Miracle-dz/UniFAR-FTUs huggingface dataset:
Observations From an Online Security Competition and Its Implications on Crowdsourced Security

The crowd sourced security industry, particularly bug bounty programs, has grown dramatically over the past years and has become the main source of software security reviews for many companies. However, the academic literature has largely omitted security teams, particularly in crowd work contexts. As such, we know very little about how distributed security teams organize, collaborate, and what technology needs they have. We ﬁll this gap by conducting focus groups with the top ﬁve teams (out of 18,201 participating teams) of a computer security Capture-the-Flag (CTF) competition. We ﬁnd that these teams adopted a set of strategies centered on specialties, which allowed them to reduce issues relating to dispersion, double work, and lack of previous collaboration. Observing the current issues of a model centered on individual workers in security crowd work platforms, our study cases that scaling security work to teams is feasible and beneﬁcial. Finally, we identify various areas which warrant future work, such as issues of social identity in high-skilled crowd work environments.
Microstructure of Charged AdS Black Hole with Minimal Length Effects

In this work, the microstructure of charged AdS black holes under minimal length effects is investigated. We study the thermodynamics of black holes in the extended phase space, where the cosmological constant is regarded as the thermodynamic pressure. The modified Hawking temperature and phase transition are obtained based on the generalized uncertainty principle (GUP). Then, using thermodynamic geometry, the microstructure of black holes can be determined by the ratio of GUP parameter to charge. For a small ratio, the black hole exhibits the typical RN-AdS microstructure with van der Waals phase transition and repulsive/attractive interactions. As the ratio increases, the reentrant phase transition takes place, and both the repulsion-attraction coexisted black hole and the attraction dominated black hole can be found in this case. For a large ratio, the black hole behaves like a Schwarzchild-AdS black hole in which neither phase transition nor repulsive interaction exists. These results suggest that the GUP effect will reduce the repulsive interaction presented by the charged AdS black hole.
The Global Glimm Property

It is known that a C ∗ -algebra with the Global Glimm Property is nowhere scattered (it has no elementary ideal-quotients), and the Global Glimm Problem asks if the converse holds. We provide a new approach to this long-standing problem by showing that a C ∗ -algebra has the Global Glimm Property if and only if it is nowhere scattered and its Cuntz semigroup is ideal- ﬁltered (the Cuntz classes generating a given ideal are downward directed) and has property (V) (a weak form of being sup-semilattice ordered). We show that ideal-ﬁlteredness and property (V) are automatic for C ∗ algebras that have stable rank one or real rank zero, thereby recovering the solutions to the Global Glimm Problem in these cases. We also use our approach to solve the Global Glimm Problem for new classes of C ∗ -algebras.
Unsupervised Learning of Unbiased Visual Representations

Deep neural networks are known for their inability to learn robust representations when biases exist in the dataset. This results in a poor generalization to unbiased datasets, as the predictions strongly rely on peripheral and confounding factors, which are erroneously learned by the network. Many existing works deal with this issue by either employing an explicit supervision on the bias attributes, or assuming prior knowledge about the bias. In this work we study this problem in a more difficult scenario, in which no explicit annotation about the bias is available, and without any prior knowledge about its nature. We propose a fully unsupervised debiasing framework, consisting of three steps: first, we exploit the natural preference for learning malignant biases, obtaining a bias-capturing model; then, we perform a pseudo-labelling step to obtain bias labels; finally we employ state-of-the-art supervised debiasing techniques to obtain an unbiased model. We also propose a theoretical framework to assess the biasness of a model, and provide a detailed analysis on how biases affect the training of neural networks. We perform experiments on synthetic and real-world datasets, showing that our method achieves state-of-the-art performance in a variety of settings, sometimes even higher than fully supervised debiasing approaches.
Identification of Inertial Modes in the Solar Convection Zone

The observation of global acoustic waves (p modes) in the Sun has been key to unveiling its internal structure and dynamics. A different kind of wave, known as sectoral Rossby modes, has been observed and identified, which potentially opens the door to probing internal processes that are inaccessible through p-mode helioseismology. Yet another set of waves, appearing as retrograde-propagating, equatorially antisymmetric vorticity waves, has also been observed but their identification remained elusive. Here, through a numerical model implemented as an eigenvalue problem, we provide evidence supporting the identification of those waves as a class of inertial eigenmodes, distinct from the Rossby-mode class, with radial velocities comparable to the horizontal ones deep in the convective zone but still small compared to the horizontal velocities toward the surface. We also suggest that the signature of tesseral-like Rossby modes might be present in recent observational data.
Magnetic Drag and 3D Effects in Theoretical High-resolution Emission Spectra of Ultrahot Jupiters: the Case of WASP-76b

Ultrahot Jupiters are ideal candidates to explore with high-resolution emission spectra. Detailed theoretical studies are necessary to investigate the range of spectra that we can expect to see from these objects throughout their orbit, because of the extreme temperature and chemical longitudinal gradients that exist across their dayside and nightside regions. Using previously published 3D general circulation models of WASP-76b with different treatments of magnetic drag, we postprocess the 3D atmospheres to generate high-resolution emission spectra for two wavelength ranges, throughout the planet’s orbit. We find that the high-resolution emission spectra vary strongly as a function of phase, at times showing emission features, absorption features, or both, which are a direct result of the 3D structure of the planet. At phases exhibiting both emission and absorption features, the Doppler shift differs in direction between the two spectral features, making them differentiable, instead of canceling each other out. Through the use of cross correlation, we find different patterns in net Doppler shift for models with different treatments of drag: the nightside spectra show opposite signs in their Doppler shift, while the dayside phases display a reversal in the trend of net shift with phase. Finally, we caution researchers against using a single spectral template throughout the planet’s orbit; this can bias the corresponding net Doppler shift returned, as it can pick up on a bright region on the edge of the planet disk that is highly redshifted or blueshifted.
On the Verification of Belief Programs

In a recent paper, Belle and Levesque proposed a framework for a type of program called belief programs, a probabilistic extension of GOLOG programs where every action and sensing result could be noisy and every test condition refers to the agent's subjective beliefs. Inherited from GOLOG programs, the action-centered feature makes belief programs fairly suitable for high-level robot control under uncertainty. An important step before deploying such a program is to verify whether it satisfies properties as desired. At least two problems exist in doing verification: how to formally specify properties of a program and what is the complexity of verification. In this paper, we propose a formalism for belief programs based on a modal logic of actions and beliefs. Among other things, this allows us to express PCTL-like temporal properties smoothly. Besides, we investigate the decidability and undecidability for the verification problem of belief programs.
MCRB-based Performance Analysis of 6G Localization under Hardware Impairments

Location information is expected to be the key to meeting the needs of communication and context-aware services in 6G systems. User localization is achieved based on delay and/or angle estimation using uplink or downlink pilot signals. However, hardware impairments (HWIs) distort the signals at both the transmitter and receiver sides and thus affect the localization performance. While this impact can be ignored at lower frequencies where HWIs are less severe, modeling and analysis efforts are needed for 6G to evaluate the localization degradation due to HWIs. In this work, we model various types of impairments and conduct a misspecified Cramér-Rao bound analysis to evaluate the HWI-induced performance loss. Simulation results with different types of HWIs show that each HWI leads to a different level of degradation in angle and delay estimation performance.
On Online Attention Dynamics

This work aims at emphasizing a number of questions that, although crucial since the early days of media studies, have not yet been the object of the empirical and computational study that they deserve: How does collective attention concentrate and dissipate in modern communication systems? How do subjects and sources rise and fall in public debates? How are these dynamics shaped by media infrastructures? In the perspective of addressing these questions, this chapter provides a review of the literature on the dynamics of online content dissemination: our goal is to prepare the ground for the necessary study, which should comprise empirical investigation, mathematical modeling, numerical simulation, and rigorous system-theoretic analysis.
Learning Eco-Driving Strategies at Signalized Intersections

Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions. There has thus been a line of work studying eco-driving control strategies to reduce fuel consumption and emission levels at intersections. However, methods to devise effective control strategies across a variety of traffic settings remain elusive. In this paper, we propose a reinforcement learning (RL) approach to learn effective eco-driving control strategies. We analyze the potential impact of a learned strategy on fuel consumption, CO2 emission, and travel time and compare with naturalistic driving and model-based baselines. We further demonstrate the generalizability of the learned policies under mixed traffic scenarios. Simulation results indicate that scenarios with 100% penetration of connected autonomous vehicles (CAV) may yield as high as 18% reduction in fuel consumption and 25% reduction in CO2 emission levels while even improving travel speed by 20%. Furthermore, results indicate that even 25% CAV penetration can bring at least 50% of the total fuel and emission reduction benefits.
Layer formation in a stably stratified fluid cooled from above: Towards an analog for Jupiter and other gas giants

In 1D evolution models of gas giant planets, an outer convection zone advances into the interior as the surface cools, and multiple convective layers form beneath that convective front. To study layer formation below an outer convection zone in a similar scenario, we investigate the evolution of a stably-stratified fluid with a linear composition gradient that is constantly being cooled from above. We use the Boussinesq approximation in a series of 2D simulations at low and high Prandtl numbers ($\mathrm{Pr} = 0.5$ and 7), initialized with constant temperature everywhere, and cooled at different rates. We find that multiple convective layers form at $\mathrm{Pr} = 7$, {as the result of an instability in the} diffusive thermal boundary layer below the outer convection zone. At low Pr, layers do not form because the temperature gradient within the boundary layer is much smaller than at large Pr and, consequently, is not large enough to overcome the stabilizing effect of the composition gradient. For the stratification used in this study, on the long-term the composition gradient is an ineffective barrier against the propagation of the outer convection zone and the entire fluid becomes fully-mixed, whether layers form or not. Our results challenge 1D evolutionary models of gas giant planets, which predict that layers are long-lived and that the outer convective envelope stops advancing inwards. We discuss what is needed for future work to build more realistic models.
On Transferability of Prompt Tuning for Natural Language Processing

Prompt tuning (PT) is a promising parameter-efficient method to utilize extremely large pre-trained language models (PLMs), which can achieve comparable performance to full-parameter fine-tuning by only tuning a few soft prompts. However, PT requires much more training time than fine-tuning. Intuitively, knowledge transfer can help to improve the efficiency. To explore whether we can improve PT via prompt transfer, we empirically investigate the transferability of soft prompts across different downstream tasks and PLMs in this work. We find that (1) in zero-shot setting, trained soft prompts can effectively transfer to similar tasks on the same PLM and also to other PLMs with a cross-model projector trained on similar tasks; (2) when used as initialization, trained soft prompts of similar tasks and projected prompts of other PLMs can significantly accelerate training and also improve the performance of PT. Moreover, to explore what decides prompt transferability, we investigate various transferability indicators and find that the overlapping rate of activated neurons strongly reflects the transferability, which suggests how the prompts stimulate PLMs is essential. Our findings show that prompt transfer is promising for improving PT, and further research shall focus more on prompts’ stimulation to PLMs. The source code can be obtained from https://github.com/thunlp/Prompt-Transferability.
Task-Aware Sampling Layer for Point-Wise Analysis

Sampling, grouping, and aggregation are three important components in the multi-scale analysis of point clouds. In this paper, we present a novel data-driven sampler learning strategy for point-wise analysis tasks. Unlike the widely used sampling technique, Farthest Point Sampling (FPS), we propose to learn sampling and downstream applications jointly. Our key insight is that uniform sampling methods like FPS are not always optimal for different tasks: sampling more points around boundary areas can make the point-wise classification easier for segmentation. Towards this end, we propose a novel sampler learning strategy that learns sampling point displacement supervised by task-related ground truth information and can be trained jointly with the underlying tasks. We further demonstrate our methods in various point-wise analysis tasks, including semantic part segmentation, point cloud completion, and keypoint detection. Our experiments show that jointly learning of the sampler and task brings better performance than using FPS in various point-based networks.
Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation

Stochastic gradient methods have enabled variational inference for high-dimensional models. However, the steepest ascent direction in the parameter space of a statistical model is actually given by the natural gradient which premultiplies the widely used Euclidean gradient by the inverse Fisher information. Use of natural gradients can improve convergence, but inverting the Fisher information matrix is daunting in high-dimensions. In Gaussian variational approximation, natural gradient updates of the mean and precision of the normal distribution can be derived analytically, but do not ensure that the precision matrix remains positive definite. To tackle this issue, we consider Cholesky decomposition of the covariance or precision matrix, and derive analytic natural gradient updates of the Cholesky factor, which depend on either the first or second derivative of the log posterior density. Efficient natural gradient updates of the Cholesky factor are also derived under sparsity constraints representing different posterior correlation structures. As Adam's adaptive learning rate does not work well with natural gradients, we propose stochastic normalized natural gradient ascent with momentum. The efficiency of proposed methods are demonstrated using logistic regression and generalized linear mixed models.
SoFaiR: Single Shot Fair Representation Learning

To avoid discriminatory uses of their data, organizations can learn to map them into a representation that filters out information related to sensitive attributes. However, all existing methods in fair representation learning generate a fairness-information trade-off. To achieve different points on the fairness-information plane, one must train different models. In this paper, we first demonstrate that fairness-information trade-offs are fully characterized by rate-distortion trade-offs. Then, we use this key result and propose SoFaiR, a single shot fair representation learning method that generates with one trained model many points on the fairness-information plane. Besides its computational saving, our single-shot approach is, to the extent of our knowledge, the first fair representation learning method that explains what information is affected by changes in the fairness / distortion properties of the representation. Empirically, we find on three datasets that SoFaiR achieves similar fairness information trade-offs as its multi-shot counterparts.
ADM formulation and Hamiltonian analysis of $f(Q)$ gravity

$f(Q)$ gravity is an extension of the symmetric teleparallel equivalent to general relativity. We demonstrate the Hamiltonian analysis of $f(Q)$ gravity with fixing the coincident gauge condition. Using the standard Dirac-Bergmann algorithm, we show that $f(Q)$ gravity has 8 physical degrees of freedom. This result reflects that the diffeomorphism symmetry of $f(Q)$ gravity is completely broken due to the gauge fixing. Moreover, in terms of the perturbations, we discuss the possible mode decomposition of these degrees of freedom.
Infrared study of the layered magnetic insulator 
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Topological insulators with intrinsic magnetic ordering, potentially hosting rare quantum effects, recently attracted extensive attention. MnBi$_2$Te$_4$ is the first established example. The Sb-doped variant Mn(Bi$_{1-x}$Sb$_x$)$_2$Te$_4$ shows a great variety of electronic properties depending on the Sb content~$x$, such as shifts in the Fermi level and the Neel temperature $T_{\mathrm N}$, and the change of the free charge carrier type from $n$- to $p$-type at high Sb substitution ratios. Here, we investigate the effect of magnetic ordering on the bulk electronic structure of Mn(Bi$_{1-x}$Sb$_x$)$_2$Te$_4$ with high Sb content $x=0.93$ by temperature-dependent reflectivity measurements over a broad frequency range. We observe anomalies in the optical response across $T_{\mathrm N}$ when the antiferromagnetic order sets, which suggests a coupling between the magnetic ordering and the electronic structure of the material.
Discrete energy analysis of the third-order variable-step BDF time-stepping for diffusion equations

This is one of our series works on discrete energy analysis of the variable-step BDF schemes. In this part, we present stability and convergence analysis of the third-order BDF (BDF3) schemes with variable steps for linear diffusion equations, see e.g. [SIAM J. Numer. Anal. , 58:2294-2314] and [Math. Comp., 90: 1207-1226] for our previous works on the BDF2 scheme. To this aim, we first build up a discrete gradient structure of the variable-step BDF3 formula under the condition that the adjacent step ratios are less than 1.4877, by which we can establish a discrete energy dissipation law. Mesh-robust stability and convergence analysis in the $L^2$ norm are then obtained. Here the mesh robustness means that the solution errors are well controlled by the maximum time-step size but independent of the adjacent time-step ratios. We also present numerical tests to support our theoretical results.
Testing the Ability of Language Models to Interpret Figurative Language

Figurative and metaphorical language are commonplace in discourse, and figurative expressions play an important role in communication and cognition. However, figurative language has been a relatively under-studied area in NLP, and it remains an open question to what extent modern language models can interpret nonliteral phrases. To address this question, we introduce Fig-QA, a Winograd-style nonliteral language understanding task consisting of correctly interpreting paired figurative phrases with divergent meanings. We evaluate the performance of several state-of-the-art language models on this task, and find that although language models achieve performance significantly over chance, they still fall short of human performance, particularly in zero- or few-shot settings. This suggests that further work is needed to improve the nonliteral reasoning capabilities of language models.
Black Hole to Photosphere: 3D GRMHD Simulations of Collapsars Reveal Wobbling and Hybrid Composition Jets

Long-duration γ-ray bursts (GRBs) accompany the collapse of massive stars and carry information about the central engine. However, no 3D models have been able to follow these jets from their birth via black hole (BH) to the photosphere. We present the first such 3D general-relativity magnetohydrodynamic simulations, which span over six orders of magnitude in space and time. The collapsing stellar envelope forms an accretion disk, which drags inwardly the magnetic flux that accumulates around the BH, becomes dynamically important, and launches bipolar jets. The jets reach the photosphere at ∼1012 cm with an opening angle θ j ∼ 6° and a Lorentz factor Γ j ≲ 30, unbinding ≳90% of the star. We find that (i) the disk–jet system spontaneously develops misalignment relative to the BH rotational axis. As a result, the jet wobbles with an angle θ t ∼ 12°, which can naturally explain quiescent times in GRB lightcurves. The effective opening angle for detection θ j + θ t suggests that the intrinsic GRB rate is lower by an order of magnitude than standard estimates. This suggests that successful GRBs are rarer than currently thought and emerge in only ∼0.1% of supernovae Ib/c, implying that jets are either not launched or choked inside most supernova Ib/c progenitors. (ii) The magnetic energy in the jet decreases due to mixing with the star, resulting in jets with a hybrid composition of magnetic and thermal components at the photosphere, where ∼10% of the gas maintains magnetization σ ≳ 0.1. This indicates that both a photospheric component and reconnection may play a role in the prompt emission.