Miracle-dz/UniFAR-FTUs huggingface dataset:
Instructional Strategies that Foster Effective Problem-Solving

Helping students become proficient problem solvers is a major goal of many physics courses from introductory to advanced levels. In fact, physics has often been used by cognitive scientists to investigate the differences between the problem-solving strategies of expert and novice problem solvers because it is a domain in which there is reasonably good agreement about what constitutes good problem-solving. Since the laws of physics are encapsulated in compact mathematical form, becoming an expert physics problem solver entails learning to unpack and interpret those physical laws as well as being able to apply them in diverse situations while solving problems. A physics expert must have a well-organized knowledge structure of relevant physics and math concepts and be able to manage cognitive load and do metacognition while solving complex problems. In this chapter, we review foundational research on expertise in physics problem-solving and then discuss research on instructional strategies that promote effective problem-solving as well as challenges in changing the instructional practices of physics instructors and teaching assistants via professional development to promote and support effective problem-solving approaches.
Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Search

In the past few years, Differentiable Neural Architecture Search (DNAS) rapidly imposed itself as the trending approach to automate the discovery of deep neural network architectures. This rise is mainly due to the popularity of DARTS, one of the first major DNAS methods. In contrast with previous works based on Reinforcement Learning or Evolutionary Algorithms, DNAS is faster by several orders of magnitude and uses fewer computational resources. In this comprehensive survey, we focus specifically on DNAS and review recent approaches in this field. Furthermore, we propose a novel challenge-based taxonomy to classify DNAS methods. We also discuss the contributions brought to DNAS in the past few years and its impact on the global NAS field. Finally, we conclude by giving some insights into future research directions for the DNAS field.
Convergence analysis of Laguerre approximations for analytic functions

Laguerre spectral approximations play an important role in the development of efficient algorithms for problems in unbounded domains. In this paper, we present a comprehensive convergence rate analysis of Laguerre spectral approximations for analytic functions. By exploiting contour integral techniques from complex analysis, we prove that Laguerre projection and interpolation methods of degree $n$ converge at the root-exponential rate $O(\exp(-2\rho\sqrt{n}))$ with $\rho>0$ when the underlying function is analytic inside and on a parabola with focus at the origin and vertex at $z=-\rho^2$. As far as we know, this is the first rigorous proof of root-exponential convergence of Laguerre approximations for analytic functions. Several important applications of our analysis are also discussed, including Laguerre spectral differentiations, Gauss-Laguerre quadrature rules, the scaling factor and the Weeks method for the inversion of Laplace transform, and some sharp convergence rate estimates are derived. Numerical experiments are presented to verify the theoretical results.
Evaluation of Short-Range Depth Sonifications for Visual-to-Auditory Sensory Substitution

Visual-to-auditory sensory substitution devices convert visual information into sound and can provide valuable assistance for blind people. Recent iterations of these devices rely on depth sensors. Rules for converting depth into sound (i.e., the sonifications) are often designed arbitrarily, with no strong evidence for choosing one over another. The purpose of this article is to compare and understand the effectiveness of five depth sonifications in order to assist the design process of future visual-to-auditory systems for blind people, which rely on depth sensors. The frequency, amplitude, and reverberation of the sound as well as the repetition rate of short high-pitched sounds and the signal-to-noise ratio of a mixture between pure sound and noise are studied. We conducted positioning experiments with 28 sighted blindfolded participants. Stage 1 incorporates learning phases followed by depth estimation tasks. Stage 2 adds the additional challenge of azimuth estimation to the first stage's protocol. Stage 3 tests learning retention by incorporating a 10-min break before retesting depth estimation. The best depth estimates in stage 1 were obtained with the sound frequency and the repetition rate of beeps. In stage 2, the beep repetition rate yielded the best depth estimation, and no significant difference was observed for the azimuth estimation. Results of stage 3 showed that the beep repetition rate was the easiest sonification to memorize. Based on the statistical analysis of the results, we discuss the effectiveness of each sonification and compare with other studies that encode depth into sounds. Finally, we provide recommendations for the design of depth encoding.
Discovery and optimization of cell-type-specific DNA methylation markers for in silico deconvolution

DNA methylation is a significant driver of cell-type heterogeneity and has been implicated in various regulatory processes ranging from cell differentiation to imprinting. As the methyl group is embedded in the DNA molecule, assessing DNA methylation is particularly promising in liquid-biopsy-based approaches, as cell-free DNA retains information related to its cell of origin. In this work, I leverage a recently profiled collection of cell-sorted whole genome bisulfite profiles of 44 healthy cell types. The high quality and purity of such data provide an ideal basis for discovering and characterizing discriminative DNA methylation regions that could serve as a reference for computational deconvolution. First, I characterize differentially methylated regions between every pair of cell types, obtaining a meaningful measure of divergence. Pairwise differences were then aggregated to identify a set of uniquely (de)methylated regions (UMRs) for each cell type. Identified UMRs are predominantly hypomethylated and their numbers vary significantly across cell types. They are mostly located in enhancer regions and strongly support cell-type-specific characteristics. As mapping onto UMRs has proven unsuitable for deconvolution, I developed a novel approach utilizing the set cover algorithm to select discriminative regions for this purpose. Based on these regions, deconvolution was performed in two distinct approaches: a beta-value-based and a read-level one. Both approaches outperform an existing deconvolution software modeled on the same data 3-fold in terms of total deconvolution error. Surprisingly, the beta-based approach slightly outperformed the read-level one. Overall, I present an adaptable, end-to-end software solution (methylcover) for obtaining accurate cell type deconvolution, with possible future applications to non-invasive assays for disease detection and monitoring.
Learning to search for and detect objects in foveal images using deep learning

The human visual system processes images with varied degrees of resolution, with the fovea, a small portion of the retina, capturing the highest acuity region, which gradually declines toward the field of view's periphery. However, the majority of existing object localization methods rely on images acquired by image sensors with space-invariant resolution, ignoring biological attention mechanisms. As a region of interest pooling, this study employs a fixation prediction model that emulates human objective-guided attention of searching for a given class in an image. The foveated pictures at each fixation point are then classified to determine whether the target is present or absent in the scene. Throughout this two-stage pipeline method, we investigate the varying results obtained by utilizing high-level or panoptic features and provide a ground-truth label function for fixation sequences that is smoother, considering in a better way the spatial structure of the problem. Finally, we present a novel dual task model capable of performing fixation prediction and detection simultaneously, allowing knowledge transfer between the two tasks. We conclude that, due to the complementary nature of both tasks, the training process benefited from the sharing of knowledge, resulting in an improvement in performance when compared to the previous approach's baseline scores.
On the Adversarial Inversion of Deep Biometric Representations

Biometric authentication service providers often claim that it is not possible to reverse-engineer a user's raw biometric sample, such as a fingerprint or a face image, from its mathematical (feature-space) representation. In this paper, we investigate this claim on the specific example of deep neural network (DNN) embeddings. Inversion of DNN embeddings has been investigated for explaining deep image representations or synthesizing normalized images. Existing studies leverage full access to all layers of the original model, as well as all possible information on the original dataset. For the biometric authentication use case, we need to investigate this under adversarial settings where an attacker has access to a feature-space representation but no direct access to the exact original dataset nor the original learned model. Instead, we assume varying degree of attacker's background knowledge about the distribution of the dataset as well as the original learned model (architecture and training process). In these cases, we show that the attacker can exploit off-the-shelf DNN models and public datasets, to mimic the behaviour of the original learned model to varying degrees of success, based only on the obtained representation and attacker's prior knowledge. We propose a two-pronged attack that first infers the original DNN by exploiting the model footprint on the embedding, and then reconstructs the raw data by using the inferred model. We show the practicality of the attack on popular DNNs trained for two prominent biometric modalities, face and fingerprint recognition. The attack can effectively infer the original recognition model (mean accuracy 83\% for faces, 86\% for fingerprints), and can craft effective biometric reconstructions that are successfully authenticated with 1-vs-1 authentication accuracy of up to 92\% for some models.
Highly structured turbulence in high-mass star formation: An evolved infrared-dark cloud G35.20-0.74 N

Context. Massive stars are generally believed to form in supersonic turbulent environment. However, recent observations have challenged this traditional view. High spatial and spectral resolution observations of the Orion Molecular Cloud and an infrared dark cloud G35.39 show a resolution-dependent turbulence, and that high-mass stars are forming exclusively in subsonic to transonic cores in those clouds. These studies demand a re-evaluation of the role of the turbulence in massive star formation. Aims. We aim to study the turbulence in a typical massive star-forming region G35.20-0.74 N with a sufficient spatial resolution to resolve the thermal Jeans length, and a spectral resolution to resolve the thermal linewidth. Methods. We use the ALMA dust continuum emission to resolve fragmentation, JVLA 1.2 cm continuum to trace ionized gas, and JVLA NH3 (1,1) to (7,7) inversion transition lines to trace linewidth, temperature, and dynamics. We fit those lines and remove line broadening due to channel width, thermal pressure, and velocity gradient to obtain a clean map of intrinsic turbulence. Results. We find that (1) the turbulence in G35.20 is overall supersonic, with mean and median Mach numbers 3.7 and 2.8, respectively. (2) Mach number decreases from 6-7 at 0.1 pc scale to<3 towards the central cores at 0.01 pc scale. (3) The central ALMA cores appear to be decoupled form the host filament, evident by an opposite velocity gradient and significantly reduced turbulence. Because of intense star formation activities in G35.20 (as compared to the relatively young and quiescent IRDC G35.39), the supersonic turbulence is likely replenished by protostellar outflows. G35.20 is, thus, representative of an evolved form of IRDC G35.39. More observations of a sample of IRDCs are highly demanded to further investigate the role of turbulence in initial conditions for the massive star formation.
Constructing the space of quasisymmetric stellarators through near-axis expansion

A simplified view of the space of optimised stellarators has the potential to guide and aid the design efforts of magnetic confinement configurations suitable for future fusion reactors. We present one such view for the class of quasisymmetric stellarators based on their approximate description near their centre (magnetic axis). The result is a space that captures existing designs and presents new ones, providing a common framework to study them. Such a simplified construction offers a basic topological approach, guided by certain theoretical and physical choices, which this paper presents in detail.
Representative Subset Selection for Efficient Fine-Tuning in Self-Supervised Speech Recognition

Self-supervised speech recognition models require considerable labeled training data for learning high-fidelity representations for Automatic Speech Recognition (ASR) which is computationally demanding and time-consuming. We consider the task of identifying an optimal subset of data for efficient fine-tuning in self-supervised speech models for ASR. We discover that the dataset pruning strategies used in vision tasks for sampling the most informative examples do not perform better than random subset selection on fine-tuning self-supervised ASR. We then present the COWERAGE algorithm for representative subset selection in self-supervised ASR. COWERAGE is based on our finding that ensuring the coverage of examples based on training Word Error Rate (WER) in the early training epochs leads to better generalization performance. Extensive experiments with the wav2vec 2.0 and HuBERT model on TIMIT, Librispeech, and LJSpeech datasets show the effectiveness of COWERAGE and its transferability across models, with up to 17% relative WER improvement over existing dataset pruning methods and random sampling. We also demonstrate that the coverage of training instances in terms of WER values ensures the inclusion of phonemically diverse examples, leading to better test accuracy in self-supervised speech recognition models.
Continual Pre-training of Language Models

Language models (LMs) have been instrumental for the rapid advance of natural language processing. This paper studies continual pre-training of LMs, in particular, continual domain-adaptive pre-training (or continual DAP-training). Existing research has shown that further pre-training an LM using a domain corpus to adapt the LM to the domain can improve the end-task performance in the domain. This paper proposes a novel method to continually DAP-train an LM with a sequence of unlabeled domain corpora to adapt the LM to these domains to improve their end-task performances. The key novelty of our method is a soft-masking mechanism that directly controls the update to the LM. A novel proxy is also proposed to preserve the general knowledge in the original LM. Additionally, it contrasts the representations of the previously learned domain knowledge (including the general knowledge in the pre-trained LM) and the knowledge from the current full network to achieve knowledge integration. The method not only overcomes catastrophic forgetting, but also achieves knowledge transfer to improve end-task performances. Empirical evaluation demonstrates the effectiveness of the proposed method.
Many versus one: The disorder operator and entanglement entropy in fermionic quantum matter

Motivated by recent development of the concept of the disorder operator and its relation with entanglement entropy in bosonic systems, here we show the disorder operator successfully probes many aspects of quantum entanglement in fermionic many-body systems. From both analytical and numerical computations in free and interacting fermion systems in 1D and 2D, we find the disorder operator and the entanglement entropy exhibit similar universal scaling behavior, as a function of the boundary length of the subsystem, but with subtle yet important differences. In 1D they both follow the \log{L}logL scaling behavior with the coefficient determined by the Luttinger parameter for disorder operator, and the conformal central charge for entanglement entropy. In 2D they both show the universal L\log LLlogL scaling behavior in free and interacting Fermi liquid states, with the coefficients depending on the geometry of the Fermi surfaces. However at a 2D quantum critical point with non-Fermi-liquid state, extra symmetry information is needed in the design of the disorder operator, so as to reveal the critical fluctuations as does the entanglement entropy. Our results demonstrate the fermion disorder operator can be used to probe quantum many-body entanglement related to global symmetry, and provide new tools to explore the still largely unknown territory of highly entangled fermion quantum matter in 2 or higher dimensions.
The scale of the problem: Recovering images of reionization with Generalized Morphological Component Analysis

The accurate and precise removal of 21-cm foregrounds from Epoch of Reionization (EoR) redshifted 21-cm emission data is essential if we are to gain insight into an unexplored cosmological era. We apply a non-parametric technique, Generalized Morphological Component Analysis (gmca), to simulated Low Frequency Array (LOFAR)-EoR data and show that it has the ability to clean the foregrounds with high accuracy. We recover the 21-cm 1D, 2D and 3D power spectra with high accuracy across an impressive range of frequencies and scales. We show that gmca preserves the 21-cm phase information, especially when the smallest spatial scale data is discarded. While it has been shown that LOFAR-EoR image recovery is theoretically possible using image smoothing, we add that wavelet decomposition is an efficient way of recovering 21-cm signal maps to the same or greater order of accuracy with more flexibility. By comparing the gmca output residual maps (equal to the noise, 21-cm signal and any foreground fitting errors) with the 21-cm maps at one frequency and discarding the smaller wavelet scale information, we find a correlation coefficient of 0.689, compared to 0.588 for the equivalently smoothed image. Considering only the pixels in a central patch covering 50 per cent of the total map area, these coefficients improve to 0.905 and 0.605, respectively, and we conclude that wavelet decomposition is a significantly more powerful method to denoise reconstructed 21-cm maps than smoothing.
Numerical Simulation of Bubble Plume in a Tank with Free Surface.

A bubble-driven plume in a tank with a free surface is investigated numerically using the bubble flow model equations which are formulated with emphasis on the relative transverse motion and volumetric change of the bubble. A numerical scheme is developed by the derivation of the pressure equation for the bubbly flow in order to treat the effects of the relative motion between two phases and the compressibility of the bubble accurately. From the numerical results, the three-dimensional flow structure of the bubble-driven plume has been shown in detail, where there are turbulences on various scales caused by the interaction of liquid convection and bubble motion.
A simple definition of food resources for the highly polyphagous Lygus lineolaris (Hemiptera: Miridae) enables prediction of adult abundance

Lygus lineolaris (Palisot de Beauvois) is a highly polyphagous herbivore with more than 300 known host plants. The high polyphagy has created logistical challenges for understanding its population dynamics. I hypothesized that the primary food resource of this species can be characterized simply, cutting across the multiple host plant species, and enabling a simpler understanding of its population dynamics. The food resource was defined as the apical buds and meristematic tissue and terminal flowers and young seeds. Adult abundance in a habitat was related to the relative abundance of food in the habitat, abundance on a host plant stem was related to the amount of food resource on the stem, and the rate of emigration was lower from host plant patches with higher amounts of food resources.These results suggest that the population dynamics of L. lineolaris depends less on the identity of the host plants and more on the amount of food resource the various host plant species provide.
Hemodynamic Tolerance of New Resistance Training Methods in Patients With Heart Failure and Coronary Artery Disease

It is believed that very short rest intervals between moderate-intensity resistance training sets may excessively increase heart rate and systolic blood pressure. We found an acceptable hemodynamic response to using small increases or decreases in the number of repetitions/set and brief rest intervals between sets. Purpose: The purpose of this study was to determine and compare the effectiveness of three different resistance training (RT) methods for cardiac rehabilitation. Methods: Individuals with heart failure with reduced ejection fraction (HFrEF, n = 23) or coronary artery disease (CAD, n = 22) and healthy controls (CTRL, n = 29) participated in this randomized crossover trial of RT exercises at 70% of the one-maximal repetition on a leg extension machine. Peak heart rate (HR) and blood pressure (BP) were measured noninvasively. The three RT methods were five sets of increasing repetitions from three to seven (RISE), of decreasing repetitions from seven to three (DROP), and three sets of nine repetitions (USUAL). Interset rest intervals were 15 sec for RISE and DROP and 60 sec for USUAL. Results: Peak HR differed on average by <4 bpm between methods in the HFrEF and CAD groups (P < .02). Rises in systolic BP (SBP) in the HFrEF group were comparable across methods. In the CAD group, mean SBP at peak exercise increased more in RISE and DROP than in USUAL (P < .001), but the increase was ≤10 mm Hg. In the CTRL group, SBP was higher for DROP than for USUAL (152 ± 22 vs 144 ± 24 mm Hg, respectively; P < .01). Peak cardiac output and perceived exertion did not differ between methods. Conclusions: The RISE, DROP, and USUAL RT methods induced a similar perception of effort and similar increases in peak HR and BP. The RISE and DROP methods appear more efficient as they allow a comparable training volume in a shorter time than the USUAL method.