Miracle-dz/UniFAR-FTUs huggingface dataset:
Using a Learning Management System to Facilitate Learning Outcomes Assessment

Increased demands for accountability among state and federal policy makers require that colleges and universities improve the process of measuring student learning outcomes. Despite a growing need, there has been limited development of integrated, electronic processes and tools that facilitate assessment of student progress toward program-level learning outcomes. Collecting student course materials, classifying by program and course-level objectives and reporting the results remains a tedious and labor-intensive task. This project demonstrates how course-level assessment data from a learning management system (LMS) can be utilized for program-level outcomes assessment. A pilot system was developed to integrate data from a LMS to provide continuous reporting of program and course-level assessment with minimal additional effort from faculty and students. This chapter shares our outcomes assessment system development approach, faculty development approach, and the lessons learned from our project, including the challenges confronted during system implementation.
Anharmonic thermodynamic properties and phase boundary across the post-perovskite transition in MgSiO$_3$

To address the effects of lattice anharmonicity across the perovskite to post-perovskite transition in MgSiO 3 , we conduct calculations using the phonon quasiparticle (PHQ) approach. The PHQ is based on ab initio molecular dynamics and, in principle, captures full anharmonicity. Free energies in the thermodynamic limit ( 𝑁 → ∞ ) are computed using temperature-dependent quasiparticle dispersions within the phonon gas model. Systematic results on anharmonic thermodynamic properties and phase boundary are reported. Both the local density approximation (LDA) and the generalized gradient approximation (GGA) calculations are performed to provide confident constraints on these properties. Anharmonic effects are demonstrated by comparing results with those obtained using the quasiharmonic approximation (QHA). The inadequacy of the QHA is indicated by its overestimation of thermal expansivity and thermodynamic Grüneisen parameter and its converged isochoric heat capacity in the high-temperature limit. The PHQ phase boundary has a Clapeyron slope ( 𝑑𝑃/𝑑𝑇 ) that increases with temperature. This result contrasts with the nearly zero curvature of the QHA phase boundary. Anharmonicity bends the phase boundary to lower temperatures at high pressures. Implications for the double-crossing of the phase boundary by the mantle geotherm are discussed.
Theory and Algorithms for Diffusion Processes on Riemannian Manifolds

We study geometric stochastic differential equations (SDEs) and their approximations on Riemannian manifolds. In particular, we introduce a simple new construction of geometric SDEs, using which with bounded curvature. In particular, we provide the first (to our knowledge) non-asymptotic bound on the error of the geometric Euler-Murayama discretization. We then bound the distance between the exact SDE and a discrete geometric random walk, where the noise can be non-Gaussian; this analysis is useful for using geometric SDEs to model naturally occurring discrete non-Gaussian stochastic processes. Our results provide convenient tools for studying MCMC algorithms that adopt non-standard noise distributions.
Worst-case dynamic power distribution network noise prediction using convolutional neural network

Worst-case dynamic PDN noise analysis is an essential step in PDN sign-off to ensure the performance and reliability of chips. However, with the growing PDN size and increasing scenarios to be validated, it becomes very time- and resource-consuming to conduct full-stack PDN simulation to check the worst-case noise for different test vectors. Recently, various works have proposed machine learning based methods for supply noise prediction, many of which still suffer from large training overhead, inefficiency, or non-scalability. Thus, this paper proposed an efficient and scalable framework for the worst-case dynamic PDN noise prediction. The framework first reduces the spatial and temporal redundancy in the PDN and input current vector, and then employs efficient feature extraction as well as a novel convolutional neural network architecture to predict the worst-case dynamic PDN noise. Experimental results show that the proposed framework consistently outperforms the commercial tool and the state-of-the-art machine learning method with only 0.63--1.02% mean relative error and 25--69× speedup.
Ultrafast Charge Relocation Dynamics in Enol–Keto Tautomerization Monitored with a Local Soft-X-ray Probe

Proton-coupled electron transfer (PCET) is the underlying mechanism governing important reactions ranging from water splitting in photosynthesis to oxygen reduction in hydrogen fuel cells. The interplay of proton and electronic charge distribution motions can vary from sequential to concerted schemes, with elementary steps occurring on ultrafast time scales. We demonstrate with a simulation study that femtosecond soft-X-ray spectroscopy provides key insights into the PCET mechanism of a photoinduced intramolecular enol* → keto* tautomerization reaction. A full quantum treatment of the electronic and nuclear dynamics of 2-(2′-hydroxyphenyl)benzothiazole upon electronic excitation reveals how spectral signatures of local excitations from core to frontier orbitals display the distinctly different stages of charge relocation for the H atom, donating, and accepting sites. Our findings indicate that ultraviolet/X-ray pump-probe spectroscopy provides a unique way to probe ultrafast electronic structure rearrangements in photoinduced chemical reactions essential to understanding the mechanism of PCET.
A high accuracy algorithm for the suppression of narrowband interference in partial discharge detection based on LS-SVM

Suppressing narrowband interference in frequency domain will result in the energy loss of partial discharge pulse. In this paper, starting from time domain, a section of narrowband interference is extracted with Least Squares-Support Vector Machine(LS-SVM) firstly. Then,this paper carries out periodic prolongation on it with autocorrelation function in order to obtain complete narrowband interference,and subtracts it from the original single.The influence of LS-SVM inserting dimension(m) on estimation accuracy and parameter(γ,σ) selection is discussed accordingly. Results show that proposed method can suppress narrowband interference at a high degree of accuracy.And the stronger the narrowband interference is, the better the suppression effect is; the more adjacent the frequencies of narrowband signal and the partial discharge pulses are, the more predominant the advantage of proposed method is. Properly choosing inserting dimension is helpful in improving accuracy and robustness for parameters.
Generative Adversarial Networks for Image Super-Resolution: A Survey

Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small samples. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We first take a look at developments of GANs. Second, we present popular architectures for GANs in big and small samples for image applications. Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners. Next, we compare performance of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR.
Person Re-Identification

Person Re-Identification (Re-ID) is an important problem in computer vision-based surveillance applications, in which one aims to identify a person across different surveillance photographs taken from different cameras having varying orientations and field of views. Due to the increasing demand for intelligent video surveillance, Re-ID has gained significant interest in the computer vision community. In this work, we experiment on some existing Re-ID methods that obtain state of the art performance in some open benchmarks. We qualitatively and quantitaively analyse their performance on a provided dataset, and then propose methods to improve the results. This work was the report submitted for COL780 final project at IIT Delhi.
Polynomial Time Algorithm for ARRIVAL on Tree-like Multigraphs

A rotor walk in a directed graph can be thought of as a deterministic version of a Markov Chain, where a pebble moves from vertex to vertex following a simple rule until a terminal vertex, or sink, is reached. The ARRIVAL problem, as defined by Dohrau and al., consists in determining which sink will be reached. While the walk itself can take an exponential number of steps, this problem belongs to the complexity class NP$\cap$co-NP without being known to be in P. Several variants have been studied where we add one or two players to the model, defining deterministic analogs of stochastic models (e.g., Markovian decision processes, Stochastic Games) with rotor-routing rules instead of random transitions. The corresponding decision problem address the existence of strategies for players that ensure some condition on the reached sink. These problems are known to be $NP$-complete for one player and $PSPACE$-complete for two players. In this work, we define a class of directed graphs, namely tree-like multigraphs, which are multigraphs having the global shape of an undirected tree. We prove that the different variants of the reachability problem with zero, one, or two players can be solved in linear time, while the number of steps of rotor walks can still be exponential. To achieve this, we define a notion of return flow, which counts the number of times the pebble will bounce back in subtrees of the graph.
UM6P-CS at SemEval-2022 Task 11: Enhancing Multilingual and Code-Mixed Complex Named Entity Recognition via Pseudo Labels using Multilingual Transformer

Building real-world complex Named Entity Recognition (NER) systems is a challenging task. This is due to the complexity and ambiguity of named entities that appear in various contexts such as short input sentences, emerging entities, and complex entities. Besides, real-world queries are mostly malformed, as they can be code-mixed or multilingual, among other scenarios. In this paper, we introduce our submitted system to the Multilingual Complex Named Entity Recognition (MultiCoNER) shared task. We approach the complex NER for multilingual and code-mixed queries, by relying on the contextualized representation provided by the multilingual Transformer XLM-RoBERTa. In addition to the CRF-based token classification layer, we incorporate a span classification loss to recognize named entities spans. Furthermore, we use a self-training mechanism to generate weakly-annotated data from a large unlabeled dataset. Our proposed system is ranked 6th and 8th in the multilingual and code-mixed MultiCoNER’s tracks respectively.
PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings

Generative models such as the variational autoencoder (VAE) and the generative adversarial networks (GAN) have proven to be incredibly powerful for the generation of synthetic data that preserves statistical properties and utility of real-world datasets, especially in the context of image and natural language text. Nevertheless, until now, there has no successful demonstration of how to apply either method for generating useful physiological sensory data. The state-of-the-art techniques in this context have achieved only limited success. We present PHYSIOGAN, a generative model to produce high fidelity synthetic physiological sensor data readings. PHYSIOGAN consists of an encoder, decoder, and a discriminator. We evaluate PHYSIOGAN against the state-of-the-art techniques using two different real-world datasets: ECG classification and activity recognition from motion sensors datasets. We compare PHYSIOGAN to the baseline models not only the accuracy of class conditional generation but also the sample diversity and sample novelty of the synthetic datasets. We prove that PHYSIOGAN generates samples with higher utility than other generative models by showing that classification models trained on only synthetic data generated by PHYSIOGAN have only 10% and 20% decrease in their classification accuracy relative to classification models trained on the real data. Furthermore, we demonstrate the use of PHYSIOGAN for sensor data imputation in creating plausible results.
A symmetric extensible protocol for quantum secret sharing

This paper introduces the Symmetric Extensible Quantum Secret Sharing protocol, a novel quantum protocol for secret sharing. At its heart, it is an entanglement-based protocol that relies on the use of maximally entangled GHZ tuples, evenly distributed among the players, endowing the spymaster with the ability to securely share a secret message with the agents. Its security stems from the fact that it is highly improbable for a malicious eavesdropper or a rogue double agent to disrupt its successful execution. It is characterized by symmetry, since all agents are treated indiscriminately, utilizing identical quantum circuits. Furthermore, it can be seamlessly extended to an arbitrary number of agents. Finally, after the completion of the quantum part of the protocol, the spymaster will have to publicly transmit some information, in order to allow the agents to unlock the secret message. This part of the protocol can be considered as an additional advantage, due to the fact that it gives the spymaster the privilege of deciding if, or when, it is the right time for the agents to unlock the secret message, after the completion of the quantum part of the protocol.
Mat2Stencil: A Modular Matrix-Based DSL for Explicit and Implicit Matrix-Free PDE Solvers on Structured Grid

Partial differential equation (PDE) solvers are extensively utilized across numerous scientific and engineering fields. However, achieving high performance and scalability often necessitates intricate and low-level programming, particularly when leveraging deterministic sparsity patterns in structured grids. In this paper, we propose an innovative domain-specific language (DSL), Mat2Stencil, with its compiler, for PDE solvers on structured grids. Mat2Stencil introduces a structured sparse matrix abstraction, facilitating modular, flexible, and easy-to-use expression of solvers across a broad spectrum, encompassing components such as Jacobi or Gauss-Seidel preconditioners, incomplete LU or Cholesky decompositions, and multigrid methods built upon them. Our DSL compiler subsequently generates matrix-free code consisting of generalized stencils through multi-stage programming. The code allows spatial loop-carried dependence in the form of quasi-affine loops, in addition to the Jacobi-style stencil’s embarrassingly parallel on spatial dimensions. We further propose a novel automatic parallelization technique for the spatially dependent loops, which offers a compile-time deterministic task partitioning for threading, calculates necessary inter-thread synchronization automatically, and generates an efficient multi-threaded implementation with fine-grained synchronization. Implementing 4 benchmarking programs, 3 of them being the pseudo-applications in NAS Parallel Benchmarks with 6.3% lines of code and 1 being matrix-free High Performance Conjugate Gradients with 16.4% lines of code, we achieve up to 1.67× and on average 1.03× performance compared to manual implementations.
Wireless at the Nanoscale: Toward Magnetically Tunable Beam Steering

Plasmonic wireless nanolinks hold great promise to overcome limitations from conventional metallic wires, namely, narrow bandwidths, Ohmic losses, dispersion, and cross-talking. However, current developments are limited to the wireless communication between two fixed points, i.e., a fixed transmitter with a fixed receiver, which in turn limits the energy efficiency and integration levels. In this work, we propose the use of magnetic fields for the active tuning of the radiation beam steering of plasmonic nanoantennas. The physical principle behind our concept is shown with a rigorous solution of the radiation pattern of a radiating dipole source in the presence of magnetic fields. The results indicate that the proposal of this work is feasible with magnetoplasmonic nanoantennas, which can be fabricated using available ferromagnetic materials.
Cyborg Beetle Achieves Efficient Autonomous Navigation Using Feedback Control

: Terrestrial cyborg insects were long discussed as potential complements for insect-scale mobile robots. These cyborgs inherit the insects’ outstanding locomotory skills, orchestrated by a sophisticated central nervous system and various sensory organs, favoring their maneuvers in complex terrains. However, the autonomous navigation of these cyborgs was not yet comprehensively studied. The struggle to select optimal stimuli for individual insects hinders reliable and accurate navigations. This study overcomes this problem and provides a detailed look at the terrestrial navigation of cyborg insects (darkling beetle) by implementing a feedback control system. Via a thrust controller for acceleration and a proportional controller for turning, the system regulates the stimulation parameters depending on the beetle’s instantaneous status. Adjusting the system’s control parameters allows reliable and precise path-following navigations (i.e., up to ~94% success rate, ~1/2 body length accuracy). Also, the system’s performance can be tuned, providing flexibility to navigation applications of terrestrial cyborg insects.
Termination Shocks and the Extended X-Ray Emission in Mrk 78

Subarcsecond imaging of the X-ray emission in the type 2 active galactic nucleus (AGN) Mrk 78 with Chandra shows complex structure with spectral variations on scales from ∼200 pc to ∼2 kpc. Overall the X-ray emission is aligned E–W with the radio (3.6 cm) and narrow emission line region as mapped in [O iii], with a marked E–W asymmetry. The eastern X-ray emission is mostly in a compact knot coincident with the location where the radio source is deflected, while the western X-ray emission forms a loop or shell ∼2 kpc from the nucleus with radius ∼0.7 kpc. There is suggestive evidence of shocks in both the eastern knot and the western arc. Both these positions coincide with large changes in the velocities of the [O iii] outflow. We discuss possible reasons why the X-ray shocks on the western side occur ∼1 kpc farther out than on the eastern side. We estimate that the thermal energy injected by the shocks into the interstellar medium corresponds to 0.05%–0.6% of the AGN bolometric luminosity.
An Improved Nearest Neighbour Classifier

A windowed version of the Nearest Neighbour (WNN) classifier for images is described. While its construction is inspired by the architecture of Artificial Neural Networks, the underlying theoretical framework is based on approximation theory. We illustrate WNN on the datasets MNIST and EMNIST of images of handwritten digits. In order to calibrate the parameters of WNN, we first study it on the classical MNIST dataset. We then apply WNN with these parameters to the challenging EMNIST dataset. It is demonstrated that WNN misclassifies 0.42% of the images of EMNIST and therefore significantly outperforms predictions by humans and shallow ANNs that both have more than 1.3% of errors.
RIS-Aided Joint Localization and Synchronization With a Single-Antenna Receiver: Beamforming Design and Low-Complexity Estimation

Reconfigurable intelligent surfaces (RISs) have attracted enormous interest thanks to their ability to overcome line-of-sight blockages in mmWave systems, enabling in turn accurate localization with minimal infrastructure. Less investigated are however the benefits of exploiting RIS with suitably designed beamforming strategies for optimized localization and synchronization performance. In this paper, a novel low-complexity method for joint localization and synchronization based on an optimized design of the base station (BS) active precoding and RIS passive phase profiles is proposed, for the challenging case of a single-antenna receiver. The theoretical position error bound is first derived and used as metric to jointly optimize the BS-RIS beamforming, assuming a priori knowledge of the user position. By exploiting the low-dimensional structure of the solution, a novel codebook-based robust design strategy with optimized beam power allocation is then proposed, which provides low-complexity while taking into account the uncertainty on the user position. Finally, a reduced-complexity maximum-likelihood based estimation procedure is devised to jointly recover the user position and the synchronization offset. Extensive numerical analysis shows that the proposed joint BS-RIS beamforming scheme provides enhanced localization and synchronization performance compared to existing solutions, with the proposed estimator attaining the theoretical bounds even at low signal-to-noise-ratio and in the presence of additional uncontrollable multipath propagation.
Hydrodynamics of $N$-urn susceptible-infected-removed epidemics

: In this paper we are concerned with N -urn susceptible-infected-removed epidemics, where each urn is in one of three states, namely ‘susceptible’, ‘infected’ and ‘removed’. We assume that recovery rates of infected urns and infection rates between infected and susceptible urns are all coordinate-dependent. We show that the hydrodynamic limit of our model is driven by a deterministic ( C [0 , 1]) ′ -valued process with density which is the solution to a nonlinear C [0 , 1]-valued ordinary diﬀerential equation consistent with a mean-ﬁeld analysis. We further show that the ﬂuctuation of our process is driven by a generalized Ornstein-Uhlenbeck process. A key step in proofs of above main results is to show that states of diﬀerent urns are approximately independent as N → + ∞ .
Research on Application of 'Buffet Style' Teaching Mode for Sports Functional Food Teaching in Private College

This study intends to analyze the feasibility of “Buffet Style” teaching mode in teaching sports functional food in college and to explore the positive utility “Buffet Style” teaching mode had towards teaching sports functional food in college. “Buffet Style” teaching mode overcame the solo-performance of the teacher and emphasizing interactions between teachers and students and also focuses on students’ skills of critical thinking and problem-solving.