Miracle-dz/UniFAR-FTUs huggingface dataset:
Is it conceivable that neurogenesis, neural Darwinism, and species evolution could all serve as inspiration for the creation of evolutionary deep neural networks?

Deep Neural Networks (DNNs) are built using artificial neural networks. They are part of machine learning methods that are capable of learning from data that have been used in a wide range of applications. DNNs are mainly handcrafted and they usually contain numerous layers. Research frontier has emerged that concerns automated construction of DNNs via evolutionary algorithms. This paper emphasizes the importance of what we call two-dimensional brain evolution and how it can inspire two dimensional DNN evolutionary modeling. We also highlight the connection between the dropout method which is widely-used in regularizing DNNs and neurogenesis of the brain, and how these concepts could benefit DNNs evolution.The paper concludes with several recommendations for enhancing the automatic construction of DNNs.
End-to-end Manipulator Calligraphy Planning via Variational Imitation Learning

Planning from demonstrations has shown promising results with the advances of deep neural networks. One of the most popular real-world applications is automated handwriting using a robotic manipulator. Classically it is simplified as a two-dimension problem. This representation is suitable for elementary drawings, but it is not sufficient for Japanese calligraphy or complex work of art where the orientation of a pen is part of the user expression. In this study, we focus on automated planning of Japanese calligraphy using a three-dimension representation of the trajectory as well as the rotation of the pen tip, and propose a novel deep imitation learning neural network that learns from expert demonstrations through a combination of images and pose data. The network consists of a combination of variational auto-encoder, bi-directional LSTM, and Multi-Layer Perceptron (MLP). Experiments are conducted in a progressive way, and results demonstrate that the proposed approach is successful in completion of tasks for real-world robots, overcoming the distribution shift problem in imitation learning. The source code and dataset will be public.
Right-handed neutrino dark matter with forbidden annihilation

The seesaw mechanism with three right-handed neutrinos has one as a well-motivated dark matter candidate if stable and the other two can explain baryon asymmetry via the thermal leptogenesis scenario. We explore the possibility of introducing additional particles to make the right-handed neutrino dark matter in thermal equilibrium and freeze out through a forbidden annihilation channel. Nowadays in the Universe, this forbidden channel can be reactivated by a strong gravitational potential such as the supermassive black hole in our galaxy center. The Fermi-LAT gamma ray data and dark matter relic density require this right-handed neutrino dark matter to have mass below $100\,$GeV and the existence of an additional boson $\phi$ that can be tested at future lepton colliders.
Optimism and Delays in Episodic Reinforcement Learning

There are many algorithms for regret minimisation in episodic reinforcement learning. This problem is well-understood from a theoretical perspective, providing that the sequences of states, actions and rewards associated with each episode are available to the algorithm updating the policy immediately after every interaction with the environment. However, feedback is almost always delayed in practice. In this paper, we study the impact of delayed feedback in episodic reinforcement learning from a theoretical perspective and propose two general-purpose approaches to handling the delays. The first involves updating as soon as new information becomes available, whereas the second waits before using newly observed information to update the policy. For the class of optimistic algorithms and either approach, we show that the regret increases by an additive term involving the number of states, actions, episode length, the expected delay and an algorithm-dependent constant. We empirically investigate the impact of various delay distributions on the regret of optimistic algorithms to validate our theoretical results.
GIF: A General Graph Unlearning Strategy via Influence Function

With the greater emphasis on privacy and security in our society, the problem of graph unlearning — revoking the influence of specific data on the trained GNN model, is drawing increasing attention. However, ranging from machine unlearning to recently emerged graph unlearning methods, existing efforts either resort to retraining paradigm, or perform approximate erasure that fails to consider the inter-dependency between connected neighbors or imposes constraints on GNN structure, therefore hard to achieve satisfying performance-complexity trade-offs. In this work, we explore the influence function tailored for graph unlearning, so as to improve the unlearning efficacy and efficiency for graph unlearning. We first present a unified problem formulation of diverse graph unlearning tasks w.r. t. node, edge, and feature. Then, we recognize the crux to the inability of traditional influence function for graph unlearning, and devise Graph Influence Function (GIF), a model-agnostic unlearning method that can efficiently and accurately estimate parameter changes in response to a ϵ -mass perturbation in deleted data. The idea is to supplement the objective of the traditional influence function with an additional loss term of the influenced neighbors due to the structural dependency. Further deductions on the closed-form solution of parameter changes provide a better understanding of the unlearning mechanism. We conduct extensive experiments on four representative GNN models and three benchmark datasets to justify the superiority of GIF for diverse graph unlearning tasks in terms of unlearning efficacy, model utility, and unlearning efficiency. Our implementations are available at https://github.com/wujcan/GIF-torch/.
Global Well-Posedness of the Primitive Equations of Large-Scale Ocean Dynamics with the Gent-McWilliams-Redi Eddy Parametrization Model

We prove global well-posedness of the ocean primitive equations coupled to advection-diffusion equations of the oceanic tracers temperature and salinity that are supplemented by the eddy parametrization model due to Gent-McWilliams and Redi. This parametrization forms a milestone in global ocean modelling and constitutes a central part of any general ocean circulation model computation. The eddy parametrization adds a secondary transport velocity to the tracer equation and renders the original Laplacian operators in the advection-diffusion equations nonlinear, with a diffusion matrix that depends via the equation of state in a nonlinear fashion on both tracers simultaneously. The eddy parametrization of Gent-McWilliams-Redi augments the complexity of the mathematical analysis of the whole system which we present here. We show first that weak solutions exist globally in time, provided the parametrization uses a regularized density. Then we prove by a detailed analysis of the eddy operators the global well-posedness. Our results apply also to the ``small-slope approximation'' that is commonly used in global ocean simulations.
Abstraction-based Probabilistic Stability Analysis of Polyhedral Probabilistic Hybrid Systems

In this paper, we consider the problem of probabilistic stability analysis of a subclass of Stochastic Hybrid Systems, namely, Polyhedral Probabilistic Hybrid Systems (PPHS), where the flow dynamics is given by a polyhedral inclusion, the discrete switching between modes happens probabilistically at the boundaries of their invariant regions and the continuous state is not reset during switching. We present an abstraction-based analysis framework that consists of constructing a finite Markov Decision Processes (MDP) such that verification of certain property on the finite MDP ensures the satisfaction of probabilistic stability on the PPHS. Further, we present a polynomial-time algorithm for verifying the corresponding property on the MDP. Our experimental analysis demonstrates the feasibility of the approach in successfully verifying probabilistic stability on PPHS of various dimensions and sizes.
Ultrafast and Bright Quantum Emitters from the Cavity-Coupled Single Perovskite Nanocrystals

Perovskite nanocrystals (NCs) have attracted increasing interest in the realization of single-photon emitters owing to their ease of chemical synthesis, wide spectral tunability, fast recombination rate constant, scalability, and high quantum yield. However, the integration of a single perovskite NC into a photonic structure has yet to be accomplished. In this work, the integration of a highly stable individual zwitterionic ligand-based CsPbBr3 perovskite NC with a circular Bragg grating (CBG) is successfully demonstrated. The far-field radiation pattern of the NC inside the CBG exhibits high directionality toward a low azimuthal angle, which is consistent with the simulation results. A 5.4-fold enhancement in brightness is observed due to an increase in collection efficiency. Moreover, a 1.95-fold increase in the recombination rate constant is achieved. This study offers ultrafast (<100 ps) single-photon emission and an improved brightness of perovskite NCs, which are critical factors for practical quantum optical applications.
Instant-NVR: Instant Neural Volumetric Rendering for Human-object Interactions from Monocular RGBD Stream

Convenient 4D modeling of human-object interactions is essential for numerous applications. However, monocular tracking and rendering of complex interaction scenarios remain challenging. In this paper, we propose Instant-NVR, a neural approach for instant volumetric human-object tracking and rendering using a single RGBD camera. It bridges traditional non-rigid tracking with recent instant radiance field techniques via a multi-thread tracking-rendering mechanism. In the tracking front-end, we adopt a robust human-object capture scheme to provide sufficient motion priors. We further introduce a separated instant neural representation with a novel hybrid deformation module for the interacting scene. We also provide an on-the-fly reconstruction scheme of the dynamic/static radiance fields via efficient motion-prior searching. Moreover, we introduce an online key frame selection scheme and a rendering-aware refinement strategy to significantly improve the appearance details for online novel-view synthesis. Extensive experiments demonstrate the effectiveness and efficiency of our approach for the instant generation of human-object radiance fields on the fly, notably achieving real-time photo-realistic novel view synthesis under complex human-object interactions. Project page: https://nowheretrix.github. io/Instant-NVR/.
Decay of dyonic black holes

Declaration I state that the work, embodied in this thesis, forms my own contribution to the research work carried out under the guidance of Professor Sunil Mukhi. I also collaborated with Anindya Mukherjee while he was a student at TIFR. This work has not been submitted for any other degree to this or any other University or body. Whenever references have been made to previous works of others, it has been clearly indicated. In my capacity as the supervisor of the candidate's thesis, I certify that the above statements are true to the best of my knowledge. Acknowledgements Firstly, I would like to thank my advisor, Sunil Mukhi for all his help during my research span in TIFR. He always supported and guided me patiently while I was struggling with the new ideas. Discussions with him were always fun and revealing at the same time. Most importantly while working with him I learnt that there is always a solution to every problem, its just one has to look for it at right place and not to give up before finding it. the help I received from them during my stay in TIFR. They were always very approachable. Other members of the theory group have also been very helpful. In particular I would like to thank Deepak Dhar, Mustansir Barma and Sridhar K. for their help and support. My collaborator and a good friend Anindya Mukherji has helped me with stimulating discussions when we were finding it difficult to solve a problem. I want to thank Debashis Ghoshal, Rajesh Gopakumar and Ashoke Sen for their help whenever I visited HRI. I had the good fortune to befriend a lot of students and postdocs in TIFR. Suresh and Suvrat made it a very joyable journey with all the serious discussions on random topics. I am thankful to Basu, Shamik and Tridib to patiently answer all my computer related queries. My special thanks to my friends Anil and Anuradha for their emotional support and for keeping me updated with the world outside institute. My thanks are due to the DTP office staff, Raju Bathija, Girish Ogale, Rajan Pawar and Mohan Shinde who have helped me with any official matters. They have always been very friendly. I wish to specially thank Ajay Salve, our system administrator for his friendship and prompt help whenever things went wrong with the student room computers. Finally I thank …
NTK-SAP: Improving neural network pruning by aligning training dynamics

Pruning neural networks before training has received increasing interest due to its potential to reduce training time and memory. One popular method is to prune the connections based on a certain metric, but it is not entirely clear what metric is the best choice. Recent advances in neural tangent kernel (NTK) theory suggest that the training dynamics of large enough neural networks is closely related to the spectrum of the NTK. Motivated by this finding, we propose to prune the connections that have the least influence on the spectrum of the NTK. This method can help maintain the NTK spectrum, which may help align the training dynamics to that of its dense counterpart. However, one possible issue is that the fixed-weight-NTK corresponding to a given initial point can be very different from the NTK corresponding to later iterates during the training phase. We further propose to sample multiple realizations of random weights to estimate the NTK spectrum. Note that our approach is weight-agnostic, which is different from most existing methods that are weight-dependent. In addition, we use random inputs to compute the fixed-weight-NTK, making our method data-agnostic as well. We name our foresight pruning algorithm Neural Tangent Kernel Spectrum-Aware Pruning (NTK-SAP). Empirically, our method achieves better performance than all baselines on multiple datasets.
Introducing the Texas Euclid Survey for Lyα (TESLA) Survey: Initial Study Correlating Galaxy Properties to Lyα Emission

We present the Texas Euclid Survey for Lyα (TESLA), a spectroscopic survey in the 10 deg2 of the Euclid North Ecliptic Pole (NEP) field. Using TESLA, we study how the physical properties of Lyα emitters (LAEs) correlate with Lyα emission to understand the escape of Lyα emission from galaxies at redshifts of 2–3.5. We present an analysis of 43 LAEs performed in the NEP field using early data from the TESLA survey. We use Subaru Hyper Suprime-Cam imaging in the grizy bands, Spitzer/IRAC channels 1 and 2 from the Hawaii 20 deg2 (H20) survey, and spectra acquired by the Visible Integral-Field Replicable Unit Spectrograph (VIRUS) on the Hobby–Eberly Telescope. We perform spectral energy distribution (SED) fitting to compute the galaxy properties of 43 LAEs, and study correlations between stellar mass, star formation rate (SFR), and dust to the Lyα rest-frame equivalent width (W Lyα ). We uncover marginal (1σ significance) correlations between stellar mass and W Lyα , and SFR and W Lyα , with a Spearman correlation coefficient of −0. 34−. 14+. 17 and −0. 37−. 14+. 16 , respectively. We show that the W Lyα distribution of the 43 LAEs is consistent with being drawn from an exponential distribution with an e-folding scale of W 0 = 150 Å. Once complete the TESLA survey will enable the study of ≳50,000 LAEs to explore more correlations between galaxy properties and W Lyα . The large sample size will allow the construction of a predictive model for W Lyα as a function of SED-derived galaxy properties, which could be used to improve Lyα-based constraints on reionization.
Exceptional Point Perspective of Periodic Leaky-Wave Antennas

Over the past decade, the issue of gain degradation at broadside in periodic leaky-wave antennas (P-LWAs) has been resolved, using a circuit modeling approach, by introducing proper asymmetry in the unit cell of the antenna structure. This paper provides a more fundamental and insightful perspective of the problem by showing, using a simple coupled-mode analysis, that the optimal level of structural asymmetry corresponds to an exceptional point of the coupling parameter between the two eigenmodes of the P-LWA. This contribution represents a key step towards the development of a full electromagnetic resolution of the broadside issue; the second and final step, which will involve a mapping between the complex temporal and spatial eigenfrequencies, will be also presented at the conference.
Performance of Data Augmentation Methods for Brazilian Portuguese Text Classification

Improving machine learning performance while increasing model generalization has been a constantly pursued goal by AI researchers. Data augmentation techniques are often used towards achieving this target, and most of its evaluation is made using English corpora. In this work, we took advantage of different existing data augmentation methods to analyze their performances applied to text classification problems using Brazilian Portuguese corpora. As a result, our analysis shows some putative improvements in using some of these techniques; however, it also suggests further exploitation of language bias and non-English text data scarcity.
Noncontrastive Representation Learning for Intervals From Well Logs

The representation learning problem in the oil and gas industry aims to construct a model that provides a representation based on logging data for a well interval. Previous attempts are mainly supervised and focus on similarity task, which estimates closeness between intervals. We desire to build informative representations without using supervised (labeled) data. One of the possible approaches is self-supervised learning (SSL). In contrast to the supervised paradigm, this one requires little or no labels for the data. Nowadays, most SSL approaches are either contrastive or noncontrastive. Contrastive methods make representations of similar (positive) objects closer and distancing different (negative) ones. Due to possible wrong marking of positive and negative pairs, these methods can provide an inferior performance. Noncontrastive methods do not rely on such labeling and are widespread in computer vision. They learn using only pairs of similar objects that are easier to identify in logging data. We are the first to introduce noncontrastive SSL for well-logging data. In particular, we exploit Bootstrap Your Own Latent (BYOL) and Barlow Twins methods that avoid using negative pairs and focus only on matching positive pairs. The crucial part of these methods is an augmentation strategy. Our augmentation strategies and adaption of BYOL and Barlow Twins together allow us to achieve superior quality on clusterization and mostly the best performance on different classification tasks. Our results prove the usefulness of the proposed noncontrastive self-supervised approaches for representation learning and interval similarity in particular.
EfﬁcientViT: Lightweight Multi-Scale Attention for On-Device Semantic Segmentation

Semantic segmentation enables many appealing real-world applications, such as computational photography, autonomous driving, etc. However, the vast computational cost makes deploying state-of-the-art semantic segmentation models on edge devices with limited hardware resources difﬁcult. This work presents EfﬁcientViT, a new family of semantic segmentation models with a novel lightweight multi-scale attention for on-device semantic segmentation. Unlike prior semantic segmentation models that rely on heavy self-attention, hardware-inefﬁcient large-kernel convolution, or complicated topology structure to obtain good performances, our lightweight multi-scale attention achieves a global receptive ﬁeld and multi-scale learning (two critical features for semantic segmentation models) with only lightweight and hardware-efﬁcient operations. As such, EfﬁcientViT delivers remarkable performance gains over previous state-of-the-art semantic segmentation models across popular benchmark datasets with signiﬁcant speedup on the mobile platform. Without performance loss on Cityscapes, our EfﬁcientViT provides up to 15 × and 9.3 × mobile latency reduction over SegFormer and SegNeXt, respectively. Maintaining the same mobile latency, EfﬁcientViT provides +7.4 mIoU gain on ADE20K over SegNeXt.
Distributed Model Predictive Control for Periodic Cooperation of Multi-Agent Systems

We consider multi-agent systems with heterogeneous, nonlinear agents subject to individual constraints that want to achieve a periodic, dynamic cooperative control goal which can be characterised by a set and a suitable cost. We propose a sequential distributed model predictive control (MPC) scheme in which agents sequentially solve an individual optimisation problem to track an artificial periodic output trajectory. The optimisation problems are coupled through these artificial periodic output trajectories, which are communicated and penalised using the cost that characterises the cooperative goal. The agents communicate only their artificial trajectories and only once per time step. We show that under suitable assumptions, the agents can incrementally move their artificial output trajectories towards the cooperative goal, and, hence, their closed-loop output trajectories asymptotically achieve it. We illustrate the scheme with a simulation example.
Natural Language Robot Programming: NLP integrated with autonomous robotic grasping

In this paper, we present a grammar-based natural language framework for robot programming, specifically for pick-and-place tasks. Our approach uses a custom dictionary of action words, designed to store together words that share meaning, allowing for easy expansion of the vocabulary by adding more action words from a lexical database. We validate our Natural Language Robot Programming (NLRP) framework through simulation and real-world experimentation, using a Franka Panda robotic arm equipped with a calibrated camera-in-hand and a microphone. Participants were asked to complete a pick-and-place task using verbal commands, which were converted into text using Google's Speech-to-Text API and processed through the NLRP framework to obtain joint space trajectories for the robot. Our results indicate that our approach has a high system usability score. The framework's dictionary can be easily extended without relying on transfer learning or large data sets. In the future, we plan to compare the presented framework with different approaches of human-assisted pick-and-place tasks via a comprehensive user study.