Miracle-dz/UniFAR-FTUs huggingface dataset:
The contributions of J‐type shocks to the H2 emission from molecular outflow sources

We present the results of modelling of the H2 emission from molecular outflow sources, induced by shock waves propagating in the gas. We emphasize the importance of proper allowance for departures from equilibrium owing to the finite flow velocity of the hot, compressed gas, with special reference to the excitation, dissociation and reformation of H2. The salient features of our computer code are described. The code is applied to interpreting the spectra of the outflow sources Cepheus A West and HH43. Particular attention is paid to determining the cooling times in shocks whose speeds are sufficient for collisional dissociation of H2 to take place; the possible observational consequences of the subsequent reformation of H2 are also examined. Because molecular outflow sources are intrinsically young objects, J-type shocks may be present in conjunction with magnetic precursors, which have a C-type structure. We note that very different physical and dynamical conditions are implied by models of C- and J-type shocks which may appear to fit the same H2 excitation diagram.
Is it the same, socially? Fully online learning and its impacts on social identification, academic performance and confidence

ABSTRACT Previous research demonstrates links between student social identification, perceived learning norms, learning approaches and academic outcomes and indicates the value of bolstering student social identification in higher education settings. The current study aimed to examine whether the models identified in this previous research replicated in a fully online environment. This is critical knowledge in the context of the industry-wide debate on the gains and losses of online university. Self-report survey data (N = 112), e-learning analytics and grades were used to examine student social identification, perceptions, behaviours, and outcomes over a 4-month period of online learning during the COVID-19 pandemic. Results were unexpected: previous models failed to replicate in our data. Exploratory analysis identified three ways forward: examination of student learning activity outside of institutionally provided online contexts, revisiting the use of SPQ as a measure of learning approach, and examining student social interactions and identification in a social media-rich online environment.
Review: Structure-Activity Relationship of Antimicrobial Peptoids

Due to their broad-spectrum activity against Gram-negative and Gram-positive bacteria, natural antimicrobial peptides (AMPs) and their synthetic analogs have emerged as prospective therapies for treating illnesses brought on by multi-drug resistant pathogens. To overcome the limitations of AMPs, such as protease degradation, oligo-N-substituted glycines (peptoids) are a promising alternative. Despite having the same backbone atom sequence as natural peptides, peptoid structures are more stable because, unlike AMP, their functional side chains are attached to the backbone nitrogen (N)-atom rather than the alpha carbon atom. As a result, peptoid structures are less susceptible to proteolysis and enzymatic degradation. The advantages of AMPs, such as hydrophobicity, cationic character, and amphipathicity, are mimicked by peptoids. Furthermore, structure-activity relationship studies (SAR) have shown that tuning the structure of peptoids is a crucial step in developing effective antimicrobials.
System API Vectorization for Malware Detection

Data is essential to the performance of artificial intelligence (AI) based malware detection models. System APIs, which allocate operating system resources, are important for identifying malicious behaviors. However, few studies have been conducted on data in the malware detection AI model. They overlooked collection of benign data, which is as important as malware data, and data characterization of system APIs. As an optimization method for data-driven artificial intelligence, this paper studied the data collection, purification, preprocessing, and vectorization for EXE files and system APIs. The objectivity of the data was ensured by using global data, and a more robust model could be created by collecting benign data from Virus Total. By analyzing the weight distribution according to the order of system API execution, we identified that major malicious behaviors occurred at the beginning of execution. We found the optimal API length and optimal dimension (feature number). Finally, accuracy of the N-gram model ranged from 97.62 to 95.73, and that of the Word2Vec model ranged from 97.44 to 95.89. In the generalization performance test using different data from the source of the training ones, we confirmed that N-gram was affected by the quantity of training data, and Word2Vec was affected by data similarity. This study systematized the entire procedure of AI data processing for malware detection, and is the first study to compare and analyze statistical vectors and word embeddings based on the characteristics of system APIs.
ON THE HYDRODYNAMICAL DEVELOPMENT LENGTH FOR PRESSURE-DRIVEN PULSATING LAMINAR FLOW IN A PIPE

In this study, pulsating laminar flow in a pipe is considered numerically. The focus is concentrated on the hydrodynamical development length. At first, steady flow in a pipe is examined and a new correlation for the hydrodynamical development length is developed based on the scale analysis. Then, the pressure-driven pulsating flow is studied. For a constant value of Reynolds number, the effect of the frequency, F and the amplitude, A of the pulsating flow on the development length are determined. It is shown that F for its range of 1≤F≤10 dramatically affects the hydrodynamical development length while this effect becomes negligible either for very high values of F (F≥100) or for low values of F (F≤0.1). It is also disclosed that the amplitude, A, has a considerable effect on the entrance length for lower values of F (F≤0.1). For some specific cases, results obtained are compared with those available in the open literature and a good agreement is obtained.
Investigating the Phonetics of Mandarin Tone Sandhi

The goal of this paper is to discuss phonetic evidence relating to the production of tone sandhi in Mandarin. Two views of tone sandhi are contrasted: the standard ”categorical” view, whereby tone 3 sandhi is treated similarly to a/an allomorphy in English, and an alternative ”gradient” view, which treats tone 3 sandhi as similar to flapping in English. Evidence is presented for two main claims. First, based on a review of the phonetic literature, it appears that native speakers of Beijing Mandarin process tone sandhi more in line with the gradient view, while speakers of other varieties (including Taiwan Mandarin) process tone sandhi in accordance with the categorical view. Second, a new phonetic study of both tone 3 sandhi and the idiosyncratic tone sandhi found with the morpheme yi (”one”) provides further support for the claim of categoricality of tone 3 sandhi in Taiwan Mandarin, yet also illustrates the empirical and theoretical pitfalls of phonetic research on tone sandhi.
Within-class and unsupervised clustering improve accuracy and extract local structure for supervised classification

Deterministic clustering methods at different levels of granularity such as within classes, at the class level and across classes are investigated for their effect on classification performance in a series of empirical studies. Specifically I have found that clustering within classes, by extracting local structure, can improve supervised learning performance in many cases. This approach, and unsupervised clustering across entire data sets are usually better for prediction than clustering or grouping within-class clusters across classes, or even global classifier approaches. These conclusions are supported by more than 3000 experiments on four benchmark datasets, using some of the most powerful automated classification methods such as regularized logistic regression and Support Vector Machines (SVMs). I have used a simple combination of unsupervised clustering and classification methods to search for local structure in the data and detect locally significant features. The approach is illustrated by analysis of lung cancer survival data from records of 200,000 patients.
Simple reflection technique for measuring the linear electro-optic coefficient of the polymer thin films

The simple reflection technique is usually used to measure the linear electro-optic (EO) coefficient (Pockels coefficient) in the development of EO polymer thin films. But there are some problems in some articles in the determination of the phase shift between the s and p light modes of a laser beam waveguided into the polymer film while a modulating voltage is applied across the electrodes, and different expressions for the linear EO coefficient measured have been given in these articles. In our research, more accurate expression of the linear EO coefficient was deduced by suitable considering the phase shift between the s and p light modes. The linear EO coefficients of several polymer thin films were measured by reflection technique, and the results of the Linear EO coefficient calculated by different expressions were compared. The limit of the simple reflection technique for measuring the linear EO coefficient of the polymer thin films was discussed.
Streamwise Vorticity: The Origin of Updraft Rotation in Supercell Storms

Abstract Linear (small amplitude) theory of shallow, inviscid, isentropic convection in a dry, unstably stratified, nonrotating atmosphere is used to investigate the rotational characteristics of an isolated, incipient convective storm in strong environmental shear. Environmental winds veering with height are associated with streamwise vorticity (i.e., a component of vorticity along the mean wind direction). We demonstrate that a roughly circular storm acquires net cyclonic (anticyclonic) rotation within its updraft (downdraft) when the storm-relative winds veer with height, or equivalently when the environmental flow possesses streamwise vorticity in a reference frame moving with the storm. A formula for the correlation coefficient between vertical velocity and vertical vorticity is obtained. The physical explanation for the correlation is as follows. Initially, the isentropic surfaces and vortex lines are horizontal. After the onset of convection, air parcels and vortex lines remain in their original is.
The Proton and Occam’s Razor

Otto Stern’s 1933 measurement of the unexpectedly large proton magnetic moment indicated to most physicists that the proton is not a point particle. At that time, many physicists modeled elementary particles as point particles, and therefore Stern’s discovery initiated the speculation that the proton might be a composite particle. In this work, we show that despite being an elementary particle, the proton is an extended particle. Our work is motivated by the experimental data, which we review in section 1. By applying Occam’s Razor principle, we identify a simple proton structure that explains the origin of its principal parameters. Our model uses only relativistic and electromagnetic concepts, highlighting the primary role of the electromagnetic potentials and of the magnetic flux quantum Φ M = h/e. Unlike prior proton models, our methodology does not violate Maxwell’s equation, Noether’s theorem, or the Pauli exclusion principle. Considering that the proton has an anapole (toroidal) magnetic moment, we propose that the proton is a spherical shaped charge that moves at the speed of light along a path that encloses a toroidal volume. A magnetic flux quantum Φ M = h/e stabilizes the proton’s charge trajectory. The two curvatures of the toroidal and poloidal current loops are determined by the magnetic forces associated with Φ M . We compare our calculations against experimental data.
Massively Parallel Reweighted Wake-Sleep

Reweighted wake-sleep (RWS) is a machine learning method for performing Bayesian inference in a very general class of models. RWS draws $K$ samples from an underlying approximate posterior, then uses importance weighting to provide a better estimate of the true posterior. RWS then updates its approximate posterior towards the importance-weighted estimate of the true posterior. However, recent work [Chattergee and Diaconis, 2018] indicates that the number of samples required for effective importance weighting is exponential in the number of latent variables. Attaining such a large number of importance samples is intractable in all but the smallest models. Here, we develop massively parallel RWS, which circumvents this issue by drawing $K$ samples of all $n$ latent variables, and individually reasoning about all $K^n$ possible combinations of samples. While reasoning about $K^n$ combinations might seem intractable, the required computations can be performed in polynomial time by exploiting conditional independencies in the generative model. We show considerable improvements over standard"global"RWS, which draws $K$ samples from the full joint.
Overleaf Example

We introduce an approach which allows detecting causal relationships between variables for which the time evolution is available. Causality is assessed by a variational scheme based on the Information Imbalance of distance ranks, a statistical test capable of inferring the relative information content of different distance measures. We test whether the predictability of a putative driven system Y can be improved by incorporating information from a potential driver system X, without making assumptions on the underlying dynamics and without the need to compute probability densities of the dynamic variables. This framework makes causality detection possible even for high-dimensional systems where only few of the variables are known or measured. Benchmark tests on coupled chaotic dynamical systems demonstrate that our approach outperforms other model-free causality detection methods, successfully handling both unidirectional and bidirectional couplings. We also show that the method can be used to robustly detect causality in human electroencephalography data.
Admissibility preserving subcell limiter for Lax-Wendroff flux reconstruction

Lax-Wendroff Flux Reconstruction (LWFR) is a single-stage, high order, quadrature free method for solving hyperbolic conservation laws. We develop a subcell based limiter by blending LWFR with a lower order scheme, either first order finite volume or MUSCL-Hancock scheme. While the blending with a lower order scheme helps to control oscillations, it may not guarantee admissibility of discrete solution, e.g., positivity property of quantities like density and pressure. By exploiting the subcell structure and admissibility of lower order schemes, we devise a strategy to ensure that the blended scheme is admissibility preserving for the mean values and then use a scaling limiter to obtain admissibility of the polynomial solution. For MUSCL-Hancock scheme on non-cell-centered subcells, we develop a slope limiter, time step restrictions and suitable blending of higher order fluxes, that ensures admissibility of lower order updates and hence that of the cell averages. By using the MUSCL-Hancock scheme on subcells and Gauss-Legendre points in flux reconstruction, we improve small-scale resolution compared to the subcell-based RKDG blending scheme with first order finite volume method and Gauss-Legendre-Lobatto points. We demonstrate the performance of our scheme on compressible Euler's equations, showcasing its ability to handle shocks and preserve small-scale structures.
Entropy of microcanonical finite-graph ensembles

The entropy of random graph ensembles has gained widespread attention in the field of graph theory and network science. We consider microcanonical ensembles of simple graphs with prescribed degree sequences. We demonstrate that the mean-field approximations of the generating function using the Chebyshev–Hermite polynomials provide estimates for the entropy of finite-graph ensembles. Our estimate reproduces the Bender–Canfield formula in the limit of large graphs.
Bohnenblust--Hille inequality for cyclic groups

For any $N>2$ and the multiplicative cyclic group $\Omega_N$ of order $N$, consider any function $f:\Omega_N^n\to\mathbf{C}$ and its Fourier expansion $f(z)=\sum_{\alpha\in\{0,1,\ldots,N-1\}^n}a_\alpha z^\alpha$ with $d=\text{deg}(f)$ denoting the degree $\text{deg}(f)$ as a multivariate polynomial. We prove a Bohnenblust--Hille (BH) inequality in this setting, which states that for any $d\ge 1$, the $\ell_{2d/(d+1)}$ norm of the Fourier coefficients of degree-$d$ polynomial $f:\Omega_N^n\to\mathbf{C}$ is bounded by $C(d,N)\|f\|_\infty$ for $C(d,N)$ independent of $n$. This is the interpolating case between the now well-understood BH inequalities for functions on the poly-torus ($N =\infty$) and the hypercube ($N=2$), but, as we explain, those extreme cases of $N$ have special properties whose absence for intermediate $N$ prevent a proof by the standard BH framework heretofore employed. New techniques are developed exploiting the group structure of $\Omega_N^n$. By known reductions (\cite{SVZ}), the cyclic group BH inequality also entails a noncommutative BH inequality for tensor products of $N \times N$ complex matrix algebra (or in the language of quantum mechanics, systems of $N$-level qudits). These new BH inequalities generalize several applications in harmonic analysis and statistical learning theory to broader classes of functions and operators.
Unsupervised Multi-channel Separation and Adaptation

A key challenge in machine learning is to generalize from training data to an application domain of interest. This work generalizes the recently-proposed mixture invariant training (MixIT) algorithm to perform unsupervised learning in the multi-channel setting. We use MixIT to train a model on far-field microphone array recordings of overlapping reverberant and noisy speech from the AMI Corpus. The models are trained on both supervised and unsupervised training data, and are tested on real AMI recordings containing overlapping speech. To objectively evaluate our models, we also use a synthetic multi-channel AMI test set. Holding network architectures constant, we find that a fine-tuned semi-supervised model yields the largest improvement to SI-SNR and to human listening ratings across synthetic and real datasets, outperforming supervised models trained on well-matched synthetic data. Our results demonstrate that unsupervised learning through MixIT enables model adaptation on both single- and multi-channel real-world speech recordings.
Active Matter under Control: Insights from Response Theory

Active constituents burn fuel to sustain individual motion, giving rise to collective effects that are not seen in systems at thermal equilibrium, such as phase separation with purely repulsive interactions. There is a great potential in harnessing the striking phenomenology of active matter to build novel controllable and responsive materials that surpass passive ones. Yet, we currently lack a systematic roadmap to predict the protocols driving active systems between different states in a way that is thermodynamically optimal. Equilibrium thermodynamics is an inadequate foundation to this end, due to the dissipation rate arising from the constant fuel consumption in active matter. Here, we derive and implement a versatile framework for the thermodynamic control of active matter. Combining recent developments in stochastic thermodynamics and nonequilibrium response theory, our approach shows how to find the optimal control for either continuous- or discrete-state active systems operating arbitrarily far from equilibrium. Our results open the door to designing novel active materials which are not only built to stabilize specific nonequilibrium collective states, but are also optimized to switch between different states at minimum dissipation.
Rate-Adaptive Coding Mechanism for Semantic Communications With Multi-Modal Data

Recently, the ever-increasing demand for bandwidth in multi-modal communication systems requires a paradigm shift. Powered by deep learning, semantic communications are applied to multi-modal scenarios to boost communication efficiency and save communication resources. However, the existing end-to-end neural network (NN) based framework without the channel encoder/decoder is incompatible with modern digital communication systems. Moreover, most end-to-end designs are task-specific and require re- design and re- training for new tasks, which limits their applications. In this paper, we propose a distributed multi-modal semantic communication framework incorporating the conventional channel encoder/decoder. We adopt NN-based semantic encoder and decoder to extract correlated semantic information contained in different modalities, including speech, text, and image. Based on the proposed framework, we further establish a general rate-adaptive coding mechanism for various types of multi-modal semantic tasks. In particular, we utilize unequal error protection based on semantic importance, which is derived by evaluating the distortion bound of each modality. We further formulate and solve an optimization problem that aims at minimizing inference delay while maintaining inference accuracy for semantic tasks. Numerical results show that the proposed mechanism fares better than both conventional communication and existing semantic communication systems in terms of task performance, inference delay, and deployment complexity.
VVC+M: Plug and Play Scalable Image Coding for Humans and Machines

Compression for machines is an emerging field, where inputs are encoded while optimizing the performance of downstream automated analysis. In scalable coding for humans and machines, the compressed representation used for machines is further utilized to enable input reconstruction. Often performed by jointly optimizing the compression scheme for both machine task and human perception, this results in sub-optimal rate-distortion (RD) performance for the machine side. We focus on the case of images, proposing to utilize the pre-existing residual coding capabilities of video codecs such as VVC to create a scalable codec from any image compression for machines (ICM) scheme. Using our approach we improve an existing scalable codec to achieve superior RD performance on the machine task, while remaining competitive for human perception. Moreover, our approach can be trained post-hoc for any given ICM scheme, and without creating a coupling between the quality of the machine analysis and human vision.