Miracle-dz/UniFAR-FTUs huggingface dataset:
Personality and Social Psychology Burnout-depression overlap: Nomological network examination and factor-analytic approach

Burnout has been defined as a condition in which individuals are left exhausted by a long-term confrontation with unmanageable job stressors. The question of whether burnout reflects anything other than depressive responses to unresolvable stress remains an object of debate. In this 911-participant study (83% female; mean age: 42.36), we further addressed the issue of burnout-depression overlap. Burnout was assessed with the exhaustion subscale of the Maslach Burnout Inventory-General Survey (MBI-GS) and depression with the PHQ-8. The relationships of burnout and depression with three jobrelated variables – illegitimate work tasks, work-nonwork interference, and job satisfaction – and three “context-free” variables – social support, general health status, and trait anxiety – were examined. Burnout and depression were found to be strongly correlated, to cluster together, and to exhibit overlapping nomological networks. Remarkably, the average correlations of burnout and depression with job-related variables were almost identical. A principal component analysis and a principal axis factor analysis both showed that the items of the MBI-GS and of the PHQ-8 loaded on a single dimension. All in all, our findings are consistent with the view that burnout is a depressive condition. The distinction between burnout and depression may be an instance of the jangle fallacy.
HIGH SCHOOL STUDENTS CONCEPTION PROFILE ON THE CELL MATERIAL IN TUBAN REGENCY USING THREE-TIER TEST METHOD Profil Konsepsi Siswa SMA pada Materi Sel di Kabupaten Tuban Dengan Metode Three-Tier Test

Conception is a person's understanding of something being observed. Cell material is prone to misconceptions owing to the fact that the material is unable to be sensed, moreover there are many subconcepts that need to be fully apprehended. This research aims to describe the high school students conception profile on the cell material in Tuban Regency using the Three-Tier Test method, to describe the differences in the conception of high school students on cell material in Tuban Regency, and to describe the differences in the conception profile of students by school area. This is a descriptive-quantitative research using Three-Tier Test instrument. Data collection was carried out online via google form and interviews via google meet. 60 high school students of class XI in Tuban Regency were involved as the target of the research. The results showed that the conception profile of high school students on cell material in Tuban Regency mastered the concept of 53.21%, did not master the concept of 14.01% and misconceptions 32.78%. The highest of school that did not mastering concept was High School 1 Bancar at 31.30% and the highest misconception was High School 3 Tuban at 36.48%. Students experienced the highest misconception on the endocytosis concept, which was 63.33%. The cause of misconceptions is from student itself, worksheet book, and the teacher’s explanation.
Quantitative forward guidance through interest rate projections*

We assess quantitative forward guidance through interest rate projections along four key dimensions: (i) predictability, (ii) credibility, (iii) redundancy and (iv) consistency, based on data for the Reserve Bank of New Zealand, the Norges Bank, the Sveriges Riksbank and the Federal Reserve. We find that market expectations of the future path of interest rates predict changes in the central bank projection path, but far from fully; and market expectations adjust to path surprises after its release, but far from a one - to - one basis. We further find that central bank interest rate projections are not redundant as they impact market expectations also when controlling for the effects of central bank macro projections that are released in parallel. Finally, the interest rate projections are consistent with the macro projections as they are empirically linked by a stabilising Taylor rule.
An Efficient Approach to Move Elements in a Distributed Geo-Replicated Tree

Replicated tree data structures are extensively used in collaborative applications and distributed file systems, where clients often perform move operations. Local move operations at different replicas may be safe. However, remote move operations may not be safe. When clients perform arbitrary move operations concurrently on different replicas, it could result in various bugs, making this operation challenging to implement. Previous work has revealed bugs such as data duplication and cycling in replicated trees. In this paper, we present an efficient algorithm to perform move operations on the distributed replicated tree while ensuring eventual consistency. The proposed technique is primarily concerned with resolving conflicts efficiently, requires no interaction between replicas, and works well with network partitions. We use the last write win semantics for conflict resolution based on globally unique timestamps of operations. The proposed solution requires only one compensation operation to avoid cycles being formed when move operations are applied. The proposed approach achieves an effective speedup of 14.6× to 68.19× over the state-of-the-art approach in a geo-replicated setting.
RetTag: hardware-assisted return address integrity on RISC-V

Memory-corruption-based return address hijacking, such as Return-oriented Programming (ROP), is a prevalent attack technique that compromises the program's control flow integrity. So far, software-based defenses against these attacks either introduce heavy performance overhead or trade-off security for performance. Meanwhile, some hardware-assisted defense mechanisms are not practical for large-scale deployment due to additional requirements of hardware features and flaws caused by complicated design. In this paper, we present RetTag, a hardware-assisted and crypto-based defense scheme on RISC-V architecture that leverages Pointer Authentication Code (PAC) embedded into the unused bits of function return address to ensure return address integrity. We extend RISC-V ISA with Return Address Authentication (RAA) instructions to generate the PAC efficiently. We integrate RetTag into the mainstream compilers GCC and LLVM to help developers transparently employ the defense and implement a prototype of RetTag on the Rocket emulator and FPGA development board to demonstrate its effectiveness by detecting various ROP attacks. Moreover, the performance evaluation shows that RetTag only introduces 0.11% performance overhead on NBench and 7.69% on Coremark.
Seasonal land-ice-flow variability in the Antarctic Peninsula

Abstract. Recent satellite-remote sensing studies have documented the multi-decadal acceleration of the Antarctic Ice Sheet in response to rapid rates of ice-sheet retreat and thinning. Unlike the Greenland Ice Sheet, where historical, high-temporal-resolution satellite and in situ observations have revealed distinct changes in land-ice flow within intra-annual timescales, observations of similar seasonal signals are limited in Antarctica. Here, we use high-spatial- and high-temporal-resolution Copernicus Sentinel-1A/B synthetic aperture radar observations acquired between 2014 and 2020 to provide the first evidence for seasonal flow variability of the land ice feeding George VI Ice Shelf (GVIIS), Antarctic Peninsula. Our observations reveal a distinct austral summertime (December–February) speed-up of ∼0.06±0.005 m d−1 (∼ 22±1.8 m yr−1) at, and immediately inland of, the grounding line of the glaciers nourishing the ice shelf, which constitutes a mean acceleration of ∼15 % relative to baseline (time-series-averaged) rates of flow. These findings are corroborated by independent, optically derived velocity observations obtained from Landsat 8 imagery. Both surface and oceanic forcing mechanisms are outlined as potential controls on this seasonality. Ultimately, our findings imply that similar surface and/or ocean forcing mechanisms may be driving seasonal accelerations at the grounding lines of other vulnerable outlet glaciers around Antarctica. Assessing the degree of seasonal ice-flow variability at such locations is important for quantifying accurately Antarctica's future contribution to global sea-level rise.
ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning

Charts are very popular for analyzing data. When exploring charts, people often ask a variety of complex reasoning questions that involve several logical and arithmetic operations. They also commonly refer to visual features of a chart in their questions. However, most existing datasets do not focus on such complex reasoning questions as their questions are template-based and answers come from a fixed-vocabulary. In this work, we present a large-scale benchmark covering 9.6K human-written questions as well as 23.1K questions generated from human-written chart summaries. To address the unique challenges in our benchmark involving visual and logical reasoning over charts, we present two transformer-based models that combine visual features and the data table of the chart in a unified way to answer questions. While our models achieve the state-of-the-art results on the previous datasets as well as on our benchmark, the evaluation also reveals several challenges in answering complex reasoning questions.
Implications of phonon anisotropy on thermal conductivity of fluorite oxides

Fluorite oxides are attractive ionic compounds for a range of applications with critical thermal management requirements. In view of recent reports alluding to anisotropic thermal conductivity in this face-centered cubic crystalline systems, we perform a detailed analysis of the impact of direction-dependent phonon group velocities and lifetimes on the thermal transport of fluorite oxides. We demonstrate that the bulk thermal conductivity of this class of materials remains isotropic despite notable anisotropy in phonon lifetime and group velocity. However, breaking the symmetry of the phonon lifetime under external stimuli including boundary scattering present in nonequilibrium molecular dynamics simulations of finite size simulation cell gives rise to apparent thermal conductivity anisotropy. We observe that for accurate determination of thermal conductivity, it is important to consider phonon properties not only along high symmetry directions commonly measured in inelastic neutron or x-ray scattering experiments but also of those along lower symmetry. Our results suggests that certain low symmetry directions have a larger contribution to thermal conductivity compared to high symmetry ones.
3D Human Pose Estimation Using Möbius Graph Convolutional Networks

3D human pose estimation is fundamental to understanding human behavior. Recently, promising results have been achieved by graph convolutional networks (GCNs), which achieve state-of-the-art performance and provide rather light-weight architectures. However, a major limitation of GCNs is their inability to encode all the transformations between joints explicitly. To address this issue, we propose a novel spectral GCN using the M\"obius transformation (M\"obiusGCN). In particular, this allows us to directly and explicitly encode the transformation between joints, resulting in a significantly more compact representation. Compared to even the lightest architectures so far, our novel approach requires 90-98% fewer parameters, i.e. our lightest M\"obiusGCN uses only 0.042M trainable parameters. Besides the drastic parameter reduction, explicitly encoding the transformation of joints also enables us to achieve state-of-the-art results. We evaluate our approach on the two challenging pose estimation benchmarks, Human3.6M and MPI-INF-3DHP, demonstrating both state-of-the-art results and the generalization capabilities of M\"obiusGCN.
Seamless lightning nowcasting with recurrent-convolutional deep learning

A deep learning model is presented to nowcast the occurrence of lightning at a five-minute time resolution 60 minutes into the future. The model is based on a recurrent-convolutional architecture that allows it to recognize and predict the spatiotemporal development of convection, including the motion, growth and decay of thunderstorm cells. The predictions are performed on a stationary grid, without the use of storm object detection and tracking. The input data, collected from an area in and surrounding Switzerland, comprise ground-based radar data, visible/infrared satellite data and derived cloud products, lightning detection, numerical weather prediction and digital elevation model data. We analyze different alternative loss functions, class weighting strategies and model features, providing guidelines for future studies to select loss functions optimally and to properly calibrate the probabilistic predictions of their model. Based on these analyses, we use focal loss in this study, but conclude that it only provides a small benefit over cross entropy, which is a viable option if recalibration of the model is not practical. The model achieves a pixel-wise critical success index (CSI) of 0.45 to predict lightning occurrence within 8 km over the 60-min nowcast period, ranging from a CSI of 0.75 at a 5-min lead time to a CSI of 0.32 at a 60-min lead time.
Emulating quantum dynamics with neural networks via knowledge distillation

We introduce an efficient training framework for constructing machine learning-based emulators and demonstrate its capability by training an artificial neural network to predict the time evolution of quantum wave packets propagating through a potential landscape. This approach is based on the idea of knowledge distillation and uses elements of curriculum learning. It works by constructing a set of simple, but rich-in-physics training examples (a curriculum). These examples are used by the emulator to learn the general rules describing the time evolution of a quantum system (knowledge distillation). We show that this emulator is capable of learning the rules of quantum dynamics from a curriculum of simple training examples (wave packet interacting with a single rectangular potential barrier), and subsequently generalizes this knowledge to solve more challenging cases (propagation through an arbitrarily complex potential landscape). Furthermore, we demonstrate, that using this framework we can not only make high-fidelity predictions, but we can also learn new facts about the underlying physical system, detect symmetries, and measure relative importance of the contributing physical processes.
Measurements of second-harmonic Fourier coefficients from azimuthal anisotropies in $p$$+$$p$, $p$$+$Au, $d$$+$Au, and $^3$He$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV

Recently, the PHENIX Collaboration has published second- and third-harmonic Fourier coefficients $v_2$ and $v_3$ for midrapidity ($|\eta|<0.35$) charged hadrons in 0\%--5\% central $p$$+$Au, $d$$+$Au, and $^3$He$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV utilizing three sets of two-particle correlations for two detector combinations with different pseudorapidity acceptance [Phys. Rev. C {\bf 105}, 024901 (2022)]. This paper extends these measurements of $v_2$ to all centralities in $p$$+$Au, $d$$+$Au, and $^3$He$+$Au collisions, as well as $p$$+$$p$ collisions, as a function of transverse momentum ($p_T$) and event multiplicity. The kinematic dependence of $v_2$ is quantified as the ratio $R$ of $v_2$ between the two detector combinations as a function of event multiplicity for $0.5$$<$$p_T$$<$$1$ and $2$$<$$p_T$$<$$2.5$ GeV/$c$. A multiphase-transport (AMPT) model can reproduce the observed $v_2$ in most-central to midcentral $d$$+$Au and $^3$He$+$Au collisions. However, the AMPT model systematically overestimates the measurements in $p$$+$$p$, $p$$+$Au, and peripheral $d$$+$Au and $^3$He$+$Au collisions, indicating a higher nonflow contribution in AMPT than in the experimental data. The AMPT model fails to describe the observed $R$ for $0.5$$<$$p_T$$<$$1$ GeV/$c$, but there is qualitative agreement with the measurements for $2$$<$$p_T$$<$$2.5$ GeV/$c$.
ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers

Novel view synthesis is a long-standing problem. In this work, we consider a variant of the problem where we are given only a few context views sparsely covering a scene or an object. The goal is to predict novel viewpoints in the scene, which requires learning priors. The current state of the art is based on Neural Radiance Field (NeRF), and while achieving impressive results, the methods suffer from long training times as they require evaluating millions of 3D point samples via a neural network for each image. We propose a 2D-only method that maps multiple context views and a query pose to a new image in a single pass of a neural network. Our model uses a two-stage architecture consisting of a codebook and a transformer model. The codebook is used to embed individual images into a smaller latent space, and the transformer solves the view synthesis task in this more compact space. To train our model efficiently, we introduce a novel branching attention mechanism that allows us to use the same model not only for neural rendering but also for camera pose estimation. Experimental results on real-world scenes show that our approach is competitive compared to NeRF-based methods while not reasoning explicitly in 3D, and it is faster to train.
Galactic angular momentum in the IllustrisTNG simulation -- I. Connection to morphology, halo spin, and black hole mass

We use the TNG100 simulation of the IllustrisTNG project to investigate the stellar speciﬁc angular momenta ( j ∗ ) of ∼ 12 000 central galaxies at z = 0 in a full cosmological context, with stellar masses ( M ∗ ) ranging from 10 9 to 10 12 M (cid:12) . We ﬁnd that the j ∗ – M ∗ relations for early-type and late-type galaxies in IllustrisTNG are in good overall agreement with observations, and that these galaxy types typically ‘retain’ ∼ 10–20 and ∼ 50–60 per cent of their host haloes’ speciﬁc angular momenta, respectively, with some dependence on the methodology used to measure galaxy morphology. We present results for kinematic as well as visual-like morphological measurements of the simulated galaxies. Next, we explore the scatter in the j ∗ – M ∗ relation with respect to the spin of the dark matter halo and the mass of the supermassive black hole (BH) at the galactic centre. We ﬁnd that galaxies residing in faster spinning haloes, as well as those hosting less massive BHs, tend to have a higher speciﬁc angular momentum. We also ﬁnd that, at ﬁxed galaxy or halo mass, halo spin and BH mass are anticorrelated with each other, probably as a consequence of more efﬁcient gas ﬂow towards the galactic centre in slowly rotating systems. Finally, we show that halo spin plays an important role in determining galaxy sizes – larger discs form at the centres of faster rotating haloes – although the trend breaks down for massive galaxies with M ∗ (cid:38) 10 11 M (cid:12) , roughly the mass scale at which a galaxy’s stellar mass becomes dominated by accreted stars.
On the Importance of Rare Kaon Decays: A Snowmass 2021 White Paper

We stress the importance of precise measurements of rare decays $K^+\rightarrow\pi^+\nu\bar\nu$, $K_L\rightarrow\pi^0\nu\bar\nu$, $K_{L,S}\to\mu^+\mu^-$ and $K_{L,S}\to\pi^0\ell^+\ell^-$ for the search of new physics (NP). This includes both branching ratios and the distributions in $q^2$, the invariant mass-squared of the neutrino system in the case of $K^+\rightarrow\pi^+\nu\bar\nu$ and $K_L\rightarrow\pi^0\nu\bar\nu$ and of the $\ell^+\ell^-$ system in the case of the remaining decays. In particular the correlations between these observables and their correlations with the ratio $\varepsilon'/\varepsilon$ in $K_L\to\pi\pi$ decays, the CP-violating parameter $\varepsilon_K$ and the $K^0-\bar K^0$ mass difference $\Delta M_K$, should help to disentangle the nature of possible NP. We stress the strong sensitivity of all observables with the exception of $\Delta M_K$ to the CKM parameter $|V_{cb}|$ and list a number of $|V_{cb}|$-independent ratios within the SM which exhibit rather different dependences on the angles $\beta$ and $\gamma$ of the unitarity triangle. The particular role of these decays in probing very short distance scales far beyond the ones explored at the LHC is emphasized. In this context the role of the Standard Model Effective Field Theory (SMEFT) is very important. We also address briefly the issue of the footprints of Majorana neutrinos in $K^+\rightarrow\pi^+\nu\bar\nu$ and $K_L\rightarrow\pi^0\nu\bar\nu$.
Explicit formulae for rank zero DT invariants and the OSV conjecture

Fix a Calabi-Yau 3-fold X satisfying the Bogomolov-Gieseker conjecture of Bayer-Macr`ı-Toda, such as the quintic 3-fold. By two diﬀerent wall-crossing arguments we prove two diﬀerent explicit formulae relating rank 0 Donaldson-Thomas invariants (counting torsion sheaves on X supported on ample divisors) in terms of rank 1 Donaldson-Thomas invariants (counting ideal sheaves of curves) and Pandharipande-Thomas invariants. In particular, we prove a slight modiﬁcation of Toda’s formulation of OSV conjecture for X . When X is of Picard rank one, we also give an explicit formula for rank two DT invariants in terms of rank zero and rank one DT invariants. technique for controlling of instability the of
Interval Dominance based Structural Results for Markov Decision Process

Structural results impose sufficient conditions on the model parameters of a Markov decision process (MDP) so that the optimal policy is an increasing function of the underlying state. The classical assumptions for MDP structural results require supermodularity of the rewards and transition probabilities. However, supermodularity does not hold in many applications. This paper uses a sufficient condition for interval dominance (called I) proposed in the microeconomics literature, to obtain structural results for MDPs under more general conditions. We present several MDP examples where supermodularity does not hold, yet I holds, and so the optimal policy is monotone; these include sigmoidal rewards (arising in prospect theory for human decision making), bi-diagonal and perturbed bi-diagonal transition matrices (in optimal allocation problems). We also consider MDPs with TP3 transition matrices and concave value functions. Finally, reinforcement learning algorithms that exploit the differential sparse structure of the optimal monotone policy are discussed.
PressureVision: Estimating Hand Pressure from a Single RGB Image

People often interact with their surroundings by applying pressure with their hands. While hand pressure can be measured by placing pressure sensors between the hand and the environment, doing so can alter contact mechanics, interfere with human tactile perception, require costly sensors, and scale poorly to large environments. We explore the possibility of using a conventional RGB camera to infer hand pressure, enabling machine perception of hand pressure from uninstrumented hands and surfaces. The central insight is that the application of pressure by a hand results in informative appearance changes. Hands share biomechanical properties that result in similar observable phenomena, such as soft-tissue deformation, blood distribution, hand pose, and cast shadows. We collected videos of 36 participants with diverse skin tone applying pressure to an instrumented planar surface. We then trained a deep model (PressureVisionNet) to infer a pressure image from a single RGB image. Our model infers pressure for participants outside of the training data and outperforms baselines. We also show that the output of our model depends on the appearance of the hand and cast shadows near contact regions. Overall, our results suggest the appearance of a previously unobserved human hand can be used to accurately infer applied pressure. Data, code, and models are available online 1 . provide a demonstration of PressureVisionNet inferring plausible pressure given conditions outside of the training data. Our demonstrations involve a