Miracle-dz/UniFAR-FTUs huggingface dataset:
MoReVis: A Visual Summary for Spatiotemporal Moving Regions

Spatial and temporal interactions are central and fundamental in many activities in our world. A common problem faced when visualizing this type of data is how to provide an overview that helps users navigate efficiently. Traditional approaches use coordinated views or 3D metaphors like the Space-time cube to tackle this problem. However, they suffer from overplotting and often lack spatial context, hindering data exploration. More recent techniques, such as MotionRugs, propose compact temporal summaries based on 1D projection. While powerful, these techniques do not support the situation for which the spatial extent of the objects and their intersections is relevant, such as the analysis of surveillance videos or tracking weather storms. In this article, we propose MoReVis, a visual overview of spatiotemporal data that considers the objects’ spatial extent and strives to show spatial interactions among these objects by displaying spatial intersections. Like previous techniques, our method involves projecting the spatial coordinates to 1D to produce compact summaries. However, our solution's core consists of performing a layout optimization step that sets the size and positions of the visual marks on the summary to resemble the actual values on the original space. We also provide multiple interactive mechanisms to make interpreting the results more straightforward for the user. We perform an extensive experimental evaluation and usage scenarios. Moreover, we evaluated the usefulness of MoReVis in a study with 9 participants. The results point out the effectiveness and suitability of our method in representing different datasets compared to traditional techniques.
Hurricane structure and evolution as simulated by an axisymmetric, nonhydrostatic numerical model

Abstract This paper reports numerical simulations of the hurricane vortex by an axisymmetric, nonhydrostatic numerical model with 2 km maximum horizontal resolution. Moist convection is modeled explicitly using two different microphysical parameterizations. The first simulates liquid water processes only, whereas the second includes ice processes as well. Although concentric rings of convection associated with local maxima of the tangential wind form in both versions of the model, they are much more common when ice processes are included. As they contract about the vortex center, the outer ones supplant the inner. Their contraction follows the mechanism suggested by balanced-vortex models. Some of the rings appear to form through symmetric instability of the vortex, and others—particularly when ice processes are included—through interactions between precipitation-induced downdrafts and the boundary layer. Both the rings’ evolution and the detailed structure of the vortex core are similar to recent aircraf.
Investigating Girls' Perspectives and Knowledge Gaps on Ethics and Fairness in Artificial Intelligence in a Lightweight Workshop

Artificial intelligence (AI) is everywhere, with many children having increased exposure to AI technologies in daily life. We aimed to understand middle school girls' (a group often excluded group in tech) perceptions and knowledge gaps about AI. We created and explored the feasibility of a lightweight (less than 3 hours) educational workshop in which learners considered challenges in their lives and communities and critically considered how existing and future AI could have an impact. After the workshop, learners had nuanced perceptions of AI, understanding AI can both help and harm. We discuss design implications for creating educational experiences in AI and fairness that embolden learners.
SoK: Evaluating Privacy and Security Concerns of Using Web Services for the Disabled Population

The online privacy and security of the disabled community is a complex field that has implications for every user who navigates web services. While many disciplines have separately researched the disabled population and their online privacy and security concerns, the overlap between the two is very high but under-researched. Moreover, a complex relationship exists between the disabled population and web services where the interaction depends on several web service developmental factors, including usability and accessibility. To this aid, we explored this intersection of privacy and security of web services as perceived by the disabled community through previous studies by conducting a detailed systematic literature review and analysis of 63 articles. Our findings encompassed several topics, including how the disabled population navigates around authentication interfaces, online privacy concerns, universal design practices, and how security methods such as CAPTCHAs can be improved to become more accessible and usable for people of all needs and abilities. We further discuss the gap in the current research, including solutions such as the universal implementation of inclusive privacy and security tools and protocols.
Phase-Field DeepONet: Physics-informed deep operator neural network for fast simulations of pattern formation governed by gradient flows of free-energy functionals

Recent advances in scientific machine learning have shed light on the modeling of pattern-forming systems. However, simulations of real patterns still incur significant computational costs, which could be alleviated by leveraging large image datasets. Physics-informed machine learning and operator learning are two new emerging and promising concepts for this application. Here, we propose"Phase-Field DeepONet", a physics-informed operator neural network framework that predicts the dynamic responses of systems governed by gradient flows of free-energy functionals. Examples used to validate the feasibility and accuracy of the method include the Allen-Cahn and Cahn-Hilliard equations, as special cases of reactive phase-field models for nonequilibrium thermodynamics of chemical mixtures. This is achieved by incorporating the minimizing movement scheme into the framework, which optimizes and controls how the total free energy of a system evolves, instead of solving the governing equations directly. The trained operator neural networks can work as explicit time-steppers that take the current state as the input and output the next state. This could potentially facilitate fast real-time predictions of pattern-forming dynamical systems, such as phase-separating Li-ion batteries, emulsions, colloidal displays, or biological patterns.
Human and Machine: Practicable Mechanisms for Measuring Performance in Partial Information Games

In this note, I introduce a new framework called n -person general-sum games with partial information , in which boundedly rational players have only limited information about the game—including actions, outcomes, and other players. For example, playing an actual game of chess is a game of partial information. To analyze these games, I introduce a set of new concepts and metrics for measuring the performance of players, with a focus on the interplay between human-and machine-based decision-making. Speciﬁcally, I introduce (i) gaming-proofness, which is a property of a mechanism that players cannot game from a practical perspective, and (ii) the Net Game Points (NGP) mechanism, which measures the success of a player’s performance in a game, taking into account both the outcome of the game and the “mistakes” made during the game. The NGP mechanism provides a practicable way to assess game outcomes and can potentially be applied to a wide range of games, from poker and football to AI systems, organizations, and companies. To illustrate the concept, I apply the NGP mechanism to select chess games played between some of the world’s top players, including the world champion.
Discovering Top-k Structural Hole Spanners in Dynamic Networks

Structural Hole (SH) theory states that the node which acts as a connecting link among otherwise disconnected communities gets positional advantages in the network. These nodes are called Structural Hole Spanners (SHS). Numerous solutions are proposed to discover SHSs; however, most of the solutions are only applicable to static networks. Since real-world networks are dynamic networks; consequently, in this study, we aim to discover SHSs in dynamic networks. Discovering SHSs is an NP-hard problem, due to which, instead of discovering exact k SHSs, we adopt a greedy approach to discover Top-k SHSs. We first propose an efficient Tracking-SHS algorithm for updating SHSs in dynamic networks. Our algorithm reuses the information obtained during the initial runs of the static algorithm and avoids the recomputations for the nodes unaffected by the updates. Besides, motivated from the success of Graph Neural Networks (GNNs) on various graph mining problems, we also design a Graph Neural Network-based model, GNN-SHS, to discover SHSs in dynamic networks, aiming to reduce the computational cost while achieving high accuracy. We provide a theoretical analysis of the Tracking-SHS algorithm, and our theoretical results prove that for a particular type of graphs, such as Preferential Attachment graphs [1], Tracking-SHS algorithm achieves 1.6 times of speedup compared with the static algorithm. We perform extensive experiments, and our results demonstrate that the Tracking-SHS algorithm attains a minimum of 3.24 times speedup over the static algorithm. Also, the proposed second model GNN-SHS is on an average 671.6 times faster than the Tracking-SHS algorithm.
Task-Oriented Explainable Semantic Communications

Semantic communications utilize the transceiver computing resources to alleviate scarce transmission resources, such as bandwidth and energy. Although the conventional deep learning (DL) based designs may achieve certain transmission efficiency, the uninterpretability issue of extracted features is the major challenge in the development of semantic communications. In this paper, we propose an explainable and robust semantic communication framework by incorporating the well-established bit-level communication system, which not only extracts and disentangles features into independent and semantically interpretable features, but also only selects task-relevant features for transmission, instead of all extracted features. Based on this framework, we derive the optimal input for rate-distortion-perception theory, and derive both lower and upper bounds on the semantic channel capacity. Furthermore, based on the $\beta $ -variational autoencoder ( $\beta $ -VAE), we propose a practical explainable semantic communication system design, which simultaneously achieves semantic features selection and is robust against semantic channel noise. We further design a real-time wireless mobile semantic communication proof-of-concept prototype. Our simulations and experiments demonstrate that our proposed explainable semantic communications system can significantly improve transmission efficiency, and also verify the effectiveness of our proposed robust semantic transmission scheme.
Comparison and Analysis of Cognitive Load under 2D/3D Visual Stimuli

With the increasing prevalence of 3D videos, understanding the distinctions between 2D and 3D video experiences has become significantly important. In our study, we explored the cognitive load imposed by 2D and 3D video stimuli under various cognitive tasks using electroencephalogram (EEG) data. To assess the cognitive load, we introduced the Cognitive Load Index (CLI), a measure that combines the oscillations of theta and alpha oscillations. Our experimental setup included four video stimuli, each associated with typical cognitive tasks. Participants were exposed to both 2D and 3D video stimuli, during which their EEG data were recorded. Then, we analyzed the power within the 0.5-45Hz frequency of EEG data, and CLI were used to evaluate brain activity. According to our experiments and analysis, videos requiring simple observational tasks (P<0.05) consistently induce a higher cognitive load in participants when watching 3D videos. However, for videos that involve calculation tasks (P>0.05), the difference in cognitive load induced by 2D and 3D stimuli is not as pronounced. Thus, we conclude that 3D videos generally induce a higher cognitive load, but the extent of this difference also depends on the content of the video stimuli.
NoPPA: Non-Parametric Pairwise Attention Random Walk Model for Sentence Representation

We propose a novel non-parametric/un-trainable language model, named Non-Parametric Pairwise Attention Random Walk Model (NoPPA), to generate sentence embedding only with pre-trained word embedding and pre-counted word frequency. To the best we know, this study is the first successful attempt to break the constraint on bag-of-words assumption with a non-parametric attention mechanism. We evaluate our method on eight different downstream classification tasks. The experiment results show that NoPPA outperforms all kinds of bag-of-words-based methods in each dataset and provides a comparable or better performance than the state-of-the-art non-parametric methods on average. Furthermore, visualization supports that NoPPA can understand contextual topics, common phrases, and word causalities. Our model is available at https://github.com/JacksonWuxs/NoPPA.
Infrared studies of circumstellar matter around Herbig Ae/Be and related stars

The study presents high spatial resolution data at 50 and 100 microns for seven young intermediate-mass stars with flat or rising spectral energy distributions. Five stars, namely, LkH-alpha 198, R Mon, CD -42 deg 11721, R CrA, and V645 Cyg, are resolved at 100 microns. The emission comes from spatially extended envelopes, with sizes varying between 0.025 and 0.4 pc. The wavelengths are optically thin at mid-IR and FIR wavelengths but become optically thick in the visual where the extinction amounts to 5-10 mag. In two cases, R CrA and V 645 Cyg, dust density distributions with steep density gradients are obtained, alpha is approximately equal to 2, while for LkH-alpha, R Mon, and CD -42 deg 11721, alpha is approximately equal to 0.5. Model fitting to the spectral energy distributions, combined with the constraints on the size of the emitting regions, indicates that inside the circumstellar envelopes a large fraction of the stellar radiation is already degraded to longer wavelengths.
Acoustic Circulation Effects and the Nonlinear Impedance of Orifices

In his theory of streaming caused by sound waves, Eckart shows that time independent streams necessarily follow as part of the solution of the complete wave equatoin, taking into account viscosity and second‐order terms. His treatment is mainly valid for liquids and it proves that the driving force of the streams is proportional to frequency squared. The effect, therefore, is especially important in the ultrasonic region (crystal winds). However, he suggests that slow streams might also be carried in air at audio frequencies.Studies of acoustical streaming phenomena around orifices have been made by the use of smoke particles in a 3‐in. diameter circular tube. These studies covered a range of orifices from thicknesses of 0.5 mm to 19 mm and diameters of 3.5 mm to 20 mm. The frequency lay between 150 to 1000 c.p.s. Velocities in the orifice cover the range of 0 to 700 cm/sec.Close studies of the flow patterns have disclosed that there exist four definite regions of flow as the particle velocity in the orif.
Thermal-Carrier-Escape Mitigation in a Quantum-Dot-In-Perovskite Intermediate Band Solar Cell via Bandgap Engineering

By harvesting a wider range of the solar spectrum, intermediate band solar cells (IBSCs) can achieve efficiencies 50% higher than conventional single-junction solar cells. For this, additional requirements are imposed to the light-absorbing semiconductor, which must contain a collection of in-gap levels, called intermediate band (IB), optically coupled to but thermally decoupled from the valence and conduction bands (VB and CB). Quantum-dot-in-perovskite (QDiP) solids, where inorganic quantum dots (QDs) are embedded in a halide perovskite matrix, have been recently suggested as a promising material platform for developing IBSCs. In this work, QDiP solids with excellent morphological and structural quality and strong absorption and emission related to the presence of in-gap QD levels are synthesized. With them, QDiP-based IBSCs are fabricated and, by means of temperature-dependent photocurrent measurements, it is shown that the IB is strongly thermally decoupled from the valence and conduction bands. The activation energy of the IB$\rightarrow$CB thermal escape of electrons is measured to be 204 meV, resulting in the mitigation of this detrimental process even under room-temperature operation, thus fulfilling the first mandatory requisite to enable high-efficiency IBSCs.
Growing patterns

Pattern forming systems allow for a wealth of states, where wavelengths and orientation of patterns varies and defects disrupt patches of monocrystalline regions. Growth of patterns has long been recognized as a strong selection mechanism. We present here recent and new results on the selection of patterns in situations where the pattern-forming region expands in time. The wealth of phenomena is roughly organised in bifurcation diagrams that depict wavenumbers of selected crystalline states as functions of growth rates. We show how a broad set of mathematical and numerical tools can help shed light into the complexity of this selection process.
Properties of InAs Lasers

Two different recombination mechanisms can be distinguished in the infrared radiation spectra of InAs. The photoluminescence of n‐type InAs corresponds to a direct band‐to‐band (or weak exciton) transition. The photoluminescence of Zn‐doped p‐type InAs and the injection luminescence of most Zn‐diffused diodes correspond to transitions between conduction‐band states (not excluding shallow donors for p‐type crystals with a heavy donor compensation) and acceptor states about 17 meV above the valence band. The diode emission, therefore, results from injection of electrons into the p‐type side of the junction, although band‐to‐band radiation has also been obtained in some diodes under certain conditions of current density and magnetic field.Present InAs diode lasers which emit near 3.1 μ (band‐to‐acceptor line) have external quantum efficiencies of about 12% and threshold current densities of about 300 A·cm−2 at 11°K, and they can be operated continuously with currents of several amperes. With magnetic fields .
Doubly charmed dibaryon states

In this work, we study the doubly charmed dibaryon states with the qqqqcc (q=u,d,s) configuration. The mass spectra of doubly charmed dibaryon states are obtained systematically within the chromomagnetic interaction model. In addition to the mass spectrum analysis, we further illustrate their two-body strong decay behaviours. Our results suggest that there may be narrow states or even stable states that cannot decay through the strong interaction. We hope that our results will provide valuable information for further experimental searches for the doubly charmed dibaryon states. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.
Optical field analysis of a circular grating-coupled surface-emitting laser with integrated focusing outcoupler

The lasing characteristics of a circular grating-coupled surface-emitting laser (GCSEL) with an integrated focusing outcoupler are investigated. Measured near and far fields suggest lasing in two near orthogonal and mutually incoherent linear elements, rather than ideal circular modes of the planar circular resonator, resulting in a degradation of the focused spot quality. Modeling of the evolution of the emitted optical field by Fresnel diffraction calculations, using a new orthogonal projection scheme to account for polarization, supports this assumption. Using the theory, we have also estimated the degradation of the focused spot quality induced by random errors in the period of the focusing outcoupler caused by the limited resolution of the electron-beam lithography system used to fabricate the device. We found this effect to be small compared to the effect of the nonideal mode characteristics.
Adapting Pre-trained Language Models for Quantum Natural Language Processing

The emerging classical-quantum transfer learning paradigm has brought a decent performance to quantum computational models in many tasks, such as computer vision, by enabling a combination of quantum models and classical pre-trained neural networks. However, using quantum computing with pre-trained models has yet to be explored in natural language processing (NLP). Due to the high linearity constraints of the underlying quantum computing infrastructures, existing Quantum NLP models are limited in performance on real tasks. We fill this gap by pre-training a sentence state with complex-valued BERT-like architecture, and adapting it to the classical-quantum transfer learning scheme for sentence classification. On quantum simulation experiments, the pre-trained representation can bring 50\% to 60\% increases to the capacity of end-to-end quantum models.
Secure End-to-End Communications with Lightweight Cryptographic Algorithm

The field of lightweight cryptography has been gaining popularity as traditional cryptographic techniques are challenging to implement in resource-limited environments. This research paper presents an approach to utilizing the ESP32 microcontroller as a hardware platform to implement a lightweight cryptographic algorithm. Our approach employs KATAN32, the smallest block cipher of the KATAN family, with an 80-bit key and 32-bit blocks. The algorithm requires less computational power as it employs an 80 unsigned 64-bit integer key for encrypting and decrypting data. During encryption, a data array is passed into the encryption function with a key, which is then used to fill a buffer with an encrypted array. Similarly, the decryption function utilizes a buffer to fill an array of original data in 32 unsigned 64-bit integers. This study also investigates the optimal implementation of cryptography block ciphers, benchmarking performance against various metrics, including memory requirements (RAM), throughput, power consumption, and security. Our implementation demonstrates that data can be securely transmitted end-to-end with good throughput and low power consumption.
Friction and particle-hole pairs

The effect induced by dissipation on quantum phenomena has recently been considered, taking into account as a starting point a phenomenological Hamiltonian in which the environment is simulated by an appropriately chosen set of harmonic oscillators. It is found that this approach should be adequate to describe the low-energy behavior of a wide class of environments. The present investigation is concerned with an analysis of the case in which the environment is a gas (or liquid) of fermions, and the relevant low-energy excitations are particle-hole pairs. A study is conducted regarding the extent to which the quantum results obtained for harmonic oscillators are also valid in the considered situation. Linear-response theory is used to derive an effective action which describes the motion of an external particle coupled to a normal Fermi fluid.