Miracle-dz/UniFAR-FTUs huggingface dataset:
The CH radical at radio wavelengths: Revisiting emission in the 3.3 GHz ground-state lines

Context. The intensities of the three widely observed radio-wavelength hyperfine structure (HFS) lines between the Λ-doublet components of the rotational ground state of CH are inconsistent with local thermodynamic equilibrium (LTE) and indicate ubiquitous population inversion. While this can be qualitatively understood assuming a pumping cycle that involves collisional excitation processes, the relative intensities of the lines and in particular the dominance of the lowest frequency satellite line has not been well understood. This has limited the use of CH radio emission as a tracer of the molecular interstellar medium. Aims. We aim to investigate the nature of the (generally) weak CH ground state masers by employing synergies between the ground state HFS transitions themselves and with the far-infrared lines, near 149 μm (2 THz), that connect these levels to an also HFS split rotationally excited level. Methods. We present the first interferometric observations, with the Karl G. Jansky Very Large Array, of the CH 9 cm ground state HFS transitions at 3.264 GHz, 3.335 GHz, and 3.349 GHz toward the four high mass star-forming regions (SFRs) Sgr B2 (M), G34.26+0.15, W49 (N), and W51. We combine this data set with our high spectral resolution observations of the N, J =2, 3/2→1, 1/2 transitions of CH near 149 μm observed toward the same sources made with the upGREAT receiver on SOFIA, which share a common lower energy levels with the HFS transitions within the rotational ground state. Results. Toward all four sources, we observe the 3.264 GHz lower satellite line in enhanced emission with its relative intensity higher than its expected value at LTE by a factor between 4 and 20. Employing recently calculated collisional rate coefficients, we perform statistical equilibrium calculations with the non-LTE radiative transfer code MOLPOP-CEP in order to model the excitation conditions traced by the ground state HFS lines of CH and to infer the physical conditions in the emitting regions. The models account for effects of far-infrared line overlap with additional constraints provided by reliable column densities of CH estimated from the 149 μm lines. Conclusions. The derived gas densities indicate that the CH radio emission lines (and the far-infrared absorption) arise from the diffuse and translucent outer regions of the SFRs’ envelopes as well as in such clouds located along the lines of sight. We infer temperatures ranging from 50 to 125 K. These elevated temperatures, together with astrochemical considerations, may indicate that CH is formed in material heated by the dissipation of interstellar turbulence, which has been invoked for other molecules. The excitation conditions we derive reproduce the observed level inversion in all three of the ground state HFS lines of CH over a wide range of gas densities with an excitation temperature of ∼−0.3 K, consistent with previous theoretical predictions.
Dissipative spin dynamics in hot quantum paramagnets

We use the functional renormalization approach for quantum spin systems developed by Krieg and Kopietz [Phys. Rev. B 99, 060403(R) (2019)] to calculate the spin-spin correlation function $G(\mathbit{k},\ensuremath{\omega})$ of quantum Heisenberg magnets at infinite temperature. For small wave vectors $\mathbit{k}$ and frequencies $\ensuremath{\omega}$ we find that $G(\mathbit{k},\ensuremath{\omega})$ assumes in dimensions $dg2$ the diffusive form predicted by hydrodynamics. In three dimensions our result for the spin-diffusion coefficient $\mathcal{D}$ is somewhat smaller than previous theoretical predictions based on the extrapolation of the short-time expansion, but is still about $30%$ larger than the measured high-temperature value of $\mathcal{D}$ in the Heisenberg ferromagnet ${\mathrm{Rb}}_{2}{\mathrm{CuBr}}_{4}\ifmmode\cdot\else\textperiodcentered\fi{}2{\mathrm{H}}_{2}\mathrm{O}$. In reduced dimensions $d\ensuremath{\le}2$ we find superdiffusion characterized by a frequency-dependent complex spin-diffusion coefficient $\mathcal{D}(\ensuremath{\omega})$ which diverges logarithmically in $d=2$, and as a power-law $\mathcal{D}(\ensuremath{\omega})\ensuremath{\propto}{\ensuremath{\omega}}^{\ensuremath{-}1/3}$ in $d=1$. Our result in one dimension implies scaling with dynamical exponent $z=3/2$, in agreement with recent calculations for integrable spin chains. Our approach is not restricted to the hydrodynamic regime and allows us to calculate the dynamic structure factor $S(\mathbit{k},\ensuremath{\omega})$ for all wave vectors. We show how the short-wavelength behavior of $S(\mathbit{k},\ensuremath{\omega})$ at high temperatures reflects the relative sign and strength of competing exchange interactions.
A DRL-Based Automated Algorithm Selection Framework for Cross-Layer QoS-Aware Scheduling and Antenna Allocation in Massive MIMO Systems

Massive multiple-input-multiple-output (MIMO) systems support advanced applications with high data rates, low latency, and high reliability in next-generation mobile networks. However, using machine learning in massive MIMO resource allocation is challenging due to quality of service (QoS) and network complexity across layers. This work presents a novel framework for adapting the scheduling and antenna allocation in massive MIMO systems using deep reinforcement learning (DRL). Rather than directly assigning execution parameters, the proposed framework utilizes DRL to select combinations of algorithms based on the current traffic conditions. The DRL model is trained using a specialized Markov decision process (MDP) model with a componentized action structure and is evaluated in realistic traffic scenarios. The results show that the proposed framework increases satisfied users by 7.2% and 12.5% compared to static algorithm combinations and other cross-layer adaptation methods. This demonstrates the effectiveness of the framework in managing and optimizing resource allocation in a flexible and adaptable manner.
Instrumental Assessments of Sub-clinical Skin Reactions induced by Cosmetic Ingredients

인체피부에서 화장품이나 화장품 원료의 안전성시험은 대체로 육안평가로 이루어져왔다. 하지만, 피부반응 초기의 미세한 변화에 대해서 육안으로는 감지하지 못하는 경우가 많다. 따라서, 본 연구에서는 수종의 화장품 원료에 의해 유발되는 홍반반응에 대해 육안평가뿐 아니라, 레이저도플러혈류이미지(LDPI) 측정장비를 이용한 혈류변화 측정, $Vapometer^{(R)}$ 를 이용한 경피수분손실량(TEWL) 측정, 분광광도계를 이용한 피부색 측정과 같은 기기평가를 병행하여 비교하였다. 30명의 건강한 여성 피험자를 대상으로 오일류 7종, 계면활성제류 6종, 보습제류 5종을 피험자의 등 부위에24 h 폐쇄첩포(D0)하여 피부반응을 유도하였고 첩포 제거 후 30 min (D1), 24 h (D2)에 육안평가와 기기평가를 각각 실시하였다. 육안평가 결과, 프로필렌글라이콜을 제외한 모든 오일류와 보습제류는 저자극 수준의 피부반응(반응도 0+ - 2.9+)을 보인 반면, 프로필렌글라이콜과 모든 계면활성제류는 중자극에서 강자극 수준의 피부반응(반응도 3+ - 5+)을 보였다. 기기측정 결과, 육안평가에서 저자극 범주의 피부반응에 대해서 혈류량이나 피부색보다 경피수분손실량이 가장 민감하게 피부변화를 감지하였다. 【The safety of cosmetics or cosmetic ingredients on human skin is generally evaluated by visual assessment but some early subtle skin changes may not be noticed by the naked eyes. Thus, the present study was conducted to detect skin reactions induced by mildly irritating cosmetic ingredients by using a laser Doppler perfusion imager (LDPI) method that measures blood flow, a $Vapometer^{(R)}$ that measure strans epidermal water loss (TEWL), and a spectrophotometer that measures the skin color as the erythema values ( $a^*$ ). Visual assessment showed that all tested oils and humectants except propylene glycol belong to the low skin irritation ranges (grades 0+ to 2.9+) while all tested surfactants and propylene glycol belong to the moderate-to strong-skin irritation ranges (grades 3+ to 5+). Among three instrumental methods, TEWL assessment appeared to be more sensitive than spectrophotometric or LDPI method and suitable for the detection of subtle skin response invisible to the naked eye (grades 0+ to 2.9+). Skin reactions of grade 3+ to 5+ could be detected by all three instrumental methods. In conclusion, the current study suggested that the sub-clinical skin reactions due to mild irritants contained in cosmetics can be best assessed by TEWL measurements. 】
Real space analysis of colloidal gels: triumphs, challenges and future directions

Colloidal gels constitute an important class of materials found in many contexts and with a wide range of applications. Yet as matter far from equilibrium, gels exhibit a variety of time-dependent behaviours, which can be perplexing, such as an increase in strength prior to catastrophic failure. Remarkably, such complex phenomena are faithfully captured by an extremely simple model—‘sticky spheres’. Here we review progress in our understanding of colloidal gels made through the use of real space analysis and particle resolved studies. We consider the challenges of obtaining a suitable experimental system where the refractive index and density of the colloidal particles is matched to that of the solvent. We review work to obtain a particle-level mechanism for rigidity in gels and the evolution of our understanding of time-dependent behaviour, from early-time aggregation to ageing, before considering the response of colloidal gels to deformation and then move on to more complex systems of anisotropic particles and mixtures. Finally we note some more exotic materials with similar properties.
Hubble tension in lepton asymmetric cosmology with an extra radiation

Osamu Seto 2, ∗ and Yo Toda † Institute for the Advancement of Higher Education, Hokkaido University, Sapporo 060-0817, Japan Department of Physics, Hokkaido University, Sapporo 060-0810, Japan Abstract We study the fit of cosmological models with two additional free parameters Neff and ξe in addition to the parameters of ΛCDM. We introduce extra radiation components such as hot axions or sterile neutrinos in addition to the energy density of neutrinos with large neutrino degeneracy. Then, a larger Neff is allowed without spoiling Big Bang Nucleosynthesis (BBN), as positive neutrino degeneracy ξe could improve BBN fit. By analysing the data from Planck, baryon acoustic oscillation (BAO), BBN and type-Ia supernovae (SNeIa), it can be seen that the Hubble tension can be ameliorated for ξe ' 0.04 and 0.3 . ∆Neff . 0.6.
Beyond the elementary representations of program invariants over algebraic data types

First-order logic is a natural way of expressing properties of computation. It is traditionally used in various program logics for expressing the correctness properties and certificates. Although such representations are expressive for some theories, they fail to express many interesting properties of algebraic data types (ADTs). In this paper, we explore three different approaches to represent program invariants of ADT-manipulating programs: tree automata, and first-order formulas with or without size constraints. We compare the expressive power of these representations and prove the negative definability of both first-order representations using the pumping lemmas. We present an approach to automatically infer program invariants of ADT-manipulating programs by a reduction to a finite model finder. The implementation called RInGen has been evaluated against state-of-the-art invariant synthesizers and has been experimentally shown to be competitive. In particular, program invariants represented by automata are capable of expressing more complex properties of computation and their automatic construction is often less expensive.
Investigating the role of magnetic fields in star formation using molecular line profiles

Determining the importance of magnetic fields in star-forming environments is hampered by the difficulty of accurately measuring both field strength and gas properties in molecular clouds. We post-process three-dimensional non-ideal magnetohydrodynamic simulations of pre-stellar cores with a time-dependent chemical network, and use radiative transfer modelling to calculate self-consistent molecular line profiles. Varying the initial mass-to-flux ratio from subcritical to supercritical results in significant changes to both the intensity and shape of several observationally important molecular lines. We identify the peak intensity ratio of N2H+ to CS lines, and the CS J = 2–1 blue-to-red peak intensity ratio, as promising diagnostics of the initial mass-to-flux ratio, with N2H+/CS values of >0.6 (<0.2) and CS blue/red values of <3 (>5) indicating subcritical (supercritical) collapse. These criteria suggest that, despite presently being magnetically supercritical, L1498 formed from subcritical initial conditions.
Larger-Context Tagging: When and Why Does It Work?

The development of neural networks and pretraining techniques has spawned many sentence-level tagging systems that achieved superior performance on typical benchmarks. However, a relatively less discussed topic is what if more context information is introduced into current top-scoring tagging systems. Although several existing works have attempted to shift tagging systems from sentence-level to document-level, there is still no consensus conclusion about when and why it works, which limits the applicability of the larger-context approach in tagging tasks. In this paper, instead of pursuing a state-of-the-art tagging system by architectural exploration, we focus on investigating when and why the larger-context training, as a general strategy, can work. To this end, we conduct a thorough comparative study on four proposed aggregators for context information collecting and present an attribute-aided evaluation method to interpret the improvement brought by larger-context training. Experimentally, we set up a testbed based on four tagging tasks and thirteen datasets. Hopefully, our preliminary observations can deepen the understanding of larger-context training and enlighten more follow-up works on the use of contextual information.
Theoretical and observational bounds on some interacting vacuum energy scenarios

The dynamics of interacting dark matter-dark energy models is characterized through an interaction rate function quantifying the energy flow between these dark sectors. In most of the interaction functions, the expansion rate Hubble function is considered and sometimes it is argued that, as the interaction function is a local property, the inclusion of the Hubble function may influence the overall dynamics. This is the starting point of the present article where we consider a very simple interacting cosmic scenario between vacuum energy and the cold dark matter characterized by various interaction functions originated from a general interaction function: $Q= \Gamma\rho_{c}^{\alpha }\rho_{x}^{1-\alpha -\beta}(\rho_{c}+\rho_{x})^{\beta}$, where $\rho_c$, $\rho_x$ are respectively the cold dark matter density and vacuum energy density; $\alpha$, $\beta$ are real numbers and $\Gamma$ is the coupling parameter with dimension equal to the dimension of the Hubble rate. We investigate four distinct interacting cosmic scenarios and constrain them both theoretically and observationally. Our analyses clearly reveal that the interaction models should be carefully handled.
Active Cytoskeletal Composites Display Emergent Tunable Contractility and Restructuring

The cytoskeleton is a model active matter system that controls diverse cellular processes from division to motility. While both active actomyosin dynamics and actin-microtubule interactions are key to the cytoskeleton’s versatility and adaptability, an understanding of their interplay is lacking. Here, we couple microscale experiments with mechanistic modeling to elucidate how connectivity, rigidity, and force-generation affect emergent material properties in in vitro composites of actin, tubulin, and myosin. We use time-resolved differential dynamic microscopy and spatial image autocorrelation to show that ballistic contraction occurs in composites with sufficient flexibility and motor density, but that a critical fraction of microtubules is necessary to sustain controlled dynamics. Our active double-network models reveal that percolated actomyosin networks are essential for contraction, but that networks with comparable actin and microtubule densities can uniquely resist mechanical stresses while simultaneously supporting substantial restructuring. Our findings provide a much-needed blueprint for designing cytoskeleton-inspired materials that couple tunability with resilience and adaptability.
A Mixed-method Study on Security and Privacy Practices in Danish Companies

Increased levels of digitalization in society expose companies to new security threats, requiring them to establish adequate security and privacy measures. Additionally, the presence of exogenous forces like new regulations, e.g., GDPR and the global COVID-19 pandemic, pose new challenges for companies that should preserve an adequate level of security while having to adapt to change. In this paper, we investigate such challenges through a two-phase study in companies located in Denmark -- a country characterized by a high level of digitalization and trust -- focusing on software development and tech-related companies. Our results show a number of issues, most notably i) a misalignment between software developers and management when it comes to the implementation of security and privacy measures, ii) difficulties in adapting company practices in light of implementing GDPR compliance, and iii) different views on the need to adapt security measures to cope with the COVID-19 pandemic.
INODE

A full-fledged data exploration system must combine different access modalities with a powerful concept of guiding the user in the exploration process, by being reactive and anticipative both for data discovery and for data linking. Such systems are a real opportunity for our community to cater to users with different domain and data science expertise. We introduce INODE - an end-to-end data exploration system - that leverages, on the one hand, Machine Learning and, on the other hand, semantics for the purpose of Data Management (DM). Our vision is to develop a classic unified, comprehensive platform that provides extensive access to open datasets, and we demonstrate it in three significant use cases in the fields of Cancer Biomarker Research, Research and Innovation Policy Making, and Astrophysics. INODE offers sustainable services in (a) data modeling and linking, (b) integrated query processing using natural language, (c) guidance, and (d) data exploration through visualization, thus facilitating the user in discovering new insights. We demonstrate that our system is uniquely accessible to a wide range of users from larger scientific communities to the public. Finally, we briefly illustrate how this work paves the way for new research opportunities in DM.
Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS

Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential—most dramatically envisioned as a trigger to employ timely and targeted interventions (i.e., voluntary and involuntary psychiatric hospitalization) to save lives. In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS). In particular, we employ two deep learning approaches: time-variant and time-invariant modeling, for user-level suicide risk assessment, and evaluate their performance against a clinician-adjudicated gold standard Reddit corpus annotated based on the C-SSRS. Our results suggest that the time-variant approach outperforms the time-invariant method in the assessment of suicide-related ideations and supportive behaviors (AUC:0.78), while the time-invariant model performed better in predicting suicide-related behaviors and suicide attempt (AUC:0.64). The proposed approach can be integrated with clinical diagnostic interviews for improving suicide risk assessments.
Risk-Aware Lane Selection on Highway with Dynamic Obstacles

This paper proposes a discretionary lane selection algorithm. In particular, highway driving is considered as a targeted scenario, where each lane has a different level of traffic flow. When lane-changing is discretionary, it is advised not to change lanes unless highly beneficial, e.g., reducing travel time significantly or securing higher safety. Evaluating such “benefit” is a challenge, along with multiple surrounding vehicles in dynamic speed and heading with uncertainty. We propose a realtime lane-selection algorithm with careful cost considerations and with modularity in design. The algorithm is a search-based optimization method that evaluates uncertain dynamic positions of other vehicles under a continuous time and space domain. For demonstration, we incorporate a state-of-the-art motion planner framework (Neural Networks integrated Model Predictive Control) under a CARLA simulation environment.
Studying Alignment in Spontaneous Speech via Automatic Methods: How Do Children Use Task-specific Referents to Succeed in a Collaborative Learning Activity?

A dialogue is successful when there is alignment between the speakers, at different linguistic levels. In this work, we consider the dialogue occurring between interlocutors engaged in a collaborative learning task, and explore how performance and learning (i.e. task success) relate to dialogue alignment processes. The main contribution of this work is to propose new measures to automatically study alignment, to consider completely spontaneous spoken dialogues among children in the context of a collaborative learning activity. Our measures of alignment consider the children’s use of expressions that are related to the task at hand, their follow up actions of these expressions, and how it links to task success. Focusing on expressions related to the task gives us insight into the way children use (potentially unfamiliar) terminology related to the task. A ﬁrst ﬁnding of this work is the discovery that the measures we propose can capture elements of lexical alignment in such a context. Through these measures, we ﬁnd that teams with bad performance often aligned too late in the dialogue to achieve task success, and that they were late to follow up each other’s instructions with actions. We also found that while interlocutors do not exhibit hesitation phenomena (which we measure by looking at ﬁllers) in introducing expressions pertaining to the task, they do exhibit hesitation before accepting the expression, in the role of clariﬁcation. Lastly, we show that information management markers (measured by the discourse marker ‘oh’) occur in the general vicinity of the follow up actions from (automatically) inferred instructions. However, good performers tend to have this marker closer to these actions. Our measures still reﬂect some ﬁne-grained aspects of learning in the dialogue, even if we cannot conclude that overall they are linked to the ﬁnal measure of learning. Our measures contribute to the research in the ﬁeld of situated dialogue and towards bridging the gap between dialogue and collaborative learning research 1 .