Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential elliptic flow of identified hadrons and constituent quark number scaling at FAIR

Differential elliptic flow v2(pT ) for identified hadrons has been investigated in the FAIR energy regime, employing a hadronic-string transport model (UrQMD) as well as a partonic transport model (AMPT). It has been observed that both the models show a mass ordering of v2 at low pT and a switch over resulting a baryon-meson crossing at intermediate pT . AMPT generates higher v2 values compared to UrQMD. In addition, constituent quark number scaling behavior of elliptic flow has also been addressed. Scaling behavior in terms of the transverse momentum pT is found to absent for both the partonic as well as the hadronic model. However UrQMD and AMPT with string melting scenario do exhibit a NCQ scaling of v2 at varying degree, with respect to transverse kinetic energy KET . But default AMPT, where partonic scatterings are not included, does not show any considerable scaling behavior. A variable α is defined to quantify the degree of KET scaling. We found that UrQMD gives better scaling than AMPT at FAIR.
Structural and mechanistic insights into the precise product synthesis by a bifunctional miltiradiene synthase

Summary Selaginella moellendorffii miltiradiene synthase (SmMDS) is a unique bifunctional diterpene synthase (diTPS) that catalyses the successive cyclization of (E,E,E)‐geranylgeranyl diphosphate (GGPP) via (+)‐copalyl diphosphate (CPP) to miltiradiene, which is a crucial precursor of important medicinal compounds, such as triptolide, ecabet sodium and carnosol. Miltiradiene synthetic processes have been studied in monofunctional diTPSs, while the precise mechanism by which active site amino acids determine product simplicity and the experimental evidence for reaction intermediates remain elusive. In addition, how bifunctional diTPSs work compared to monofunctional enzymes is attractive for detailed research. Here, by mutagenesis studies of SmMDS, we confirmed that pimar‐15‐en‐8‐yl+ is an intermediate in miltiradiene synthesis. Moreover, we determined the apo‐state and the GGPP‐bound state crystal structures of SmMDS. By structure analysis and mutagenesis experiments, possible contributions of key residues both in class I and II active sites were suggested. Based on the structural and functional analyses, we confirmed the copal‐15‐yl+ intermediate and unveiled more details of the catalysis process in the SmMDS class I active site. Moreover, the structural and experimental results suggest an internal channel for (+)‐CPP produced in the class II active site moving towards the class I active site. Our research is a good example for intermediate identification of diTPSs and provides new insights into the product specificity determinants and intermediate transport, which should greatly facilitate the precise controlled synthesis of various diterpenes.
UBE2T promotes glioblastoma malignancy through ubiquitination‐mediated degradation of RPL6

Glioblastoma (GBM) is the most frequent and aggressive malignant glioma. Due to patients’ poor prognosis, it is of great clinical significance to determine new targets that may improve GBM treatment. In the present study, we showed that ubiquitin (Ub)‐conjugating enzyme E2T (UBE2T) was significantly overexpressed in GBM and could promote proliferation, invasion, and inhibit apoptosis of GBM cells. Mechanistically, UBE2T functioned as the Ub enzyme of ribosomal protein L6 (RPL6) and induced the ubiquitination and degradation of RPL6 in an E3 ligase‐independent manner through direct modification by K48‐linked polyubiquitination, thus contributing to the malignant progression of GBM cells. Furthermore, inhibiting the expression of RPL6 by UBE2T could not only reduce the expression of wild‐type p53, but also enhance the gain‐of‐function of mutant p53. Moreover, knockdown of UBE2T in LN229 cells obviously suppressed tumor growth in LN229 xenograft mouse models. Collectively, our study demonstrated that UBE2T promotes GBM malignancy through ubiquitination‐mediated degradation of RPL6 regardless of the p53 mutation status. It will provide new candidates for molecular biomarkers and therapeutic targets for clinical application in GBM.
Device-led versus human-led feedback on chest compressions for cardiopulmonary resuscitation and providers experience and preference: a randomised crossover study

Background: High cardiopulmonary resuscitation (CPR) quality is associated with better patient survival from cardiac arrest. However, CPR providers may not have an accurate perception of the depth and rate of their chest compressions (CC). Realtime feedback during resuscitation improves CPR quality compared to no feedback. Evidence comparing audio-visual feedback device (AVF) and team leaders feedback (TLF) in improving CPR performance is limited and conflicting. Methodology: We performed a randomized crossover study to evaluate CC performance with AVF and TLF. Seventy participants performed CC for 1 minute on a CPR manikin connected to ZOLL R series defibrillator with CPR-sensing capability in a randomised crossover sequence. We interviewed participants to explore their perception and preference with both feedback methods. Results: Mean CC rate was higher with AVF than with TLF (121.8 min -1 {+/-} 17.7 vs. 117.4 min -1 {+/-} 13.5, p = 0.005). There was no significant difference in proportions of participants performing CC within the recommended rate of 100-120 beats per minute between AVF and TLF (48.6% and 51.4%, p = 0.824). Overall, CC depth was below the recommended target regardless of feedback method with mean CC depth of 4.4 cm {+/-} 0.8 in AVF and 4.3 cm {+/-} 0.9 in TLF respectively ( p = 0.479). Most participants felt that TLF was easier to follow, more motivating and preferable compared to AVF. Those who preferred TLF performed CC at rates above the recommended range with AVF compared to TLF (124.1 min -1 {+/-} 19.4 versus 118.2 min -1 {+/-} 14.9, p = 0.004). Conclusion: A well-trained team leader is as effective as an AVF device in leading high-quality CC. CPR providers performance may be influenced by their preferred feedback method.
Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video Grounding

Spatio-Temporal video grounding (STVG) focuses on retrieving the spatio-temporal tube of a specific object depicted by a free-form textual expression. Existing approaches mainly treat this complicated task as a parallel frame-grounding problem and thus suffer from two types of inconsistency drawbacks: feature alignment inconsistency and prediction inconsistency. In this paper, we present an end-to-end one-stage framework, termed Spatio-Temporal Consistency-Aware Transformer (STCAT), to alleviate these issues. Specially, we introduce a novel multi-modal template as the global objective to address this task, which explicitly constricts the grounding region and associates the predictions among all video frames. Moreover, to generate the above template under sufficient video-textual perception, an encoder-decoder architecture is proposed for effective global context modeling. Thanks to these critical designs, STCAT enjoys more consistent cross-modal feature alignment and tube prediction without reliance on any pre-trained object detectors. Extensive experiments show that our method outperforms previous state-of-the-arts with clear margins on two challenging video benchmarks (VidSTG and HC-STVG), illustrating the superiority of the proposed framework to better understanding the association between vision and natural language. Code is publicly available at https://github.com/jy0205/STCAT.
On polynomial-time solvability of combinatorial Markov random fields

The problem of inferring Markov random fields (MRFs) with a sparsity or robustness prior can be naturally modeled as a mixed-integer program. This motivates us to study a general class of convex submodular optimization problems with indicator variables, which we show to be polynomially solvable in this paper. The key insight is that, possibly after a suitable reformulation, indicator constraints preserve submodularity. Fast computations of the associated Lov\'asz extensions are also discussed under certain smoothness conditions, and can be implemented using only linear-algebraic operations in the case of quadratic objectives.
Duality viewpoint of criticality

In this work, we study quantum many-body systems which are self-dual under duality transformation connecting diﬀerent symmetry protected topological (SPT) phases. We provide a geometric explanation of the criticality of these self-dual models. More precisely, we show a ground state (quasi-)degeneracy under the periodic boundary conditions,i.e., the ingappability of the bulk spectrum. Equivalently, the symmetry group at criticality, including the duality symmetry, has a mixed ’t Hooft anomaly. This approach can not only predict the spectrum of the self-dual model with ordinary 0-form symmetry, but also be applied to that with generalized symmetry, such as higher form and subsystem symmetry. As an application, we illustrate our results with several examples in one and two dimensions, which separate two diﬀerent SPTs.
Top partners and scalar dark matter: A nonminimal reappraisal

A large set of models beyond the Standard Model of particle physics suggest that the top quark plays a special role in fundamental interactions. At the same time some of these models predict that a particle responsible for dark matter is present in the physical spectrum of the theory, and in particular cases dark matter may be specially linked to the top sector. Such a new physics configuration occurs, for instance, in composite frameworks that additionally feature vector-like quarks as mediators of the interactions of the dark matter with the Standard Model. We investigate the interplay of top-philic dark matter and the presence of several vector-like top partners. Such a non-minimal setup with several vector-like fermions is typical of composite scenarios, and we make use of an effective model to study to which extent such non-minimality modifies the collider and cosmological phenomenology of the minimal model, the latter only including one vector-like mediator of the dark matter interactions with the Standard Model.
Unsupervised Domain Adaptation with Histogram-gated Image Translation for Delayered IC Image Analysis

Deep learning has achieved great success in the challenging circuit annotation task by employing Convolutional Neural Networks (CNN) for the segmentation of circuit structures. The deep learning approaches require a large amount of manually annotated training data to achieve a good performance, which could cause a degradation in performance if a deep learning model trained on a given dataset is applied to a different dataset. This is commonly known as the domain shift problem for circuit annotation, which stems from the possibly large variations in distribution across different image datasets. The different image datasets could be obtained from different devices or different layers within a single device. To address the domain shift problem, we propose Histogram-gated Image Translation (HGIT), an unsupervised domain adaptation framework which transforms images from a given source dataset to the domain of a target dataset, and utilize the transformed images for training a segmentation network. Specifically, our HGIT performs generative adversarial network (GAN)-based image translation and utilizes histogram statistics for data curation. Experiments were conducted on a single labeled source dataset adapted to three different target datasets (without labels for training) and the segmentation performance was evaluated for each target dataset. We have demonstrated that our method achieves the best performance compared to the reported domain adaptation techniques, and is also reasonably close to the fully supervised benchmark.
Overleaf Example

Mobile manipulators in the home can provide increased autonomy to individuals with severe motor impairments, who often cannot complete activities of daily living (ADLs) without the help of a caregiver. Teleoperation of an assistive mobile manipulator could enable an individual with motor impairments to independently perform self-care and household tasks, yet limited motor function can impede one’s ability to interface with a robot. In this work, we present a unique inertial-based wearable assistive interface, embedded in a familiar head-worn garment, for individuals with severe motor impairments to teleoperate and perform physical tasks with a mobile manipulator. We evaluate this wearable interface with both able-bodied (N = 16) and individuals with motor impairments (N = 2) for performing ADLs and everyday household tasks. Our results show that the wearable interface enabled participants to complete physical tasks with low error rates, high perceived ease of use, and low workload measures. Overall, this inertial-based wearable serves as a new assistive interface option for control of mobile manipulators in the home.
From Ranked Lists to Carousels: A Carousel Click Model

Carousel-based recommendation interfaces allow users to explore recommended items in a structured, efficient, and visually-appealing way. This made them a de-facto standard approach to recommending items to end users in many real-life recommenders. In this work, we try to explain the efficiency of carousel recommenders using a \emph{carousel click model}, a generative model of user interaction with carousel-based recommender interfaces. We study this model both analytically and empirically. Our analytical results show that the user can examine more items in the carousel click model than in a single ranked list, due to the structured way of browsing. These results are supported by a series of experiments, where we integrate the carousel click model with a recommender based on matrix factorization. We show that the combined recommender performs well on held-out test data, and leads to higher engagement with recommendations than a traditional single ranked list.
3D non-LTE iron abundances in FG-type dwarfs

Iron is one of the most important elements in-stellar astrophysics. However, spectroscopic measurements of its abundance are prone to systematic modelling errors. We present three dimensional non-local thermodynamic equilibrium (3D non-LTE) calculations across 32 STAGGER-grid models with effective temperatures from 5000 K to 6500 K, surface gravities of 4.0 dex and 4.5 dex, and metallicities from −3 dex to 0 dex, and we study the effects on 171 Fe i and 12 Fe ii optical lines. In warm metal-poor stars, the 3D non-LTE abundances are up to 0.5 dex larger than 1D LTE abundances inferred from Fe i lines of an intermediate excitation potential. In contrast, the 3D non-LTE abundances can be 0.2 dex smaller in cool metal-poor stars when using Fe i lines of a low excitation potential. The corresponding abundance differences between 3D non-LTE and 1D non-LTE are generally less severe but can still reach ±0.2 dex. For Fe ii lines, the 3D abundances range from up to 0.15 dex larger to 0.10 dex smaller than 1D abundances, with negligible departures from 3D LTE except for the warmest stars at the lowest metallicities. The results were used to correct 1D LTE abundances of the Sun and Procyon (HD 61421), and of the metal-poor stars HD 84937 and HD 140283, using an interpolation routine based on neural networks. The 3D non-LTE models achieve an improved ionisation balance in all four stars. In the two metal-poor stars, they removed excitation imbalances amounting to 250 K to 300 K errors in effective temperature. For Procyon, the 3D non-LTE models suggest [Fe/H] = 0.11 ± 0.03, which is significantly larger than literature values based on simpler models. We make the 3D non-LTE interpolation routine for FG-type dwarfs publicly available, in addition to 1D non-LTE departure coefficients for standard MARCS models of FGKM-type dwarfs and giants. These tools, together with an extended 3D LTE grid for Fe ii from 2019, can help improve the accuracy of stellar parameter and iron abundance determinations for late-type stars.
Efficient View Path Planning for Autonomous Implicit Reconstruction

Implicit neural representations have shown promising potential for 3D scene reconstruction. Recent work applies it to autonomous 3D reconstruction by learning information gain for view path planning. Effective as it is, the computation of the information gain is expensive, and compared with that using volumetric representations, collision checking using the implicit representation for a 3D point is much slower. In the paper, we propose to 1) leverage a neural network as an implicit function approximator for the information gain field and 2) combine the implicit fine-grained representation with coarse volumetric representations to improve efficiency. Further with the improved efficiency, we propose a novel informative path planning based on a graph-based planner. Our method demonstrates significant improvements in the reconstruction quality and planning efficiency compared with autonomous reconstructions with implicit and explicit representations. We deploy the method on a real UAV and the results show that our method can plan informative views and reconstruct a scene with high quality.
Exploiting GPU/SIMD Architectures for Solving Linear-Quadratic MPC Problems*

We report numerical results on solving linear-quadratic model predictive control (MPC) problems by exploiting graphics processing units (GPUs). The presented method reduces the MPC problem by eliminating the state variables and applies a condensed-space interior-point method to remove the inequality constraints in the KKT system. The final condensed matrix is positive definite and can be efficiently factorized in parallel on GPU/SIMD architectures. In addition, the size of the condensed matrix depends only on the number of controls in the problem, rendering the method particularly effective when the problem has many states but few inputs and moderate horizon length. Our numerical results for PDE-constrained problems show that the approach is an order of magnitude faster than a standard CPU implementation. We also provide an open-source Julia framework that facilitates modeling (DynamicNLPModels.jl) and solution (MadNLP.jl) of MPC problems on GPUs.
Resilient Distributed Optimization*

This paper considers a distributed optimization problem in the presence of Byzantine agents capable of introducing untrustworthy information into the communication network. A resilient distributed subgradient algorithm is proposed based on graph redundancy and objective redundancy. It is shown that the algorithm causes all non-Byzantine agents’ states to asymptotically converge to the same optimal point under appropriate assumptions. A partial convergence rate result is also provided.
Towards Real Time Thermal Simulations for Design Optimization using Graph Neural Networks

This paper presents a method to simulate the thermal behavior of 3D systems using a graph neural network. The method discussed achieves a significant speed-up with respect to a traditional finite-element simulation. The graph neural network is trained on a diverse dataset of 3D CAD designs and the corresponding finite-element simulations, representative of the different geometries, material properties and losses that appear in the design of electronic systems. We present for the transient thermal behavior of a test system. The accuracy of the network result for one-step predictions is remarkable (0.003% error). After 400 time steps, the accumulated error reaches 0.78 %. The computing time of each time step is 50 ms. Reducing the accumulated error is the current focus of our work. In the future, a tool such as the one we are presenting could provide nearly instantaneous approximations of the thermal behavior of a system that can be used for design optimization.
High order approximations of the Cox-Ingersoll-Ross process semigroup using random grids

We present new high order approximations schemes for the Cox–Ingersoll–Ross (CIR) process that are obtained by using a recent technique developed by Alfonsi and Bally (2021, A generic construction for high order approximation schemes of semigroups using random grids. Numer. Math., 148, 743–793) for the approximation of semigroups. The idea consists in using a suitable combination of discretization schemes calculated on different random grids to increase the order of convergence. This technique coupled with the second order scheme proposed by Alfonsi (2010, High order discretization schemes for the CIR process: application to affine term structure and Heston models. Math. Comp., 79, 209–237) for the CIR leads to weak approximations of order $2k$, for all $k\in{{\mathbb{N}}}^{\ast }$. Despite the singularity of the square-root volatility coefficient, we show rigorously this order of convergence under some restrictions on the volatility parameters. We illustrate numerically the convergence of these approximations for the CIR process and for the Heston stochastic volatility model and show the computational time gain they give.
A wide-area view of the Phoenix dwarf galaxy from VLT/FORS imaging

We present results from a wide-area photometric survey of the Phoenix dwarf galaxy, one of the rare dwarf irregular/ dwarf spheroidal transition type galaxies (dTs) of the Local Group (LG). These objects offer the opportunity to study the existence of possible evolutionary links between the late- and early- type LG dwarf galaxies, since the properties of dTs suggest that they may be dwarf irregulars in the process of transforming into dwarf spheroidals. Using FORS at the VLT we have acquired VI photometry of Phoenix. The data reach a S/N~10 just below the horizontal branch of the system and consist of a mosaic of images that covers an area of 26' x 26' centered on the coordinates of the optical center of the galaxy. Examination of the colour-magnitude diagram and luminosity function revealed the presence of a bump above the red clump, consistent with being a red giant branch bump. The deep photometry combined with the large area covered allows us to put on a secure ground the determination of the overall structural properties of the galaxy and to derive the spatial distribution of stars in different evolutionary phases and age ranges, from 0.1 Gyr to the oldest stars. The best-fitting profile to the overall stellar population is a Sersic profile of Sersic radius R_S = 1.82'+-0.06' and m=0.83+-0.03. We confirm that the spatial distribution of stars is found to become more and more centrally concentrated the younger the stellar population, as reported in previous studies. This is similar to the stellar population gradients found for close-by Milky Way dwarf spheroidal galaxies. We quantify such spatial variations by analyzing the surface number density profiles of stellar populations in different age ranges; [Abridged]
Radiation-free and non-Hermitian topology inertial defect states of on-chip magnons

Radiative damping is a strong dissipation source for the quantum emitters hybridized with propagating photons, electrons, or phonons, which is not easily avoidable for on-chip magnonic emitters as well that can radiate via the surface acoustic waves of the substrate. Here we demonstrate in an array of on-chip nano-magnets coupled in a long range via exchanging the surface acoustic waves that a point defect in the array, which can be introduced by the local magnon frequency shift by a local biased magnetic ﬁeld or the absence of a magnetic wire, strongly localizes the magnons, in contrast to the well spreading Bloch-like collective magnon modes in such an array setting. The radiation of the magnon defect states is exponentially suppressed by the distance of the defect to the array edges. Moreover, this defect state is strikingly inertial to the non-Hermitian topology that localizes all the extended states at one boundary. Such conﬁguration robust magnon defect states towards radiation-free limit may be suitable for high-ﬁdelity magnon quantum information storage in the future on-chip magnonic devices.