Miracle-dz/UniFAR-FTUs huggingface dataset:
HiStore: Rethinking Hybrid Index in RDMA-based Key-Value Store

RDMA (Remote Direct Memory Access) is widely exploited in building key-value stores to achieve ultra low latency. In RDMA-based key-value stores, the indexing time takes a large fraction (up to 74%) of the overall operation latency as RDMA enables fast data accesses. However, the single index structure used in existing RDMA-based key-value stores, either hash-based or sorted index, fails to support range queries efficiently while achieving high performance for single-point operations. In this paper, we reconsider the adoption of hybrid index in the key-value stores based on RDMA, to combine the benefits of hash table and sorted index. We propose HiStore, an RDMA-based key-value store using hash table for single-point lookups and leveraging skiplist for range queries. To maintain strong consistency in a lightweight and efficient approach, HiStore introduces index groups where a skiplist corresponds to a hash table, and asynchronously applies updates to the skiplist within a group. Guided by previous work on using RDMA for key-value services, HiStore dedicatedly chooses different RDMA primitives to optimize the read and write performance. Furthermore, HiStore tolerates the failures of servers that maintain index structures with index replication for high availability. Our evaluation results demonstrate that HiStore improves the performance of both GET and SCAN operations (by up to 2.03x) with hybrid index.
ECP SOLLVE: Validation and Verification Testsuite Status Update and Compiler Insight for OpenMP

The OpenMP language continues to evolve with every new specification release, as does the need to validate and verify the new features that have been implemented by the different vendors. With the release of OpenMP 5.0 and OpenMP 5.1, new target offload and host-based features have been introduced to the programming model. While OpenMP continues to grow in maturity, there is an observable growth in the number of compiler and hardware vendors that support OpenMP. In this manuscript, the main focus is on evaluating the conformity and OpenMP implementation progress of various compiler vendors such as Cray, IBM, GNU, Clang/LLVM, NVIDIA, and Intel. More specifically, the 4.5, 5.0, and 5.1 versions of the OpenMP specification are analyzed. For our experimental setup, the Crusher and Summit computing systems hosted by Oak Ridge National Lab’s Computing Facilities are utilized. The effort of vendor agnostic analysis of these implementations is especially valuable for application developers who are using new OpenMP features to accelerate their scientific codes. Insights are presented into the current implementation status of various vendors, the progression of specific compiler’s support for OpenMP overtime, the subset of OpenMP 4.5, 5.0, and 5.1 that is supported by all compilers, and examples of how our test suite has influenced discussion regarding the correct interpretation of the OpenMP specification. By evaluating OpenMP conformity of pre-Exascale computing systems, the aim is to detail progress and status of AMD + Cray ecosystem before the system and their OpenMP implementation is used for mission critical applications when the first Exascale Computer Frontier is made available to applications.
Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy

Background Deep learning-based head and neck lymph node level (HN_LNL) autodelineation is of high relevance to radiotherapy research and clinical treatment planning but still underinvestigated in academic literature. In particular, there is no publicly available open-source solution for large-scale autosegmentation of HN_LNL in the research setting. Methods An expert-delineated cohort of 35 planning CTs was used for training of an nnU-net 3D-fullres/2D-ensemble model for autosegmentation of 20 different HN_LNL. A second cohort acquired at the same institution later in time served as the test set (n = 20). In a completely blinded evaluation, 3 clinical experts rated the quality of deep learning autosegmentations in a head-to-head comparison with expert-created contours. For a subgroup of 10 cases, intraobserver variability was compared to the average deep learning autosegmentation accuracy on the original and recontoured set of expert segmentations. A postprocessing step to adjust craniocaudal boundaries of level autosegmentations to the CT slice plane was introduced and the effect of autocontour consistency with CT slice plane orientation on geometric accuracy and expert rating was investigated. Results Blinded expert ratings for deep learning segmentations and expert-created contours were not significantly different. Deep learning segmentations with slice plane adjustment were rated numerically higher (mean, 81.0 vs. 79.6, p = 0.185) and deep learning segmentations without slice plane adjustment were rated numerically lower (77.2 vs. 79.6, p = 0.167) than manually drawn contours. In a head-to-head comparison, deep learning segmentations with CT slice plane adjustment were rated significantly better than deep learning contours without slice plane adjustment (81.0 vs. 77.2, p = 0.004). Geometric accuracy of deep learning segmentations was not different from intraobserver variability (mean Dice per level, 0.76 vs. 0.77, p = 0.307). Clinical significance of contour consistency with CT slice plane orientation was not represented by geometric accuracy metrics (volumetric Dice, 0.78 vs. 0.78, p = 0.703). Conclusions We show that a nnU-net 3D-fullres/2D-ensemble model can be used for highly accurate autodelineation of HN_LNL using only a limited training dataset that is ideally suited for large-scale standardized autodelineation of HN_LNL in the research setting. Geometric accuracy metrics are only an imperfect surrogate for blinded expert rating.
Constraints on dark matter scattering with long lived mediators from observations of the Sun with the Fermi Large Area Telescope

The Sun represents a promising target for indirect dark matter searches, as dark matter particles from the Galactic halo can be gravitationally trapped in its core or in external orbits, and their annihilations can lead to final states with standard model particles that are able to reach the Earth. In this work we have considered a scenario in which dark matter particles can annihilate into pairs of long-lived mediators, which in turn can escape from the Sun and decay into pairs of gamma rays or into the bb̅, τ + τ -, μ + μ - channels, with the production of gamma rays in the final states. All these processes are expected to yield an excess in the energy spectrum of gamma rays towards the Sun. We have therefore analyzed the data collected by the Fermi Large Area Telescope during its first 13.5 years of operation, searching for possible excesses in the solar gamma-ray spectrum. Since no statistically significant excess is found, we have set constraints on the dark matter-nucleon scattering cross sections in both the spin-dependent and spin-independent cases. For all the mediator decay channels explored and for dark matter masses between a few GeV/c2 and 1 TeV/c2, we have found that the upper limits on the spin-dependent and spin-independent cross sections are in the ranges from 10-45 to 10-39 cm2 and from 10-47 up to 10-42 cm2, respectively.
Room temperature spin-orbit torque efficiency and magnetization switching in 
SrRuO3
-based heterostructures

Spin-orbit torques (SOTs) from transition metal oxides (TMOs) in conjunction with magnetic materials have recently attracted tremendous attention for realizing high-efficient spintronic devices. SrRuO3 is a promising candidate among TMOs due to its large and tunable SOT-efficiency as well as high conductivity and chemical stability. However, a further study for benchmarking the SOT-efficiency and realizing SOT-driven magnetization switching in SrRuO3 is still highly desired so far. Here, we systematically study the SOT properties of high-quality SrRuO3 thin film heterostructuring with different magnetic alloys of both IMA and PMA configuration by the harmonic Hall voltage technique. Our results indicate that SrRuO3 possesses pronounced SOT-efficiency of about 0.2 at room temperature regardless of the magnetic alloys, which is comparable to typical heavy metals (HMs). Furthermore, we achieve SOT-driven magnetization switching with a low threshold current density of 3.8x10^10 A/m^2, demonstrating the promising potential of SrRuO3 for practical devices. By making a comprehensive comparison with HMs, our work unambiguously benchmarks the SOT properties and concludes the advantages of SrRuO3, which may bring more diverse choices for SOT applications by utilizing hybrid-oxide/metal and all-oxide systems.
Some results on degenerate harmonic numbers and degenerate Fubini polynomials

A BSTRACT . In recent years, some degenerate versions of quite a few special numbers and polynomials are introduced and investigated by means of various methods. The aim of this paper is to study some results on degenerate harmonic numbers, degenerate hyperharmonic numbers, degenerate Fubi polynomials and degenerate r -Fubini polynomials from a general identity which is valid for any two formal power series and involves the degenerate r -Stirling numbers of the second kind.
Kinematic data rebuild the nuclear star cluster as the most metal-rich region of the Galaxy

The Galactic centre (GC) is located at only 8 kpc from Earth and constitutes a unique template to understand Galactic nuclei. Nevertheless, the high crowding and extinction towards the GC hamper the study of its main stellar components, the nuclear stellar disc (NSD) and the nuclear star cluster (NSC). Recent work has suggested that the NSD and the NSC can be distinguished along the line of sight towards the NSC via the different extinction of their stars. This motivated us to analyse the proper motion, radial velocity, and the metallicity distributions of the different extinction groups. We use photometric, kinematic, and metallicity data to distinguish between probable NSD and NSC stars in a region centred on the NSC. We detected two different extinction groups of stars and obtained significantly different proper motion distributions for each of them, in agreement with the expected kinematics for the NSD and the NSC. We derived radial velocity maps that appear to be different for the NSD and the NSC. We also found different metallicities for each of the components, with the largest one measured for the most extinguished group of stars. We obtained that the metallicity distribution of each extinction group is best fitted by a bimodal distribution, indicating the presence of two metallicity components for each of them (a broad one slightly below solar metallicity, and a more metal rich narrower one, that is largest for the high extinction group of stars). We conclude that both extinction groups are distinct GC components with different kinematics and metallicity, and correspond to the NSD and the NSC. Therefore, it is possible to distinguish them via their different extinction. The high mean metallicity, $[M/H]\sim0.3$\,dex, obtained for the NSC metal rich stars, supports that the NSC is arguabily the most metal rich region of the Galaxy.
Towards Accurate Reconstruction of 3D Scene Shape From A Single Monocular Image

Despite significant progress made in the past few years, challenges remain for depth estimation using a single monocular image. First, it is nontrivial to train a metric-depth prediction model that can generalize well to diverse scenes mainly due to limited training data. Thus, researchers have built large-scale relative depth datasets that are much easier to collect. However, existing relative depth estimation models often fail to recover accurate 3D scene shapes due to the unknown depth shift caused by training with the relative depth data. We tackle this problem here and attempt to estimate accurate scene shapes by training on large-scale relative depth data, and estimating the depth shift. To do so, we propose a two-stage framework that first predicts depth up to an unknown scale and shift from a single monocular image, and then exploits 3D point cloud data to predict the depth shift and the camera's focal length that allow us to recover 3D scene shapes. As the two modules are trained separately, we do not need strictly paired training data. In addition, we propose an image-level normalized regression loss and a normal-based geometry loss to improve training with relative depth annotation. We test our depth model on nine unseen datasets and achieve state-of-the-art performance on zero-shot evaluation. Code is available at: https://github.com/aim-uofa/depth/.
Gauging Industry's Perspectives on Soft Skills of Graduate Architects: Importance vs Satisfaction

The roles of architects today are becoming more diverse and demanding. Given the higher market expectation and broad job roles, architects need diverse qualities ranging from technical expertise to soft skills. To meet with these escalating requirements in the workplace, architectural firms nowadays demand for graduate architects that are appropriately equipped with relevant skills. This study focused on identifying the soft skills of graduate architects deemed important by the industry and to gauge their satisfaction with the level of skills possessed by the graduates. The results of a survey of professional/senior architects from 65 architectural firms suggested that proficiencies in English language (in terms of oral, written, oral presentation and written presentation) were rated as most important skills for graduate architects. Other highly valued soft skills include time management, listening, teamwork, problem solving, leadership, and decision makings. The findings further revealed that the employers were least satisfied with the graduates’ English languages abilities in terms of written presentation, written communication and oral presentation. In addition, negotiation skills and analytical & critical thinking were also rated low in terms of satisfaction. The findings were useful in recognizing employers' expectations of soft skills and identifying gaps that exist in the quality of soft skills among graduate architects. Implications of the findings are discussed and recommendations for future research are put forward.
Investigating student interpretations of the differences between classical and quantum computers: Are quantum computers just analog classical computers?

Significant attention in the PER community has been paid to student cognition and reasoning processes in undergraduate quantum mechanics. Until recently, however, these same topics have remained largely unexplored in the context of emerging interdisciplinary quantum information science (QIS) courses. We conducted exploratory interviews with 22 students in an upper-division quantum computing course at a large R1 university crosslisted in physics and computer science, as well as 6 graduate students in a similar graduate-level QIS course offered in physics. We classify and analyze students' responses to a pair of questions regarding the fundamental differences between classical and quantum computers. We specifically note two key themes of importance to educators: (1) when reasoning about computational power, students often struggled to distinguish between the relative effects of exponential and linear scaling, resulting in students frequently focusing on distinctions that are arguably better understood as analog-digital than classical-quantum, and (2) introducing the thought experiment of analog classical computers was a powerful tool for helping students develop a more expertlike perspective on the differences between classical and quantum computers.
Reputation and the occurrence and success of mediation in civil wars

In this article, we argue that because governments fear that accepting mediation and reaching agreements with opponents may signal weakness to other potential challengers, civil war governments tend to resist mediation as a means of demonstrating their resolve. Building on current theories of reputation in civil war, we argue that the threat of future challenges from other groups is likely to be particularly acute in states with multiple ethnic groups, especially states with high levels of ethnic exclusion. We therefore expect that civil wars in these states will be less likely to see mediation and to produce agreements when they do. By examining all instances of mediation in intrastate conflict from 1990 to 2008, we test this argument empirically and find that mediation is more likely to emerge in civil wars where there are a large number of ethnic groups, but is less likely to occur when many of those ethnic groups are excluded from the political process. Once mediation is underway, however, it is less likely to yield a negotiated settlement when there are a large number of ethnic groups, but more likely to end in a negotiated settlement when many of the ethnic groups are excluded from the political process.
POSITIVITY OF ZERO–REGULAR BUNDLES, CONTINUOUS CM–REGULARITY, AND GENERIC VANISHING

A BSTRACT . The purpose of this article is twofold. Firstly, we study the positivity propertiesof 0–regular bundles (in the sense of Castelnuovo–Mumford) on a polarized smooth projective variety ( X , O X (1)). Secondly, we study the continuous CM–regularity of torsion–free coherent sheaves on ( X , O X (1)), and its relation with the theory of generic vanishing. This continuous variant of CM–regularity was introduced by Mustopa, and he raised the question whether a continuously 1–regular such sheaf F is GV. Here we answer the question in the afﬁrmative for many pairs ( X , O X (1)) which includes the case of any polarized abelian variety. Moreover, for these pairs, we show that if F is continuously k –regular for some integer 1 ≤ k ≤ dim X , then F is a GV − ( k − 1) sheaf. Further, we extend the notion of continuous CM–regularity to a real valued function on the Q –twisted bundles on polarizedabelian varieties( X , O X (1)), and we show thatthis functioncan be extended to a continuous function on N 1 ( X ) R . We also provide syzygetic consequences of our results for O P ( E ) (1) on P ( E ) associated to a 0–regular bundle E on polarized smooth projective varieties. In particular, when X is abelian, we showthat O P ( E ) (1) satisﬁes N p propertyif thebase–point freeness thresholdofthe class of O X (1) in N 1 ( X ) is less than 1 p + 2 . This result is obtained using a theorem in the Appendix A written by Atsushi Ito.
Temporal Label Smoothing for Early Prediction of Adverse Events

Models that can predict adverse events ahead of time with low false-alarm rates are critical to the acceptance of decision support systems in the medical community. This challenging machine learning task remains typically treated as simple binary classiﬁcation, with few bespoke methods proposed to leverage temporal dependency across samples. We propose Temporal Label Smoothing (TLS), a novel learning strategy that modulates smoothing strength as a function of proximity to the event of interest. This regularization technique reduces model conﬁdence at the class boundary, where the signal is often noisy or uninformative, thus allowing training to focus on clinically informative data points away from this boundary region. From a theoretical perspective, we also show that our method can be framed as an extension of multi-horizon prediction, a learning heuristic proposed in other early prediction work. TLS empirically matches or outperforms considered competing methods on various early prediction benchmark tasks. In particular, our approach signiﬁcantly improves performance on clinically-relevant metrics such as event recall at low false-alarm rates.
Towards Explaining Demographic Bias through the Eyes of Face Recognition Models

Biases inherent in both data and algorithms make the fairness of widespread machine learning (ML)-based decision-making systems less than optimal. To improve the trustfulness of such ML decision systems, it is crucial to be aware of the inherent biases in these solutions and to make them more transparent to the public and developers. In this work, we aim at providing a set of explainability tool that analyse the difference in the face recognition models' behaviors when processing different demographic groups. We do that by leveraging higher-order statistical information based on activation maps to build explainability tools that link the FR models' behavior differences to certain facial regions. The experimental results on two datasets and two face recognition models pointed out certain areas of the face where the FR models react differently for certain demographic groups compared to reference groups. The outcome of these analyses interestingly aligns well with the results of studies that analyzed the anthropometric differences and the human judgment differences on the faces of different demographic groups. This is thus the first study that specifically tries to explain the biased behavior of FR models on different demographic groups and link it directly to the spatial facial features. The code is publicly available here11https://github.com/fbiying87/Demographic-Bias-Visualization.git.
Thermal keV dark matter in a gauged 
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 model with cosmic inflation

We investigate the possibility of keV scale thermal dark matter (DM) in a gauged $B - L$ extension of the standard model with three right-handed neutrinos (RHN) and one vector like fermion in the context of cosmic inflation. The complex singlet scalar field responsible for the spontaneous breaking of $B-L$ gauge symmetry is non-minimally coupled to gravity and serves the role of inflaton. The keV scale vector like fermion DM gives rise to the possibility of warm dark matter, but it gets overproduced thermally. The subsequent entropy dilution due to one of the RHN decay can bring the thermal abundance of DM within the observed limit. The dynamics of both the DM and the diluter are regulated by the $B-L$ model parameters which are also restricted by the requirement of successful inflationary dynamics.We constrain the model parameter space from the requirement of producing sufficient entropy dilution to obtain correct DM relic, inflationary observables along with other phenomenological constraints. Interestingly, we obtain unique predictions for the order of lightest active neutrino mass ($m_{\nu_l}\lesssim 10^{-14}$ eV) as function of $B-L$ gauge coupling for keV scale DM. The proposed framework also explains the origin of observed baryon asymmetry from the decay of other two heavier RHN by overcoming the entropy dilution effect.