Miracle-dz/UniFAR-FTUs huggingface dataset:
Methodical Advice Collection and Reuse in Deep Reinforcement Learning

Reinforcement learning (RL) has shown great success in solving many challenging tasks via use of deep neural networks. Although using deep learning for RL brings immense representational power, it also causes a well-known sample-inefficiency problem. This means that the algorithms are data-hungry and require millions of training samples to converge to an adequate policy. One way to combat this issue is to use action advising in a teacher-student framework, where a knowledgeable teacher provides action advice to help the student. This work considers how to better leverage uncertainties about when a student should ask for advice and if the student can model the teacher to ask for less advice. The student could decide to ask for advice when it is uncertain or when both it and its model of the teacher are uncertain. In addition to this investigation, this paper introduces a new method to compute uncertainty for a deep RL agent using a secondary neural network. Our empirical results show that using dual uncertainties to drive advice collection and reuse may improve learning performance across several Atari games.
Relaxing Equivariance Constraints with Non-stationary Continuous Filters

Equivariances provide useful inductive biases in neural network modeling, with the translation equivariance of convolutional neural networks being a canonical example. Equivariances can be embedded in architectures through weight-sharing and place symmetry constraints on the functions a neural network can represent. The type of symmetry is typically fixed and has to be chosen in advance. Although some tasks are inherently equivariant, many tasks do not strictly follow such symmetries. In such cases, equivariance constraints can be overly restrictive. In this work, we propose a parameter-efficient relaxation of equivariance that can effectively interpolate between a (i) non-equivariant linear product, (ii) a strict-equivariant convolution, and (iii) a strictly-invariant mapping. The proposed parameterisation can be thought of as a building block to allow adjustable symmetry structure in neural networks. In addition, we demonstrate that the amount of equivariance can be learned from the training data using backpropagation. Gradient-based learning of equivariance achieves similar or improved performance compared to the best value found by cross-validation and outperforms baselines with partial or strict equivariance on CIFAR-10 and CIFAR-100 image classification tasks.
Cross-Frequency Coupling Increases Memory Capacity in Oscillatory Neural Networks

: An open problem in neuroscience is to explain the functional role of oscillations in neural networks, contributing, for example, to perception, attention, and memory. Cross-frequency coupling (CFC) is associated with information integration across populations of neurons [1]. Impaired CFC is linked to neurological disease [2]. It is unclear what role CFC has in information processing and brain functional connectivity. We construct a model of CFC which predicts a computational role for observed θ − γ oscillatory circuits in the hippocampus and cortex [3]. Our model predicts that the complex dynamics in recurrent and feedforward networks of coupled oscillators performs robust information storage and pattern retrieval. Based on phasor associative memories (PAM) [4], we present a novel oscillator neural network (ONN) model [5] that includes subharmonic injection locking (SHIL) [6] and which reproduces experimental observations of CFC. We show that the presence of CFC increases the memory capacity of a population of neurons connected by plastic synapses. CFC enables error-free pattern retrieval whereas pattern retrieval fails without CFC. In addition, the trade-oﬀs between sparse connectivity, capacity, and information per connection are identiﬁed. The associative memory is based on a complex-valued neural network, or phasor neural network (PNN). We show that for values of Q which are the same as the ratio of γ to θ oscillations observed in the hippocampus and the cortex, the associative memory achieves greater capacity and information storage than previous models. The novel contri-butions of this work are providing a computational framework based on oscillator dynamics which predicts the functional role of neural oscillations and connecting concepts in neural network theory and dynamical system theory. Description: Identifying the mechanisms which integrate activity across neural circuits is needed in order to understand cognitive processes. CFC is the observation
Testing Distributional Assumptions of Learning Algorithms

There are many important high dimensional function classes that have fast agnostic learning algorithms when strong assumptions on the distribution of examples can be made, such as Gaussianity or uniformity over the domain. But how can one be sufficiently confident that the data indeed satisfies the distributional assumption, so that one can trust in the output quality of the agnostic learning algorithm? We propose a model by which to systematically study the design of tester-learner pairs (A,T), such that if the distribution on examples in the data passes the tester T then one can safely trust the output of the agnostic learner A on the data. To demonstrate the power of the model, we apply it to the classical problem of agnostically learning halfspaces under the standard Gaussian distribution and present a tester-learner pair with a combined run-time of nÕ(1/є4). This qualitatively matches that of the best known ordinary agnostic learning algorithms for this task. In contrast, finite sample Gaussian distribution testers do not exist for the L1 and EMD distance measures. Previously it was known that half-spaces are well-approximated with low-degree polynomials relative to the Gaussian distribution. A key step in our analysis is showing that this is the case even relative to distributions whose low-degree moments approximately match those of a Gaussian. We also go beyond spherically-symmetric distributions, and give a tester-learner pair for halfspaces under the uniform distribution on {0,1}n with combined run-time of nÕ(1/є4). This is achieved using polynomial approximation theory and critical index machinery of [Diakonikolas, Gopalan, Jaiswal, Servedio, and Viola 2009]. Can one design agnostic learning algorithms under distributional assumptions and count on future technical work to produce, as a matter of course, tester-learner pairs with similar run-time? Our answer is a resounding no, as we show there exist some well-studied settings for which 2Õ(√n) run-time agnostic learning algorithms are available, yet the combined run-times of tester-learner pairs must be as high as 2Ω(n). On that account, the design of tester-learner pairs is a research direction in its own right independent of standard agnostic learning. To be specific, our lower bounds apply to the problems of agnostically learning convex sets under the Gaussian distribution and for monotone Boolean functions under the uniform distribution over {0,1}n.
Detecting Violence in Video Based on Deep Features Fusion Technique

With the rapid growth of surveillance cameras in many public places to monitor human activities such as in malls, streets, schools and, prisons, there is a strong demand for such systems to detect violence events automatically. Automatic analysis of video to detect violence is significant for law enforcement. Moreover, it helps to avoid any social, economic and environmental damages. Mostly, all systems today require manual human supervisors to detect violence scenes in the video which is inefficient and inaccurate. in this work, we interest in physical violence that involved two persons or more. This work proposed a novel method to detect violence using a fusion technique of two significantly different convolutional neural networks (CNNs) which are AlexNet and SqueezeNet networks. Each network followed by separate Convolution Long Short Term memory (ConvLSTM) to extract robust and richer features from a video in the final hidden state. Then, making a fusion of these two obtained states and fed to the max-pooling layer. Finally, features were classified using a series of fully connected layers and softmax classifier. The performance of the proposed method is evaluated using three standard benchmark datasets in terms of detection accuracy: Hockey Fight dataset, Movie dataset and Violent Flow dataset. The results show an accuracy of 97%, 100%, and 96% respectively. A comparison of the results with the state of the art techniques revealed the promising capability of the proposed method in recognizing violent videos.
Unsupervised Probabilistic Models for Sequential Electronic Health Records

We develop an unsupervised probabilistic model for heterogeneous Electronic Health Record (EHR) data. Utilizing a mixture model formulation, our approach directly models sequences of arbitrary length, such as medications and laboratory results. This allows for subgrouping and incorporation of the dynamics underlying heterogeneous data types. The model consists of a layered set of latent variables that encode underlying structure in the data. These variables represent subject subgroups at the top layer, and unobserved states for sequences in the second layer. We train this model on episodic data from subjects receiving medical care in the Kaiser Permanente Northern California integrated healthcare delivery system. The resulting properties of the trained model generate novel insight from these complex and multifaceted data. In addition, we show how the model can be used to analyze sequences that contribute to assessment of mortality likelihood.
Pushing the Limits of Additive Fabrication Technologies

In this thesis, I will present the results of three projects addressing some of the major chanllenges of applying computational design for 3D printing. Printability test has long been the bottleneck that prevents 3D printing from scaling up. Oftentimes, designers of 3D models lack the expertise or tools to ensure 3D printability. 3D printing service providers typically rely human inspections to filter out unprintable designs. This process is manual and error-prone. As designs become ever more complex, manual printability check becomes increasingly difficult. To tackle this problem, my colleagues and I proposed an algorithm to automatically determine structurally weak regions and the worst-case usage scenario to break a given model. We validate the algorithm by physically break a number of real 3D printed designs. The second project focuses on designing, analyzing and fabricating complex shapes. A key distinctive feature of 3D printing technologies is that the cost and time of fabrication is uncorrelated with geometric complexity. This opens up many exciting new possibilities. In particular, by pushing geometric complexity to the extreme, 3D printing has the potential of fabricating soft, deformable shapes with microscopic structures using a single raw material. In our recent SIGGRAPH publication, my colleagues and I have not only demonstrated fabricating microscopic frame structures is possible but also proposed an entire pipeline for designing spatially varying microstructures to satisfy target material properties or deformation goals. An essential component of computational design for 3D printing is elasticity simulation, which requires meshes satisfying a number of criteria. With the boost of 3D printing technologies, 3D models have become more abundant and easily accessible than ever before. However, a significant portion of these models violate such criteria. This poses a serious challenge in robustly analyzing 3D designs. Many state-of-the-art geometry processing algorithms/libraries are ill-prepared for dealing with models that are nonmanifold, self-intersecting, locally degenerate and/or containing multiple and possibly
ORCNet: A context-based network to simultaneously segment the ocular region components

Accurate extraction of the Region of Interest is critical for successful ocular region-based biometrics. In this direction, we propose a new context-based segmentation approach, entitled Ocular Region Context Network (ORCNet), introducing a specific loss function, i.e., he Punish Context Loss (PC-Loss). The PC-Loss punishes the segmentation losses of a network by using a percentage difference value between the ground truth and the segmented masks. We obtain the percentage difference by taking into account Biederman's semantic relationship concepts, in which we use three contexts (semantic, spatial, and scale) to evaluate the relationships of the objects in an image. Our proposal achieved promising results in the evaluated scenarios: iris, sclera, and ALL (iris + sclera) segmentations, utperforming the literature baseline techniques. The ORCNet with ResNet-152 outperforms the best baseline (EncNet with ResNet-152) on average by 2.27%, 28.26% and 6.43% in terms of F-Score, Error Rate and Intersection Over Union, respectively. We also provide (for research purposes) 3,191 manually labeled masks for the MICHE-I database, as another contribution of our work.
Evaluating few shot and Contrastive learning Methods for Code Clone Detection

Context: Code Clone Detection (CCD) is a software engineering task that is used for plagiarism detection, code search, and code comprehension. Recently, deep learning-based models have achieved an F1 score (a metric used to assess classifiers) of $\sim$95\% on the CodeXGLUE benchmark. These models require many training data, mainly fine-tuned on Java or C++ datasets. However, no previous study evaluates the generalizability of these models where a limited amount of annotated data is available. Objective: The main objective of this research is to assess the ability of the CCD models as well as few shot learning algorithms for unseen programming problems and new languages (i.e., the model is not trained on these problems/languages). Method: We assess the generalizability of the state of the art models for CCD in few shot settings (i.e., only a few samples are available for fine-tuning) by setting three scenarios: i) unseen problems, ii) unseen languages, iii) combination of new languages and new problems. We choose three datasets of BigCloneBench, POJ-104, and CodeNet and Java, C++, and Ruby languages. Then, we employ Model Agnostic Meta-learning (MAML), where the model learns a meta-learner capable of extracting transferable knowledge from the train set; so that the model can be fine-tuned using a few samples. Finally, we combine contrastive learning with MAML to further study whether it can improve the results of MAML.
Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making

Multi-robot decision-making is the process where multiple robots coordinate actions. In this paper, we aim for efficient and effective multi-robot decision-making despite the robots’ limited on-board resources and the often resource-demanding complexity of their tasks. We introduce the first algorithm enabling the robots to choose with which few other robots to coordinate and provably balance the trade-off of centralized vs. decentralized coordination. Particularly, centralization favors globally near-optimal decision-making but at the cost of increased on-board resource requirements; whereas, decentralization favors minimal resource requirements but at a global suboptimality cost. All robots can thus afford our algorithm, irrespective of their resources. We are motivated by the future of autonomy that involves multiple robots coordinating actions to complete resource-demanding tasks, such as target tracking, area covering, and monitoring. To provide closed-form guarantees, we focus on maximization problems involving monotone and "doubly" submodular functions. To capture the cost of decentralization, we introduce the notion of Centralization Of Information among non-Neighbors (COIN). We validate our algorithm in simulated scenarios of image covering.
Convergence of the Discrete Minimum Energy Path

The minimum energy path (MEP) describes the mechanism of reaction, and the energy barrier along the path can be used to calculate the reaction rate in thermal systems. The nudged elastic band (NEB) method is one of the most commonly used schemes to compute MEPs numerically. It approximates an MEP by a discrete set of configuration images, where the discretization size determines both computational cost and accuracy of the simulations. In this paper, we consider a discrete MEP to be a stationary state of the NEB method and prove an optimal convergence rate of the discrete MEP with respect to the number of images. Numerical simulations for the transitions of some several proto-typical model systems are performed to support the theory.
Sensitivity of Sparse Codes to Image Distortions

Abstract Sparse coding has been proposed as a theory of visual cortex and as an unsupervised algorithm for learning representations. We show empirically with the MNIST data set that sparse codes can be very sensitive to image distortions, a behavior that may hinder invariant object recognition. A locally linear analysis suggests that the sensitivity is due to the existence of linear combinations of active dictionary elements with high cancellation. A nearest-neighbor classifier is shown to perform worse on sparse codes than original images. For a linear classifier with a sufficiently large number of labeled examples, sparse codes are shown to yield higher accuracy than original images, but no higher than a representation computed by a random feedforward net. Sensitivity to distortions seems to be a basic property of sparse codes, and one should be aware of this property when applying sparse codes to invariant object recognition.
Semantic-Aware Pretraining for Dense Video Captioning

This report describes the details of our approach for the event dense-captioning task in ActivityNet Challenge 2021. We present a semantic-aware pretraining method for dense video captioning, which empowers the learned features to recognize high-level semantic concepts. Diverse video features of different modalities are fed into an event captioning module to generate accurate and meaningful sentences. Our final ensemble model achieves a 10.00 METEOR score on the test set.
-Magneto-optical trap of a Group III atom

We realize the ﬁrst magneto-optical trap of an atom in main group III of the Periodic Table. Our atom of choice (indium) does not have a transition out of its ground state suitable for laser cooling; therefore, laser cooling is performed on the | 5 P 3 / 2 , F = 6 i → | 5 D 5 / 2 , F = 7 i transition, where | 5 P 3 / 2 , F = 6 i is a long-lived metastable state. Optimization of our trap parameters results in atoms numbers as large as 5 × 10 8 atoms with temperatures of order 1 mK. Additionally, through trap decay measurements, we infer a one-body trap lifetime of 12 . 3 s. This lifetime is consistent with background gas collisions and indicates that our repumpers have closed all leakage pathways. We also infer a two-body loss rate of 1 . 6 × 10 − 11 cm 3 / s, which is comparable to those measured in alkali atoms. The techniques demonstrated in this work can be straightforwardly applied to other group III atoms, and our results pave the way for realizing quantum degenerate gases of these particles. The magneto-optical trap (MOT) is the workhorse of ultracold With the repumper lasers, the MOT lifetime extends up to two orders of magnitude. IN-SET: Trapped atom number as a function of repumper power under optimal MOT parameters. The trapped atom number increases up to two orders of magnitude with the application of repumpers.
An Introductory Review of Spiking Neural Network and Artificial Neural Network: From Biological Intelligence to Artificial Intelligence

Stemming from the rapid development of artificial intelligence, which has gained expansive success in pattern recognition, robotics, and bioinformatics, neuroscience is also gaining tremendous progress. A kind of spiking neural network with biological interpretability is gradually receiving wide attention, and this kind of neural network is also regarded as one of the directions toward general artificial intelligence. This review summarizes the basic properties of artificial neural networks as well as spiking neural networks. Our focus is on the biological background and theoretical basis of spiking neurons, different neuronal models, and the connectivity of neural circuits. We also review the mainstream neural network learning mechanisms and network architectures. This review hopes to attract different researchers and advance the development of brain intelligence and artificial intelligence.
UNDECIDABILITY AND NON-AXIOMATIZABILITY OF MODAL MANY-VALUED LOGICS

Abstract In this work we study the decidability of a class of global modal logics arising from Kripke frames evaluated over certain residuated lattices, known in the literature as modal many-valued logics. We exhibit a large family of these modal logics which are undecidable, in contrast with classical modal logic and propositional logics defined over the same classes of algebras. This family includes the global modal logics arising from Kripke frames evaluated over the standard Łukasiewicz and Product algebras. We later refine the previous result, and prove that global modal Łukasiewicz and Product logics are not even recursively axiomatizable. We conclude by closing negatively the open question of whether each global modal logic coincides with its local modal logic closed under the unrestricted necessitation rule.
Design and evaluation of a web-based training tool for the User Action Framework Explorer

The proliferation of personal computing power and its associated affordability has seen an explosion in the use of computing and interactive systems of all kinds. This growth has brought with it an awareness among developers of interactive systems about the importance of user centered design and usability. In the Virginia Tech Usability tools lab, efforts are underway to develop tools and processes that assist in usability evaluation. This has led to the development of the User Action Framework (UAF) (Andre et al., 1999), a framework that forms the basis of several usability inspection tools like the Explorer, UPI (Usability Problem Inspector), UPC (Usability Problem Classifier) and the usability problem database. The UAF explorer is the tool in the toolkit, which allows users to explore and learn the structure of the UAF. The framework, in its final stages of development, is based on an interaction cycle derived from Normans action model (1986). For its acceptance, the user action framework, which is based on human factors and cognitive concepts, needed a training program to make it accessible and understandable to the usability practioners in industry and academia. This thesis addressed the following research activities: (1) Developing a webbased training tool for the User Action Framework explorer and (2) Evaluating a webbased training tool using various formative evaluation techniques and a final summative evaluation to measure effectiveness of the training, transfer of training, knowledge/skill acquisition and reaction to the training. The summative evaluation used a pretest-posttest between subjects experimental design to determine the effectiveness of the training tool with the use of lecture-based training as a control group. The data collection included objective measures of performance and subjective measures through questionnaires and rating scales. The summative evaluation found no significant differences between gain scores on lecturebased training and web-based training under controlled conditions. Opportunities for future research were identified and the training tool is expected to contribute to the efforts of the VT usability tools lab towards educating usability professionals and researchers alike on the usefulness of the User Action Framework and its associated tools. This work also seeks to proliferate the use of web based training methods as a valuable way to train remote learners on such developing frameworks and toolkits.