Miracle-dz/UniFAR-FTUs huggingface dataset:
Delivering Instruction During the Pandemic: Insights for Special Education Administrators and Teacher Preparation Programs

This study examined considerations for delivering online instruction and related services to students with disabilities. The purpose of the study was to uncover teachers’ reflections about their experiences with online learning for students with disabilities as a response to COVID-19. We used an online open-ended survey to collect reflections from practicing teachers, who were graduates of a special education program at a university in Florida. Our findings highlighted challenges and unanticipated benefits of online service delivery. We discussed how administrators should streamline instructional-delivery platforms and procedures for teachers, students, and families. We recommended ensuring that Individualized Education Planning (IEP) teams consider priorities for future pandemics and natural disasters. Keywords: Online learning, special education teachers, students with disabilities, COVID-19, administrators.
Graph embedding via dissimilarity mapping for network comparison

A key issue in social network analysis is related to the comparison betweenK observed networks. To this end, a special graph embedding procedurewithin the so-called dissimilarity representation framework is proposed. The procedureis mainly based on the use of an appropriate metric among the observednetworks, that is the graph edit distance. Particular attention is devoted also to thedimensionality reduction of the embedding. The purpose is to provide a measure ofthe distances among the K observed networks, embedding them as points in a multivariatespace.We will show applications in both exploratory social network analysisand network statistical modeling.
CHEERS Results from NGC 3393. III. Chandra X-Ray Spectroscopy of the Narrow Line Region

We present spatially resolved Chandra narrow-band imaging and imaging spectroscopy of NGC 3393. This galaxy hosts a Compton-thick Seyfert 2 active galactic nucleus (AGN) with sub-kpc bipolar outflows that are strongly interacting with the circumnuclear gas. We identify narrow-band excess emission associated with the Ne ix 0.905 keV transition (with likely contributions due to intermediate-state iron emission) that points to strong shocks driven by AGN feedback. Imaging spectroscopy resolves outflow-interstellar medium (ISM) interaction sites and the surrounding ISM at ∼100 pc scales, and suggests the presence of a hot AGN wind above the plane at radii beyond the shock sites. The cross-cone shows evidence for reprocessing of photoionization that has passed through gaps in the torus, and also for collisionally excited plasma that may be powered by a shock-confined equatorial outflow. Deep X-ray observations at subarcsecond resolution (such as may be performed very efficiently by Lynx, which would also energetically resolve the complex line emission) are necessary to eliminate model degeneracies and reduce uncertainties in local feedback properties.
Nanoscale angular lattice formed by light with high orbital angular

Standing waves generated by the interference of Laguerre-Gaussian (LG) beams can be used for dipole trap. We propose a scheme to create a nanometer (nm) scale ring lattice based on the interference of two high order LG beams without decrease the wavelength. Both of the two LG beams have a monocyclic intensity distribution, and they have the same orbital angular momentum (OAM) quantum number on the order of $10^6$. We are able to theoretically demonstrate a dipole potential along angular direction with the period of $\sim$1 nm , given the waist of the Gaussian beams to be $\sim$0.8 $\mu$m. The atoms in this lattice can be trapped along the radial and azimuthal direction in the potential wells of with $\sim$100 nm and $\sim$0.85 nm distance. The proposed method opens up a convenient pathway towards sub-wavelength atom traps that could directly lead to overlap of wave function of atoms in adjacent wells and the formation of molecular bonds.
Non-linear Ion-wake Excitation by Ultra-relativistic Electron Wakefields

The excitation of a non-linear ion-wake by a train of ultra-relativistic electron bubble wake [1]-[8] is modeled. The ion-wake is shown to be a driven non-linear ion-acoustic wave in the form of a cylindrical ion-soliton [11]-[14]. The phases of the oscillating radial electric fields of the slowly-propagating [21] electron bubble is asymmetric in time and excites time-averaged inertial ion motion radially. The electron compression in the back of the bubble sucks-in the ions and the space-charge within the bubble cavity expels them, driving a cylindrical ion-soliton structure with on-axis and bubble-edge density-spikes [5][6]. Once formed, the channel-edge density-spike is driven radially outwards by the thermal pressure of the wake energy [12]. Its channel-like structure due to the flat-residue left behind by the propagating ion-soliton, is independent of the energy-source driving the bubble [3][4] electron wake. We explore the use of the partially- filled channel formed by the cylindrical ion-soliton for a novel regime of positron acceleration [18]. OSIRIS PIC [25] simulations are used to study the ion-wake soliton structure, its driven propagation and its use for positron acceleration.
‘Do you know a few words?’ – developing an evidence-based model to analyse multilingual classroom interaction

ABSTRACT In the field of applied linguistics, the use of multiple languages in educational settings has often been studied from a pedagogical perspective, focusing on teacher practices. In order for multilingual teaching pedagogies, such as translanguaging to reach their full potential, pupils should participate actively in classroom interaction and be stimulated to discuss different ideas, as in dialogic interaction (Alexander. 2008. Towards Dialogic Teaching: Rethinking Classroom Talk. Dialogos). The current study aims to explore multilingual pedagogical practices in primary education from an interactionist perspective and further develop a model of multilingual classroom interaction (Gajo and Berthoud. 2018. “Multilingual Interaction and Construction of Knowledge in Higher Education.” International Journal of Bilingual Education and Bilingualism 21 (7): 853–866) based on empirical analyses. Whole class conversations taking place in three Dutch primary schools participating in an Educational Design Research programme were analysed using a mixed methods approach. The analyses demonstrate discourse practices largely in line with monologic interaction and a rather limited and only symbolic use of other languages than Dutch. We discuss the use of the model of multilingual classroom interaction in primary education on a micro-level and the possible relationship between the use of multilingualism and pupils' participation in classroom interaction.
Molecular gas in the H ii-region complex RCW 166: Possible evidence for an early phase of cloud–cloud collision prior to the bubble formation

Young HII regions are an important site to study O star formation based on distributions of ionized and molecular gas. We revealed that two molecular clouds at $\sim 48$ km s$^{-1}$ and $\sim 53$ km s$^{-1}$ are associated with the HII regions G018.149-00.283 in RCW 166 by using the JCMT CO High-Resolution Survey (COHRS) of the $^{12}$CO ($J$=3--2) emission. G018.149-00.283 comprises a bright ring at 8 $\mu$m and an extended HII region inside the ring. The $\sim 48$ km s$^{-1}$ cloud delineates the ring, and the $\sim 53$ km s$^{-1}$ cloud is located within the ring, indicating a complementary distribution between the two molecular components. We propose a hypothesis that high-mass stars within G018.149-00.283 were formed by triggering in cloud-cloud collision at a projected velocity separation of $\sim 5$ km s$^{-1}$. We argue that G018.149-00.283 is in an early evolutionary stage, $\sim 0.1$ Myr after the collision according to the scheme by [hab92] which will be followed by a bubble formation stage like RCW 120. We also suggested that nearby HII regions N21 and N22 are candidates for bubbles possibly formed by cloud-cloud collision. [ino13] showed that the interface gas becomes highly turbulent and realizes a high-mass accretion rate of $10^{-3}$ -- $10^{-4}$ $M_{\odot}$ $/$yr by magnetohydrodynamical numerical simulations, which offers an explanation of the O-star formation. A fairly high frequency of cloud-cloud collision in RCW 166 is probably due to the high cloud density in this part of the Scutum arm.
Solving Large-Scale Real-World Minimum-Cost Flow Problems by a Network Simplex Method

This paper presents a large-scale real-world application of the minimum-cost flow problem, describes some details of a new implementation of the network simplex algorithm, and reports on computational comparisions. The real-world test sets include minimum-cost flow problems that are based on single-depot vehicle scheduling problems and on a Lagrangean relaxation of multiple-depot vehicle scheduling problems. Some of the problems are extremely large with up to 42,000 nodes and 20,000,000 arcs. The standard test problems are generated with NETGEN and include parts of the DIMACS standard problems. Our network simplex code is compared with \mbox{RELAX-IV}, Cost Scaling 2 version 3.4, and CPLEX's network solver NETOPT.
Enhancing Photoluminescence Intensity and Spectral Bandwidth of Hybrid Nanofiber/Thin-Film Multilayer Tm3+-Doped SiO2–HfO2

Multilayering of optical thin films is widely used for a range of purposes in photonic technology, but the development of nanofiber structures that can outperform thin films and nanoparticles in optical applications cannot simply be disregarded. Hybrid structures composed of Tm3+-doped SiO2–HfO2 in the form of nanofibers (NFs) and thin films (TFs) are deposited on a single substrate using the electrospinning and dip-coating methods, respectively. Ultrafine nanofiber strands with a diameter of 10–60 nm were fabricated in both single and multilayer samples. Enhanced photoluminescence emission intensity of about 10 times was attained at wavelengths of around 457, 512 and 634 nm under an excitation of 350 nm for NF-TF-NF* hybrid structures when compared with single-layered NF and TF structures. The arrangement of nanofibers and thin films in a multilayer structure influenced the luminescence intensity and spectral bandwidth. High transparency in the range of 75–95% transparency across the wavelength of 200–2000 nm was achieved, making it ideal for photonic application. Theoretical findings obtained through IMD software were compared with experimental results, and they were found to be in good agreement.
Effect of Postharvest Treatments on Biochemical and Bioactive Compounds of Custard Apple (Annona Squamosa L.) Cv. Balanagar

ABSTRACT An experiment was conducted to find out the effects of various postharvest treatments on the biochemical and bioactive compounds of custard apple cv. Balanagar fruits. In the present experiment, three chemicals viz. oxalic acid (OA) (2.5 mM, 5.0 mM, 7.5 mM, and 10 mM; salicylic acid (SA) (0.5 mM, 1.0 mM, 1.5 mM, and 2.0 mM) and Sodium Nitro Prusside (SNP) at 0.5 mM, 1.0 mM, 1.5 mM, and 2.0 mM along with control were applied. Fruits were stored at ambient condition (Temp. 25 ± 2°C and relative humidity (RH) 44 ± 2%). Results indicated that fruits treated with SNP (2.0 mM) had significantly (p≤ 0.05) higher total soluble solids (30.80°B), titratable acidity (TA) (0.33%) and retained higher total sugar content (TS) (25.09%) at the end of the 9th day of storage. At the end of storage, the least reduction in total phenol content (TPC) (492.52 mg gallic acid equivalent (GAE)/100 g (11.62%) was found in SNP (2.0 mM) treated fruits. Likewise, SNP (2.0 mM) significantly (p≤ 0.05) retained higher ascorbic acid (AA) (43.20 mg/100 g), antioxidant activity (AOX) (0.92 µmol Trolox Equivalent (TE)/g), total flavonoids (TF) (71.49 µg CE/100 g) and tannin content (TC) (1.28 mg/g) compared to control during 9 days of storage. Thus, in the present investigation, SNP treatment was most suited to preserve the nutritional quality and bioactive compounds.
Structural Embedding of Data Files with MAGRITTE

Large amounts of tabular data are encoded in plain-text files, e.g., CSV , TSV and TXT . Plain-text formats allow freedom of expression and encoding, fostering the use of non-standard syntaxes and dialects. Before analyzing the content of such files, it is necessary to understand their structure , e.g., recognize their dialect [8], extract metadata [6, 4], or detect tables [1]. Previous work on table representation focused on learning the semantics of data cells [5, 2, 9], with the assumption that the syntactical properties of a file are known to end users. We propose MAGRiTTE, an approach to synthetically represent the structural features of a data file. MAGRiTTE is a self-supervised machine learning model trained to learn structural embeddings from data files. The architecture of MAGRiTTE is composed of two components. The first is a transformer-encoder architecture, based on BERT [3] and pre-trained to learn row embeddings. The second is a DCGAN-autoencoder [7] trained to produce file-level embeddings. To pre-train the transformer architecture on structural features, we propose two core adaptations: a novel tokenization stage and specialized training objectives. To abstract the data content of a file, and train the transformer architecture on structural features, we in-troduce “pattern tokenization”: Assuming that structural properties are identifiable through special characters, we reduce all alphanumeric characters to a set of few general patterns. After tokenization, the rows of the input files are split on newline characters and a percentage of the special character tokens is masked before feeding it to the row encoder model. The row-transformer model is then trained on two objectives, reconstructing
Dark matter relics and the expansion rate in scalar-tensor theories

We study the impact of a modified expansion rate on the dark matter relic abundance in a class of scalar-tensor theories. The scalar-tensor theories we consider are motivated from string theory constructions, which have conformal as well as disformally coupled matter to the scalar. We investigate the effects of such a conformal coupling to the dark matter relic abundance for a wide range of initial conditions, masses and cross-sections. We find that exploiting all possible initial conditions, the annihilation cross-section required to satisfy the dark matter content can differ from the thermal average cross-section in the standard case. We also study the expansion rate in the disformal case and find that physically relevant solutions require a nontrivial relation between the conformal and disformal functions. We study the effects of the disformal coupling in an explicit example where the disformal function is quadratic.
Housing Prices and Macro-prudential Policies in Taiwan

This paper develops a small open economy dynamic stochastic general equilibrium (DSGE) model with the collateral constraints. Using Bayesian methods, the model is applied to the Taiwan’s data. We assess the welfare implications of monetary and macro-prudential policies setting counter-cyclically regulatory loan-to-value (LTV) ratio. Our analysis finds that both the optimal interest-rate rules and optimal LTV ratio rules lead to the Pareto im-provements. The optimized interest-rate rule in this economy is a muted response to either credit growth or changes in housing prices, and it will increase volatility of inflation. The LTV ratio rule leaning against total credit growth could significantly increase the social welfare and the individual welfare with similar inflation variation, and it could stabilize the credit-to-GDP ratio most effectively.
Open comment on wcd-2022-47: Some concerns regarding the methodology and interpretation of a valuable study on Agulhas Current inﬂuence on South African precipitation.

This is an important article on an understudied subject. I would strongly support the publication of a paper focussed on the evidence of inﬂuence of Agulhas Current changes on southern African precipitation from the high resolution model experiments described in this work. The comments provided herein are not intended as a complete evaluation of the submitted work. They focus primarily on claims made relating to my area of expertise. In particular, I have some signiﬁcant concerns regarding the framing of the study, observational data used and representation and interpretation of some results, namely:
Using an Infrared Pen as an Input Device for Projected Augmented Reality Tabletops

Interactive tabletops do not only offer a large surface for collaborative interaction. They also offer quick access to digital tools directly at the table - where a large number of everyday activities take place. Tabletops with an embedded display are generally less flexible and more fragile than ordinary massive tabletops. Physical objects on the tabletop occlude the digital content. In contrast, top-down-projected interfaces using an overhead camera-projector system allow for augmenting arbitrary tables and the object lying on them. However, detecting pointing input only via a camera image captured from above requires robustly recognizing whether a finger or pen touches the tabletop or whether it hovers slightly above it. In this paper, we present a solution for reliably tracking a pen on arbitrary tabletop surfaces. The pen emits infrared light via a tip made of optical fiber. A camera captures position and shape of the light point on the surface. Our open-source tracking algorithm combines heuristics and a machine learning model to distinguish between drawing and hovering. A pilot study with 7 participants shows that that this system can be reliably used for drawing and writing on tabletops. However, occlusion by users’ hands can deteriorate tracking of the pen.
Enhancing Transfer of Reinforcement Learning Agents with Abstract Contextual Embeddings

Deep reinforcement learning (DRL) algorithms have seen great success in perform- 1 ing a plethora of tasks, but often have trouble adapting to changes in the environ- 2 ment. We address this issue by using reward machines (RM), a graph-based ab- 3 straction of the underlying task to represent the current setting or context . Using a 4 graph neural network (GNN), we embed the RMs into deep latent vector represen- 5 tations and provide it to the agent to enhance its ability to adapt to new contexts. 6 To the best of our knowledge, this is the first work to embed contextual abstractions 7 and let the agent decide how to use them. Our preliminary empirical evaluation 8 demonstrates improved sample efficiency of our approach upon context transfer on 9 a set of grid navigation tasks. 10
International Journal of Electrical and Computer Engineering (IJECE)

Received Mar 21, 2022 Revised Jul 17, 2022 Accepted Aug 21, 2022 The proposed work gives a comparative study on performance of various pretrained deep learning models for classifying Malayalam documents such as agreement documents, notebook images, and palm leaves. The documents are classified based on their visual and structural features. The dataset was manually collected from different sources. The method of research proceeds with preprocessing, feature extraction, and classification. The proposed work deals with three fine-tuned deep learning models such as visual geometry group-16 (VGG-16), convolutional neural network (CNN) and AlexNet. The models attained high accuracies of 99.7%, 96%, and 95%, respectively. Among the three models, the fine-tuned VGG-16 model was found to perform better attaining a very high accuracy on the dataset. As a future work, methods to classify the documents based on content as well as spectral features can be developed.
Amortized Variational Inference with Gaussian Mixture Models

Amortized variational inference is essential to many areas of machine learning. However, most approaches still rely on simple distributions, leading to a potentially large approximation error. In this work, we investigate to use Gaussian Mixture Models, which are a promising alternative as they provide a flexible and powerful representation. Building on a recently proposed lower bound to decompose the mixture objective, we adopt trust region optimization in order to encourage exploration and promote mixture diversity. We describe a numerical instability in the objective function and prevent it using a covariance regularization technique based on eigenvalue bounds. Sample efficiency is improved by applying resampling to evaluate a mixture component only on relevant inputs. Furthermore, we investigate strategies to adapt the number of mixture components and discuss the feasibility of sharing parameters between components. We evaluate the proposed algorithm on a set of inverse kinematic tasks and show results competitive to other generative modeling techniques in terms of task space accuracy. Compared to standard amortized inference using Gaussian Mixture Models, the algorithm can generate more accurate and diverse samples.
Guiding Offline Reinforcement Learning Using a Safety Expert

Offline reinforcement learning is used to train policies in situations where it is expensive or infeasible to access the environment during training. An agent trained under such a scenario does not get corrective feedback once the learned policy starts diverging and may fall prey to the overestimation bias commonly seen in this setting. This increases the chances of the agent choosing potentially unsafe actions, especially in states with insufficient representation in the training dataset. In this paper, we explore the problem of acting safely in sparsely observed regions of the state space. We propose to leverage a safety expert to nudge an offline RL agent towards choosing safe actions in under-represented states in the dataset. The proposed framework in this paper transfers the safety expert’s knowledge into an offline setting for states with high uncertainty to prevent catastrophic failures from occurring in safety-critical domains. We use a simple but effective approach to quantify the state uncertainty based on how frequently they appear in a training dataset. In states with high uncertainty, the offline RL agent mimics the safety expert, otherwise maximizing the long-term reward. Our approach has a plug-and-play nature, i.e., any existing value-based or actor-critic style offline RL algorithm can be guided by a safety expert. We finally show that such guided offline RL algorithms can outperform their state-of-the-art counterparts, reducing the chance of taking unsafe actions while simultaneously retaining competitive performance.
Squeezing more value out of your historical data: data-augmented behavioural cloning as launchpad for reinforcement learning

In many real-world applications collecting large, high-quality datasets may be too costly or impractical. Offline reinforcement learning (RL) aims to infer an optimal decision-making policy from a fixed set of data. Getting the most information from this dataset is then vital for good performance. We pro-pose a model-based data augmentation strategy, Trajectory Stitching (TS), to improve the quality of sub-optimal trajectories. TS introduces unseen actions joining previously disconnected states: using a probabilistic notion of state reachability, it effectively ‘stitches’ together parts of the historical demonstrations to generate new, higher quality ones. A stitching event consists of a transition between a pair of observed states through a synthetic and highly probable action. New actions are introduced only when they are expected to be beneficial, according to an estimated state-value function. We show that using supervised learning, behavioural cloning (BC), to extract a decision-making policy from the new TS dataset, leads to improvements over the behaviour-cloned policy from the original dataset. Improving over the BC policy could then be used as a launchpad for online RL through planning and demonstration-guided RL.