Miracle-dz/UniFAR-FTUs huggingface dataset:
VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer

Since visual perception can give rich information beyond text descriptions for world understanding, there has been increasing interest in leveraging visual grounding for language learning. Recently, vokenization (Tan and Bansal, 2020) has attracted attention by using the predictions of a text-to-image retrieval model as labels for language model supervision. Despite its success, the method suffers from approximation error of using finite image labels and the lack of vocabulary diversity of a small image-text dataset. To overcome these limitations, we present VidLanKD, a video-language knowledge distillation method for improving language understanding. We train a multi-modal teacher model on a video-text dataset, and then transfer its knowledge to a student language model with a text dataset. To avoid approximation error, we propose to use different knowledge distillation objectives. In addition, the use of a large-scale video-text dataset helps learn diverse and richer vocabularies. In our experiments, VidLanKD achieves consistent improvements over text-only language models and vokenization models, on several downstream language understanding tasks including GLUE, SQuAD, and SWAG. We also demonstrate the improved world knowledge, physical reasoning, and temporal reasoning capabilities of our model by evaluating on the GLUE-diagnostics, PIQA, and TRACIE datasets. Lastly, we present comprehensive ablation studies as well as visualizations of the learned text-to-video grounding results of our teacher and student language models. Our code and models are available at: https://github.com/zinengtang/VidLanKD
Agents that Listen: High-Throughput Reinforcement Learning with Multiple Sensory Systems

Humans and other intelligent animals evolved highly sophisticated perception systems that combine multiple sensory modalities. On the other hand, state-of-the-art artificial agents rely mostly on visual inputs or structured low-dimensional observations provided by instrumented environments. Learning to act based on combined visual and auditory inputs is still a new topic of research that has not been explored beyond simple scenarios. To facilitate progress in this area we introduce a new version of VizDoom simulator to create a highly efficient learning environment that provides raw audio observations. We study the performance of different model architectures in a series of tasks that require the agent to recognize sounds and execute instructions given in natural language. Finally, we train our agent to play the full game of Doom and find that it can consistently defeat a traditional vision-based adversary. We are currently in the process of merging the augmented simulator with the main ViZDoom code repository. Video demonstrations and experiment code can be found at https://sites.google. com/view/sound-rl.
Dueling Bandits with Team Comparisons

We introduce the dueling teams problem, a new online-learning setting in which the learner observes noisy comparisons of disjoint pairs of $k$-sized teams from a universe of $n$ players. The goal of the learner is to minimize the number of duels required to identify, with high probability, a Condorcet winning team, i.e., a team which wins against any other disjoint team (with probability at least $1/2$). Noisy comparisons are linked to a total order on the teams. We formalize our model by building upon the dueling bandits setting (Yue et al.2012) and provide several algorithms, both for stochastic and deterministic settings. For the stochastic setting, we provide a reduction to the classical dueling bandits setting, yielding an algorithm that identifies a Condorcet winning team within $\mathcal{O}((n + k \log (k)) \frac{\max(\log\log n, \log k)}{\Delta^2})$ duels, where $\Delta$ is a gap parameter. For deterministic feedback, we additionally present a gap-independent algorithm that identifies a Condorcet winning team within $\mathcal{O}(nk\log(k)+k^5)$ duels.
Tactile Sensing with a Tendon-Driven Soft Robotic Finger

In this paper, a novel tactile sensing mechanism for soft robotic fingers is proposed. Inspired by the proprioception mechanism found in mammals, the proposed approach infers tactile information from a tension sensor attached on the finger’s tendon. We perform experiments to test the tactile sensing capabilities of the proposed structures, and our results indicate this method is capable of palpating texture and stiffness in both abduction and flexion contact. Under systematic cross validation, the proposed system achieved 100% and 99.7% accuracy in texture and stiffness discrimination respectively, which validate the viability of this approach. Furthermore, we used statistics tools to determine the significance of various features extracted for classification.
Learning a Generative Transition Model for Uncertainty-Aware Robotic Manipulation

Robot learning of real-world manipulation tasks remains challenging and time consuming, even though actions are often simplified by single-step manipulation primitives. In order to compensate the removed time dependency, we additionally learn an image-to-image transition model that is able to predict a next state including its uncertainty. We apply this approach to bin picking, the task of emptying a bin using grasping as well as pre-grasping manipulation as fast as possible. The transition model is trained with up to 42 000 pairs of real-world images before and after a manipulation action. Our approach enables two important skills: First, for applications with flange-mounted cameras, picks per hours (PPH) can be increased by around 15 % by skipping image measurements. Second, we use the model to plan action sequences ahead of time and optimize time-dependent rewards, e.g. to minimize the number of actions required to empty the bin. We evaluate both improvements with real-robot experiments and achieve over 700 PPH in the YCB Box and Blocks Test.
Goodness-of-fit testing for H\"older continuous densities under local differential privacy

We address the problem of goodness-of-fit testing for H\"older continuous densities under local differential privacy constraints. We study minimax separation rates when only non-interactive privacy mechanisms are allowed to be used and when both non-interactive and sequentially interactive can be used for privatisation. We propose privacy mechanisms and associated testing procedures whose analysis enables us to obtain upper bounds on the minimax rates. These results are complemented with lower bounds. By comparing these bounds, we show that the proposed privacy mechanisms and tests are optimal up to at most a logarithmic factor for several choices of $f_0$ including densities from uniform, normal, Beta, Cauchy, Pareto, exponential distributions. In particular, we observe that the results are deteriorated in the private setting compared to the non-private one. Moreover, we show that sequentially interactive mechanisms improve upon the results obtained when considering only non-interactive privacy mechanisms.
CONVERGENCE RATES IN THE STRONG, WEAK AND FUNCTIONAL INVARIANCE PRINCIPLES FOR NON-STATIONARY MIXING ARRAYS VIA VARIANCE LINEARIZATION

We obtain convergence rates in the central limit theorem (CLT), the functional CLT and the almost sure invariance principle for self normalized partial sums Sn = ∑n j=1 ξj,n of certain classes of triangular arrays {ξ1,n, . , ξn,n} satisfying some mixing and moment conditions (which might not be uniform in n). In particular our results hold when ξj,n = ξj and {ξj} is a non-stationary sufficiently fast mixing sequence. We also obtain a moderate deviations principle and some Rosenthal type inequalities. The main novelty here is that our results are obtained without any assumptions about the growth rate of the variance of Sn and without the condition ∑n j=1 Var(ξj,n) = O(Var(Sn)). The main new idea in the proof is a “lineariztion” of the variance of the partial sums, which, to some extent, allows us to reduce the limit theorems to the case when Var(Sn) grows linearly fast in n.
InfoNCE is a variational autoencoder

Unsupervised learning typically involves learning a full generative model of the inputs, and goes back at least to the Boltzmann (Ackley et al. 1985) and Helmholtz machines (Dayan et al. 1995). More recently, it was noted that we can get good representations of unlabelled datapoints without the need to learn a full generative model, and this gave birth to the modern field of self-supervised learning (SSL). We reconcile these critically important families of machine learning method, by showing that modern SSL methods including InfoNCE which maximize mutual information are equivalent to a particular unsupervised learning method, the variational autoencoder. Additionally, our approach resolves mysteries purely in SSL. In particular, recent work (Tschannen et al., 2019) has argued that mutual information objectives can give arbitrarily entangled representations. Instead, they argue that the excellent performance of InfoNCE arises from the use of a simplified, linear mutual information estimator. How can we understand the success of InfoNCE if better mutual information estimators lead to worse representations? Remarkably, under one choice of prior, the VAE objective (i.e. the ELBO) is exactly equal to the mutual information (up to constants). Under an alternative choice of prior, the SSVAE objective is exactly equal to the simplified parametric mutual information estimator used in InfoNCE (up to constants). As such, the SSVAE framework naturally provides a principled justification for using simplified mututal information estimators, because they are equivalent to structured priors.
Dueling Bandits with Adversarial Sleeping

We introduce the problem of sleeping dueling bandits with stochastic preferences and adversarial availabilities (DB-SPAA). In almost all dueling bandit applications, the decision space often changes over time; eg, retail store management, online shopping, restaurant recommendation, search engine optimization, etc. Surprisingly, this `sleeping aspect' of dueling bandits has never been studied in the literature. Like dueling bandits, the goal is to compete with the best arm by sequentially querying the preference feedback of item pairs. The non-triviality however results due to the non-stationary item spaces that allow any arbitrary subsets items to go unavailable every round. The goal is to find an optimal `no-regret' policy that can identify the best available item at each round, as opposed to the standard `fixed best-arm regret objective' of dueling bandits. We first derive an instance-specific lower bound for DB-SPAA $\Omega( \sum_{i =1}^{K-1}\sum_{j=i+1}^K \frac{\log T}{\Delta(i,j)})$, where $K$ is the number of items and $\Delta(i,j)$ is the gap between items $i$ and $j$. This indicates that the sleeping problem with preference feedback is inherently more difficult than that for classical multi-armed bandits (MAB). We then propose two algorithms, with near optimal regret guarantees. Our results are corroborated empirically.
A deep-learning-based multimodal depth-aware dynamic hand gesture recognition system

The dynamic hand gesture recognition task has seen studies on various unimodal and multimodal methods. Previously, researchers have explored depth and 2D-skeleton-based multimodal fusion CRNNs (Convolutional Recurrent Neural Networks) but have had limitations in getting expected recognition results. In this paper, we revisit this approach to hand gesture recognition and suggest several improvements. We observe that raw depth images possess low contrast in the hand regions of interest (ROI). They do not highlight important fine details, such as finger orientation, overlap between the finger and palm, or overlap between multiple fingers. We thus propose quantizing the depth values into several discrete regions, to create a higher contrast between several key parts of the hand. In addition, we suggest several ways to tackle the high variance problem in existing multimodal fusion CRNN architectures. We evaluate our method on two benchmarks: the DHG-14/28 dataset and the SHREC'17 track dataset. Our approach shows a significant improvement in accuracy and parameter efficiency over previous similar multimodal methods, with a comparable result to the state-of-the-art.
COVID‐19 and Motivated Reasoning: The Influence of Knowledge on COVID‐Related Policy and Health Behavior

Objective The spread of misinformation about COVID‐19 severely influences the governments' ability to address the COVID‐19 pandemic. This study explores the predictors of accurate beliefs about COVID‐19 and its influence on COVID‐related policy and behavior. Methods Data from an original survey collected by Lucid in July 2020 are used. Ordinary Least Squares regression (OLS) is used to predict accurate beliefs about COVID‐19. Ordered logistic regression models are estimated to examine the relationship between COVID‐19 knowledge, policy preferences, and health behavior intentions. Results Ideology and education were found to have a positive effect on knowledge about COVID‐19. Moreover, low levels of knowledge about COVID‐19 were found to reduce support for mandatory vaccination policy and willingness to get a coronavirus vaccine when available. Conclusion These findings will help policymakers develop communication strategies for the public on the coronavirus vaccination.
Chemithermomechanical and kraft pulping of Pinus radiata wood chips after the hydrothermal extraction of hemicelluloses

Abstract Pinus radiata D. Don wood chips were submitted to a hydrothermal (HT) process, which is a pretreatment with pressurized water at P-factors of 20 (E1) and 1200 (E2), leading to 27% and 56% of hemicellulose removal, respectively. The residual wood chips were pulped by the chemithermomechanical (CTMP) and kraft processes. The pulp yield in the HT/CTMP process was in the range of 56–75%. The cellulose yields were not affected by the HT pretreatment, whereas the solubilization of hemicelluloses and lignin was intensified. The HT process provided energy savings in the refining of CTMP pulps, and the E1/CTMP pulp had a similar tensile and increased tear strength as the control wood. For kraft pulping, the HT wood chips demanded more active alkali (AA) to achieve a κ number (KN) of 30, and consequently, a decrease in pulp yield was observed (47.2% yield in the control and 44.4% and 37.8% in the E1 and E2 pulps, respectively). The fiber length decreased in the E1 and E2 kraft pulps compared with their control samples. In the E1 pulps, the tensile, tear, and burst indexes decreased by 10%, 25%, and 30%, respectively. Considering the low hemicellulose content and pulp viscosity in the range of 800–900 ml g-1, the mild HT process of P. radiata would be better suited for preparing cellulose-rich materials instead of paper-grade pulps under the biorefinery concept.
An efficient deep learning-based detection and 2 classification system for cyber-attacks in IoT 3 communication networks

: With the rapid expansion of intelligent resource-constrained devices and high-speed 9 communication technologies, Internet of Things (IoT) has earned a wide recognition as the primary 10 standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to 11 cyber-attacks due to the constraints in computation, storage, and communication capacity of the 12 endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by 13 slightly mutating formerly established cyber-attacks to produce a new attack tending to be treated 14 as a normal traffic through the IoT network. From the other side, the influence of coupling the deep 15 learning techniques with cybersecurity field has become a recent inclination of many security 16 applications due to their impressive performance. In this paper, we provide a comprehensive
Novel modulator for endothelial adhesion molecules : Adipocyte-derived plasma protein adiponectin

suits suggest that the increased expressions of visceral fat AQPap and hepatic AQP9 may be involved in the development of diabetes mellitus through the increased glycerol release and uptake (Fig. 1). Wealso analyzed the expressed genes in adipose tissue by large scale random sequencing. Unexpectedly, adipocytes, especially visceral adipocytes, expressed a variety of genes for secretory proteins including various bioactive substances; we designated them 'adipocytokines'. One of them, plasminogen activator inhibitor1 (PAI1 ) was overproduced in accumulated visceral fat and it might contribute to the development of vascular disease (5). Wehave also cloned a novel adipose-specific gene namedadiponectin. Adiponectin is a collagen-like plasma protein and its plasma levels are paradoxically decreased in obesity especially in visceral obesity and diabetes mellitus with insulin resistance (6). Adiponectin has been suggested to have potent antiatherogenicity, since this moleculewasshownto attenuate the TNF-oc-inducedexpression of adhesion molecules in vascular endothelial cells, and moreover, we found that the plasma adiponectin levels were significantly lower in patients with coronary artery disease than in ageand BMI-matched control subjects (Fig. 2) (7). Based on these results, it is thought that adipocytokines may play important and direct roles in the development of atherosclerosis by changing the plasma levels in the subjects with visceral fat syndrome. In summary, an over-accumulation of adipocytes, in particular, visceral adipocytes, causes the elevated portal FFAand glycerol to become hyperlipidemia or diabetes mellitus. Further, the elevated portal FFA brings about the insulin resistance, and through this, hyperlipidemia and diabetes mellitus get worse, and hypertension occurs. Then the cluster of these lifestyle related disorders leads to vascular disease. Furthermore, adipocytokines originating from visceral fat such as PAI-1, adiponectin and so on, directly or indirectly transmit numerous effects on the development of vascular disease. Thus, it is very important for us to pay attention to visceral fat accumulation and to prevent the development of life-style related disease.
Fatigue, Stress, and Performance during Alternating Physical and Cognitive Tasks—Effects of the Temporal Pattern of Alternations

Abstract In occupational life, performing cognitive work tasks in between fatiguing physical work tasks may allow recovery and reduce stress without losing productive working time. The temporal pattern of such alternations is likely a determinant of the recovery effect, influencing both stress and fatigue; the difficulty of the cognitive task (CT) would also be a likely determinant. The aim of this study was to determine to what extent the temporal pattern of alternations between a repetitive physical task and a CT of different difficulties influenced perceived fatigability, performance fatigability, stress-related outcomes, and performance. Fifteen women performed four work sessions comprising 110 min of repeated bouts of a repetitive physical task (pipetting), alternating with a CT (n-back). Sessions differed in bout cycle time (short: 7 + 3 min versus long: 14 + 6 min) and CT difficulty (CTdiff; easy versus difficult). Fatigue was assessed from recordings of maximal voluntary contraction force in shoulder elevations and handgrip pre- and post-work, electromyography (EMG) from the right trapezius and right forearm extensors during work, and repeated self-ratings of fatigue and pain throughout the session. Stress was assessed using electrocardiography (heart rate variability), salivary alpha-amylase, and self-reports. Perceived fatigue increased significantly over time for all protocols and more in long-cycle than short-cycle conditions. EMG activity did not increase markedly over time in any condition. Neither objective nor subjective indicators suggested that stress increased over time, regardless of the temporal pattern. Pipetting performance remained stable in all conditions. Cognitive performance, measured by the proportions of correct positive and false positive answers, differed between CTdiff levels but remained stable over time, with no significant difference between temporal patterns. In summary, temporal patterns of alternating tasks influenced fatigue to some extent but had no obvious influence on stress indicators or performance. Thus, designing job rotation with alternating physical and cognitive work should consider the temporal patterns of alternations to minimize fatigue.
Vedolizumab as first-line biological therapy in elderly patients and those with contraindications for anti-TNF therapy: a real-world, nationwide cohort of patients with inflammatory bowel diseases

Abstract Background Data from real-life populations about vedolizumab as first-line biological therapy for ulcerative colitis (UC) and Crohn’s disease (CD) are emerging. Objective To investigate the efficacy and safety of vedolizumab in bio-naïve patients with UC and CD. Methods A Danish nationwide cohort study was conducted between November 2014 and November 2019. Primary outcomes were clinical remission, steroid-free clinical remission, and sustained clinical remission from weeks 14 through 52. Results The study included 56 patients (UC:31, CD:25) who initiated treatment with vedolizumab mainly because of contraindications to anti-TNFs, of whom 54.8 and 24.0%, respectively received systemic steroids at the initiation. Rates of clinical remission at weeks 6, 14, and 52 were 32.0, 48.0, and 40.0%, respectively, in UC, and 36.8, 36.8, and 47.4% in CD. Steroid-free clinical remission at week 52 was achieved among 36.0 and 47.4% of UC and CD patients, while sustained clinical remission was achieved in 32.0 and 36.8%. Lack of remission was associated with being female (68.8 vs. 11.1%, p = .01) in UC and non‐structuring, non‐penetrating behavior in CD (90.0 vs. 44.4%, p = .03); however, this was not confirmed in multivariate analysis. Discontinuation due to primary non-response occurred in 20.0 and 5.3% of UC and CD patients, respectively, while rates of secondary loss of response were 12.0 and 5.3% after 52 weeks of follow-up. Vedolizumab was well-tolerated as only one UC patient experienced a serious adverse event. Conclusion Vedolizumab is effective in the achievement of short-term, long-term, and steroid-free clinical remission in bio-naïve UC and CD patients.
Antibacterial and anti‐inflammatory activity of valproic acid‐pyrazole conjugates as a potential agent against periodontitis

Periodontitis is a serious global concern. Therefore, in the present study, we intend to synthesize novel valproic‐acid pyrazole conjugates as a novel agent against periodontitis. The molecules were developed in a facile synthetic route and obtained in excellent yields. The entire set of molecules were screened for antibacterial activity against a battery of micro‐organisms responsible for periodontitis such as P. gingivalis, P. intermedia, F. nucleatum, and E. coli, where they exhibit considerable inhibitory activity. The most potent compound among the tested series, compound 7c showed bactericidal activity in the time‐kill curve against E. coli. Compound 7c also showed inhibition of NF‐ĸB transcriptional activity in LPS‐stimulated RAW264.7 cells with IC50 of 19.23 μM. The effect of compound 7c was also investigated in experimentally induced periodontitis in rats on various indices of oxidative stress (MDA, SOD, and GSH), inflammation (TNF‐α, IL‐1β, and IL‐6), and apoptosis. It has been found that compound 7c significantly inhibits oxidative stress, inflammation, and apoptosis in a dose‐dependent manner. Compound 7c also inhibits the expression of COX‐2 and iNOS as shown by western blot analysis.