Miracle-dz/UniFAR-FTUs huggingface dataset:
Self Similarity Matrix based CNN Filter Pruning

In recent years, most of the deep learning solutions are targeted to be deployed in mobile devices. This makes the need for development of lightweight models all the more imminent. Another solution is to optimize and prune regular deep learning models. In this paper, we tackle the problem of CNN model pruning with the help of Self-Similarity Matrix (SSM) computed from the 2D CNN filters. We propose two novel algorithms to rank and prune redundant filters which contribute similar activation maps to the output. One of the key features of our method is that there is no need of finetuning after training the model. Both the training and pruning process is completed simultaneously. We benchmark our method on two of the most popular CNN models - ResNet and VGG and record their performance on the CIFAR-10 dataset.
Martian Ionosphere Electron Density Prediction Using Bagged Trees

The availability of Martian atmospheric data provided by several Martian missions broadened the opportunity to investigate and study the conditions of the Martian ionosphere. As such, ionospheric models play a crucial part in improving our understanding of ionospheric behavior in response to different spatial, temporal, and space weather conditions. This work represents an initial attempt to construct an electron density prediction model of the Martian ionosphere using machine learning. The model targets the ionosphere at solar zenith ranging from 70 to 90 degrees, and as such only utilizes observations from the Mars Global Surveyor mission. The performance of different machine learning methods was compared in terms of root mean square error, coefficient of determination, and mean absolute error. The bagged regression trees method performed best out of all the evaluated methods. Furthermore, the optimized bagged regression trees model outperformed other Martian ionosphere models from the literature (MIRI and NeMars) in finding the peak electron density value, and the peak density height in terms of root-mean-square error and mean absolute error.
Dynamic Subgoal-based Exploration via Bayesian Optimization

Reinforcement learning in sparse-reward navigation environments with expensive and limited interactions is challenging and poses a need for effective exploration. Motivated by complex navigation tasks that require real-world training (when cheap simulators are not available), we consider an agent that faces an unknown distribution of environments and must decide on an exploration strategy. It may leverage a series of training environments to improve its policy before it is evaluated in a test environment drawn from the same environment distribution. Most existing approaches focus on fixed exploration strategies, while the few that view exploration as a meta-optimization problem tend to ignore the need for cost-efficient exploration. We propose a cost-aware Bayesian optimization approach that efficiently searches over a class of dynamic subgoal-based exploration strategies. The algorithm adjusts a variety of levers -- the locations of the subgoals, the length of each episode, and the number of replications per trial -- in order to overcome the challenges of sparse rewards, expensive interactions, and noise. An experimental evaluation demonstrates that the new approach outperforms existing baselines across a number of problem domains. We also provide a theoretical foundation and prove that the method asymptotically identifies a near-optimal subgoal design.
Task Tree Retrieval for Robotic Cooking

Robotics is used to foster creativity. Humans can perform jobs in their unique manner, depending on the circumstances. This situation applies to food cooking. Robotic technology in the kitchen can speed up the process and reduce its workload. However, the potential of robotics in the kitchen is still unrealized. In this essay, the idea of FOON, a structural knowledge representation built on insights from human manipulations, is introduced. To reduce the failure rate and ensure that the task is effectively completed, three different algorithms have been implemented where weighted values have been assigned to the manipulations depending on the success rates of motion. This knowledge representation was created using videos of open-sourced recipes
Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation

Topic taxonomies display hierarchical topic structures of a text corpus and provide topical knowledge to enhance various NLP applications. To dynamically incorporate new topic information, several recent studies have tried to expand (or complete) a topic taxonomy by inserting emerging topics identified in a set of new documents. However, existing methods focus only on frequent terms in documents and the local topic-subtopic relations in a taxonomy, which leads to limited topic term coverage and fails to model the global topic hierarchy. In this work, we propose a novel framework for topic taxonomy expansion, named TopicExpan, which directly generates topic-related terms belonging to new topics. Specifically, TopicExpan leverages the hierarchical relation structure surrounding a new topic and the textual content of an input document for topic term generation. This approach encourages newly-inserted topics to further cover important but less frequent terms as well as to keep their relation consistency within the taxonomy. Experimental results on two real-world text corpora show that TopicExpan significantly outperforms other baseline methods in terms of the quality of output taxonomies.
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach

Interpretable and explainable machine learning has seen a recent surge of interest. We focus on safety as a key motivation behind the surge and make the relationship between interpretability and safety more quantitative. Toward assessing safety, we introduce the concept of maximum deviation via an optimization problem to find the largest deviation of a supervised learning model from a reference model regarded as safe. We then show how interpretability facilitates this safety assessment. For models including decision trees, generalized linear and additive models, the maximum deviation can be computed exactly and efficiently. For tree ensembles, which are not regarded as interpretable, discrete optimization techniques can still provide informative bounds. For a broader class of piecewise Lipschitz functions, we leverage the multi-armed bandit literature to show that interpretability produces tighter (regret) bounds on the maximum deviation. We present case studies, including one on mortgage approval, to illustrate our methods and the insights about models that may be obtained from deviation maximization.
The ice surface in premelting and freezing: transition dynamics and metastable states

The premelting of ice is well known, but not about how the premelted and solid surfaces convert into each other. In this work, the transition dynamics between two phases are revealed with large-scale molecular dynamics simulations. Supercooling and superheating states exist in the transition, and are overcome by nucleation-like processes. The natural inhomogeneity of ice surface enhances nuclei formation, while it only accelerates premelting but not freezing. Furthermore, the complete freezing of ice surfaces may be hindered by the stacking order mismatch between nuclei. This work points out the importance of metastable states in premelting studies, and calls attention to their potential effects on related physics.
Factorization of Linear Quantum Systems with Delayed Feedback

We consider the transfer functions describing the input-output relation for a class of linear open quantum systems involving feedback with nonzero time delays. We show how such transfer functions can be factorized into a product of terms which are transfer functions of canonical physically realizable components. We prove under certain conditions that this product converges, and can be approximated on compact sets. Thus our factorization can be interpreted as a (possibly infinite) cascade. Our result extends past work where linear open quantum systems with a state-space realization have been shown to have a pure cascade realization [Nurdin, H. I. , Grivopoulos, S. , & Petersen, I. R. (2016). The transfer function of generic linear quantum stochastic systems has a pure cascade realization. Automatica, 69, 324-333. ]. The functions we consider are inherently non-Markovian, which is why in our case the resulting product may have infinitely many terms.
A switchable controlled-NOT gate in a spin-chain NMR quantum computer

A method of switching a controlled-NOT gate in a solid-stae NMR quantum computer is pre-sented. Qubits of I = 1 / 2 nuclear spins are placed periodically along a quantum spin chain (1-D antiferromagnet) having a singlet ground state with a ﬁnite spin gap to the lowest excited state caused by some quantum eﬀect. Irradiation of a microwave tuned to the spin gap energy excites a packet of triplet magnons at a speciﬁc part of the chain where control and target qubits are in-volved. The packet switches on the Suhl-Nakamura interaction between the qubits, which serves as a controlled NOT gate. The qubit initialization is achieved by a qubit initializer consisting of semiconducting sheets attached to the spin chain, where spin polarizations created by the optical pumping method in the semiconductors are transferred to the spin chain. The scheme allows us to separate the initialization process from the computation, so that one can optimize the computation part without being restricted by the initialization scheme, which provides us with a wide selection of materials for a quantum computer.
Investigating the robustness of a deep learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions

Objective. Quantitative phase retrieval (QPR) in propagation-based x-ray phase contrast imaging of heterogeneous and structurally complicated objects is challenging under laboratory conditions due to partial spatial coherence and polychromaticity. A deep learning-based method (DLBM) provides a nonlinear approach to this problem while not being constrained by restrictive assumptions about object properties and beam coherence. The objective of this work is to assess a DLBM for its applicability under practical scenarios by evaluating its robustness and generalizability under typical experimental variations. Approach. Towards this end, an end-to-end DLBM was employed for QPR under laboratory conditions and its robustness was investigated across various system and object conditions. The robustness of the method was tested via varying propagation distances and its generalizability with respect to object structure and experimental data was also tested. Main results. Although the end-to-end DLBM was stable under the studied variations, its successful deployment was found to be affected by choices pertaining to data pre-processing, network training considerations and system modeling. Significance. To our knowledge, we demonstrated for the first time, the potential applicability of an end-to-end learning-based QPR method, trained on simulated data, to experimental propagation-based x-ray phase contrast measurements acquired under laboratory conditions with a commercial x-ray source and a conventional detector. We considered conditions of polychromaticity, partial spatial coherence, and high noise levels, typical to laboratory conditions. This work further explored the robustness of this method to practical variations in propagation distances and object structure with the goal of assessing its potential for experimental use. Such an exploration of any DLBM (irrespective of its network architecture) before practical deployment provides an understanding of its potential behavior under experimental settings.
NaRPA: Navigation and Rendering Pipeline for Astronautics

This paper presents Navigation and Rendering Pipeline for Astronautics (NaRPA) - a novel ray-tracing-based computer graphics engine to model and simulate light transport for space-borne imaging. NaRPA incorporates lighting models with attention to atmospheric and shading eﬀects for the synthesis of space-to-space and ground-to-space virtual observations. In addition to image rendering, the engine also possesses point cloud, depth, and contour map generation capabilities to simulate passive and active vision-based sensors and to facilitate the designing, testing, or veriﬁcation of visual navigation algorithms. Physically based rendering capabilities of NaRPA and the eﬃcacy of the proposed rendering algorithm are demonstrated using applications in representative space-based environments. A key demonstration includes NaRPA as a tool for generating stereo imagery and application in 3D coordinate estimation using triangulation. Another prominent application of NaRPA includes a novel diﬀerentiable rendering approach for image-based attitude estimation is proposed to highlight the eﬃcacy of the NaRPA engine for simulating vision-based navigation and guidance operations.
Automatic Crater Shape Retrieval using Unsupervised and Semi-Supervised Systems

Impact craters are formed due to continuous impacts on the surface of planetary bodies. Most recent deep learning-based crater detection methods treat craters as circular shapes, and less attention is paid to extracting the exact shapes of craters. Extracting precise shapes of the craters can be helpful for many advanced analyses, such as crater formation. This paper proposes a combination of unsupervised non-deep learning and semi-supervised deep learning approach to accurately extract shapes of the craters and detect missing craters from the existing catalog. In unsupervised non-deep learning, we have proposed an adaptive rim extraction algorithm to extract craters' shapes. In this adaptive rim extraction algorithm, we utilized the elevation profiles of DEMs and applied morphological operation on DEM-derived slopes to extract craters' shapes. The extracted shapes of the craters are used in semi-supervised deep learning to get the locations, size, and refined shapes. Further, the extracted shapes of the craters are utilized to improve the estimate of the craters' diameter, depth, and other morphological factors. The craters' shape, estimated diameter, and depth with other morphological factors will be publicly available.
Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform

To address practical challenges in establishing and maintaining robust wireless connectivity such as multi-path effects, low latency, size reduction, and high data rate, we have deployed the digital beamformer, as a spatial filter, by using the hybrid antenna array at an operating frequency of 10 GHz. The proposed digital beamformer utilizes a combination of the two well-established beamforming techniques of minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV). In this case, the MVDR beamforming method updates weight vectors on the FPGA board, while the LCMV beamforming technique performs nullsteering in directions of interference signals in the real environment. The most well-established machine learning technique of support vector machine (SVM) for the Direction of Arrival (DoA) estimation is limited to problems with linearly-separable datasets. To overcome the aforementioned constraint, the quadratic surface support vector machine (QS-SVM) classifier with a small regularizer has been used in the proposed beamformer for the DoA estimation in addition to the two beamforming techniques of LCMV and MVDR. In this work, we have assumed that five hybrid array antennas and three sources are available, at which one of the sources transmits the signal of interest. The QS-SVM-based beamformer has been deployed on the FPGA board for spatially filtering two signals from undesired directions and passing only one of the signals from the desired direction. The simulation results have verified the strong performance of the QS-SVM-based beamformer in suppressing interference signals, which are accompanied by placing deep nulls with powers less than −10 dB in directions of interference signals, and transferring the desired signal. Furthermore, we have verified that the performance of the QS-SVM-based beamformer yields other advantages including average latency time in the order of milliseconds, performance efficiency of more than 90%, and throughput of nearly 100%.
Optimal Algorithms for Stochastic Complementary Composite Minimization

Inspired by regularization techniques in statistics and machine learning, we study complementary composite minimization in the stochastic setting. This problem corresponds to the minimization of the sum of a (weakly) smooth function endowed with a stochastic first-order oracle, and a structured uniformly convex (possibly nonsmooth and non-Lipschitz) regularization term. Despite intensive work on closely related settings, prior to our work no complexity bounds for this problem were known. We close this gap by providing novel excess risk bounds, both in expectation and with high probability. Our algorithms are nearly optimal, which we prove via novel lower complexity bounds for this class of problems. We conclude by providing numerical results comparing our methods to the state of the art.
The Authenticity Gap in Human Evaluation

Human ratings are the gold standard in NLG evaluation. The standard protocol is to collect ratings of generated text, average across annotators, and rank NLG systems by their average scores. However, little consideration has been given as to whether this approach faithfully captures human preferences. Analyzing this standard protocol through the lens of utility theory in economics, we identify the implicit assumptions it makes about annotators. These assumptions are often violated in practice, in which case annotator ratings cease to reflect their preferences. The most egregious violations come from using Likert scales, which provably reverse the direction of the true preference in certain cases. We suggest improvements to the standard protocol to make it more theoretically sound, but even in its improved form, it cannot be used to evaluate open-ended tasks like story generation. For the latter, we propose a new human evaluation protocol called system-level probabilistic assessment (SPA). When human evaluation of stories is done with SPA, we can recover the ordering of GPT-3 models by size, with statistically significant results. However, when human evaluation is done with the standard protocol, less than half of the expected preferences can be recovered (e.g., there is no significant difference between curie and davinci, despite using a highly powered test).
Signature Guide Tap on the Screen Move ” Down ” Write ” Name

Filling out printed forms (e.g., checks) independently is currently impossible for blind people, since they cannot pinpoint the locations of the form fields, and quite often, they cannot even figure out what fields (e.g., name) are present in the form. Hence, they always depend on sighted people to write on their behalf, and help them affix their signatures. Extant
Virtual and augmented reality applications to improve older adults’ quality of life: A systematic mapping review and future directions

Objective The use of virtual reality and augmented reality to improve older adults’ quality of life has rapidly increased in recent years. This systematic mapping review aimed to provide a comprehensive overview of existing research that identifies and classifies current virtual reality and augmented reality applications that enhance the quality of life of older adults to increase the understanding of the impact of these technologies. Methods To reach this objective, a systematic mapping review was conducted of the studies published between 2009 and 2020 in major scientific databases, such as IEEE Xplore, Web of Science, Scopus, and PubMed. A total of 57 studies were analyzed and classified into four main quality of life domains: physical, cognitive, psychological, and social well-being. Results The findings showed that virtual reality and augmented reality have found their places in many quality of life studies of older adults. Although virtual reality and augmented reality applications are notably growing in the physical and cognitive well-being domains in training and rehabilitation settings, they are still in the early stages of development in psychological and social well-being research as well as healthcare settings. Our findings also revealed that virtual reality games, particularly motion-based exergames, and 3D augmented reality systems are the most common virtual reality and augmented reality types among the reviewed studies. Moreover, balance and attention were the most prevalent physical and cognitive functions when using motion-based and immersive virtual reality exergames and augmented reality systems and games, respectively, while confidence and interaction were the most dominant psychological and social functions. Conclusion This mapping review provides a comprehensive overview of potential areas for further research in this field, thereby assisting researchers, technologists, and health practitioners in expanding this field of research.
Estimates of Reverse Shock Emission from Short Gamma-ray Bursts.

We investigate the expected radio emission from the reverse shock of short GRBs, using the afterglow parameters derived from the observed short GRB light curves. In light of recent results suggesting that in some cases the radio afterglow is due to emission from the reverse shock, we examine the extent to which this component is detectable for short GRBs. In some GRBs, the standard synchrotron shock model predicts detectable radio emission from the reverse shock when none was seen. Because many physical parameters play a role in these estimates, our results highlight the need to more deeply explore the fundamental processes involved in GRB particle acceleration and emission. However, with more rapid follow-up, we can test our standard model of GRBs, which predicts an early, radio bright reverse shock in many cases.
A Study of the Depolarization of Lunar Radar Echoes

The radar backscattering characteristics of the lunar surface are examined in detail at a wavelength of 23 cm. The backscattered waves are studied both for circular and for linear polarization of the transmitted wave. Effects relating to the orientation of the local plane of incidence on the moon with respect to the polarization of transmitted or scattered waves are investigated. The experimental results appear to strongly support the hypothesis that the returns at oblique angles of incidence arise through single scattering from discrete objects, as opposed to the returns at near normal incidence which are dominated by quasi-specular reflection.
On the role and in

In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and modeling approaches used in the field. This issue is particularly complex in neuroscience because the field studies phenomena across a wide range of scales and often requires consideration of these phenomena at varying degrees of abstraction, from precise biophysical interactions to the computations they implement. We argue that a pragmatic perspective of science, in which descriptive, mechanistic, and normative approaches each play a distinct role in defining and bridging levels of abstraction will facilitate neuroscientific practice. This analysis leads to methodological suggestions, including selecting a level of abstraction that is appropriate for a given problem, identifying transfer functions to connect models and data, and the use of models themselves as a form of experiment.