Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing Sousveillance Tools for Gig Workers

As independently-contracted employees, gig workers disproportionately suffer the consequences of workplace surveillance, which include increased pressures to work, breaches of privacy, and decreased digital autonomy. Despite the negative impacts of workplace surveillance, gig workers lack the tools, strategies, and workplace social support to protect themselves against these harms. Meanwhile, some critical theorists have proposed sousveillance as a potential means of countering such abuses of power, whereby those under surveillance monitor those in positions of authority (e.g., gig workers collect data about requesters/platforms). To understand the benefits of sousveillance systems in the gig economy, we conducted semi-structured interviews and led co-design activities with gig workers. We use"care ethics"as a guiding concept to understand our interview and co-design data, while also focusing on empathic sousveillance technology design recommendations. Through our study, we identify gig workers' attitudes towards and past experiences with sousveillance. We also uncover the type of sousveillance technologies imagined by workers, provide design recommendations, and finish by discussing how to create empowering, empathic spaces on gig platforms.
Repoformer: Selective Retrieval for Repository-Level Code Completion

Recent advances in retrieval-augmented generation (RAG) have initiated a new era in repository-level code completion. However, the invariable use of retrieval in existing methods exposes issues in both efficiency and robustness, with a large proportion of the retrieved contexts proving unhelpful or harmful to code language models (code LMs). To tackle the challenges, this paper proposes a selective RAG framework where retrieval is avoided when unnecessary. To power this framework, we design a self-supervised learning approach that enables a code LM to accurately self-evaluate whether retrieval can improve its output quality and robustly leverage the potentially noisy retrieved contexts. Using this LM as both the selective retrieval policy and the generation model, our framework consistently outperforms the state-of-the-art prompting with an invariable retrieval approach on diverse benchmarks including RepoEval, CrossCodeEval, and a new benchmark. Meanwhile, our selective retrieval strategy results in strong efficiency improvements by as much as 70% inference speedup without harming the performance. We demonstrate that our framework effectively accommodates different generation models, retrievers, and programming languages. These advancements position our framework as an important step towards more accurate and efficient repository-level code completion.
MaiBaam: A Multi-Dialectal Bavarian Universal Dependency Treebank

Despite the success of the Universal Dependencies (UD) project exemplified by its impressive language breadth, there is still a lack in `within-language breadth': most treebanks focus on standard languages. Even for German, the language with the most annotations in UD, so far no treebank exists for one of its language varieties spoken by over 10M people: Bavarian. To contribute to closing this gap, we present the first multi-dialect Bavarian treebank (MaiBaam) manually annotated with part-of-speech and syntactic dependency information in UD, covering multiple text genres (wiki, fiction, grammar examples, social, non-fiction). We highlight the morphosyntactic differences between the closely-related Bavarian and German and showcase the rich variability of speakers' orthographies. Our corpus includes 15k tokens, covering dialects from all Bavarian-speaking areas spanning three countries. We provide baseline parsing and POS tagging results, which are lower than results obtained on German and vary substantially between different graph-based parsers. To support further research on Bavarian syntax, we make our dataset, language-specific guidelines and code publicly available.
Thermal radiation forces on planar structures with asymmetric optical response

Light carries momentum and, upon interaction with material structures, can exert forces on them. Here, we show that a planar structure with asymmetric optical response is spontaneously accelerated when placed in an environment at a different temperature. This phenomenon originates from the imbalance in the exchange rates of photons between both sides of the structure and the environment. Using a simple theoretical model, we calculate the force acting on the planar structure and its terminal velocity in vacuum, and analyze their dependence on the initial temperature and the geometrical properties of the system for different realistic materials. Our results unravel an alternative approach to manipulating objects in the nano and microscale that does not require an external source of radiation.
WRTA - Wake-Up-Receiver optimised routing and topology optimisation approach

In the domain of distributed embedded systems, available resources regarding computing power, energy, and memory are strongly limited. With focus on the energy resources, communication hardware and communication tasks represent critical consumers for most of the common application scenarios. Accordingly, optimised approaches for the communication have to be found, including routing, topology control, and scheduling. Wake-Up-Receivers (WuRx) represent a promising approach for minimising the energy consumption in wireless communication environments. To use the conceptual benefits of such technology, an adapted communication behaviour is required. Communication paradigm, communication protocols as well as the runtime behaviour must fit together. In this paper, we introduce a WuRx-optimised routing and topology optimisation approach - WRTA. We discuss the hardware integration as well as the adaptation of the communication task scheduling on application layer. The design matching process of all these aspects allows significant improvements of the energy-efficiency in typical sensor network scenarios. For achieving these goals, WRTA represents a lightweight protocol for data-centric WSN environments. The approach combines complex route path calculations and topology optimisation mechanisms, considering a given asynchronous communication environment. For proof of concept, we implement several heterogeneous test benches in both soft- and hardware. Hence, the presented simulation results as well as the respective real world measurements provide interesting results regarding the scalability and the efficiency. The analysis of the data shows minimum protocol overhead and outstanding characteristics regarding scalability and robustness. We clarify that application-specific adaptations & configurations within the overall system architecture are essential to ensure an reliable communication behaviour in energy self-sufficient WuRx environments.
Delayed interactions in the noisy voter model through the periodic polling mechanism

We investigate the effects of delayed interactions on the stationary distribution of the noisy voter model. We assume that the delayed interactions occur through the periodic polling mechanism and replace the original instantaneous two-agent interactions. In our analysis, we require that the polling period aligns with the delay in announcing poll outcomes. As expected, when the polling period is relatively short, the model with delayed interactions is effectively identical to the original model. As the polling period increases, oscillatory behavior emerges, but the model with delayed interactions still converges to stationary distribution. The stationary distribution resembles a Beta-binomial distribution, with its shape parameters scaling with the polling period. The observed scaling behavior is non-trivial. As the polling period increases, fluctuation damping also intensifies, yet there is a critical intermediate polling period for which fluctuation damping reaches its maximum intensity.
PnP Restoration with Domain Adaptation for SANS

Small Angle Neutron Scattering (SANS) is a non-destructive technique utilized to probe the nano- to mesoscale structure of materials by analyzing the scattering pattern of neutrons. Accelerating SANS acquisition for in-situ analysis is essential, but it often reduces the signal-to-noise ratio (SNR), highlighting the need for methods to enhance SNR even with short acquisition times. While deep learning (DL) can be used for enhancing SNR of low quality SANS, the amount of experimental data available for training is usually severely limited. We address this issue by proposing a Plug-and-play Restoration for SANS (PR-SANS) that uses domain-adapted priors. The prior in PR-SANS is initially trained on a set of generic images and subsequently fine-tuned using a limited amount of experimental SANS data. We present a theoretical convergence analysis of PR-SANS by focusing on the error resulting from using inexact domain-adapted priors instead of the ideal ones. We demonstrate with experimentally collected SANS data that PR-SANS can recover high-SNR 2D SANS detector images from low-SNR detector images, effectively increasing the SNR. This advancement enables a reduction in acquisition times by a factor of 12 while maintaining the original signal quality.
ATOM: Asynchronous Training of Massive Models for Deep Learning in a Decentralized Environment

The advent of the Transformer architecture has propelled the growth of natural language processing (NLP) models, leading to remarkable achievements in numerous NLP tasks. Yet, the absence of specialized hardware like expansive GPU memory and high-speed interconnects poses challenges for training large-scale models. This makes it daunting for many users to experiment with pre-training and fine-tuning large language models (LLMs). In this study, we introduce \atom, a resilient distributed training framework designed for asynchronous training of vast models in a decentralized setting using cost-effective hardware, including consumer-grade GPUs and Ethernet. Unlike conventional model partitioning methods that distribute sub-models across GPUs, \atom aims to accommodate a complete LLM on one host (peer) through seamlessly model swapping and concurrently trains multiple copies across various peers to optimize training throughput. Through static analysis, \atom identifies the best model partitioning strategy and flawlessly merges model execution with swapping. Key benefits of \atom include: Avoiding the central point of failure found in pipeline parallelism methods. Demonstrating superior performance and scalability compared to closely-integrated pipeline parallelism in slower networks. Our experiments using different GPT-3 model configurations reveal that, in scenarios with suboptimal network connections, \atom can enhance training efficiency up to $20 \times$ when juxtaposed with the state-of-the-art decentralized pipeline parallelism approaches.
Multi-Layer Kernel Machines: Fast and Optimal Nonparametric Regression with Uncertainty Quantification

Kernel ridge regression (KRR) is widely used for nonparametric regression over reproducing kernel Hilbert spaces. It offers powerful modeling capabilities at the cost of significant computational costs, which typically require $O(n^3)$ computational time and $O(n^2)$ storage space, with the sample size n. We introduce a novel framework of multi-layer kernel machines that approximate KRR by employing a multi-layer structure and random features, and study how the optimal number of random features and layer sizes can be chosen while still preserving the minimax optimality of the approximate KRR estimate. For various classes of random features, including those corresponding to Gaussian and Matern kernels, we prove that multi-layer kernel machines can achieve $O(n^2\log^2n)$ computational time and $O(n\log^2n)$ storage space, and yield fast and minimax optimal approximations to the KRR estimate for nonparametric regression. Moreover, we construct uncertainty quantification for multi-layer kernel machines by using conformal prediction techniques with robust coverage properties. The analysis and theoretical predictions are supported by simulations and real data examples.
PQDynamicISP: Dynamically Controlled Image Signal Processor for Any Image Sensors Pursuing Perceptual Quality

Full DNN-based image signal processors (ISPs) have been actively studied and have achieved superior image quality compared to conventional ISPs. In contrast to this trend, we propose a lightweight ISP that consists of simple conventional ISP functions but achieves high image quality by increasing expressiveness. Specifically, instead of tuning the parameters of the ISP, we propose to control them dynamically for each environment and even locally. As a result, state-of-the-art accuracy is achieved on various datasets, including other tasks like tone mapping and image enhancement, even though ours is lighter than DNN-based ISPs. Additionally, our method can process different image sensors with a single ISP through dynamic control, whereas conventional methods require training for each sensor.
Increasing Black Hole Feedback-induced Quenching with Anisotropic Thermal Conduction

Feedback from central supermassive black holes is often invoked to explain the low star formation rates (SFRs) in the massive galaxies at the centers of galaxy clusters. However, the detailed physics of the coupling of the injected feedback energy with the intracluster medium (ICM) is still unclear. Using high-resolution magnetohydrodynamic cosmological simulations of galaxy cluster formation, we investigate the role of anisotropic thermal conduction in shaping the thermodynamic structure of clusters, and in particular, in modifying the impact of black hole feedback. Stratified anisotropically conducting plasmas are formally always unstable, and thus more prone to mixing, an expectation borne out by our results. The increased mixing efficiently isotropizes the injected feedback energy, which in turn significantly improves the coupling between the feedback energy and the ICM. This facilitates an earlier disruption of the cool-core, reduces the SFR by more than an order of magnitude, and results in earlier quenching despite an overall lower amount of feedback energy injected into the cluster core. With conduction, the metallicity gradients and dispersions are lowered, aligning them better with observational constraints. These results highlight the important role of thermal conduction in establishing and maintaining the quiescence of massive galaxies.
Security analysis of online centroid anomaly detection

Security issues are crucial in a number of machine learning applications, especially in scenarios dealing with human activity rather than natural phenomena (e.g., information ranking, spam detection, malware detection, etc.). In such cases, learning algorithms may have to cope with manipulated data aimed at hampering decision making. Although some previous work addressed the issue of handling malicious data in the context of supervised learning, very little is known about the behavior of anomaly detection methods in such scenarios. In this contribution, we analyze the performance of a particular method--online centroid anomaly detection--in the presence of adversarial noise. Our analysis addresses the following security-related issues: formalization of learning and attack processes, derivation of an optimal attack, and analysis of attack efficiency and limitations. We derive bounds on the effectiveness of a poisoning attack against centroid anomaly detection under different conditions: attacker's full or limited control over the traffic and bounded false positive rate. Our bounds show that whereas a poisoning attack can be effectively staged in the unconstrained case, it can be made arbitrarily difficult (a strict upper bound on the attacker's gain) if external constraints are properly used. Our experimental evaluation, carried out on real traces of HTTP and exploit traffic, confirms the tightness of our theoretical bounds and the practicality of our protection mechanisms.
Counterfactual Image Editing

Counterfactual image editing is an important task in generative AI, which asks how an image would look if certain features were different. The current literature on the topic focuses primarily on changing individual features while remaining silent about the causal relationships between these features, as present in the real world. In this paper, we formalize the counterfactual image editing task using formal language, modeling the causal relationships between latent generative factors and images through a special type of model called augmented structural causal models (ASCMs). Second, we show two fundamental impossibility results: (1) counterfactual editing is impossible from i.i. d. image samples and their corresponding labels alone; (2) even when the causal relationships between the latent generative factors and images are available, no guarantees regarding the output of the model can be provided. Third, we propose a relaxation for this challenging problem by approximating non-identifiable counterfactual distributions with a new family of counterfactual-consistent estimators. This family exhibits the desirable property of preserving features that the user cares about across both factual and counterfactual worlds. Finally, we develop an efficient algorithm to generate counterfactual images by leveraging neural causal models.
Simple online and realtime tracking with a deep association metric

Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms. In this paper, we integrate appearance information to improve the performance of SORT. Due to this extension we are able to track objects through longer periods of occlusions, effectively reducing the number of identity switches. In spirit of the original framework we place much of the computational complexity into an offline pre-training stage where we learn a deep association metric on a largescale person re-identification dataset. During online application, we establish measurement-to-track associations using nearest neighbor queries in visual appearance space. Experimental evaluation shows that our extensions reduce the number of identity switches by 45%, achieving overall competitive performance at high frame rates.
Redundancy Transmission in UAV-Aided LoRa Networks Featuring Wake-Up Radios

We consider a LoRa sensor network featuring a UAV-mounted gateway for collecting sensor data (messages). Wake-up radios (WuR) are employed to inform the sensors of the UAV's arrival. Building on an existing random access scheme for such setups, we propose and evaluate two redundancy transmission protocols for enhancing the reliability of the data transfer. One protocol employs fountain-coded transmissions, whereas the other performs message replication. Our results illustrate how redundancy transmission can be beneficial under the time constraints imposed by the UAV's limited hovering duration, and how node density, hovering time, and sensor energy budget impact the performance of the schemes.
Signatures of the accelerating black holes with a cosmological constant from the $\textrm{Sgr~A}^\star$ and $\textrm{M87}^\star$ shadow prospects

Recently, the Event Horizon Telescope (EHT) achieved the realization of an image of the supermassive black hole $\textrm{Sgr~A}^\star$ showing an angular shadow diameter $\mathcal{D}= 48.7 \pm 7\mu as$ and the fractional deviation $\mathbf{\delta} = -0.08^{+0.09}_{-0.09}~\text{(VLTI)},-0.04^{+0.09}_{-0.10}~\text{(Keck)}$, alongside the earlier image of $\textrm{M87}^\star$ with angular diameter $ \mathcal{D}=42 \pm 3 \mu as$, deviation $\mathbf{\delta}=-0.01^{+0.17}_{-0.17}$ and deviations from circularity estimated to be $\Delta \mathcal{C}\lesssim 10\%$. In addition, the shadow radii are assessed within the ranges $3.38 \le \frac{r_{\text{s}}}{M} \le 6.91$ for $\textrm{M87}^\star$ and $3.85 \le \frac{r_{\text{s}}}{M} \le 5.72$ as well as $3.95 \le \frac{r_{\text{s}}}{M} \le 5.92$ for $\textrm{Sgr~A}^\star$ using the Very Large Telescope Interferometer (VLTI) and Keck observatories, respectively. These values are provided with $1$-$\sigma$ and $2$-$\sigma$ measurements. Such realizations can unveil a better comprehension of gravitational physics at the horizon scale. In this paper, we use the EHT observational results for $\textrm{M87}^\star$ and $\textrm{Sgr~A}^\star$ to elaborate the constraints on parameters of accelerating black holes with a cosmological constant. Concretely, we utilize the mass and distance of both black holes to derive the observables associated with the accelerating black hole shadow. First, we compare our findings with observed quantities such as angular diameter, circularity, shadow radius, and the fractional deviation from the $\textrm{M87}^\star$ data. This comparison reveals constraints within the acceleration parameter and the cosmological constant. Lastly, one cannot rule out the possibility of the negative values for the cosmological constant on the emergence of accelerated black hole solutions within the context of minimal gauged supergravity.
Open-Universe Indoor Scene Generation using LLM Program Synthesis and Uncurated Object Databases

We present a system for generating indoor scenes in response to text prompts. The prompts are not limited to a fixed vocabulary of scene descriptions, and the objects in generated scenes are not restricted to a fixed set of object categories -- we call this setting indoor scene generation. Unlike most prior work on indoor scene generation, our system does not require a large training dataset of existing 3D scenes. Instead, it leverages the world knowledge encoded in pre-trained large language models (LLMs) to synthesize programs in a domain-specific layout language that describe objects and spatial relations between them. Executing such a program produces a specification of a constraint satisfaction problem, which the system solves using a gradient-based optimization scheme to produce object positions and orientations. To produce object geometry, the system retrieves 3D meshes from a database. Unlike prior work which uses databases of category-annotated, mutually-aligned meshes, we develop a pipeline using vision-language models (VLMs) to retrieve meshes from massive databases of un-annotated, inconsistently-aligned meshes. Experimental evaluations show that our system outperforms generative models trained on 3D data for traditional, closed-universe scene generation tasks; it also outperforms a recent LLM-based layout generation method on open-universe scene generation.
Tomography of near-field radiative heat exchange between mesoscopic bodies immersed in a thermal bath

A tomographic study of near-field radiative heat exchanges between a mesoscopic object and a substrate immersed in a thermal bath is carried out within the theoretical framework of fluctuational electrodynamics. By using the discrete-dipole-approximation method, we compute the power density distribution for radiative exchanges and highlight the major role played by many-body interactions in these transfers. Additionally, we emphasize the close relationship between power distribution and eigenmodes within the solid paving the way to applications for hot-spot targeting at deep sub-wavelength scale by shape optimization.
Open quantum system simulation of time and frequency resolved spectroscopy

The dynamics of excitonic energy transfer in molecular complexes triggered by interaction with laser pulses offers a unique window into the underlying physical processes. The absorbed energy moves through the network of interlinked pigments and in photosynthetic complexes reaches a reaction center. The efficiency and time-scale depend not only on the excitonic couplings, but are also affected by the dissipation of energy to vibrational modes of the molecules. An open quantum system description provides a suitable tool to describe the involved processes and connects the decoherence and relaxation dynamics to measurements of the time-dependent polarization.