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In this paper, we derive a Chen-Strichartz formula for stochastic differential equations driven by Levy processes, that is, we derive a series expansion of the logarithm of the flowmap of the stochastic differential equation in terms of commutators of vector fields with stochastic coefficients, and we provide an explicit formula for the components in this series. The stochastic components are generated by the Levy processes that drive the stochastic differential equation and their quadratic variation and power jumps; the vector fields are given as linear combinations of commutators of elements in the pre-Lie Magnus expansion generated by the original vector fields governing our stochastic differential equation. In particular, we show the logarithm of the flowmap is a Lie series. These results extend previous results for deterministic differential equations and continuous stochastic differential equations. For these, the Chen-Strichartz series has shown to play a pivotal role in the design of numerical integration schemes that preserve qualitative properties of the solution such as the construction of geometric numerical schemes and in the context of efficient numerical schemes., Comment: 25 pages

As a promising technology, physical layer security (PLS) enhances security by leveraging the physical characteristics of communication channels. However, the conventional PLS approach leads to a considerable disparity in the effort legitimate users need to secure data compared to eavesdroppers. To address this issue, we propose a physical layer deception (PLD) framework, which applies random deceptive ciphering and orthogonal frequency-division multiplexing (OFDM) to defend against eavesdropping proactively. While ensuring the same level of confidentiality as traditional PLS methods, the PLD approach additionally introduces a deception mechanism, even when the eavesdropper possesses the same knowledge about the transmitter end as the legitimate receiver. Through thorough theoretical analyses and numerical simulations, we prove the superiority of our method over the conventional PLS approach., Comment: Submitted to ICC 2025 (appendices are excluded from the submitted version due to length limit)

Multi-task learning (MTL) leverages a shared model to accomplish multiple tasks and facilitate knowledge transfer. Recent research on task arithmetic-based MTL demonstrates that merging the parameters of independently fine-tuned models can effectively achieve MTL. However, existing merging methods primarily seek a static optimal solution within the original model parameter space, which often results in performance degradation due to the inherent diversity among tasks and potential interferences. To address this challenge, in this paper, we propose a Weight-Ensembling Mixture of Experts (WEMoE) method for multi-task model merging. Specifically, we first identify critical (or sensitive) modules by analyzing parameter variations in core modules of Transformer-based models before and after finetuning. Then, our WEMoE statically merges non-critical modules while transforming critical modules into a mixture-of-experts (MoE) structure. During inference, expert modules in the MoE are dynamically merged based on input samples, enabling a more flexible and adaptive merging approach. Building on WEMoE, we further introduce an efficient-and-effective WEMoE (E-WEMoE) method, whose core mechanism involves eliminating non-essential elements in the critical modules of WEMoE and implementing shared routing across multiple MoE modules, thereby significantly reducing both the trainable parameters, the overall parameter count, and computational overhead of the merged model by WEMoE. Experimental results across various architectures and tasks demonstrate that both WEMoE and E-WEMoE outperform state-of-the-art (SOTA) model merging methods in terms of MTL performance, generalization, and robustness.

Model merging has gained significant attention as a cost-effective approach to integrate multiple single-task fine-tuned models into a unified one that can perform well on multiple tasks. However, existing model merging techniques primarily focus on resolving conflicts between task-specific models, they often overlook potential security threats, particularly the risk of backdoor attacks in the open-source model ecosystem. In this paper, we first investigate the vulnerabilities of existing model merging methods to backdoor attacks, identifying two critical challenges: backdoor succession and backdoor transfer. To address these issues, we propose a novel Defense-Aware Merging (DAM) approach that simultaneously mitigates task interference and backdoor vulnerabilities. Specifically, DAM employs a meta-learning-based optimization method with dual masks to identify a shared and safety-aware subspace for model merging. These masks are alternately optimized: the Task-Shared mask identifies common beneficial parameters across tasks, aiming to preserve task-specific knowledge while reducing interference, while the Backdoor-Detection mask isolates potentially harmful parameters to neutralize security threats. This dual-mask design allows us to carefully balance the preservation of useful knowledge and the removal of potential vulnerabilities. Compared to existing merging methods, DAM achieves a more favorable balance between performance and security, reducing the attack success rate by 2-10 percentage points while sacrificing only about 1% in accuracy. Furthermore, DAM exhibits robust performance and broad applicability across various types of backdoor attacks and the number of compromised models involved in the merging process. We will release the codes and models soon., Comment: 21 pages,8 figures

Carbon-rich (C-rich) stars can be found at all metallicities and evolutionary stages. They are often the result of mass-transfer from a companion, but some of the most metal-poor C-rich objects are likely carrying the imprint of the metal-free First Stars from birth. In this work, we employ a neural network to predict metallicities and carbon abundances for over 10 million stars with Gaia low-resolution XP spectra, down to [Fe/H] = -3.0 and up to [C/Fe] $\approx$ +2. We identify ~2000 high-confidence bright (G<16) carbon-enhanced metal-poor (CEMP) stars with [Fe/H] < -2.0 and [C/Fe] > +0.7. The majority of our C-rich candidates have [Fe/H] > -2.0 and are expected to be binary mass-transfer products, supported by high barium abundances in GALAH and/or their Gaia RUWE and radial velocity variations. We confirm previous findings of an increase in C-rich stars with decreasing metallicity, adopting a definition of $3\sigma$ outliers from the [C/Fe] distribution, although our frequency appears to flatten for -3.0 < [Fe/H] < -2.0 at a level of 6-7%. We also find that the fraction of C-rich stars is low among globular cluster stars (connected to their lower binary fraction), and that it decreases for field stars more tightly bound to the Milky Way. We interpret these last results as evidence that disrupted globular clusters contribute more in the inner Galaxy, supporting previous work. Homogeneous samples like these are key to understanding the full population properties of C-rich stars, and this is just the beginning., Comment: 15 pages and 15 figures + appendix, submitted to MNRAS

Physical layer deception (PLD) is a novel security mechanism that combines physical layer security (PLS) with deception technologies to actively defend against eavesdroppers. In this paper, we establish a novel semantic model for PLD that evaluates its performance in terms of semantic distortion. By analyzing semantic distortion at varying levels of knowledge on the receiver's part regarding the key, we derive the receiver's optimal decryption strategy, and consequently, the transmitter's optimal deception strategy. The proposed semantic model provides a more generic understanding of the PLD approach independent from coding or multiplexing schemes, and allows for efficient real-time adaptation to fading channels., Comment: Submitted to ICC 2025

We report on XMM-Newton, NuSTAR, and NICER X-ray observations of CTCV J2056-3014, a cataclysmic variable (CV) with one of the fastest-spinning white dwarfs (WDs) at P = 29.6 s. While previously classified as an intermediate polar (IP), CJ2056 also exhibits the properties of WZ-Sge-type CVs, such as dwarf novae and superoutbursts. With XMM-Newton and NICER, we detected the spin period up to approximately 2 keV with 7-$\sigma$ significance. We constrained its derivative to |$\dot{P}$| < 1.8e-12 s/s after correcting for binary orbital motion. The pulsed profile is characterized by a single broad peak with approximately 25% modulation. NuSTAR detected a four-fold increase in unabsorbed X-ray flux coincident with an optical flare in November 2022. The XMM-Newton and NICER X-ray spectra in 0.3-10 keV are best characterized by an absorbed optically-thin three-temperature thermal plasma model (kT = 0.3, 1.0, and 4.9 keV), while the NuSTAR spectra in 3-30 keV are best fit by a single-temperature thermal plasma model (kT = 8.4 keV), both with Fe abundance $Z_{Fe}/Z_\odot$ = 0.3. CJ2056 exhibits similarities to other fast-spinning CVs, such as low plasma temperatures, and no significant X-ray absorption at low energies. As the WD's magnetic field strength is unknown, we applied both non-magnetic and magnetic CV spectral models (MKCFLOW and MCVSPEC) to determine the WD mass. The derived WD mass range (M = 0.7-1.0 $M_\odot$) is above the centrifugal break-up mass limit of 0.56 $M_\odot$ and consistent with the mean WD mass of local CVs (M $\approx$ 0.8-0.9 $M_\odot$)., Comment: 22 pages, 12 figures, accepted to ApJ

The climate crisis is a salient issue in online discussions, and hypocrisy accusations are a central rhetorical element in these debates. However, for large-scale text analysis, hypocrisy accusation detection is an understudied tool, most often defined as a smaller subtask of fallacious argument detection. In this paper, we define hypocrisy accusation detection as an independent task in NLP, and identify different relevant subtypes of hypocrisy accusations. Our Climate Hypocrisy Accusation Corpus (CHAC) consists of 420 Reddit climate debate comments, expert-annotated into two different types of hypocrisy accusations: personal versus political hypocrisy. We evaluate few-shot in-context learning with 6 shots and 3 instruction-tuned Large Language Models (LLMs) for detecting hypocrisy accusations in this dataset. Results indicate that the GPT-4o and Llama-3 models in particular show promise in detecting hypocrisy accusations (F1 reaching 0.68, while previous work shows F1 of 0.44). However, context matters for a complex semantic concept such as hypocrisy accusations, and we find models struggle especially at identifying political hypocrisy accusations compared to personal moral hypocrisy. Our study contributes new insights in hypocrisy detection and climate change discourse, and is a stepping stone for large-scale analysis of hypocrisy accusation in online climate debates., Comment: cite the public version, published at CPSS 2024 @ KONVENS

The commissioning and operation of future large-scale scientific experiments will challenge current tuning and control methods. Reinforcement learning (RL) algorithms are a promising solution thanks to their capability of autonomously tackling a control problem based on a task parameterized by a reward function. The conventionally utilized machine learning (ML) libraries are not intended for microsecond latency applications, as they mostly optimize for throughput performance. On the other hand, most of the programmable logic implementations are meant for computation acceleration, not being intended to work in a real-time environment. To overcome these limitations of current implementations, RL needs to be deployed on-the-edge, i.e. on to the device gathering the training data. In this paper we present the design and deployment of an experience accumulator system in a particle accelerator. In this system deep-RL algorithms run using hardware acceleration and act within a few microseconds, enabling the use of RL for control of ultra-fast phenomena. The training is performed offline to reduce the number of operations carried out on the acceleration hardware. The proposed architecture was tested in real experimental conditions at the Karlsruhe research accelerator (KARA), serving also as a synchrotron light source, where the system was used to control induced horizontal betatron oscillations in real-time. The results showed a performance comparable to the commercial feedback system available at the accelerator, proving the viability and potential of this approach. Due to the self-learning and reconfiguration capability of this implementation, its seamless application to other control problems is possible. Applications range from particle accelerators to large-scale research and industrial facilities.

We analyse high-resolution ESPaDOnS/CFHT spectra of 20 very metal-poor stars ([Fe/H]~$<-2.0$) in the solar neighbourhood (within $\sim2$ kpc) selected to be on planar orbits (with a maximum height of $\lesssim4$ kpc). Targets include 11 prograde and 9 retrograde stars, spanning a wide range of eccentricities ($0.20-0.95$). Their chemical abundances are consistent with those observed in the Galactic halo but show a smaller spread, with no notable difference between progrades and retrogrades. This suggests a common chemical evolution and likely a shared formation site (except for one star). In this case, chemical evolution models indicate that the formation site would have had a baryonic mass of $\sim1.4\times10^9\msun$, similar to classical dwarf galaxies. High-energy supernovae and hypernovae are needed to reproduce the [X/Fe] up to the Fe-peak, while fast-rotating massive stars and neutron star merger events explain the [X/Fe] of the neutron-capture elements. The absence of Type Ia supernova signatures suggests a star formation duration of $\lesssim1$~Gyr. Cosmological zoom-in simulations support the scenario that an in-plane infall of a single system could disperse stars over a wide range of angular momenta during the early Galactic assembly. We propose that these stars originated in a proto-Galactic building block, which we name Loki. Less likely, if progrades and retrogrades formed in two different systems, their chemical evolution must have been very similar, with a combined baryonic mass twice that of a single system. Forthcoming surveys will provide a large and homogeneous dataset to investigate whether Loki is associated with any of the known detected structures. A comparison (primarily [$\alpha$/Fe]) with other VMPs moving in planar orbits suggests multiple systems contributed to the Galactic planar population, presenting some differences in their kinematical parameters., Comment: REference list updated; discussion on GSE updated

This paper presents LiteVSR2, an enhanced version of our previously introduced efficient approach to Visual Speech Recognition (VSR). Building upon our knowledge distillation framework from a pre-trained Automatic Speech Recognition (ASR) model, we introduce two key improvements: a stabilized video preprocessing technique and feature normalization in the distillation process. These improvements yield substantial performance gains on the LRS2 and LRS3 benchmarks, positioning LiteVSR2 as the current best CTC-based VSR model without increasing the volume of training data or computational resources utilized. Furthermore, we explore the scalability of our approach by examining performance metrics across varying model complexities and training data volumes. LiteVSR2 maintains the efficiency of its predecessor while significantly enhancing accuracy, thereby demonstrating the potential for resource-efficient advancements in VSR technology.

Thermography is especially valuable for the military and other users of surveillance cameras. Some recent methods based on Neural Radiance Fields (NeRF) are proposed to reconstruct the thermal scenes in 3D from a set of thermal and RGB images. However, unlike NeRF, 3D Gaussian splatting (3DGS) prevails due to its rapid training and real-time rendering. In this work, we propose ThermalGaussian, the first thermal 3DGS approach capable of rendering high-quality images in RGB and thermal modalities. We first calibrate the RGB camera and the thermal camera to ensure that both modalities are accurately aligned. Subsequently, we use the registered images to learn the multimodal 3D Gaussians. To prevent the overfitting of any single modality, we introduce several multimodal regularization constraints. We also develop smoothing constraints tailored to the physical characteristics of the thermal modality. Besides, we contribute a real-world dataset named RGBT-Scenes, captured by a hand-hold thermal-infrared camera, facilitating future research on thermal scene reconstruction. We conduct comprehensive experiments to show that ThermalGaussian achieves photorealistic rendering of thermal images and improves the rendering quality of RGB images. With the proposed multimodal regularization constraints, we also reduced the model's storage cost by 90\%. The code and dataset will be released., Comment: 10 pages, 7 figures

Globular clusters (GCs) are sites of extremely efficient star formation, and recent studies suggest they significantly contributed to the early Milky Way's stellar mass build-up. Although their role has since diminished, GCs' impact on the Galaxy's initial evolution can be traced today by identifying their most chemically unique stars--those with anomalous nitrogen and aluminum overabundances and oxygen depletion. While they are a perfect tracer of clusters, be it intact or fully dissolved, these high-[N/O], high-[Al/Fe] GC-origin stars are extremely rare within the current Galaxy. To address the scarcity of these unusual, precious former GC members, we train a neural network (NN) to identify high-[N/O], high-[Al/Fe] stars using low-resolution Gaia BP/RP spectra. Our NN achieves a classification accuracy of approximately $\approx99\%$ and a false positive rate of around $\approx7\%$, identifying 878 new candidates in the Galactic field. We validate our results with several physically-motivated sanity checks, showing, for example, that the incidence of selected stars in Galactic GCs is significantly higher than in the field. Moreover, we find that most of our GC-origin candidates reside in the inner Galaxy, having likely formed in the proto-Milky Way, consistent with previous research. The fraction of GC candidates in the field drops at a metallicity of [Fe/H]$\approx-1$, approximately coinciding with the completion of spin-up, i.e. the formation of the Galactic stellar disk., Comment: 18 pages, 12 figures + 4 in the appendix. Submitted to MNRAS

Context: In the context of Galactic archaeology, the outer halo remains relatively unexplored with respect to its metallicity distribution, merger debris, and the abundance of known very/extremely metal-poor ([Fe/H]<-2.5) stars. Aim: We utilize the Pristine survey's publicly available, Pristine data release 1 (PDR1) and Pristine-Gaia synthetic (PGS) catalogues of photometric metallicities, to select Red Giant Branch (RGB) stars in the outer halo. Methods: The RGB selection pipeline selects giants based on the absence of a well-measured parallax in the brightness range where dwarfs have reasonable parallax estimate from Gaia DR3 data. The photometric distances are calculated using a BaSTI-isochrone fitting code and the photometric metallicities. Results: Photometric distances derived from PDR1- and PGS-giants show typical uncertainties of 12% and a scatter of up to 20% and 40% respectively, when validated against inverted-parallax and Starhorse-code distances. The PDR1-giants catalogue provides a low-to-no bias view of the metallicity structure versus distance compared to the PGS-giants catalogue (with a distance-metallicity selection bias), while the PGS-giants catalogue provides an all-sky view of the outer halo, especially in the VMP end. The PDR1-giants catalogue is used to study the metallicity distribution function of the halo out to ~100 kpc, using Gaussian Mixture Model decomposition. Additionally, we use the PDR1-giants with radial velocities from spectroscopic surveys to map the metallicity view of the integrals-of-motion space where accreted dwarf galaxy debris conserve their orbital parameters for a long time. Using the PGS-giants catalogue, we associate 41 stars tentatively to the stellar counterpart of the Magellanic stream in the VMP end, out to 70 kpc. Conclusions: We publish two RGB catalogues between -4.0<[Fe/H]<+0.1 with reliable photometric distances and metallicities., Comment: Submitted to A&A, 25 pages (19 Figures) + 4 pages (6 Figures) in Appendix. Comments are very welcome, enjoy reading! The catalogues will be published upon acceptance

We present a novel method to constrain the formation time of the Milky Way disc using the chrono-kinematic signatures of the inner Galaxy. We construct an O-rich Mira variable sample from the Gaia Long-period Variable catalogue to study the kinematic behaviour of stars with different ages in the inner Galaxy. From the Auriga suite of cosmological zoom-in simulations, we find that the age of the oldest stellar population with imprints of the bar in density and kinematics matches the disc spin-up epoch. This is because stars born before the spin-up show insufficient rotation and are not kinematically cold enough to be efficiently trapped by the bar. We find that the bar kinematic signature disappears for Mira variables with a period shorter than 190 days. Using the period-age relation of Mira variables, we constrain the spin-up epoch of the Milky Way to be younger than $\sim11-12$~Gyr (redshift $\sim3$). We also discuss and compare our method and result to other evidence of the Milky Way spin-up epoch under the context of a realistic age uncertainty. Age uncertainty leads to an overestimation of the disc formation time when performing backward modelling. Our constrain of the spin-up epoch is independent from previous studies because it relies on the kinematics of the inner Galaxy instead of the solar vicinity., Comment: 16 pages, 10 figures. Submitted to MNRAS. Comments welcome!

Deep model training on extensive datasets is increasingly becoming cost-prohibitive, prompting the widespread adoption of deep model fusion techniques to leverage knowledge from pre-existing models. From simple weight averaging to more sophisticated methods like AdaMerging, model fusion effectively improves model performance and accelerates the development of new models. However, potential interference between parameters of individual models and the lack of interpretability in the fusion progress remain significant challenges. Existing methods often try to resolve the parameter interference issue by evaluating attributes of parameters, such as their magnitude or sign, or by parameter pruning. In this study, we begin by examining the fine-tuning of linear layers through the lens of subspace analysis and explicitly define parameter interference as an optimization problem to shed light on this subject. Subsequently, we introduce an innovative approach to model fusion called zero-shot Sparse MIxture of Low-rank Experts (SMILE) construction, which allows for the upscaling of source models into an MoE model without extra data or further training. Our approach relies on the observation that fine-tuning mostly keeps the important parts from the pre-training, but it uses less significant or unused areas to adapt to new tasks. Also, the issue of parameter interference, which is intrinsically intractable in the original parameter space, can be managed by expanding the dimensions. We conduct extensive experiments across diverse scenarios, such as image classification and text generation tasks, using full fine-tuning and LoRA fine-tuning, and we apply our method to large language models (CLIP models, Flan-T5 models, and Mistral-7B models), highlighting the adaptability and scalability of SMILE. Code is available at https://github.com/tanganke/fusion_bench, Comment: Code is available at https://github.com/tanganke/fusion_bench

Content moderation in online platforms is crucial for ensuring activity therein adheres to existing policies, especially as these platforms grow. NLP research in this area has typically focused on automating some part of it given that it is not feasible to monitor all active discussions effectively. Past works have focused on revealing deletion patterns with like sentiment analysis, or on developing platform-specific models such as Wikipedia policy or stance detectors. Unsurprisingly, however, this valuable body of work is rather scattered, with little to no agreement with regards to e.g., the deletion discussions corpora used for training or the number of stance labels. Moreover, while efforts have been made to connect stance with rationales (e.g., to ground a deletion decision on the relevant policy), there is little explanability work beyond that. In this paper, we introduce a suite of experiments on Wikipedia deletion discussions and wide-analyis (Wikipedia Deletion Analysis), a Python package aimed at providing one click analysis to content moderation discussions. We release all assets associated with wide-analysis, including data, models and the Python package, and a HuggingFace space with the goal to accelerate research on automating content moderation in Wikipedia and beyond., Comment: System Demonstration

For vector-valued Maass cusp forms for~$SL_2(\mathbb{Z})$ with real weight~$k\in\mathbb{R}$ and spectral parameter $s\in\mathbb{C}$, $\mathrm{Re} s\in (0,1)$, $s\not\equiv \pm k/2$ mod $1$, we propose a notion of vector-valued period functions, and we establish a linear isomorphism between the spaces of Maass cusp forms and period functions by means of a cohomological approach. The period functions are a generalization of those for the classical Maass cusp forms, being solutions of a finite-term functional equation or, equivalently, eigenfunctions with eigenvalue $1$ of a transfer operator deduced from the geodesic flow on the modular surface. We apply this result to deduce a notion of period functions and related linear isomorphism for Jacobi Maass forms of weight $k+1/2$ for the semi-direct product of $SL_2(\mathbb{Z})$ with the integer points $Hei(\mathbb{Z})$ of the Heisenberg group., Comment: 58 pages, 7 figure

The expansion of IoT devices and the demands of deep learning have highlighted significant challenges in distributed deep learning systems. Parallel split learning has emerged as a promising derivative of split learning well suited for distributed learning on resource-constrained devices. However, parallel split learning faces several challenges, such as large effective batch sizes, non-independent and identically distributed data, and the straggler effect. We view these issues as a sampling dilemma and propose to address them by orchestrating a mini-batch sampling process on the server side. We introduce a new method called uniform global sampling to decouple the effective batch size from the number of clients and reduce the mini-batch deviation. To address the straggler effect, we introduce a novel method called Latent Dirichlet Sampling, which generalizes uniform global sampling to balance the trade-off between batch deviation and training time. Our simulations reveal that our proposed methods enhance model accuracy by up to 34.1% in non-independent and identically distributed settings and reduce the training time in the presence of stragglers by up to 62%. In particular, Latent Dirichlet Sampling effectively mitigates the straggler effect without compromising model accuracy or adding significant computational overhead compared to uniform global sampling. Our results demonstrate the potential of our methods to mitigate common challenges in parallel split learning.

Large language models (LLMs) have been shown to propagate and amplify harmful stereotypes, particularly those that disproportionately affect marginalised communities. To understand the effect of these stereotypes more comprehensively, we introduce GlobalBias, a dataset of 876k sentences incorporating 40 distinct gender-by-ethnicity groups alongside descriptors typically used in bias literature, which enables us to study a broad set of stereotypes from around the world. We use GlobalBias to directly probe a suite of LMs via perplexity, which we use as a proxy to determine how certain stereotypes are represented in the model's internal representations. Following this, we generate character profiles based on given names and evaluate the prevalence of stereotypes in model outputs. We find that the demographic groups associated with various stereotypes remain consistent across model likelihoods and model outputs. Furthermore, larger models consistently display higher levels of stereotypical outputs, even when explicitly instructed not to., Comment: Accepted to EMNLP Main 2024

In the case of single-digit picosecond bunch length, synchrotron light sources produce intense coherent radiation up to the THz range. The reduction of the bunch length by lowering the momentum compaction factor (low-$\alpha$) gives rise to the micro-bunching instability, which is on one hand a crucial roadblock in the X-ray range during to the resulting effective bunch lengthening but on the other hand also an opportunity for the generation of intense THz radiation if it can be controlled appropriately. In the KIT storage ring KARA (Karlsruhe Research Accelerator), two parallel plates with periodic rectangular corrugations are planned to be installed in an electron storage ring. These plates create an additional longitudinal impedance based on their geometry, which can affect the beam dynamics. The resulting impedance manipulation will be used to study and control the longitudinal electron beam dynamics and the emitted coherent synchrotron radiation (CSR). This paper presents the results of systematic studies in simulation of the impact of additional corrugated plate impedances on the longitudinal beam dynamics using the example of the KARA storage ring. If the periodicity of the wake function of the corrugated plates matches the size of the substructures in the longitudinal bunch profile, the instability threshold can be effectively manipulated. This extends intense THz radiation to different beam current regimes., Comment: 11 pages, 13 figures, to be published in Physical Review Accelerators and Beams

Physical layer security (PLS) is a promising technology to secure wireless communications by exploiting the physical properties of the wireless channel. However, the passive nature of PLS creates a significant imbalance between the effort required by eavesdroppers and legitimate users to secure data. To address this imbalance, in this article, we propose a novel framework of physical layer deception (PLD), which combines PLS with deception technologies to actively counteract wiretapping attempts. Combining a two-stage encoder with randomized ciphering and non-orthogonal multiplexing, the PLD approach enables the wireless communication system to proactively counter eavesdroppers with deceptive messages. Relying solely on the superiority of the legitimate channel over the eavesdropping channel, the PLD framework can effectively protect the confidentiality of the transmitted messages, even against eavesdroppers who possess knowledge equivalent to that of the legitimate receiver. We prove the validity of the PLD framework with in-depth analyses and demonstrate its superiority over conventional PLS approaches with comprehensive numerical benchmarks., Comment: Submitted to IEEE Transactions on Wireless Communications

We introduce CHEW, a novel dataset of changing events in Wikipedia expressed in naturally occurring text. We use CHEW for probing LLMs for their timeline understanding of Wikipedia entities and events in generative and classification experiments. Our results suggest that LLMs, despite having temporal information available, struggle to construct accurate timelines. We further show the usefulness of CHEW-derived embeddings for identifying meaning shift., Comment: Short Paper

We aim to constrain the chemo-dynamical properties of the Sagittarius (Sgr) dwarf galaxy using carbon abundances. Our sample from the \textit{Pristine} Inner Galaxy Survey (PIGS) includes $\sim 350$ metal-poor ([Fe/H]~$<-1.5$) stars in the main body of Sgr with good quality spectroscopic observations. Our metal-poor Sgr population has a larger velocity dispersion than metal-rich Sgr from the literature, which could be explained by outside-in star formation, extreme Galactic tidal perturbations and/or the presence of a metal-rich disc/bar $+$ a metal-poor halo. The average carbon abundance [C/Fe] in Sgr is similar to that of other classical dwarf galaxies (DGs) and consistently lower than in the Milky Way by $\sim0.2-0.3$~dex at low metallicity. The interstellar medium in DGs, including Sgr, may have retained yields from more energetic Population~III~and~II supernovae (SNe), thereby reducing the average [C/Fe]. Additionally, SNe~Ia, producing more Fe than C, would start to contribute at lower metallicity in DGs/Sgr than in the Galaxy. The presence of a [C/Fe] gradient for Sgr stars with [Fe/H]~$\gtrsim-2.0$ ($\sim 6.8\times 10^{-4}\ \rm{dex \ arcmin^{-1}}$) suggests that SNe~Ia contributed in the system at those metallicities, especially in its inner regions. There is a low frequency of carbon-enhanced metal-poor (CEMP) stars in our Sgr sample. At higher metallicity/carbon abundance (mostly CEMP-s) this may be due to photometric selection effects, but those are less likely to affect CEMP-no stars. We propose that, given the lower average [C/Fe] in DGs, using the same CEMP definition ([C/Fe]~$>+0.7$) as in the Galaxy under-predicts the number of CEMP stars in DGs, and for Sgr a cut at [C/Fe]$~\sim +0.35$ may be more appropriate, which brings the frequency of CEMP stars in agreement with that in the Galaxy., Comment: Accepted for publication in A&A. The abstract in the metadata is a shorter version than the actual one

Solving multi-objective optimization problems for large deep neural networks is a challenging task due to the complexity of the loss landscape and the expensive computational cost of training and evaluating models. Efficient Pareto front approximation of large models enables multi-objective optimization for various tasks such as multi-task learning and trade-off analysis. Existing algorithms for learning Pareto set, including (1) evolutionary, hypernetworks, and hypervolume-maximization methods, are computationally expensive and have restricted scalability to large models; (2) Scalarization algorithms, where a separate model is trained for each objective ray, which is inefficient for learning the entire Pareto set and fails to capture the objective trade-offs effectively. Inspired by the recent success of model merging, we propose a practical and scalable approach to Pareto set learning problem via mixture of experts (MoE) based model fusion. By ensembling the weights of specialized single-task models, the MoE module can effectively capture the trade-offs between multiple objectives and closely approximate the entire Pareto set of large neural networks. Once the routers are learned and a preference vector is set, the MoE module can be unloaded, thus no additional computational cost is introduced during inference. We conduct extensive experiments on vision and language tasks using large-scale models such as CLIP-ViT and GPT-2. The experimental results demonstrate that our method efficiently approximates the entire Pareto front of large models. Using only hundreds of trainable parameters of the MoE routers, our method even has lower memory usage compared to linear scalarization and algorithms that learn a single Pareto optimal solution, and are scalable to both the number of objectives and the size of the model., Comment: code is available at https://github.com/tanganke/pareto_set_learning

Through the chemodynamical characterisation of metal-poor stars, one can efficiently probe the early history of the Milky Way. We aim at decontaminating a sample of $\sim$ 3M giant stars with Gaia DR3 XP-based \textit{Pristine-Gaia} metallicities, to investigate a subset of very metal-poor stars ([Fe/H] < -1.7) with disc-like orbits. We construct a statistically robust sample of $\sim$ 36 000 very metal-poor giants, using APOGEE and LAMOST to estimate and remove contamination from high $V_\phi$ stars. We investigate the spatial and kinematic properties of the decontaminated sample, using $V_\phi$ and the action space, both powerful to disentangle stellar populations. As in previous works, we find a pronounced asymmetry in $L_z$ and $V_\phi$ in favour of prograde stars. This excess is mostly made of prograde-planar stars (10% of the very metal-poor population), and contains stars with $V_\phi$ > 180 km s$^{-1}$ and $Z_{\text{max}}$ < 1.5 kpc, down to [Fe/H] = -2.9 at a 2$\sigma$ confidence level. While the overall orbital distributions of our sample match that of a halo, the highly prograde and planar subset (2% of the very metal-poor population) also bears characteristics classically associated with a thick disc, i.e., a spatial distribution compatible with a short-scaled thick disc, and a thick disc-like $Z_{\text{max}}$ - $R_{\text{max}}$ distribution. Additionally, assuming a stationary or prograde halo with $\overline{V_\phi}$ $\sim$ 30-40 km.s$^{-1}$ is not sufficient to suppress the kinematic signature of the highly prograde and planar subset. These results rule out any link with a thin disc, and instead, support a contribution from a \textit{metal-weak thick disc}., Comment: Accepted for publication in A&A on June 24, 2024. The abstract has been modified to comply with arXiv's abstract requirements (word limit). The paper numbering has been modified from XXV to XXIV to remain consistent with the recent submissions of the Pristine collaboration

Millimeter wave (mmWave) cell-free massive MIMO (CF mMIMO) is a promising solution for future wireless communications. However, its optimization is non-trivial due to the challenging channel characteristics. We show that mmWave CF mMIMO optimization is largely an assignment problem between access points (APs) and users due to the high path loss of mmWave channels, the limited output power of the amplifier, and the almost orthogonal channels between users given a large number of AP antennas. The combinatorial nature of the assignment problem, the requirement for scalability, and the distributed implementation of CF mMIMO make this problem difficult. In this work, we propose an unsupervised machine learning (ML) enabled solution. In particular, a graph neural network (GNN) customized for scalability and distributed implementation is introduced. Moreover, the customized GNN architecture is hierarchically permutation-equivariant (HPE), i.e., if the APs or users of an AP are permuted, the output assignment is automatically permuted in the same way. To address the combinatorial problem, we relax it to a continuous problem, and introduce an information entropy-inspired penalty term. The training objective is then formulated using the augmented Lagrangian method (ALM). The test results show that the realized sum-rate outperforms that of the generalized serial dictatorship (GSD) algorithm and is very close to the upper bound in a small network scenario, while the upper bound is impossible to obtain in a large network scenario.

Deep model fusion is an emerging technique that unifies the predictions or parameters of several deep neural networks into a single model in a cost-effective and data-efficient manner. This enables the unified model to take advantage of the original models' strengths, potentially exceeding their performance. Although a variety of deep model fusion techniques have been introduced, their evaluations tend to be inconsistent and often inadequate to validate their effectiveness and robustness against distribution shifts. To address this issue, we introduce FusionBench, which is the first comprehensive benchmark dedicated to deep model fusion. FusionBench covers a wide range of tasks, including open-vocabulary image classification, text classification, and text-to-text generation. Each category includes up to eight tasks with corresponding task-specific models, featuring both full fine-tuning and LoRA fine-tuning, as well as models of different sizes, to ensure fair and balanced comparisons of various multi-task model fusion techniques across different tasks, model scales, and fine-tuning strategies. We implement and evaluate a broad spectrum of deep model fusion techniques. These techniques range from model ensemble methods, which combine the predictions to improve the overall performance, to model merging, which integrates different models into a single one, and model mixing methods, which upscale or recombine the components of the original models. FusionBench now contains 26 distinct tasks, 74 fine-tuned models, and 16 fusion techniques, and we are committed to consistently expanding the benchmark with more tasks, models, and fusion techniques. In addition, we offer a well-documented set of resources and guidelines to aid researchers in understanding and replicating the benchmark results. Homepage https://github.com/tanganke/fusion_bench, Comment: Project homepage: https://github.com/tanganke/fusion_bench

The Pristine-\textit{Gaia} synthetic catalogue provides reliable photometric metallicities for $\sim$30 million FGK stars using the Pristine survey model and Gaia XP spectra. We perform the first low-to-medium-resolution spectroscopic follow-up of bright (G<15) and distant (up to 35 kpc) very and extremely metal-poor (V/EMP, [Fe/H]<-2.5) red giant branch stars from this. We use Isaac Newton Telescope/Intermediate Dispersion Spectrograph (INT/IDS) observations centred around the calcium triplet region ideal for V/EMP stars. We find that 76\% of our stars indeed have [Fe/H]<-2.5 with these inferred spectroscopic metallicities and only 3\% are outliers with [Fe/H] > -2.0. We report a success rate of 77\% and 38\% in finding stars with [Fe/H]<-2.5 and -3.0 respectively. This will allow for 10,000-20,000 homogeneously analysed EMP stars using the WEAVE survey follow-up of Pristine EMP candidates. We associate 20\%, 46\%, and 34\% of the stars to be confined to the disc plane, or to have inner and outer halo orbits, respectively. We also associate these V/EMP stars to known accretion events such as Gaia-Enceladus-Sausage (GES), LMS-1/Wukong, Thamnos, Helmi streams, Sagittarius, Sequoia, etc. For the stars that orbit close to the disc plane, we find that the prograde region with low vertical action is overdense with a significance of 4$\sigma$ as compared to its retrograde counterpart. We also find three new (brightest) members of the most metal-poor stellar stream, C-19, one of which is 50$^\circ$ away from the main body of the stream. Our measured mean metallicity, velocity dispersion, and stream width are consistent with the literature, but our results favour a higher distance ($\sim$21.5 kpc) for the stream. We publish a catalogue (and 1D spectra) of 215 V/EMP stars from this spectroscopic follow-up and showcase the power of chemokinematic analysis of V/EMP end., Comment: Submitted to A&A. 17 pages (9 figures) + 3 pages (3 figures) in Appendix. Comments are very welcome! The catalogue and 1D spectra will be made available public after acceptance and before upon reasonable request to the first author

We combine stellar orbits with the abundances of the heavy, $r$-process element europium and the light, $\alpha$-element, silicon to separate in-situ and accreted populations in the Milky Way across all metallicities. At high orbital energy, the accretion-dominated halo shows elevated values of [Eu/Si], while at lower energies, where many of the stars were born in-situ, the levels of [Eu/Si] are lower. These systematically different levels of [Eu/Si] in the MW and the accreted halo imply that the scatter in [Eu/$\alpha$] within a single galaxy is smaller than previously thought. At the lowest metallicities, we find that both accreted and in-situ populations trend down in [Eu/Si], consistent with enrichment via neutron star mergers. Through compiling a large dataset of abundances for 46 globular clusters (GCs), we show that differences in [Eu/Si] extend to populations of in-situ/accreted GCs. We interpret this consistency as evidence that in $r$-process elements, GCs trace the star formation history of their hosts, motivating their use as sub-Gyr timers of galactic evolution. Furthermore, fitting the trends in [Eu/Si] using a simple galactic chemical evolution model, we find that differences in [Eu/Si] between accreted and in-situ MW field stars cannot be explained through star formation efficiency alone. Finally, we show that the use of [Eu/Si] as a chemical tag between GCs and their host galaxies extends beyond the Local Group, to the halo of M31 - potentially offering the opportunity to do Galactic Archaeology in an external galaxy., Comment: Accepted for publication in MNRAS. Some edits, new figures and the discussion on GCE modeling made clearer following referee report. Core science results remain unchanged

Hoaxes are a recognised form of disinformation created deliberately, with potential serious implications in the credibility of reference knowledge resources such as Wikipedia. What makes detecting Wikipedia hoaxes hard is that they often are written according to the official style guidelines. In this work, we first provide a systematic analysis of similarities and discrepancies between legitimate and hoax Wikipedia articles, and introduce Hoaxpedia, a collection of 311 hoax articles (from existing literature and official Wikipedia lists), together with semantically similar legitimate articles, which together form a binary text classification dataset aimed at fostering research in automated hoax detection. In this paper, We report results after analyzing several language models, hoax-to-legit ratios, and the amount of text classifiers are exposed to (full article vs the article's definition alone). Our results suggest that detecting deceitful content in Wikipedia based on content alone is hard but feasible, and complement our analysis with a study on the differences in distributions in edit histories, and find that looking at this feature yields better classification results than context.

The most metal-poor stars provide valuable insights into the early chemical enrichment history of a system, carrying the chemical imprints of the first generations of supernovae. The most metal-poor region of the Sagittarius dwarf galaxy remains inadequately observed and characterised. To date, only $\sim4$ stars with [Fe/H]~$<-2.0$ have been chemically analysed with high-resolution spectroscopy. In this study, we present the most extensive chemical abundance analysis of 12 low-metallicity stars with metallicities down to [Fe/H]~$=-3.26$ and located in the main body of Sagittarius. These targets, selected from the Pristine Inner Galaxy Survey, were observed using the MIKE high-resolution spectrograph at the {\it Magellan-Clay} telescope, which allowed us to measure up to 17 chemical species. The chemical composition of these stars reflects the imprint of a variety of type~II supernovae (SNe~II). A combination of low- to intermediate-mass high-energy SNe and hypernovae ($\sim10-70\msun$) is required to account for the abundance patterns of the lighter elements up to the Fe-peak. The trend of the heavy elements suggests the involvement of compact binary merger events and fast-rotating (up to $\sim300\kms$) intermediate-mass to massive metal-poor stars ($\sim25-120\msun$) that are the sources of rapid and slow processes, respectively. Additionally, asymptotic giant branch stars contribute to a wide dispersion of [Ba/Mg] and [Ba/Eu]. The absence of an $\alpha-$knee in our data indicates that type Ia supernovae did not contribute in the very metal-poor region ([Fe/H]~$\leq-2.0$). However, they might have started to pollute the interstellar medium at [Fe/H]~$>-2.0$, given the relatively low [Co/Fe] in this metallicity region., Comment: Accepted for publication in A&A. New plot on [Eu/Mg]. Some refs updated

We present an analysis of the sensitivity of current and future LHC searches for new spin-0 particles in top-anti-top-quark ($t\bar{t}$) final states, focusing on generic axion-like particles (ALPs) that are coupled to top quarks and gluons. As a first step, we derive new limits on the effective ALP Lagrangian in terms of the Wilson coefficients $c_t$ and $c_{\tilde{G}}$ based on the results of the CMS search using $35.9$ fb$^{-1}$ of data, collected at $\sqrt{s} = 13$ TeV. We then investigate how the production of an ALP with generic couplings to gluons and top quarks can be distinguished from the production of a pseudoscalar which couples to gluons exclusively via a top-quark loop. To this end, we make use of the invariant $t\bar{t}$ mass distribution and angular correlations that are sensitive to the $t\bar{t}$ spin correlation. Using a mass of 400 GeV as an example, we find that already the data collected during Run 2 and Run 3 of the LHC provides an interesting sensitivity to the underlying nature of a possible new particle. We also analyze the prospects for data anticipated to be collected during the high-luminosity phase of the LHC. Finally, we compare the limits obtained from the $t \bar t$ searches to existing experimental bounds from LHC searches for narrow di-photon resonances, from measurements of the production of four top quarks, and from global analyses of ALP-SMEFT interference effects., Comment: 28 pages, 9 figures

Measuring the quality of contributions in political online discussions is crucial in deliberation research and computer science. Research has identified various indicators to assess online discussion quality, and with deep learning advancements, automating these measures has become feasible. While some studies focus on analyzing specific quality indicators, a comprehensive quality score incorporating various deliberative aspects is often preferred. In this work, we introduce AQuA, an additive score that calculates a unified deliberative quality score from multiple indices for each discussion post. Unlike other singular scores, AQuA preserves information on the deliberative aspects present in comments, enhancing model transparency. We develop adapter models for 20 deliberative indices, and calculate correlation coefficients between experts' annotations and the perceived deliberativeness by non-experts to weigh the individual indices into a single deliberative score. We demonstrate that the AQuA score can be computed easily from pre-trained adapters and aligns well with annotations on other datasets that have not be seen during training. The analysis of experts' vs. non-experts' annotations confirms theoretical findings in the social science literature.

Swarm robots have sparked remarkable developments across a range of fields. While it is necessary for various applications in swarm robots, a fast and robust coordinate initialization in vision-based drone swarms remains elusive. To this end, our paper proposes a complete system to recover a swarm's initial relative pose on platforms with size, weight, and power (SWaP) constraints. To overcome limited coverage of field-of-view (FoV), the drones rotate in place to obtain observations. To tackle the anonymous measurements, we formulate a non-convex rotation estimation problem and transform it into a semi-definite programming (SDP) problem, which can steadily obtain global optimal values. Then we utilize the Hungarian algorithm to recover relative translation and correspondences between observations and drone identities. To safely acquire complete observations, we actively search for positions and generate feasible trajectories to avoid collisions. To validate the practicability of our system, we conduct experiments on a vision-based drone swarm with only stereo cameras and inertial measurement units (IMUs) as sensors. The results demonstrate that the system can robustly get accurate relative poses in real time with limited onboard computation resources. The source code is released.

Nonlinear transport is a unique functionality of noncentrosymmetric systems, which reflects profound physics, such as spin-orbit interaction, superconductivity and band geometry. However, it remains highly challenging to enhance the nonreciprocal transport for promising rectification devices. Here, we observe a light-induced giant enhancement of nonreciprocal transport at the superconducting and epitaxial CaZrO3/KTaO3 (111) interfaces. The nonreciprocal transport coefficient undergoes a giant increase with three orders of magnitude up to 105 A-1T-1. Furthermore, a strong Rashba spin-orbit coupling effective field of 14.7 T is achieved with abundant high-mobility photocarriers under ultraviolet illumination, which accounts for the giant enhancement of nonreciprocal transport coefficient. Our first-principles calculations further disclose the stronger Rashba spin-orbit coupling strength and the longer relaxation time in the photocarrier excitation process, bridging the light-property quantitative relationship. Our work provides an alternative pathway to boost nonreciprocal transport in noncentrosymmetric systems and facilitates the promising applications in opto-rectification devices and spin-orbitronic devices., Comment: 38 pages, 17 figures

Precisely measuring pressure in microfluidic flows is essential for flow control, fluid characterization, and monitoring, but faces specific challenges such as \RE{achieving} sufficient resolution, non-invasiveness, or ease of use. Here, we demonstrate a fully integrated multiplexed optofluidic pressure sensor, entirely decoupled from the flow path, that enables local pressure measurements along any microfluidic channel without altering its flow geometry. The sensor itself relies on the compression of a soft mechano-actuated hydrogel, changing color in response to a pressure change. The hydrogel is separated from the fluid circulating in the channel by a thin membrane, allowing for the unrestricted use of different types of fluids. Imaging the gel through the transparent PDMS with a color camera provides a direct, easy, and contact-free determination of the fluid pressure at the sensing location for pressures as small as \SI{20}{\milli\bar} with a resolution of around \SI{10}{\milli\bar}. The sensitivity and accessible pressure range can be tuned via the mechanical properties \RE{of the sensing unit}. The photonic gel can also be used to acquire 2D pressure or deformation maps, taking advantage of the fast response time and fine spatial resolution.