Miracle-dz/UniFAR-FTUs huggingface dataset:
Offline Meta-Reinforcement Learning for Industrial Insertion

Reinforcement learning (RL) can in principle let robots automatically adapt to new tasks, but current RL methods require a large number of trials to accomplish this. In this paper, we tackle rapid adaptation to new tasks through the framework of meta-learning, which utilizes past tasks to learn to adapt with a specific focus on industrial insertion tasks. Fast adaptation is crucial because prohibitively large number of on-robot trials will potentially damage hardware pieces. Additionally, effective adaptation is also feasible in that experience among different insertion applications can be largely leveraged by each other. In this setting, we address two specific challenges when applying meta-learning. First, conventional meta-RL algorithms require lengthy online meta-training. We show that this can be replaced with appropriately chosen offline data, resulting in an offline meta- RL method that only requires demonstrations and trials from each of the prior tasks, without the need to run costly meta-RL procedures online. Second, meta-RL methods can fail to generalize to new tasks that are too different from those seen at meta-training time, which poses a particular challenge in industrial applications, where high success rates are critical. We address this by combining contextual meta-learning with direct online finetuning: if the new task is similar to those seen in the prior data, then the contextual meta-learner adapts immediately, and if it is too different, it gradually adapts through finetuning. We show that our approach is able to quickly adapt to a variety of different insertion tasks, with a success rate of 100% using only a fraction of the samples needed for learning the tasks from scratch. Experiment videos and details are available at //sites.google. com/view/offline-metarl-insertion.https:
Yield and quality characteristics of paneer made from milk blend containing homogenized milk

The research was carried out to ascertain the feasibility of preparing paneer from milk blend containing low pressure homogenized milk to avail the benefits rendered by homogenization. Standardized milk was subjected to two-stage homogenization (4.90 and 0.98 MPa respectively) and then blended with unhomogenized standardized milk in three proportions (i.e. 3:7, 4:6 and 1:1, w/w). It was necessary to add calcium chloride to the milk blend to improve the firmness of resultant paneer. The experimental paneer samples obtained from ‘milk blend’ containing homogenized milk, as well as control sample (only from unhomogenized milk) were studied for their proximate composition, physico-chemical characteristics, textural properties and sensory quality. Paneer obtained from milk blend (homogenized:unhomogenized; 4:6 w/w) resulted in greater fat recovery and moisture content culminating in significantly higher yield compared to control paneer (CP). The hardness of paneer obtained from blended milks was lower, but the springiness of BMP3:7 sample was greater than that of CP. The total sensory score of experimental paneer BMP4:6 was similar to the score associated with CP, but greater than the scores associated with paneer samples BMP3:7 and BMP1:1. Blending of homogenized (low pressure) milk with unhomogenized milk in 4:6 proportion helped in obtaining paneer with superior fat recovery and yield compared to use of unhomogenized milk, without any adverse effect on sensory properties and with concomitant cost savings.
Orbit equivalence rigidity of irreducible actions of right-angled Artin groups

Let $G_\Gamma \curvearrowright X$ and $G_\Lambda \curvearrowright Y$ be two free measure-preserving actions of one-ended right-angled Artin groups with trivial center on standard probability spaces. Assume they are irreducible, i.e. every element from a standard generating set acts ergodically. We prove that if the two actions are stably orbit equivalent (or merely stably $W^*$-equivalent), then they are automatically conjugate through a group isomorphism between $G_\Gamma$ and $G_\Lambda$. Through work of Monod and Shalom, we derive a superrigidity statement: if the action $G_\Gamma \curvearrowright X$ is stably orbit equivalent (or merely stably $W^*$-equivalent) to a free, measure-preserving, mildly mixing action of a countable group, then the two actions are virtually conjugate. We also use the works of Popa and Ioana, Popa and Vaes to establish the $W^*$-superrigidity of Bernoulli actions of all infinite conjugacy classes groups having a finite generating set made of infinite-order elements where two consecutive elements commute, and one has a nonamenable centralizer: these include one-ended nonabelian right-angled Artin groups, but also many other Artin groups and most mapping class groups of finite-type surfaces.
Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions

Understanding the convergence properties of learning dynamics in repeated auctions is a timely and important question in the area of learning in auctions, with numerous applications in, e.g., online advertising markets. This work focuses on repeated first price auctions where bidders with fixed values for the item learn to bid using mean-based algorithms – a large class of online learning algorithms that include popular no-regret algorithms such as Multiplicative Weights Update and Follow the Perturbed Leader. We completely characterize the learning dynamics of mean-based algorithms, in terms of convergence to a Nash equilibrium of the auction, in two senses: (1) time-average: the fraction of rounds where bidders play a Nash equilibrium approaches 1 in the limit; (2) last-iterate: the mixed strategy profile of bidders approaches a Nash equilibrium in the limit. Specifically, the results depend on the number of bidders with the highest value: Our discovery opens up new possibilities in the study of convergence dynamics of learning algorithms.
Scalar triplet flavor leptogenesis with dark matter

We investigate a simple variant of type-II seesaw, responsible for neutrino mass generation, where the particle spectrum is extended with one singlet right-handed neutrino and an inert Higgs doublet, both odd under an additional $Z_2$ symmetry. While the role of the dark matter is played by the lightest neutral component of the inert Higgs doublet, its interaction with the Standard Model lepton doublets and the right-handed neutrino turns out to be crucial in generating the correct baryon abundance of the Universe through flavored leptogenesis from the decay of the $SU(2)_L$ scalar triplet, involved in type-II framework.
Lightweight Convolutional Neural Networks By Hypercomplex Parameterization

Hypercomplex neural networks have proved to reduce the overall number of parameters while ensuring valuable performances by leveraging the properties of Clifford algebras. Recently, hypercomplex linear layers have been further improved by involving efficient parameterized Kronecker products. In this paper, we define the parameterization of hypercomplex convolutional layers to develop lightweight and efficient large-scale convolutional models. Our method grasps the convolution rules and the filters organization directly from data without requiring a rigidly predefined domain structure to follow. The proposed approach is flexible to operate in any user-defined or tuned domain, from 1D to nD regardless of whether the algebra rules are preset. Such a malleability allows processing multidimensional inputs in their natural domain without annexing further dimensions, as done, instead, in quaternion neural networks for 3D inputs like color images. As a result, the proposed method operates with 1/n free parameters as regards its analog in the real domain. We demonstrate the versatility of this approach to multiple domains of application by performing experiments on various image datasets as well as audio datasets in which our method outperforms real and quaternionvalued counterparts.
Combining Differential Privacy and Byzantine Resilience in Distributed SGD

Privacy and Byzantine resilience (BR) are two crucial requirements of modern-day distributed machine learning. The two concepts have been extensively studied individually but the question of how to combine them effectively remains unanswered. This paper contributes to addressing this question by studying the extent to which the distributed SGD algorithm, in the standard parameter-server architecture, can learn an accurate model despite (a) a fraction of the workers being malicious (Byzantine), and (b) the other fraction, whilst being honest, providing noisy information to the server to ensure differential privacy (DP). We first observe that the integration of standard practices in DP and BR is not straightforward. In fact, we show that many existing results on the convergence of distributed SGD under Byzantine faults, especially those relying on $(\alpha,f)$-Byzantine resilience, are rendered invalid when honest workers enforce DP. To circumvent this shortcoming, we revisit the theory of $(\alpha,f)$-BR to obtain an approximate convergence guarantee. Our analysis provides key insights on how to improve this guarantee through hyperparameter optimization. Essentially, our theoretical and empirical results show that (1) an imprudent combination of standard approaches to DP and BR might be fruitless, but (2) by carefully re-tuning the learning algorithm, we can obtain reasonable learning accuracy while simultaneously guaranteeing DP and BR.
Learning Higher-Order Dynamics in Video-Based Cardiac Measurement

Computer vision methods typically optimize for first-order dynamics (e.g., optical flow). However, in many cases the properties of interest are subtle variations in higher-order changes, such as acceleration. This is true in the cardiac pulse, where the second derivative can be used as an indicator of blood pressure and arterial disease. Recent developments in camera-based vital sign measurement have shown that cardiac measurements can be recovered with impressive accuracy from videos; however, most of the research has focused on extracting summary statistics such as heart rate. Less emphasis has been put on the accuracy of waveform morphology that is necessary for many clinically meaningful assessments. In this work, we provide evidence that higher-order dynamics are better estimated by neural models when explicitly optimized for in the loss function. Furthermore, adding second-derivative inputs also improves performance when estimating second-order dynamics. We illustrate this, by showing that incorporating the second derivative of both the input frames and the target vital sign signals into the training procedure, models are better able to estimate left ventricle ejection time (LVET) intervals.
Social Recommendation with Self-Supervised Metagraph Informax Network

In recent years, researchers attempt to utilize online social information to alleviate data sparsity for collaborative filtering, based on the rationale that social networks offers the insights to understand the behavioral patterns. However, due to the overlook of inter-dependent knowledge across items (e.g., knowledge graph dependencies between products), existing social recommender systems are insufficient to distill the heterogeneous collaborative signals from both user and item side. In this work, we propose Self- Supervised Metagraph Informax Network (SMIN) which investigates the potential of jointly incorporating social- and knowledge-aware relational structures into the user preference representation framework. To model relation heterogeneity, we design a metapath-guided heterogeneous graph neural network to aggregate feature embeddings from different types of meta-relations across users and items, empowering SMIN to maintain dedicated representations for multifaceted user- and item-wise dependencies. Additionally, to inject high-order collaborative signals into recommendation, we generalize the mutual information learning paradigm from vector space to a self-supervised graph-based collaborative filtering. This endows the expressive modeling of user-item interactive patterns, by exploring global-level collaborative relations and underlying isomorphic transformation property of graph topology. Experimental results on several real-world datasets demonstrate the effectiveness of our model over various state-of-the-art recommendation methods. Further analysis provides insights into the performance superiority of our new recommendation framework. We release our source code at https://github.com/SocialRecsys/SMIN.
On the Limitations of Multimodal VAEs

Multimodal variational autoencoders (VAEs) have shown promise as efficient generative models for weakly-supervised data. Yet, despite their advantage of weak supervision, they exhibit a gap in generative quality compared to unimodal VAEs, which are completely unsupervised. In an attempt to explain this gap, we uncover a fundamental limitation that applies to a large family of mixture-based multimodal VAEs. We prove that the sub-sampling of modalities enforces an undesirable upper bound on the multimodal ELBO and thereby limits the generative quality of the respective models. Empirically, we showcase the generative quality gap on both synthetic and real data and present the tradeoffs between different variants of multimodal VAEs. We find that none of the existing approaches fulfills all desired criteria of an effective multimodal generative model when applied on more complex datasets than those used in previous benchmarks. In summary, we identify, formalize, and validate fundamental limitations of VAE-based approaches for modeling weakly-supervised data and discuss implications for real-world applications.
milliTRACE-IR: Contact Tracing and Temperature Screening via mmWave and Infrared Sensing

Social distancing and temperature screening have been widely employed to counteract the COVID-19 pandemic, sparking great interest from academia, industry and public administrations worldwide. While most solutions have dealt with these aspects separately, their combination would greatly benefit the continuous monitoring of public spaces and help trigger effective countermeasures. This work presents milliTRACE-IR, a joint mmWave radar and infrared imaging sensing system performing unobtrusive and privacy preserving human body temperature screening and contact tracing in indoor spaces. milliTRACE-IR combines, via a robust sensor fusion approach, mmWave radars and infrared thermal cameras. It achieves fully automated measurement of distancing and body temperature, by jointly tracking the subjects’s faces in the thermal camera image plane and the human motion in the radar reference system. Moreover, milliTRACE-IR performs contact tracing: a person with high body temperature is reliably detected by the thermal camera sensor and subsequently traced across a large indoor area in a non-invasive way by the radars. When entering a new room, a subject is re-identified among several other individuals by computing gait-related features from the radar reflections through a deep neural network and using a weighted extreme learning machine as the final re-identification tool. Experimental results, obtained from a real implementation of milliTRACE-IR, demonstrate decimeter-level accuracy in distance/trajectory estimation, inter-personal distance estimation (effective for subjects getting as close as 0.2 m), and accurate temperature monitoring (max. errors of 0.5 °C). Furthermore, milliTRACE-IR provides contact tracing through highly accurate (95%) person re-identification, in less than 20 seconds.
A simple equivariant machine learning method for dynamics based on scalars

Physical systems obey strict symmetry principles. We expect that machine learning methods that intrinsically respect these symmetries should have higher prediction accuracy and better generalization in prediction of physical dynamics. In this work we implement a principled model based on invariant scalars, and release open-source code. We apply this Scalars method to a simple chaotic dynamical system, the springy double pendulum. We show that the Scalars method outperforms state-of-the-art approaches for learning the properties of physical systems with symmetries, both in terms of accuracy and speed. Because the method incorporates the fundamental symmetries, we expect it to generalize to different settings, such as changes in the force laws in the system.
The systematic consideration of the large-scale fed-batch fermentation inhomogeneities using a genetically modified C. glutamicum strain as a model organism

The loss of efficiency and performance of bioprocesses on scale-up is well known, but not fully understood. This work addresses this problem, by studying the effect of some fermentation gradients (pH, glucose and oxygen) at a larger scale in a bench-scale two compartment reactor (PFR + STR) using the cadaverine-producing recombinant bacterium, Corynebacterium glutamicum DM1945 Δact3 Ptuf-ldcC_OPT. The initial scale down strategy increased the magnitude of these gradients by only increasing the mean cell residence time in the plug flow reactor (τ_PFR). The cell growth and product related rate constants were compared as the τ_PFR was increased; differences were significant in some cases, but only up to 2 min residence time. For example, losses in cadaverine productivity when compared to the control fed-batch fermentation on average for the τ_PFR of 1 min, 2 min and 5 min were 25 %, 42 % and 46 % respectively. This indicated that the increasing the τ_PFR alone does not necessarily increase the magnitude of fermentation gradients. The new scale-down strategy developed here, increased the magnitude of fermentation gradients by not only increasing the τ_PFR, but also considering the mean frequency at which the bacterial cells entered the PFR section (f_m). The f_m was kept constant by reducing the broth volume in the STR. Hence, the bacterial cells also spent shorter times in the well mixed STR, as the τ_PFR was increased (hypothesised as giving the bacterial cells less time to recover the non-ideal PFR section of the SDR). On adoption of this strategy cadaverine productivity decreases for the τ_PFR of 1 min, 2 min and 5 min were 25 %, 32 % and 53 % respectively. Thus, highlighting that loss in performance is most likely to occur as the magnitude of heterogeneity within the fermentation environment increases. However, Corynebacterium glutamicum DM1945 Δact3 Ptuf-ldcC_OPT did show some resilience in its biomass productivity. It was only marginally affected in the harshest of conditions simulated here.
Wake-Cough: cough spotting and cougher identification for personalised long-term cough monitoring

We present ‘wake-cough’, an application of wake-word spotting to coughs using a Resnet50 and the identification of coughers using i-vectors, for the purpose of a long-term, personalised cough monitoring system. Coughs, recorded in a quiet ($73\pm 5\ \text{dB}$) and noisy ($34\pm 17\ \text{dB}$) environment, were used to extract i-vectors, x-vectors and d-vectors, used as features to the classifiers. The system achieves 90.02% accuracy when using an MLP to discriminate between 51 coughers using 2-sec long cough segments in the noisy environment. When discriminating between 5 and 14 coughers using longer (100 sec) segments in the quiet environment, this accuracy improves to 99.78% and 98.39% respectively. Unlike speech, i-vectors outperform x-vectors and d-vectors in identifying coughers. These coughs were added as an extra class to the Google Speech Commands dataset and features were extracted by preserving the end-to-end time-domain information in a trigger phrase. The highest accuracy of 88.58% is achieved in spotting coughs among 35 other trigger phrases using a Resnet50. Thus, wake-cough represents a personalised, non-intrusive cough monitoring system, which is power-efficient as on-device wake-word detection can keep a smartphone-based monitoring device mostly dormant. This makes wake-cough extremely attractive in multi-bed ward environments to monitor patients' long-term recovery from lung ailments such as tuberculosis (TB) and COVID-19.
Stability of large complex systems with heterogeneous relaxation dynamics

We study the probability of stability of a large complex system of size N within the framework of a generalized May model, which assumes a linear dynamics of each population size n i (with respect to its equilibrium value): dnidt=−aini−T∑jJijnj . The a i > 0’s are the intrinsic decay rates, J ij is a real symmetric (N × N) Gaussian random matrix and T measures the strength of pairwise interaction between different species. Our goal is to study how inhomogeneities in the intrinsic damping rates a i affect the stability of this dynamical system. As the interaction strength T increases, the system undergoes a phase transition from a stable phase to an unstable phase at a critical value T = T c. We reinterpret the probability of stability in terms of the hitting time of the level b = 0 of an associated Dyson Brownian motion (DBM), starting at the initial position a i and evolving in ‘time’ T. In the large N → ∞ limit, using this DBM picture, we are able to completely characterize T c for arbitrary density μ(a) of the a i ’s. For a specific flat configuration ai=1+σi−1N , we obtain an explicit parametric solution for the limiting (as N → ∞) spectral density for arbitrary T and σ. For finite but large N, we also compute the large deviation properties of the probability of stability on the stable side T < T c using a Coulomb gas representation.
Active learning for interactive satellite image change detection

We introduce in this paper a novel active learning algorithm for satellite image change detection. The proposed solution is interactive and based on a question and answer model, which asks an oracle (annotator) the most informative questions about the relevance of sampled satellite image pairs, and according to the oracle's responses, updates a decision function iteratively. We investigate a novel framework which models the probability that samples are relevant; this probability is obtained by minimizing an objective function capturing representativity, diversity and ambiguity. Only data with a high probability according to these criteria are selected and displayed to the oracle for further annotation. Extensive experiments on the task of satellite image change detection after natural hazards (namely tornadoes) show the relevance of the proposed method against the related work.
Perceived and Intended Sarcasm Detection with Graph Attention Networks

Existing sarcasm detection systems focus on exploiting linguistic markers, context, or user-level priors. However, social studies suggest that the relationship between the author and the audience can be equally relevant for the sarcasm usage and interpretation. In this work, we propose a framework jointly leveraging (1) a user context from their historical tweets together with (2) the social information from a user’s conversational neighborhood in an interaction graph, to contextualize the interpretation of the post. We use graph attention networks (GAT) over users and tweets in a conversation thread, combined with dense user history representations. Apart from achieving state-of-the-art results on the recently published dataset of 19k Twitter users with 30K labeled tweets, adding 10M unlabeled tweets as context, our results indicate that the model contributes to interpreting the sarcastic intentions of an author more than to predicting the sarcasm perception by others.