Miracle-dz/UniFAR-FTUs huggingface dataset:
Provable Privacy with Non-Private Pre-Processing

When analysing Differentially Private (DP) machine learning pipelines, the potential privacy cost of data-dependent pre-processing is frequently overlooked in privacy accounting. In this work, we propose a general framework to evaluate the additional privacy cost incurred by non-private data-dependent pre-processing algorithms. Our framework establishes upper bounds on the overall privacy guarantees by utilising two new technical notions: a variant of DP termed Smooth DP and the bounded sensitivity of the pre-processing algorithms. In addition to the generic framework, we provide explicit overall privacy guarantees for multiple data-dependent pre-processing algorithms, such as data imputation, quantization, deduplication and PCA, when used in combination with several DP algorithms. Notably, this framework is also simple to implement, allowing direct integration into existing DP pipelines.
Probabilistic Circuits with Constraints via Convex Optimization

This work addresses integrating probabilistic propositional logic constraints into the distribution encoded by a probabilistic circuit (PC). PCs are a class of tractable models that allow efficient computations (such as conditional and marginal probabilities) while achieving state-of-the-art performance in some domains. The proposed approach takes both a PC and constraints as inputs, and outputs a new PC that satisfies the constraints. This is done efficiently via convex optimization without the need to retrain the entire model. Empirical evaluations indicate that the combination of constraints and PCs can have multiple use cases, including the improvement of model performance under scarce or incomplete data, as well as the enforcement of machine learning fairness measures into the model without compromising model fitness. We believe that these ideas will open possibilities for multiple other applications involving the combination of logics and deep probabilistic models.
Working in Clients' Homes: The Impact on the Mental Health and Well-Being of Visiting Home Care Workers

ABSTRACT The purpose of this paper is to examine the effects of working in clients' homes on the mental health and well-being of visiting home care workers. This paper reports the results of a survey of 674 visiting staff from three non-profit home care agencies in a medium-sized city in Ontario, Canada. Survey results are also complimented by data from 9 focus groups with 50 employees. For purposes of this study, home care workers include visiting therapists, nurses, and home support workers. Mental health and well-being is measured by three dependent variables: stress; job stress; and intrinsic job satisfaction. Multiple least squared regression analyses show several structural, emotional, physical, and organizational working conditions associated with the health and well-being of visiting home care workers. Overall, results show that workload, difficult clients, clients who take advantage of workers, sexual harassment, safety hazards, a repetitious job, and work-related injuries are associated with poorer health. Being fairly paid, having good benefits, emotional labour, organizational support, control over work, and peer support are associated with better health. Results suggest that policy change is needed to encourage healthier work environments for employees who work in clients' homes.
Graph Neural Network-based Multi-agent Reinforcement Learning for Resilient Distributed Coordination of Multi-Robot Systems

Existing multi-agent coordination techniques are often fragile and vulnerable to anomalies such as agent attrition and communication disturbances, which are quite common in the real-world deployment of systems like field robotics. To better prepare these systems for the real world, we present a graph neural network (GNN)-based multi-agent reinforcement learning (MARL) method for resilient distributed coordination of a multi-robot system. Our method, Multi-Agent Graph Embedding-based Coordination (MAGEC), is trained using multi-agent proximal policy optimization (PPO) and enables distributed coordination around global objectives under agent attrition, partial observability, and limited or disturbed communications. We use a multi-robot patrolling scenario to demonstrate our MAGEC method in a ROS 2-based simulator and then compare its performance with prior coordination approaches. Results demonstrate that MAGEC outperforms existing methods in several experiments involving agent attrition and communication disturbance, and provides competitive results in scenarios without such anomalies.
Federated reinforcement learning for robot motion planning with zero-shot generalization

This paper considers the problem of learning a control policy for robot motion planning with zero-shot generalization, i.e., no data collection and policy adaptation is needed when the learned policy is deployed in new environments. We develop a federated reinforcement learning framework that enables collaborative learning of multiple learners and a central server, i.e., the Cloud, without sharing their raw data. In each iteration, each learner uploads its local control policy and the corresponding estimated normalized arrival time to the Cloud, which then computes the global optimum among the learners and broadcasts the optimal policy to the learners. Each learner then selects between its local control policy and that from the Cloud for next iteration. The proposed framework leverages on the derived zero-shot generalization guarantees on arrival time and safety. Theoretical guarantees on almost-sure convergence, almost consensus, Pareto improvement and optimality gap are also provided. Monte Carlo simulation is conducted to evaluate the proposed framework.
A Hybrid Framework for 3-D Human Motion Tracking

In this paper, we present a hybrid framework for articulated 3-D human motion tracking from multiple synchronized cameras with potential uses in surveillance systems. Although the recovery of 3-D motion provides richer information for event understanding, existing methods based on either deterministic search or stochastic sampling lack robustness or efficiency. We therefore propose a hybrid sample-and-refine framework that combines both stochastic sampling and deterministic optimization to achieve a good compromise between efficiency and robustness. Similar motion patterns are used to learn a compact low-dimensional representation of the motion statistics. Sampling in a low-dimensional space is implemented during tracking, which reduces the number of particles drastically. We also incorporate a local optimization method based on simulated physical force/moment into our framework, which further improves the optimality of the tracking. Experimental results on several real human motion sequences show the accuracy and robustness of our method, which also has a higher sampling efficiency than most particle filtering-based methods.
The weak Harnack inequality for unbounded minimizers of elliptic functionals with generalized Orlicz growth

In this work we prove that the non-negative functions $u \in L^s_{loc}(\Omega)$, for some $s>0$, belonging to the De Giorgi classes \begin{equation}\label{eq0.1} \fint\limits_{B_{r(1-\sigma)}(x_{0})} \big|\nabla \big(u-k\big)_{-}\big|^{p}\, dx \leqslant \frac{c}{\sigma^{q}} \,\Lambda\big(x_{0}, r, k\big)\bigg(\frac{k}{r}\bigg)^{p}\bigg(\frac{\big|B_{r}(x_{0})\cap\big\{u\leqslant k\big\}\big|}{|B_{r}(x_{0})|}\bigg)^{1-\delta}, \end{equation} under proper assumptions on $\Lambda$, satisfy a weak Harnack inequality with a constant depending on the $L^s$-norm of $u$. Under suitable assumptions on $\Lambda$, the minimizers of elliptic functionals with generalized Orlicz growth belong to De Giorgi classes satisfying \eqref{eq0.1}; thus this study gives a wider interpretation of Harnack-type estimates derived to double-phase, degenerate double-phase functionals and functionals with variable exponents.
Microcompartments in Prokaryotes: Carboxysomes and Related Polyhedra

All cyanobacteria and many chemoautotrophs contain polyhedral inclusion bodies that are bound by a unilamellar protein shell (15, 63). Isolation and enzymatic analysis of the bodies from Halothiobacillus neapolitanus (previously Thiobacillus neapolitanus) revealed that they are filled with ribulose 1,5bisphosphate carboxylase/oxygenase (RuBisCO); therefore they were given the name “carboxysomes” (59). Subsequent studies of both cyanobacteria and chemoautotrophic bacteria have led to the well-accepted conclusion that the “organelles” or microcompartments function to enhance the catalytic properties of the RuBisCO they contain, although the mechanism of this catalytic enhancement is unclear (51, 65). Localization and characterization of the genes encoding carboxysome components has underscored the apparent common function of these bodies in carboxysome-containing autotrophic bacteria. More surprising is the finding that a number of heterotrophic prokaryotes harbor genes homologous to those for carboxysome shell proteins (9, 32, 62). Under proper growth conditions, these bacteria produce polyhedral inclusion bodies that are morphologically similar to carboxysomes, although the cells expressing these bodies contain no RuBisCO and do not fix CO 2 via the Calvin-Benson-Bassham cycle as a major part of their carbon metabolism. This review evaluates the evidence that relates carboxysome structure to function in the carbon metabolism of autotrophic prokaryotes and examines similarities to newly discovered particles found in heterotrophs. The possibility is explored that microcompartmentalization of key metabolic enzymes by carboxysomes and their relatives is a more widely utilized regulatory mechanism in prokaryotes than was previously envisioned.
DOR3D-Net: Dense Ordinal Regression Network for 3D Hand Pose Estimation

Depth-based 3D hand pose estimation is an important but challenging research task in human-machine interaction community. Recently, dense regression methods have attracted increasing attention in 3D hand pose estimation task, which provide a low computational burden and high accuracy regression way by densely regressing hand joint offset maps. However, large-scale regression offset values are often affected by noise and outliers, leading to a significant drop in accuracy. To tackle this, we re-formulate 3D hand pose estimation as a dense ordinal regression problem and propose a novel Dense Ordinal Regression 3D Pose Network (DOR3D-Net). Specifically, we first decompose offset value regression into sub-tasks of binary classifications with ordinal constraints. Then, each binary classifier can predict the probability of a binary spatial relationship relative to joint, which is easier to train and yield much lower level of noise. The estimated hand joint positions are inferred by aggregating the ordinal regression results at local positions with a weighted sum. Furthermore, both joint regression loss and ordinal regression loss are used to train our DOR3D-Net in an end-to-end manner. Extensive experiments on public datasets (ICVL, MSRA, NYU and HANDS2017) show that our design provides significant improvements over SOTA methods.
Hexagonal assembly of a restricting TRIM5α protein

TRIM5α proteins are restriction factors that protect mammalian cells from retroviral infections by binding incoming viral capsids, accelerating their dissociation, and preventing reverse transcription of the viral genome. Individual TRIM5 isoforms can often protect cells against a broad range of retroviruses, as exemplified by rhesus monkey TRIM5α and its variant, TRIM5-21R, which recognize HIV-1 as well as several distantly related retroviruses. Although capsid recognition is not yet fully understood, previous work has shown that the C-terminal SPRY/B30.2 domain of dimeric TRIM5α binds directly to viral capsids, and that higher-order TRIM5α oligomerization appears to contribute to the efficiency of capsid recognition. Here, we report that recombinant TRIM5-21R spontaneously assembled into two-dimensional paracrystalline hexagonal lattices comprising open, six-sided rings. TRIM5-21R assembly did not require the C-terminal SPRY domain, but did require both protein dimerization and a B-box 2 residue (Arg121) previously implicated in TRIM5α restriction and higher-order assembly. Furthermore, TRIM5-21R assembly was promoted by binding to hexagonal arrays of the HIV-1 CA protein that mimic the surface of the viral capsid. We therefore propose that TRIM5α proteins have evolved to restrict a range of different retroviruses by assembling a deformable hexagonal scaffold that positions the capsid-binding domains to match the symmetry and spacing of the capsid surface lattice. Capsid recognition therefore involves a synergistic combination of direct binding interactions, avidity effects, templated assembly, and lattice complementarity.
Weakly convergent nonparametric forecasting of stationary time series

The conditional distribution of the next outcome given the infinite past of a stationary process can be inferred from finite but growing segments of the past. Several schemes are known for constructing pointwise consistent estimates, but they all demand prohibitive amounts of input data. We consider real-valued time series and construct conditional distribution estimates that make much more efficient use of the input data. The estimates are consistent in a weak sense, and the question whether they are pointwise-consistent is still open. For finite-alphabet processes one may rely on a universal data compression scheme like the Lempel-Ziv (1978) algorithm to construct conditional probability mass function estimates that are consistent in expected information divergence. Consistency in this strong sense cannot be attained in a universal sense for all stationary processes with values in an infinite alphabet, but weak consistency can. Some applications of the estimates to on-line forecasting, regression, and classification are discussed.
Unifying Local and Global Multimodal Features for Place Recognition in Aliased and Low-Texture Environments

Perceptual aliasing and weak textures pose significant challenges to the task of place recognition, hindering the performance of Simultaneous Localization and Mapping (SLAM) systems. This paper presents a novel model, called UMF (standing for Unifying Local and Global Multimodal Features) that 1) leverages multi-modality by cross-attention blocks between vision and LiDAR features, and 2) includes a re-ranking stage that re-orders based on local feature matching the top-k candidates retrieved using a global representation. Our experiments, particularly on sequences captured on a planetary-analogous environment, show that UMF outperforms significantly previous baselines in those challenging aliased environments. Since our work aims to enhance the reliability of SLAM in all situations, we also explore its performance on the widely used RobotCar dataset, for broader applicability. Code and models are available at https://github.com/DLR-RM/UMF
Map-Aware Human Pose Prediction for Robot Follow-Ahead

In the robot follow-ahead task, a mobile robot is tasked to maintain its relative position in front of a moving human actor while keeping the actor in sight. To accomplish this task, it is important that the robot understand the full 3D pose of the human (since the head orientation can be different than the torso) and predict future human poses so as to plan accordingly. This prediction task is especially tricky in a complex environment with junctions and multiple corridors. In this work, we address the problem of forecasting the full 3D trajectory of a human in such environments. Our main insight is to show that one can first predict the 2D trajectory and then estimate the full 3D trajectory by conditioning the estimator on the predicted 2D trajectory. With this approach, we achieve results comparable or better than the state-of-the-art methods three times faster. As part of our contribution, we present a new dataset where, in contrast to existing datasets, the human motion is in a much larger area than a single room. We also present a complete robot system that integrates our human pose forecasting network on the mobile robot to enable real-time robot follow-ahead and present results from real-world experiments in multiple buildings on campus. Our project page, including supplementary material and videos, can be found at: https://qingyuan-jiang.github. io/iros2024_poseForecasting/
Selenium Supplementation Reduced Oxidative DNA Damage in Adnexectomized BRCA1 Mutations Carriers

Some experimental evidence suggests that BRCA1 plays a role in repair of oxidative DNA damage. Selenium has anticancer properties that are linked with protection against oxidative stress. To assess whether supplementation of BRCA1 mutation carriers with selenium have a beneficial effect concerning oxidative stress/DNA damage in the present double-blinded placebo control study, we determined 8-oxodG level in cellular DNA and urinary excretion of 8-oxodG and 8-oxoGua in the mutation carriers. We found that 8-oxodG level in leukocytes DNA is significantly higher in BRCA1 mutation carriers. In the distinct subpopulation of BRCA1 mutation carriers without symptoms of cancer who underwent adnexectomy and were supplemented with selenium, the level of 8-oxodG in DNA decreased significantly in comparison with the subgroup without supplementation. Simultaneously in the same group, an increase of urinary 8-oxoGua, the product of base excision repair (hOGG1 glycosylase), was observed. Therefore, it is likely that the selenium supplementation of the patients is responsible for the increase of BER enzymes activities, which in turn may result in reduction of oxidative DNA damage. Importantly, in a double-blinded placebo control prospective study, it was shown that in the same patient groups, reduction in cancer incidents was observed. Altogether, these results suggest that BRCA1 deficiency contributes to 8-oxodG accumulation in cellular DNA, which in turn may be a factor responsible for cancer development in women with mutations, and that the risk to developed breast cancer in BRCA1 mutation carriers may be reduced in selenium-supplemented patients who underwent adnexectomy. (Cancer Epidemiol Biomarkers Prev 2009;18(11):2923–8)
KI-Zeitschrift-Auszug als Leseprobe

Despite the last decade’s research and new insights into human-machine communication, much of today’s technology is too complicated to use, e.g., [6]. This is true for consumer electronics as well as household devices—much of the cutting-edge products do not fit in an every-day environment for every-day users. Current home automation solutions are for technophile users. The lack of intuitive or even understandable interaction concepts actually opens up the market for profitable business companies whose business model is centered around the idea of helping people out as they fail in installing or use newly bought computerized technology. For elderly and disabled persons the situation is even worse. At the same time, these people form exactly the user group that could and should profit the most from modern technology. i2home, a EU-funded project, has as its main goal to develop technology for elderly and persons with mild cognitively impairments that supports them in their daily life in their digital home. Rather than developing interface technology for a few, one of the project’s main allegations is design-for-all. This is further underpinned by the methodology chosen: the project is build around the user-centred design (henceforth UCD) paradigm. One of the main ideas with UCD is to include the users of the technology right from the start. In fact, their needs and requirements are the driving force behind the development of the technology. Some of the main obstacles for possibly remotely managing the digital home in a convenient way are that remote control devices usually are device oriented, device dependent and tied to specific target devices. Therefore, the project implements the Universal Remote Console (URC) standard [3, 8]. This approach makes it possible to develop user interfaces that are generic, individually adaptable and allows for controlling multiple devices seamlessly, i.e., in one coherent user interface. The i2home consortium consists of nine partners from five countries and brings a wide variety of experiences, ranging from definition and implementation of open stan-
System-level power analysis methodology applied to the AMBA AHB bus [SoC applications]

The specification on power consumption of a digital system is extremely important due to the growing relevance of the market of portable devices and must be taken into account since the early phases of a complex system-on-chip design. In this paper, some guidelines are provided for the integration of the information on power consumption in the executable model of parameterized cores, with particular attention to the AMBA AHB bus. This gives important information for the analysis and choice between different design architectures driven by functional, timing and power constraints of the system-on-chip.
Low-cost, open source bioelectric signal acquisition system

Bioelectric potentials provide an intuitive source of control in human-machine interfaces. In this work, a low-cost system for bioelectric signals acquisition and processing was developed and made available as open source. A single module based on the ADS1299 (Texas Instruments, USA) can acquire up to 8 differential or single-ended channels with a resolution of 24 bits and programmable gain up to 24 V/V. Several modules can be daisy-chained together to increase the number of input channels. Opto-isolated USB communication was included in the design to interface safely with a personal computer. The system was designed to be compatible with a low-cost and widely available microcontroller development platform, namely the Tiva LaunchPad (Texas Instruments, USA) featuring an ARM Cortex-M4 core. We made the source files for the PCB, firmware, and high-level software available online (GitHub: ADS_BP). Digital processing was used for float conversion and filtering. The high-level software for control and acquisition was integrated into an already existent open source platform for advanced myoelectric control, namely BioPatRec. This integration provide a complete system for intuitive myoelectric control where signal processing, machine learning, and control algorithms are used for the prediction of motor volition and control of robotic and virtual devices.
Predicate-argument Structure based Textual Entailment Recognition System of KYOTO Team for NTCIR9 RITE

We participated in Japanese tasks of RITE in NTCIR9 (team id: “KYOTO”). Our proposed method regards predicateargument structure as a basic unit of handling the meaning of text/hypothesis, and performs the matching between text and hypothesis. Our system first performs predicateargument structure analysis to both a text and a hypothesis. Then, we perform the matching between text and hypothesis. In matching text and hypothesis, wide-coverage relations between words/phrases such as synonym and is-a are utilized, which are automatically acquired from a dictionary, Web corpus and Wikipedia.