Miracle-dz/UniFAR-FTUs huggingface dataset:
Phenomenology of Trimaximal Mixing with One Texture Equality

<jats:p>We study neutrino mass matrices with one texture equality and the neutrino mixing matrix having either its first (<jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mtext>T</mtext> <msub> <mrow> <mtext>M</mtext> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> </jats:inline-formula>) or second (<jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mtext>T</mtext> <msub> <mrow> <mtext>M</mtext> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </math> </jats:inline-formula>) column identical to that of the tribimaximal mixing matrix. We found that out of total fifteen possible neutrino mass matrices with one texture equality, only six textures are compatible with <jats:inline-formula> <math xmlns="http://www.w3. org/1998/Math/MathML" id="M3"> <mtext>T</mtext> <msub> <mrow> <mtext>M</mtext> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> </jats:inline-formula> mixing and six textures are compatible with <jats:inline-formula> <math xmlns="http://www.w3. org/1998/Math/MathML" id="M4"> <mtext>T</mtext> <msub> <mrow> <mtext>M</mtext> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </math> </jats:inline-formula> mixing in the light of the current neutrino oscillation data. These textures have interesting implications for the presently unknown parameters such as the neutrino mass scale, effective Majorana neutrino mass, effective neutrino mass, the atmospheric mixing, and the Dirac- and Majorana-type CP violating phases. We, also, present the <jats:inline-formula> <math xmlns="http://www.w3. org/1998/Math/MathML" id="M5"> <msub> <mrow> <mi>S</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </math> </jats:inline-formula> group motivation for some of these textures. </jats:p>
Model Checking on Interpretations of Classes of Bounded Local Cliquewidth

An interpretation is an operation that maps an input graph to an output graph by redefining its edge relation using a first-order formula. This rich framework includes operations such as taking the complement or a fixed power of a graph as (very) special cases. We prove that there is an FPT algorithm for the first-order model checking problem on classes of graphs which are first-order interpretable in classes of graphs with bounded local cliquewidth. Notably, this includes interpretations of planar graphs, and of classes of bounded genus in general. To obtain this result we develop a new tool which works in a very general setting of NIP classes and which we believe can be an important ingredient in obtaining similar results in the future.
Explainable deepfake and spoofing detection: an attack analysis using SHapley Additive exPlanations

Despite several years of research in deepfake and spoof-ing detection for automatic speaker veriﬁcation, little is known about the artefacts that classiﬁers use to distinguish between bona ﬁde and spoofed utterances. An understanding of these is crucial to the design of trustworthy, explainable solutions. In this paper we report an extension of our previous work to better understand classiﬁer behaviour to the use of SHapley Additive exPlanations (SHAP) to attack analysis. Our goal is to identify the artefacts that characterise utterances generated by different attacks algorithms. Using a pair of classiﬁers which operate either upon raw waveforms or magnitude spectrograms, we show that visualisations of SHAP results can be used to identify attack-speciﬁc artefacts and the differences and consistencies between synthetic speech and converted voice spooﬁng attacks.
Automated Identification of Toxic Code Reviews: How Far Can We Go?

Toxic conversations during software development interactions may have serious repercussions on a Free and Open Source Software (FOSS) development project. For example, victims of toxic conversations may become afraid to express themselves, therefore get demotivated, and may eventually leave the project. Automated filtering of toxic conversations may help a FOSS community to maintain healthy interactions among its members. However, off-the-shelf toxicity detectors perform poorly on Software Engineering (SE) dataset, such as one curated from code review comments. To encounter this challenge, we present ToxiCR , a supervised learning-based toxicity identification tool for code review interactions. ToxiCR includes a choice to select one of the ten supervised learning algorithms, an option to select text vectorization techniques, five mandatory and three optional SE domain specific processing steps, and a large scale labeled dataset of 19,571 code review comments. With our rigorous evaluation of the models with various combinations of preprocessing steps and vectorization techniques, we have identified the best combination for our dataset that boosts 95.8% accuracy and 88.9% 𝐹 1 1 score. ToxiCR significantly outperforms existing toxicity detectors on our dataset. We have released our dataset, pretrained models, evaluation results, and source code publicly available at: https://github.com/WSU-SEAL/ToxiCR CCS Concepts: • Software and its engineering → Collaboration in software development ; Integrated and visual development environments ; • Computing methodologies → Supervised learning .
On applications of Herglotz-Nevanlinna functions in material sciences, II: extended applications and generalized theory

Part II of the review article focuses on the applications of Herglotz-Nevanlinna functions in material sciences. It presents a diverse set of applications with details and the role of Herglotz-Nevanlinna functions clearly pointed out. This paper is concluded by a collection of existent generalizations of the class of Herglotz-Nevanlinna functions that are motivated by potential applications.
Plug‐and‐Play Single‐Photon Devices with Efficient Fiber‐Quantum Dot Interface

Incorporating solid‐state quantum emitters into optical fiber networks enables the long‐distance transmission of quantum information and the remote connection of distributed quantum nodes. However, interfacing quantum emitters with fiber optics encounters several challenges, including low coupling efficiency and delicate configuration. In this study, a highly efficient fiber‐interfacing photonic device that directly launches single photons from quantum dots into a standard FC/PC‐connectorized single‐mode fiber is demonstrated. Optimally designed photonic structures based on hole gratings produce an ultra‐narrow directional beam that matches the small numerical aperture of a single‐mode fiber. A pick‐and‐place technique precisely integrates a single miniaturized device into the core of the fiber. This approach realizes a plug‐and‐play single‐photon device that does not require optical alignment and thus guarantees long‐term stability. The results represent a major step toward practical and reliable transmission of quantum light across a fiber network.
An Empirical Comparison of Machine Learning Models for Student’s Mental Health Illness Assessment

Student’s mental health problems have been explored previously in higher education literature in various contexts including empirical work involving quantitative and qualitative methods. Nevertheless, comparatively few research could be found, aiming for computational methods that learn information directly from data without relying on set parameters for a predetermined equation as an analytical method. This study aims to investigate the performance of Machine learning (ML) models used in higher education. ML models considered are Naïve Bayes, Support Vector Machine, K-Nearest Neighbor, Logistic Regression, Stochastic Gradient Descent, Decision Tree, Random Forest, XGBoost (Extreme Gradient Boosting Decision Tree), and NGBoost (Natural) algorithm. Considering the factors of mental health illness among students, we follow three phases of data processing: segmentation, feature extraction, and classification. We evaluate these ML models against classification performance metrics such as accuracy, precision, recall, F1 score, and predicted run time. The empirical analysis includes two contributions: 1. It examines the performance of various ML models on a survey-based educational dataset, inferring a significant classification performance by a tree-based XGBoost algorithm; 2. It explores the feature importance [variables] from the datasets to infer the significant importance of social support, learning environment, and childhood adversities on a student’s mental health illness.
Three-dimensional Propagation of the Global Extreme-ultraviolet Wave Associated with a Solar Eruption on 2021 October 28

We present a case study for the global extreme-ultraviolet (EUV) wave and its chromospheric counterpart the Moreton-Ramsey Wave associated with the second X-class flare in Solar Cycle 25 and a halo coronal mass ejection (CME). The EUV wave was observed in the Hα and EUV passbands with different characteristic temperatures. In the 171 Å and 193/195 Å images, the wave propagates circularly with an initial velocity of 600–720 km s−1 and a deceleration of 110–320 m s−2. The local coronal plasma is heated from log(T/K) ≈ 5.9 to log(T/K) ≈ 6.2 during the passage of the wave front. The Hα and 304 Å images also reveal signatures of wave propagation with a velocity of 310–540 km s−1. With multiwavelength and dual-perspective observations, we found that the wave front likely propagates forwardly inclined to the solar surface with a tilt angle of ∼53°. 2. Our results suggest that this EUV wave is a fast-mode magnetohydrodynamic wave or shock driven by the expansion of the associated CME, whose wave front is likely a dome-shaped structure that could impact the upper chromosphere, transition region, and corona.
Investigating the Disk–Jet Structure in M87 through Flux Separation in the Linear and Circular Polarization Images

For testing different electron temperature (T e) prescriptions in general relativistic magnetohydrodynamics (GRMHD) simulations through observations, we propose to utilize linear polarization (LP) and circular polarization (CP) images. We calculate the polarization images based on a semi-magnetically arrested disk GRMHD model for various T e parameters, bearing M87 in mind. We find an LP–CP separation in the images of the low-T e disk cases at 230GHz; namely, the LP flux mainly originates from downstream of the jet, and the CP flux comes from the counter-side jet, while the total intensity is maximum at the jet base. This can be understood as follows: although the LP flux is generated through synchrotron emission widely around the black hole, most of the LP flux from the jet base does not reach the observer, since it undergoes Faraday rotation ( ∝Te−2 ) when passing through the outer cold disk and is thus depolarized. Hence, only the LP flux from the downstream (not passing the cold dense plasmas) can survive. Meanwhile, the CP flux is generated from the LP flux by Faraday conversion ( ∝ T e) in the inner hot region. Stronger CP flux is thus observed from the counter-side jet. Moreover, the LP–CP separation is more enhanced at a lower frequency, such as 86 GHz, but is rather weak at 43 GHz, since the media in the latter case is optically thick for synchrotron self-absorption so that all of the fluxes should come from the photosphere. The same is true for cases with higher mass accretion rates and/or larger inclination angles.
Regularization of the Factorization Method with Applications to Inverse Scattering

Here we discuss a regularized version of the factorization method for positive operators acting on a Hilbert Space. The factorization method is a qualitative reconstruction method that has been used to solve many inverse shape problems. In general, qualitative methods seek to reconstruct the shape of an unknown object using little to no a priori information. The regularized factorization method presented here seeks to avoid numerical instabilities in the inversion algorithm. This allows one to recover unknown structures in a computationally simple and analytically rigorous way. We will discuss the theory and application of the regularized factorization method to examples coming from acoustic inverse scattering. Numerical examples will also be presented using synthetic data to show the applicability of the method.
Virial Clumps in Central Molecular Zone Clouds

CMZoom survey observations with the Submillimeter Array are analyzed to describe the virial equilibrium (VE) and star-forming potential of 755 clumps in 22 clouds in the Central Molecular Zone (CMZ) of the Milky Way. In each cloud, nearly all clumps follow the column density–mass trend N ∝ M s , where s = 0.38 ± 0.03 is near the pressure-bounded limit s p = 1/3. This trend is expected when gravitationally unbound clumps in VE have similar velocity dispersion and external pressure. Nine of these clouds also harbor one or two distinctly more massive clumps. These properties allow a VE model of bound and unbound clumps in each cloud, where the most massive clump has the VE critical mass. These models indicate that 213 clumps have velocity dispersion 1–2 km s−1, mean external pressure (0.5–4) × 108 cm−3 K, bound clump fraction 0.06, and typical virial parameter α = 4–15. These mostly unbound clumps may be in VE with their turbulent cloud pressure, possibly driven by inflow from the Galactic bar. In contrast, most Sgr B2 clumps are bound according to their associated sources and N–M trends. When the CMZ clumps are combined into mass distributions, their typical power-law slope is analyzed with a model of stopped accretion. It also indicates that most clumps are unbound and cannot grow significantly, due to their similar timescales of accretion and dispersal, ∼0.2 Myr. Thus, virial and dynamical analyses of the most extensive clump census available indicate that star formation in the CMZ may be suppressed by a significant deficit of gravitationally bound clumps.
Feasibility and Acceptability of Remote Neuromotor Rehabilitation Interactions Using Social Robot Augmented Telepresence: A Case Study

There is a growing need to deliver rehabilitation care to patients remotely. Long term demographic changes, geographic shortages of care providers, and now a global pandemic contribute to this need. Telepresence provides an option for delivering this care. However, telepresence using video and audio alone does not provide an interaction of the same quality as in-person. To bridge this gap, we propose the use of social robot augmented telepresence (SRAT). We have constructed a demonstration SRAT system for upper extremity rehab, in which a humanoid, with a head, body, face, and arms, is attached to a mobile telepresence system, to collaborate with the patient and clinicians as an independent social entity. The humanoid can play games with the patient and demonstrate activities. These activities could be used to perform assessments in support of self-directed rehab and to perform exercises.In this paper, we present a case series with six subjects who completed interactions with the robot, three subjects who have previously suffered a stroke and three pediatric subjects who are typically developing. Subjects performed a Simon Says activity and a target touch activity in person, using classical telepresence (CT), and using SRAT. Subjects were able to effectively work with the social robot guiding interactions and 5 of 6 rated SRAT better than CT. This study demonstrates the feasibility of SRAT and some of its benefits.
Provably Efficient Convergence of Primal-Dual Actor-Critic with Nonlinear Function Approximation

We study the convergence of the actor-critic algorithm with nonlinear function approximation under a nonconvex-nonconcave primal-dual formulation. Stochastic gradient descent ascent is applied with an adaptive proximal term for robust learning rates. We show the first efficient convergence result with primal-dual actor-critic with a convergence rate of $\mathcal{O}\left(\sqrt{\frac{\ln \left(N d G^2 \right)}{N}}\right)$ under Markovian sampling, where $G$ is the element-wise maximum of the gradient, $N$ is the number of iterations, and $d$ is the dimension of the gradient. Our result is presented with only the Polyak-\L{}ojasiewicz condition for the dual variables, which is easy to verify and applicable to a wide range of reinforcement learning (RL) scenarios. The algorithm and analysis are general enough to be applied to other RL settings, like multi-agent RL. Empirical results on OpenAI Gym continuous control tasks corroborate our theoretical findings.
Effective field theory of pairing rotations

Pairing rotations are the low-energy excitations of finite superfluid systems, connecting systems that differ in their number of Cooper pairs. This paper presents a model-independent derivation of pairing rotations within an effective theory that exploits the emergent breaking of U(1) phase symmetries. The symmetries are realized nonlinearly and the Nambu-Goldstone modes depend only on time because the system is finite. Semi-magic nuclei exhibit pairing rotational bands while the pairing spectrum becomes an elliptical paraboloid for open-shell nuclei. Model-independent relations between double charge-exchange reactions and α particle capture or knockout in open-shell nuclei are in analogy to the pair transfer reactions in a single superfluid. Odd semi-magic nuclei are described by coupling a fermion to the superfluid. The leading-order theories reproduce data for pairing rotational bands within uncertainty estimates.
Quantum Tanner codes

Tanner codes are long error correcting codes obtained from short codes and a graph, with bits on the edges and parity-check constraints from the short codes enforced at the vertices of the graph. Combining good short codes together with a spectral expander graph yields the celebrated expander codes of Sipser and Spielman, which are asymptotically good classical LDPC codes. In this work we apply this prescription to the left-right Cayley complex that lies at the heart of the recent construction of a c3 locally testable code by Dinur et at. Specifically, we view this complex as two graphs that share the same set of edges. By defining a Tanner code on each of those graphs we obtain two classical codes that together define a quantum code. This construction can be seen as a simplified variant of the Panteleev and Kalachev asymptotically good quantum LDPC code, with improved estimates for its minimum distance. This quantum code is closely related to the Dinur et at. code in more than one sense: indeed, we prove a theorem that simultaneously gives a linearly growing minimum distance for the quantum code and recovers the local testability of the Dinur et at. code.
Hyperbolic Graph Neural Networks: A Review of Methods and Applications

Graph neural networks generalize conventional neural networks to graph-structured data and have received widespread attention due to their impressive representation ability. In spite of the remarkable achievements, the performance of Euclidean models in graph-related learning is still bounded and limited by the representation ability of Euclidean geometry, especially for datasets with highly non-Euclidean latent anatomy. Recently, hyperbolic space has gained increasing popularity in processing graph data with tree-like structure and power-law distribution, owing to its exponential growth property. In this survey, we comprehensively revisit the technical details of the current hyperbolic graph neural networks, unifying them into a general framework and summarizing the variants of each component. More importantly, we present various HGNN-related applications. Last, we also identify several challenges, which potentially serve as guidelines for further flourishing the achievements of graph learning in hyperbolic spaces.
Conformal prediction beyond exchangeability

Conformal prediction is a popular, modern technique for providing valid predictive inference for arbitrary machine learning models. Its validity relies on the assumptions of exchangeability of the data, and symmetry of the given model fitting algorithm as a function of the data. However, exchangeability is often violated when predictive models are deployed in practice. For example, if the data distribution drifts over time, then the data points are no longer exchangeable; moreover, in such settings, we might want to use a nonsymmetric algorithm that treats recent observations as more relevant. This paper generalizes conformal prediction to deal with both aspects: we employ weighted quantiles to introduce robustness against distribution drift, and design a new randomization technique to allow for algorithms that do not treat data points symmetrically. Our new methods are provably robust, with substantially less loss of coverage when exchangeability is violated due to distribution drift or other challenging features of real data, while also achieving the same coverage guarantees as existing conformal prediction methods if the data points are in fact exchangeable. We demonstrate the practical utility of these new tools with simulations and real-data experiments on electricity and election forecasting.
Towards Machine Learning for Placement and Routing in Chip Design: a Methodological Overview

Placement and routing are two indispensable and challenging (NP-hard) tasks in modern chip design flows. Compared with traditional solvers using heuristics or expert-well-designed algorithms, machine learning has shown promising prospects by its data-driven nature, which can be of less reliance on knowledge and priors, and potentially more scalable by its advanced computational paradigms (e.g. deep networks with GPU acceleration). This survey starts with the introduction of basics of placement and routing, with a brief description on classic learning-free solvers. Then we present detailed review on recent advance in machine learning for placement and routing. Finally we discuss the challenges and opportunities for future research.
A Rich Satellite Population of the NGC 4437 Group and Implications of a Magnitude Gap for Galaxy Group Assembly History

Both observations and cosmological simulations have recently shown that there is a large scatter in the number of satellites of Milky Way (MW)–like galaxies. In this study, we investigate the relation between the satellite number and galaxy group assembly history using the r-band magnitude gap (Δm 12) between the brightest and second-brightest galaxies as an indicator. From 20 deg2 of the Hyper Suprime-Cam Subaru Strategic Program Wide layer, we identify 17 dwarf satellite candidates around NGC 4437, a spiral galaxy with about one-fourth of the MW stellar mass. We estimate their distances using the surface brightness fluctuation method. Then we confirm five candidates as members of the NGC 4437 group, resulting in a total of seven group members. Combining the NGC 4437 group (with Δm 12 = 2.5 mag) with other groups in the literature, we find a stratification of the satellite number by Δm 12 for a given host stellar mass. The satellite number for the given host stellar mass decreases as Δm 12 increases. The same trend is found in simulated galaxy groups in the TNG50 simulation of the IllustrisTNG project. We also find that the host galaxies in groups with a smaller Δm 12 (like NGC 4437) have assembled their halo mass more recently than those in larger gap groups, and that their stellar-to-halo mass ratios increase as Δm 12 increases. These results show that the large scatter in the satellite number is consistent with a large range of Δm 12, indicating diverse group assembly histories.