Miracle-dz/UniFAR-FTUs huggingface dataset:
Vers un traitement passif des drainages miniers acides (DMA) riches en arsenic par oxydation biologique du fer et de l'arsenic

Les dechets sulfures issus de l’extraction des minerais metalliques generent des drainages miniers acides (DMA) contenant des elements toxiques tels que l’arsenic. Les procedes de traitement passifs bases sur l’oxydation bacterienne du fer et de l’arsenic, en favorisant la precipitation de ces elements sous une forme stable, pourraient representer une solution efficace et economique pour traiter cette pollution. Dans ce contexte, l’objectif general de cette these etait de mieux comprendre les facteurs environnementaux et operationnels qui controlent l’efficacite d’elimination de l’arsenic. Une approche en pilote a flux continu a ete mise en oeuvre afin de se rapprocher des conditions reelles d’un traitement. L’etude a ete conduite d’abord a l’echelle d’un bioreacteur de paillasse en conditions controlees (temperature, lumiere, debit, temps de sejour et hauteur d’eau), puis dans un dispositif de taille superieure, fonctionnant de maniere totalement passive et in situ. Ces dispositifs ont ete alimentes avec de l’eau d’un DMA riche en arsenic, issue de l’ancien site minier de Carnoules, dans le Gard. Les caracteristiques de l’eau et des bioprecipites au sein de ces pilotes, en particulier le redox du fer et de l’arsenic, ont ete suivis dans differentes conditions environnementales et d’operation par des methodes de speciation liquide et solide (HPLC-ICP-MS, EXAFS, XANES), des analyses mineralogiques (DRX) et des analyses microbiologiques (ARISA, sequencage haut debit du gene de l'ARNr 16S, quantification du gene aioA). Les resultats issus des experiences en laboratoire ont mis en evidence l’effet de differents parametres operationnels (hauteur d’eau, temps de retention hydraulique, et presence/absence d’une pellicule flottante) sur les performances du traitement, ainsi que sur la microbiologie et la mineralogie des bioprecipites formes. Le dispositif de terrain a permis de tester les performances du procede dans des conditions environnementales fluctuantes (variabilite de la physico-chimie de l’eau d’entree et de la temperature) et d’acquerir des connaissances nouvelles sur l’evolution des bioprecipites au cours de six mois de traitement. Les connaissances acquises dans cette these pourront servir de base a la conception d’une etape d’elimination de l’arsenic dans les processus de traitement des DMA.
GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing

Multimodal language models (MLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. Despite its success in language understanding, it is critical to evaluate the performance of downstream tasks for better human-centric applications. This paper assesses the application of MLMs with 5 crucial abilities for affective computing, spanning from visual affective tasks and reasoning tasks. The results show that GPT4 has high accuracy in facial action unit recognition and micro-expression detection while its general facial expression recognition performance is not accurate. We also highlight the challenges of achieving fine-grained micro-expression recognition and the potential for further study and demonstrate the versatility and potential of GPT4 for handling advanced tasks in emotion recognition and related fields by integrating with task-related agents for more complex tasks, such as heart rate estimation through signal processing. In conclusion, this paper provides valuable insights into the potential applications and challenges of MLMs in human-centric computing. The interesting samples are available at \url{https://github.com/LuPaoPao/GPT4Affectivity}.
A Spectral Element Enrichment Wall-Model

We develop an enrichment wall-model within the spectral element method (SEM) framework for large-eddy simulations of wall-bounded flows. The method augments the polynomial solution in the wall-adjacent elements with an analytical law-of-the-wall enrichment function representing the mean velocity near the wall. In the solution representation, this enrichment function captures the large gradients in the boundary layer, which allows the polynomial modes to represent the turbulent fluctuations. The enriched solution is able to resolve the shear stress at the wall without any modification to the no-slip wall boundary conditions. The performance of the SEM enrichment wall-modeling approach is assessed in turbulent channel flow LES for a range of Reynolds numbers. The results show that the proposed method improves solution accuracy on under-resolved near-wall grids as compared to the traditional shear stress wall-models.
Are LLMs ready for Visualization?

Generative models have received a lot of attention in many areas of academia and the industry. Their capabilities span many areas, from the invention of images given a prompt to the generation of concrete code to solve a certain programming issue. These two paradigmatic cases fall within two distinct categories of requirements, ranging from"creativity"to"precision", as characterized by Bing Chat, which employs ChatGPT-4 as its backbone. Visualization practitioners and researchers have wondered to what end one of such systems could accomplish our work in a more efficient way. Several works in the literature have utilized them for the creation of visualizations. And some tools such as Lida, incorporate them as part of their pipeline. Nevertheless, to the authors' knowledge, no systematic approach for testing their capabilities has been published, which includes both extensive and in-depth evaluation. Our goal is to fill that gap with a systematic approach that analyzes three elements: whether Large Language Models are capable of correctly generating a large variety of charts, what libraries they can deal with effectively, and how far we can go to configure individual charts. To achieve this objective, we initially selected a diverse set of charts, which are commonly utilized in data visualization. We then developed a set of generic prompts that could be used to generate them, and analyzed the performance of different LLMs and libraries. The results include both the set of prompts and the data sources, as well as an analysis of the performance with different configurations.
Hufu: A Modality-Agnositc Watermarking System for Pre-Trained Transformers via Permutation Equivariance

With the blossom of deep learning models and services, it has become an imperative concern to safeguard the valuable model parameters from being stolen. Watermarking is considered an important tool for ownership verification. However, current watermarking schemes are customized for different models and tasks, hard to be integrated as an integrated intellectual protection service. We propose Hufu, a modality-agnostic watermarking system for pre-trained Transformer-based models, relying on the permutation equivariance property of Transformers. Hufu embeds watermark by fine-tuning the pre-trained model on a set of data samples specifically permuted, and the embedded model essentially contains two sets of weights -- one for normal use and the other for watermark extraction which is triggered on permuted inputs. The permutation equivariance ensures minimal interference between these two sets of model weights and thus high fidelity on downstream tasks. Since our method only depends on the model itself, it is naturally modality-agnostic, task-independent, and trigger-sample-free. Extensive experiments on the state-of-the-art vision Transformers, BERT, and GPT2 have demonstrated Hufu's superiority in meeting watermarking requirements including effectiveness, efficiency, fidelity, and robustness, showing its great potential to be deployed as a uniform ownership verification service for various Transformers.
BoostER: Leveraging Large Language Models for Enhancing Entity Resolution

Entity resolution, which involves identifying and merging records that refer to the same real-world entity, is a crucial task in areas like Web data integration. This importance is underscored by the presence of numerous duplicated and multi-version data resources on the Web. However, achieving high-quality entity resolution typically demands significant effort. The advent of Large Language Models (LLMs) like GPT-4 has demonstrated advanced linguistic capabilities, which can be a new paradigm for this task. In this paper, we propose a demonstration system named BoostER that examines the possibility of leveraging LLMs in the entity resolution process, revealing advantages in both easy deployment and low cost. Our approach optimally selects a set of matching questions and poses them to LLMs for verification, then refines the distribution of entity resolution results with the response of LLMs. This offers promising prospects to achieve a high-quality entity resolution result for real-world applications, especially to individuals or small companies without the need for extensive model training or significant financial investment.
Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys

The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a beginner but still struggles to consistently achieve precision when synthesis parameters are coupled. The time to optimize synthesis becomes a barrier to exploring scientifically and technologically exciting compositionally complex materials. This investigation demonstrates an Active Learning (AL) approach for optimizing physical vapor deposition synthesis of thin-film alloys with up to five principal elements. We compared AL based on Gaussian Process (GP) and Random Forest (RF) models. The best performing models were able to discover synthesis parameters for a target quinary alloy in 14 iterations. We also demonstrate the capability of these models to be used in transfer learning tasks. RF and GP models trained on lower dimensional systems (i.e. ternary, quarternary) show an immediate improvement in prediction accuracy compared to models trained only on quinary samples. Furthermore, samples that only share a few elements in common with the target composition can be used for model pre-training. We believe that such AL approaches can be widely adapted to significantly accelerate the exploration of compositionally complex materials.
Velocity Dispersion of the open cluster NGC 2571 by Radial Velocities and Proper Motions

We use a Kernel Density Estimator method to evaluate the stellar velocity dispersion in the open cluster NGC 2571. We derive the 3-D velocity dispersion using both proper motions as extracted from Gaia DR3 and single epoch radial velocities as obtained with the instrument FLAMES at ESO VLT. The mean-square velocity along the line-of-sight is found to be larger than the one in the tangential direction by a factor in the interval [6,8]. We argue that the most likely explanation for such an occurrence is the presence of a significant quantity of unresolved binary and multiple stars in the radial velocity sample. Special attention should be paid to single line spectroscopic binaries (SB1) since in this case we observe the spectral lines of the primary component only, and therefore the derived radial velocity is not the velocity of the binary system center of mass. To investigate this scenario, we performed numerical experiments at varying the fractional abundance of SB1 in the observed sample. These experiments show that the increase of the mean-square radial velocity depends actually on the fractional abundance of SB1 to a power in the range of [0.39,0.45]. We used the 3-D velocity dispersion obtained by the dispersions in the tangential directions and the assumption that the radial velocity dispersion is the same as a tangential one to estimate the virial cluster mass and the cluster mass taking into account the gravitational field of the Galaxy and the non-stationarity of the cluster. These estimates are $650\pm30 \; M_\odot$ and $310\pm80 \; M_\odot$, respectively, and they are in substantial agreement with the photometric cluster mass.
Towards Zero-Shot Interpretable Human Recognition: A 2D-3D Registration Framework

Large vision models based in deep learning architectures have been consistently advancing the state-of-the-art in biometric recognition. However, three weaknesses are commonly reported for such kind of approaches: 1) their extreme demands in terms of learning data; 2) the difficulties in generalising between different domains; and 3) the lack of interpretability/explainability, with biometrics being of particular interest, as it is important to provide evidence able to be used for forensics/legal purposes (e.g., in courts). To the best of our knowledge, this paper describes the first recognition framework/strategy that aims at addressing the three weaknesses simultaneously. At first, it relies exclusively in synthetic samples for learning purposes. Instead of requiring a large amount and variety of samples for each subject, the idea is to exclusively enroll a 3D point cloud per identity. Then, using generative strategies, we synthesize a very large (potentially infinite) number of samples, containing all the desired covariates (poses, clothing, distances, perspectives, lighting, occlusions,.). Upon the synthesizing method used, it is possible to adapt precisely to different kind of domains, which accounts for generalization purposes. Such data are then used to learn a model that performs local registration between image pairs, establishing positive correspondences between body parts that are the key, not only to recognition (according to cardinality and distribution), but also to provide an interpretable description of the response (e.g.:"both samples are from the same person, as they have similar facial shape, hair color and legs thickness").
Debiased Projected Two-Sample Comparisonscfor Single-Cell Expression Data

We study several variants of the high-dimensional mean inference problem motivated by modern single-cell genomics data. By taking advantage of low-dimensional and localized signal structures commonly seen in such data, our proposed methods not only have the usual frequentist validity but also provide useful information on the potential locations of the signal if the null hypothesis is rejected. Our method adaptively projects the high-dimensional vector onto a low-dimensional space, followed by a debiasing step using the semiparametric double-machine learning framework. Our analysis shows that debiasing is unnecessary under the global null, but necessary under a ``projected null'' that is of scientific interest. We also propose an ``anchored projection'' to maximize the power while avoiding the degeneracy issue under the null. Experiments on synthetic data and a real single-cell sequencing dataset demonstrate the effectiveness and interpretability of our methods.
Optimistic Safety for Linearly-Constrained Online Convex Optimization

The setting of online convex optimization (OCO) under unknown constraints has garnered significant attention in recent years. In this work, we consider a version of this problem with static linear constraints that the player receives noisy feedback of and must always satisfy. By leveraging our novel design paradigm of optimistic safety, we give an algorithm for this problem that enjoys $\tilde{\mathcal{O}}(\sqrt{T})$ regret. This improves on the previous best regret bound of $\tilde{\mathcal{O}}(T^{2/3})$ while using only slightly stronger assumptions of independent noise and an oblivious adversary. Then, by recasting this problem as OCO under time-varying stochastic linear constraints, we show that our algorithm enjoys the same regret guarantees in such a setting and never violates the constraints in expectation. This contributes to the literature on OCO under time-varying stochastic constraints, where the state-of-the-art algorithms enjoy $\tilde{\mathcal{O}}(\sqrt{T})$ regret and $\tilde{\mathcal{O}}(\sqrt{T})$ violation when the constraints are convex and the player receives full feedback. Additionally, we provide a version of our algorithm that is more computationally efficient and give numerical experiments comparing it with benchmark algorithms.
FLAP: Flow Adhering Planning with Constrained Decoding in LLMs

Planning is a crucial task for agents in task oriented dialogs (TODs). Human agents typically resolve user issues by following predefined workflows, decomposing workflow steps into actionable items, and performing actions by executing APIs in order; all of which require reasoning and planning. With the recent advances in LLMs, there have been increasing attempts to use LLMs for task planning and API usage. However, the faithfulness of the plans to predefined workflows and API dependencies, is not guaranteed with LLMs because of their bias towards pretraining data. Moreover, in real life, workflows are custom-defined and prone to change, hence, quickly adapting agents to the changes is desirable. In this paper, we study faithful planning in TODs to resolve user intents by following predefined flows and preserving API dependencies. We propose a constrained decoding algorithm based on lookahead heuristic for faithful planning. Our algorithm alleviates the need for finetuning LLMs using domain specific data, outperforms other decoding and prompting-based baselines, and applying our algorithm on smaller LLMs (7B) we achieve comparable performance to larger LLMs (30B-40B).
Put Myself in Your Shoes: Lifting the Egocentric Perspective from Exocentric Videos

We investigate exocentric-to-egocentric cross-view translation, which aims to generate a first-person (egocentric) view of an actor based on a video recording that captures the actor from a third-person (exocentric) perspective. To this end, we propose a generative framework called Exo2Ego that decouples the translation process into two stages: high-level structure transformation, which explicitly encourages cross-view correspondence between exocentric and egocentric views, and a diffusion-based pixel-level hallucination, which incorporates a hand layout prior to enhance the fidelity of the generated egocentric view. To pave the way for future advancements in this field, we curate a comprehensive exo-to-ego cross-view translation benchmark. It consists of a diverse collection of synchronized ego-exo tabletop activity video pairs sourced from three public datasets: H2O, Aria Pilot, and Assembly101. The experimental results validate that Exo2Ego delivers photorealistic video results with clear hand manipulation details and outperforms several baselines in terms of both synthesis quality and generalization ability to new actions.
Vad kan tidigare forskning säga om kvaliteten i idéburen välfärd?

Kvalitet är ett centralt begrepp inom välfärden. Det behövs för att kunna be-döma och jämföra värdet av en välfärdstjänst, och det utgör en grund för verksamhetsutveckling hos organisationer som tillhandahåller sådana tjäns-ter. Men hur definieras, mäts och jämförs då kvalitet av välfärdstjänster? I denna artikel analyserar vi forskning om kvalitet i de välfärdstjänster som idéburna organisationer tillhandahåller, en grupp av organisationer som ofta glöms bort i den allmänna debatten i Sverige. Forskningsöversikten visar att det finns få tydliga definitioner av kvalitet, och att forskningen främst ba-serats på studier av äldre- och omsorgsboenden. Det förekommer också ett stort antal sätt att mäta, analysera och jämföra kvalitet. Vidare är det relativt sällan som patienter, brukare och klienter avgör kvaliteten. I stället baseras forskningen på offentliga register och återkommande kontroller av andra mått. Knappt hälften av artiklarna redovisar entydigt positiva relatio-ner mellan kvalitet, civilsamhälle och välfärd, och då utifrån jämförelser med antingen offentliga eller privata organisationer, eller med båda. Artikeln av-slutas med några förslag till framtida forskning.
Effects of early predation and social cues on the relationship between laterality and personality

Abstract Individual differences in laterality and personality are expected to covary, as emotions are processed differently by the two hemispheres, and personality involves emotional behavior. Fish species are often used to investigate this topic due to the large variability in personality and laterality patterns. While some species show a positive relationship between lateralization strength and boldness, others show a negative relationship, and some show no relationship. A new way to assess the robustness of such a relationship is to manipulate both laterality and personality to examine how this affects their relationship. To this end, we conducted a fully factorial design experiment manipulating predation and group size during early development. Results showed that the strength of laterality was influenced by predation threat, while social tendency and boldness were influenced by group size. These findings suggest that early life conditions can have an impact on laterality and social behavior. The relationship between laterality and personality traits, while present, was heavily influenced by the specific trial conditions but not by the different developmental conditions. In summary, the relationship between laterality and behaviors appears to be context-dependent, yet resilient to early environmental manipulations.
Dynamics of quantum cellular automata electron transition in triple quantum dots

The quantum cellular automata (QCA) effect is a transition in which multiple electron move coordinately by Coulomb interactions and observed in multiple quantum dots. This effect will be useful for realizing and improving quantum cellular automata and information transfer using multiple electron transfer. In this paper, we investigate the real-time dynamics of the QCA charge transitions in a triple quantum dot by using fast charge-state readout realized by rf reflectometry. We observe real-time charge transitions and analyze the tunneling rate comparing with the first-order tunneling processes. We also measure the gate voltage dependence of the QCA transition and show that it can be controlled by the voltage.
PAPER-HILT: Personalized and Adaptive Privacy-Aware Early-Exit for Reinforcement Learning in Human-in-the-Loop Systems

Reinforcement Learning (RL) has increasingly become a preferred method over traditional rule-based systems in diverse human-in-the-loop (HITL) applications due to its adaptability to the dynamic nature of human interactions. However, integrating RL in such settings raises significant privacy concerns, as it might inadvertently expose sensitive user information. Addressing this, our paper focuses on developing PAPER-HILT, an innovative, adaptive RL strategy through exploiting an early-exit approach designed explicitly for privacy preservation in HITL environments. This approach dynamically adjusts the tradeoff between privacy protection and system utility, tailoring its operation to individual behavioral patterns and preferences. We mainly highlight the challenge of dealing with the variable and evolving nature of human behavior, which renders static privacy models ineffective. PAPER-HILT's effectiveness is evaluated through its application in two distinct contexts: Smart Home environments and Virtual Reality (VR) Smart Classrooms. The empirical results demonstrate PAPER-HILT's capability to provide a personalized equilibrium between user privacy and application utility, adapting effectively to individual user needs and preferences. On average for both experiments, utility (performance) drops by 24%, and privacy (state prediction) improves by 31%.