Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal (and Hybrid Thermal/Audio) Side-Channel Attacks on Keyboard Input

To date, there has been no systematic investigation of thermal proﬁles of keyboards, and thus no efforts have been made to secure them. This serves as our main motivation for constructing a means for password harvesting from keyboard thermal emanations. Speciﬁcally, we introduce Thermanator : a new post-factum insider attack based on heat transfer caused by a user typing a password on a typical external (plastic) keyboard. We conduct and describe a user study that collected thermal residues from 30 users entering 10 unique passwords (both weak and strong) on 4 popular commodity keyboards. Results show that entire sets of key-presses can be recovered by non-expert users as late as 30 seconds after initial password entry, while partial sets can be recovered as late as 1 minute after entry. However, the thermal residue side-channel lacks information about password length, duplicate key-presses, and key-press ordering. To overcome these limitations, we leverage keyboard acoustic emanations and combine the two to yield AcuTherm , the ﬁrst hybrid side-channel attack on keyboards. AcuTherm signiﬁcantly reduces password search without the need for any training on the victim’s typing. We report results gathered for many representative passwords based on a user study involving 19 subjects. The takeaway of this work is three-fold: (1) using plastic keyboards to enter secrets (such as passwords and PINs) is even less secure than previously recognized, (2) post-factum thermal imaging attacks are realistic, and (3) hybrid (multiple side-channel) attacks are both realistic and effective.
Intracluster stars in simulations with active galactic nucleus feedback

We use a set of high-resolution hydrodynamical simulations of clusters of galaxies to study the build-up of the intracluster light (ICL), an interesting and likely significant component of their total stellar mass. Our sample of groups and clusters includes active galactic nucleus (AGN) feedback and is of high enough resolution to accurately resolve galaxy populations down to the smallest galaxies that are expected to significantly contribute to the stellar mass budget. We describe and test four different methods to identify the ICL in cluster simulations, thereby allowing us to assess the reliability of the measurements. For all of the methods, we consistently find a very significant ICL stellar fraction (∼45 per cent) which exceeds the values typically inferred from observations. However, we show that this result is robust with respect to numerical resolution and integration accuracy, remarkably insensitive to changes in the star formation model, and almost independent of halo mass. It is also almost invariant when black hole growth is included, even though AGN feedback successfully prevents excessive overcooling in clusters and leads to a drastically improved agreement of the simulated cluster galaxy population with observations. In particular, the luminosities of central cluster galaxies and the ages of their stellar populations are much more realistic when including AGN. In the light of these findings, it appears challenging to construct a simulation model that simultaneously matches the cluster galaxy population and at the same time produces a low ICL component. We find that intracluster stars are preferentially stripped in a cluster's densest region from massive galaxies that fall into the forming cluster at z > 1. Surprisingly, some of the intracluster stars also form in the intracluster medium inside cold gas clouds that are stripped out of infalling galaxies.
Nodeless superconductivity in the topological nodal-line semimetal 
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CaSb2 is a topological nodal-line semimetal that becomes superconducting below 1.6 K, providing an ideal platform to investigate the interplay between topologically nontrivial electronic bands and superconductivity. In this work, we investigated the superconducting order parameter of CaSb2 by measuring its magnetic penetration depth change {\Delta}{\lambda}(T) down to 0.07 K, using a tunneling diode oscillator (TDO) based technique. Well inside the superconducting state, {\Delta}{\lambda}(T) shows an exponential activated behavior, and provides direct evidence for a nodeless superconducting gap. By analyzing the temperature dependence of the superfluid density and the electronic specific heat, we find both can be consistently described by a two-gap s-wave model, in line with the presence of multiple Fermi surfaces associated with distinct Sb sites in this compound. These results demonstrate fully-gapped superconductivity in CaSb2 and constrain the allowed pairing symmetry.
Weak error analysis for the stochastic Allen–Cahn equation

We prove strong rate resp. weak rate $${{\mathcal {O}}}(\tau )$$ O ( τ ) for a structure preserving temporal discretization (with $$\tau $$ τ the step size) of the stochastic Allen–Cahn equation with additive resp. multiplicative colored noise in $$d=1,2,3$$ d = 1 , 2 , 3 dimensions. Direct variational arguments exploit the one-sided Lipschitz property of the cubic nonlinearity in the first setting to settle first order strong rate. It is the same property which allows for uniform bounds for the derivatives of the solution of the related Kolmogorov equation, and then leads to weak rate $${{\mathcal {O}}}(\tau )$$ O ( τ ) in the presence of multiplicative noise. Hence, we obtain twice the rate of convergence known for the strong error in the presence of multiplicative noise.
RELAXATION IN N-BODY SIMULATIONS OF DISK GALAXIES

I use N-body simulations with two mass species of particles to demonstrate that disk galaxy simulations are subject to collisional relaxation at a higher rate than is widely assumed. Relaxation affects the vertical thickness of the disk most strongly, and drives the velocity ellipsoid to a moderately flattened shape similar to that observed for disk stars in the solar neighborhood. The velocity ellipsoid in simulations with small numbers of particles quickly approaches this shape, but shot noise also dominates the in-plane behavior. Simulations with higher, but reachable, numbers of particles relax slowly enough to be considered collisionless, allowing the in-plane dispersions to rise due to spiral activity without heating the vertical motions. Relaxation may have affected many previously published simulations of the formation and evolution of galaxy disks.
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions

Contrastive learning is a powerful framework for learning self-supervised representations that generalize well to downstream supervised tasks. We show that multiple existing contrastive learning methods can be reinterpreted as learning kernel functions that approximate a fixed positive-pair kernel. We then prove that a simple representation obtained by combining this kernel with PCA provably minimizes the worst-case approximation error of linear predictors, under a straightforward assumption that positive pairs have similar labels. Our analysis is based on a decomposition of the target function in terms of the eigenfunctions of a positive-pair Markov chain, and a surprising equivalence between these eigenfunctions and the output of Kernel PCA. We give generalization bounds for downstream linear prediction using our Kernel PCA representation, and show empirically on a set of synthetic tasks that applying Kernel PCA to contrastive learning models can indeed approximately recover the Markov chain eigenfunctions, although the accuracy depends on the kernel parameterization as well as on the augmentation strength.
Faster parameterized algorithms for modification problems to minor-closed classes

Let ${\cal G}$ be a minor-closed graph class and let $G$ be an $n$-vertex graph. We say that $G$ is a $k$-apex of ${\cal G}$ if $G$ contains a set $S$ of at most $k$ vertices such that $G\setminus S$ belongs to ${\cal G}$. Our first result is an algorithm that decides whether $G$ is a $k$-apex of ${\cal G}$ in time $2^{{\sf poly}(k)}\cdot n^2$, where ${\sf poly}$ is a polynomial function depending on ${\cal G}$. This algorithm improves the previous one, given by Sau, Stamoulis, and Thilikos [ICALP 2020], whose running time was $2^{{\sf poly}(k)}\cdot n^3$. The elimination distance of $G$ to ${\cal G}$, denoted by ${\sf ed}_{\cal G}(G)$, is the minimum number of rounds required to reduce each connected component of $G$ to a graph in ${\cal G}$ by removing one vertex from each connected component in each round. Bulian and Dawar [Algorithmica 2017] provided an FPT-algorithm, with parameter $k$, to decide whether ${\sf ed}_{\cal G}(G)\leq k$. However, its dependence on $k$ is not explicit. We extend the techniques used in the first algorithm to decide whether ${\sf ed}_{\cal G}(G)\leq k$ in time $2^{2^{2^{{\sf poly}(k)}}}\cdot n^2$. This is the first algorithm for this problem with an explicit parametric dependence in $k$. In the special case where ${\cal G}$ excludes some apex-graph as a minor, we give two alternative algorithms, running in time $2^{2^{{\cal O}(k^2\log k)}}\cdot n^2$ and $2^{{\sf poly}(k)}\cdot n^3$ respectively, where $c$ and ${\sf poly}$ depend on ${\cal G}$. As a stepping stone for these algorithms, we provide an algorithm that decides whether ${\sf ed}_{\cal G}(G)\leq k$ in time $2^{{\cal O}({\sf tw}\cdot k+{\sf tw}\log{\sf tw})}\cdot n$, where ${\sf tw}$ is the treewidth of $G$. Finally, we provide explicit upper bounds on the size of the graphs in the minor-obstruction set of the class of graphs ${\cal E}_k({\cal G})=\{G\mid{\sf ed}_{\cal G}(G)\leq k\}$.
SUZAKU OBSERVATIONS OF THE TYPE 2 QSO IN THE CENTRAL GALAXY OF THE PHOENIX CLUSTER

We report the Suzaku/XIS and HXD and Chandra/ACIS-I results on the X-ray spectra of the Phoenix cluster at the redshift z = 0.596. The spectrum of the intracluster medium (ICM) is well reproduced with the emissions from low-temperature (∼3.0 keV and ∼0.76 solar) and high-temperature (∼11 keV and ∼0.33 solar) plasmas; the former is localized at the cluster core, while the latter distributes over the cluster. In addition to these ICM emissions, a strongly absorbed power-law component is found, which is due to an active galactic nucleus (AGN) in the cluster center. The absorption column density and unobscured luminosity of the AGN are ∼3.2 × 1023 cm−2 and ∼4.7 × 1045 erg s−1 (2–10 keV), respectively. Furthermore, a neutral iron (Fe i) K-shell line is discovered for the first time with the equivalent width (EW) of ∼150 eV at the rest frame. The column density and the EW of the Fe i line are exceptionally large for such a high-luminosity AGN, and hence the AGN is classified as a type 2 quasi-stellar object (QSO). We speculate that a significant fraction of the ICM cooled gas would be consumed to maintain the torus and to activate the type 2 QSO. The Phoenix cluster has a massive starburst in the central galaxy, indicating that suppression in the cooling flow is less effective. This may be because the onset of the latest AGN feedback has occurred recently and has not yet been effective. Alternatively, the AGN feedback is predominantly in radiative mode, not in kinetic mode, and the torus may work as a shield to reduce its effect.
Buoyancy-driven bubbly flows: role of meso-scale structures on the relative motion between phases in bubble columns operated in the heterogeneous regime

Abstract The hydrodynamics of bubble columns in the heterogeneous regime is investigated from experiments with bubbles at large particle Reynolds numbers and without coalescence. The void fraction field $\varepsilon$ at small scales, analysed with Voronoï tessellations, corresponds to a random Poisson process (RPP) in homogeneous conditions but it significantly differs from an RPP in the heterogeneous regime. The distance to an RPP allows identifying meso-scale structures, namely clusters, void regions and intermediate regions. A series of arguments demonstrate that the bubble motion is driven by the dynamics of these structures. Notably, bubbles in clusters (respectively in intermediate regions) are moving up faster, up to 3.5 (respectively 2) times the terminal velocity, than bubbles in void regions whose absolute velocity equals the mean liquid velocity. In addition, the mean unconditional relative velocity of bubbles is recovered from mean relative velocities conditional to meso-scale structures, weighted by the proportion of bubbles in each structure. Assuming buoyancy–inertia equilibrium for each structure, the relative velocity is related to the characteristic size and concentration of meso-scale structures. By considering the latter quantity's values at large gas superficial velocities, a cartoon of the internal flow structure is proposed. Arguments are proposed to help understanding why the relative velocity scales as $(gD\varepsilon )^{1/2}$ (with $D$ the column's diameter and $g$ gravity's acceleration). The proposed cartoon seems consistent with a fast-track mechanism that, for the moderate Rouse numbers studied, leads to liquid velocity fluctuations proportional to the relative velocity. The potential impact of coalescence on the above analysis is also commented.
Distributed Online Generalized Nash Equilibrium Tracking for Prosumer Energy Trading Games

With the proliferation of distributed generations, traditional passive consumers in distribution networks are evolving into "prosumers", which can both produce and consume energy. Energy trading with the main grid or between prosumers is inevitable if the energy surplus and shortage exist. To this end, this paper investigates the peer-to-peer (P2P) energy trading market, which is formulated as a generalized Nash game. We first prove the existence and uniqueness of the generalized Nash equilibrium (GNE). Then, an distributed online algorithm is proposed to track the GNE in the time-varying environment. Its regret is proved to be bounded by a sublinear function of learning time, which indicates that the online algorithm has an acceptable accuracy in practice. Finally, numerical results with six microgrids validate the performance of the algorithm.
A NONUNIFORM MARKUS–YAMABE CONJECTURE: TRIANGULAR CASE VIA UNIFORMIZATION

We introduce a nonautonomous nonuniform Markus-Yamabe Conjecture, namely, a global problem on nonuniform asymptotic stability for nonau- tonomous diﬀerential systems, whose restriction to the autonomous case is related to the classical Markus–Yamabe Conjecture. Additionally, we prove that the conjecture is veriﬁed in dimension one and for a family of triangular systems of nonautonomous diﬀerential equations satisfying technical boundedness assumptions. An essential tool to carry out the proof in the triangular case is a necessary and suﬃcient condition ensuring the property of nonuniform expo- nential dichotomy for upper block triangular linear diﬀerential systems, which has been developed previously for the uniform case. We extend this result for a speciﬁc nonuniform exponential dichotomy and we obtain some byproducts having interest on itself, such as, the diagonal signiﬁcance property.
Joint Reconfigurable Intelligent Surface Location and Passive Beamforming Optimization for Maximizing the Secrecy-Rate

The physical layer security (PLS) is investigated for reconfigurable intelligent surface (RIS) assisted wireless networks, where a source transmits its confidential information to a legitimate destination with the aid of a single small RIS in the presence of a malicious eavesdropper. A new joint RIS location and passive beamforming (J-LPB) optimization scheme is proposed for the sake of maximizing the secrecy rate under the RIS location constraint and the constraint that the modulus of the reflecting coefficient at each RIS's unit is not larger than 1. Specifically, we analyze the optimal location of the RIS, and conclude that the product involving the source-RIS distance and the RIS-destination distance should be minimized. Since the product minimization problem is nonconvex, we then propose a two-tier optimization algorithm for solving it. Based on the near-optimal RIS 3D location obtained, we further formulate the passive beamforming optimization problem, and then propose to apply the Charnes-Cooper transformation along with the sequential rank-one constraint relaxation (SROCR) algorithm to solve it. Our numerical results show that the secrecy rate of the proposed J-LPB optimization scheme is higher than that of the benchmarks. Explicitly, we use the following benchmarks: the near-source-based RIS location and passive beamforming (NSB-LPB) optimization scheme, the near-destination-based RIS location and passive beamforming (NDB-LPB) optimization scheme, and the random RIS location and passive beamforming (R-LPB) optimization scheme. Finally, the benefits of our J-LPB scheme are further increased with the number of RIS units.
Ask Me Anything: A simple strategy for prompting language models

Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt that demonstrates how to perform the task and no additional training. Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore significant effort is dedicated towards designing a painstakingly"perfect prompt"for a task. To mitigate the high degree of effort involved in prompt-design, we instead ask whether producing multiple effective, yet imperfect, prompts and aggregating them can lead to a high quality prompting strategy. Our observations motivate our proposed prompting method, ASK ME ANYTHING (AMA). We first develop an understanding of the effective prompt formats, finding that question-answering (QA) prompts, which encourage open-ended generation ("Who went to the park? ") tend to outperform those that restrict the model outputs ("John went to the park. Output True or False."). Our approach recursively uses the LLM itself to transform task inputs to the effective QA format. We apply the collected prompts to obtain several noisy votes for the input's true label. We find that the prompts can have very different accuracies and complex dependencies and thus propose to use weak supervision, a procedure for combining the noisy predictions, to produce the final predictions for the inputs. We evaluate AMA across open-source model families (e.g., EleutherAI, BLOOM, OPT, and T0) and model sizes (125M-175B parameters), demonstrating an average performance lift of 10.2% over the few-shot baseline. This simple strategy enables the open-source GPT-J-6B model to match and exceed the performance of few-shot GPT3-175B on 15 of 20 popular benchmarks. Averaged across these tasks, the GPT-J-6B model outperforms few-shot GPT3-175B. We release our code here: https://github.com/HazyResearch/ama_prompting
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse

As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means for recourse. While several approaches have been proposed to construct recourses for affected individuals, the recourses output by these methods either achieve low costs (i.e., ease-of-implementation) or robustness to small perturbations (i.e., noisy implementations of recourses), but not both due to the inherent trade-offs between the recourse costs and robustness. Furthermore, prior approaches do not provide end users with any agency over navigating the aforementioned trade-offs. In this work, we address the above challenges by proposing the first algorithmic framework which enables users to effectively manage the recourse cost vs. robustness trade-offs. More specifically, our framework Probabilistically ROBust rEcourse (\texttt{PROBE}) lets users choose the probability with which a recourse could get invalidated (recourse invalidation rate) if small changes are made to the recourse i.e., the recourse is implemented somewhat noisily. To this end, we propose a novel objective function which simultaneously minimizes the gap between the achieved (resulting) and desired recourse invalidation rates, minimizes recourse costs, and also ensures that the resulting recourse achieves a positive model prediction. We develop novel theoretical results to characterize the recourse invalidation rates corresponding to any given instance w.r. t. different classes of underlying models (e.g., linear models, tree based models etc.), and leverage these results to efficiently optimize the proposed objective. Experimental evaluation with multiple real world datasets demonstrates the efficacy of the proposed framework.
A first catalogue of automatically selected ultraviolet-excess sources from the UVEX survey

We present the first catalogue of point-source ultraviolet (UV)-excess sources selected from the UV-Excess Survey of the Northern Galactic Plane (UVEX). UVEX images the Northern Galactic Plane in the U, g, r and He Iota lambda 5875 bands in the Galactic latitude range -5 degrees < b < +5 degrees. Through an automated algorithm, which works on a field-to-field basis, we select blue UV-excess sources in 211 square degrees from the (U-g) versus (g-r) colour-colour diagram and the g versus (U-g) and g versus (g-r) colour-magnitude diagrams. The UV-excess catalogue covers the magnitude range 14 < g < 22.5, contains 2170 sources and consists of a mix of white dwarfs, post-common-envelope objects, interacting binaries, quasars and active galactic nuclei. Two other samples of outliers were found during the selection: (i) a subdwarf sample, consisting of no less than 9872 candidate metal-poor stars or lightly reddened main-sequence stars, and (ii) a purple sample consisting of 803 objects, most likely a mix of reddened late M giants, T Tauri stars, planetary nebulae, symbiotic stars and carbon stars. Cross-matching the selected UV-excess catalogue with other catalogues aids with the first classification of the different populations and shows that more than 99 per cent of our selected sources are unidentified sources.