting thousands of small nodes (sensors or things) to the Internet, also known as the IoT. These protocols usually assume that nodes operate with few energy resources. Therefore, they cannot be fully active all the time. The vast majority of these protocols focus on increasing the probability that two nodes become active simultaneously, thus enabling mutual discovery. In addition, these protocols assume that successful discovery is guaranteed once two nodes are simultaneously active, with very few exceptions. However, many problems can disrupt the discovery, such as channel errors, collisions, synchronization mismatches, energy availability, and so forth. Most ND protocols did not consider these factors, making them vulnerable to severe performance degradation when transmission errors occur. This paper proposes a new framework to evaluate the performance of deterministic neighbor discovery protocols when transmission errors are present. The proposed framework facilitates obtaining an analytical CDF of the discovery time of such protocols with transmissions errors without having to implement the protocol in a simulator, since is time-consuming and prone to implementation errors. We applied the framework to analyze the effect of transmission errors on the discovery time in four of the most representative ND protocols in the literature. Finally, we validate the framework accuracy for the selected protocols using extensive simulations. The results show that the CDF of discovery times provided by the framework closely matches the performance results obtained through simulating these protocols. In general, neighbor discovery protocols are deeply affected as a result of transmission errors.
Design, Manufacturing, and Control of a Pneumatic-Driven Passive Robotic Gait Training System for Muscle-Weakness in a Lower Limb

We designed and manufactured a pneumatic-driven robotic passive gait training system (PRPGTS), providing the functions of body-weight support, postural support, and gait orthosis for patients who suffer from weakened lower limbs. The PRPGTS was designed as a soft-joint gait training rehabilitation system. The soft joints provide passive safety for patients. The PRPGTS features three subsystems: a pneumatic body weight support system, a pneumatic postural support system, and a pneumatic gait orthosis system. The dynamic behavior of these three subsystems are all involved in the PRPGTS, causing an extremely complicated dynamic behavior; therefore, this paper applies five individual interval type-2 fuzzy sliding controllers (IT2FSC) to compensate for the system uncertainties and disturbances in the PRGTS. The IT2FSCs can provide accurate and correct positional trajectories under passive safety protection. The feasibility of weight reduction and gait training with the PRPGTS using the IT2FSCs is demonstrated with a healthy person, and the experimental results show that the PRPGTS is stable and provides a high-trajectory tracking performance.
Comparison of DOFS Attachment Methods for Time-Dependent Strain Sensing

Structural health monitoring (SHM) is a challenge for many industries. Over the last decade, novel strain monitoring methods using optical fibers have been implemented for SHM in aerospace, energy storage, marine, and civil engineering structures. However, the practical attachment of optical fibers (OFs) to the component is still problematic. While monitoring, the amount of substrate strain lost by the OF attachment is often unclear, and difficult to predict under long-term loads. This investigation clarifies how different attachment methods perform under time-dependent loading. Optical fibers are attached on metal, thermoset composite, and thermoplastic substrates for distributed strain sensing. Strains along distributed optical fiber sensors (DOFS) are measured by optical backscatter reflectometry (OBR) and compared to contact extensometer strains under tensile creep loading. The quality of the bondline and its influence on the strain transfer is analyzed. Residual strains and strain fluctuations along the sensor fiber are correlated to the fiber attachment method. Results show that a machine-controlled attachment process (such as in situ 3-D printing) holds great promise for the future as it achieves a highly uniform bondline and provides accurate strain measurements.
Wearable Assistive Robotics: A Perspective on Current Challenges and Future Trends

Wearable assistive robotics is an emerging technology with the potential to assist humans with sensorimotor impairments to perform daily activities. This assistance enables individuals to be physically and socially active, perform activities independently, and recover quality of life. These benefits to society have motivated the study of several robotic approaches, developing systems ranging from rigid to soft robots with single and multimodal sensing, heuristics and machine learning methods, and from manual to autonomous control for assistance of the upper and lower limbs. This type of wearable robotic technology, being in direct contact and interaction with the body, needs to comply with a variety of requirements to make the system and assistance efficient, safe and usable on a daily basis by the individual. This paper presents a brief review of the progress achieved in recent years, the current challenges and trends for the design and deployment of wearable assistive robotics including the clinical and user need, material and sensing technology, machine learning methods for perception and control, adaptability and acceptability, datasets and standards, and translation from lab to the real world.
ZIKV Infection and miRNA Network in Pathogenesis and Immune Response

Over the years, viral infections have caused severe illness in humans. Zika Virus (ZIKV) is a flavivirus transmitted by mosquito vectors that leads to notable neurological impairment, whose most dramatic impact is the Congenital ZIKV Syndrome (CZS). ZIKV targets neuronal precursor cells leading to apoptosis and further impairment of neuronal development, causing microcephaly, lissencephaly, ventriculomegaly, and calcifications. Several regulators of biological processes are involved in CZS development, and in this context, microRNAs (miRNAs) seem to have a fundamental role. miRNAs are important regulators of protein translation, as they form the RISC silencing complex and interact with complementary mRNA target sequences to further post-transcriptional repression. In this context, little is known about their participation in the pathogenesis of viral infections. In this review, we discuss how miRNAs could relate to ZIKV and other flavivirus infections.