Miracle-dz/UniFAR-FTUs huggingface dataset:
Hydrodynamics and freshwater diversion within Barataria Basin

In order to understand the physical processes in the Barataria estuary, a previously developed two-dimensional, depth-integrated hydrodynamic model is applied to simulate estuarine processes. The equations for conservation of mass and momentum predict specific physical processes when forced by tidal and salinity variations at the open boundaries, wind forcing patterns, precipitation and evaporation over the model domain, and freshwater runoff as point sources. This study is focused on examining the impact of freshwater dispersion from the freshwater sources. Thus, a hydrologic model was developed to estimate runoff. Furthermore grid size was reduced, a new advective code added, and baroclinic effects included. The model was run with and without freshwater diversions from the Mississippi River. When compared to observations the correlation coefficients ( 2 r ) of model water levels are larger than 0.9 at all but one station. For the hydrologic calibration, a big flood event was tested. Agreement between observed and model results with runoff is surprisingly good. The observed agreement provides a justification for adopting 100 % coupling between the hydrologic model and hydrodynamic model, at least for this flood event. In terms of water level within the basin, the freshwater diversions seem to affect most of the Barataria Basin system water level within 3 days. Using a Hovmüller diagram, tidal phase speed was estimated within the basin as 16 hours to travel from the mouth of Barataria Bay to the top of the basin, Lac des Allemands. In terms of salinity within the basin, the impact of freshwater diversion reaches Barataria Bay within 5 and 10 days from Naomi and West Pointe à la Hache, respectively, and the Gulf of Mexico 15 to 20 days after freshwater release.
Unveiling NPT bound problem: From Distillability Sets to Inequalities and Multivariable Insights

Equivalence between Positive Partial Transpose (PPT) entanglement and bound entanglement is a long-standing open problem in quantum information theory. So far limited progress has been made, even on the seemingly simple case of Werner states bound entanglement. The primary challenge is to give a concise mathematical representation of undistillability. To this end, we propose a decomposition of the N-undistillability verification into $log(N)$ repeated steps of 1-undistillability verification. For Werner state N-undistillability verification, a bound for N-undistillability is given, which is independent of the dimensionality of Werner states. Equivalent forms of inequalities for both rank one and two matrices are presented, before transforming the two-undistillability case into a matrix analysis problem. A new perspective is also attempted by seeing it as a non-convex multi-variable function, proving its critical points and conjecturing Hessian positivity, which would make them local minimums.
Challenges in Restructuring Community-based Moderation

Content moderation practices and technologies need to change over time as requirements and community expectations shift. However, attempts to restructure existing moderation practices can be difficult, especially for platforms that rely on their communities to conduct moderation activities, because changes can transform the workflow and workload of moderators and contributors' reward systems. Through the study of extensive archival discussions around a prepublication moderation technology on Wikipedia named Flagged Revisions, complemented by seven semi-structured interviews, we identify various challenges in restructuring community-based moderation practices. We learn that while a new system might sound good in theory and perform well in terms of quantitative metrics, it may conflict with existing social norms. Our findings also highlight how the intricate relationship between platforms and self-governed communities can hinder the ability to assess the performance of any new system and introduce considerable costs related to maintaining, overhauling, or scrapping any piece of infrastructure.
A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems

This survey provides a comprehensive review of research on multi-turn dialogue systems, with a particular focus on multi-turn dialogue systems based on large language models (LLMs). This paper aims to (a) give a summary of existing LLMs and approaches for adapting LLMs to downstream tasks; (b) elaborate recent advances in multi-turn dialogue systems, covering both LLM-based open-domain dialogue (ODD) and task-oriented dialogue (TOD) systems, along with datasets and evaluation metrics; (c) discuss some future emphasis and recent research problems arising from the development of LLMs and the increasing demands on multi-turn dialogue systems.
Personalizing Smart Home Privacy Protection With Individuals' Regulatory Focus: Would You Preserve or Enhance Your Information Privacy?

In this study, we explore the effectiveness of persuasive messages endorsing the adoption of a privacy protection technology (IoT Inspector) tailored to individuals' regulatory focus (promotion or prevention). We explore if and how regulatory fit (i.e., tuning the goal-pursuit mechanism to individuals' internal regulatory focus) can increase persuasion and adoption. We conducted a between-subject experiment (N = 236) presenting participants with the IoT Inspector in gain ("Privacy Enhancing Technology"-- PET) or loss ("Privacy Preserving Technology"-- PPT) framing. Results show that the effect of regulatory fit on adoption is mediated by trust and privacy calculus processes: prevention-focused users who read the PPT message trust the tool more. Furthermore, privacy calculus favors using the tool when promotion-focused individuals read the PET message. We discuss the contribution of understanding the cognitive mechanisms behind regulatory fit in privacy decision-making to support privacy protection.
Model Predictive Control with adaptive resilience for Denial-of-Service Attacks mitigation on a Regulated Dam

In recent years, SCADA (Supervisory Control and Data Acquisition) systems have increasingly become the target of cyber attacks. SCADAs are no longer isolated, as web-based applications expose strategic infrastructures to the outside world connection. In a cyber-warfare context, we propose a Model Predictive Control (MPC) architecture with adaptive resilience, capable of guaranteeing control performance in normal operating conditions and driving towards resilience against DoS (controller-actuator) attacks when needed. Since the attackers' goal is typically to maximize the system damage, we assume they solve an adversarial optimal control problem. An adaptive resilience factor is then designed as a function of the intensity function of a Hawkes process, a point process model estimating the occurrence of random events in time, trained on a moving window to estimate the return time of the next attack. We demonstrate the resulting MPC strategy's effectiveness in 2 attack scenarios on a real system with actual data, the regulated Olginate dam of Lake Como.
Understanding all-terrain vehicle user behaviour: the human dimensions of ATV use in Northeastern New Brunswick, Canada

This study examined all-terrain vehicle (ATV) users in northeastern New Brunswick, Canada, providing a human dimensions approach to understanding the activity and inform future management decisions. A methodological comparison between self-classification and multivariate applications of Bryan's (1977) recreation specialization framework was conducted to assess variation in participant engagement within the activity. The impact of activity consumption on levels of recreation specialization was also examined to investigate how differences in ATV use affects user engagement. Self-administered questionnaires were randomly distributed to three New Brunswick communities (n = 144). Results suggest that both applications of the recreation specialization framework did not similarly classify participants as expected. Specialization levels were found to differ across three activity consumption sub-groups, suggesting different types of ATV use may impact user engagement. Resource managers should consider differences in user recreation specialization and activity consumption when designing strategies to manage the heterogeneous activity.
Emergence of Large-Scale Structures in Holographic Superfluid Turbulence

In two-dimensional turbulence systems, the emergence of large-scale structures holds profound physical implications, particularly as it indicates the occurrence of inverse energy cascades, thereby garnering significant attention. In this paper, we report a novel vortex clusters formation in the background of near-extreme Reissner-Nordstr$\ddot{o}$m black hole holographic model. At temperatures nearing absolute zero, we observe not only the formation of vortex clusters but also the emergence of an inverse energy cascade. Distinct from typical quantum systems, the genesis of holographic vortex clusters is rooted in unique quantum dissipation properties, characterized by the near immobilization of vortex dipoles at low temperatures. Through a comparative analysis with the dynamics of the Gross-Pitaevskii equation, our investigation enhances the understanding of inverse energy cascades under these extreme conditions, thereby broadening our comprehension of quantum turbulence.
Quantum state of Yang-Mills fields in $SU(\infty)$ Quantum Gravity ($SU(\infty)$-QGR)

Our Universe is ruled by quantum mechanics and should be treated as a quantum system. $SU(\infty)$-QGR is a recently proposed quantum model for the Universe, in which gravity is associated to $SU(\infty)$ symmetry of its Hilbert space. Clustering of its infinite dimensional state due to random quantum fluctuations divides the Universe to approximately isolated subsystems. In addition to parameters of their internal finite rank symmetries, states and dynamics of subsystems are characterized by 4 continuous parameters, and the perceived classical spacetime is their effective representation, reflecting quantum states of subsystems and theirrelative evolution. At lowest order the effective Lagrangian of $SU(\infty)$-QGR has the form of Yang-Mills gauge theories for both $SU(\infty)$ - gravity - and internal symmetries defined on the aforementioned 4D parameter space. In the present work we study more thoroughly some of the fundamental aspects of $SU(\infty)$-QGR. Specifically, we clarify impact of the degeneracy of $\mathcal{SU}(\infty)$; describe mixed states of subsystems and their purification; calculate measures of their entanglement to the rest of the Universe; and discuss their role in the emergence of local gauge symmetries. We also describe the relationship between what is called internal space of $SU(\infty)$ Yang-Mills with the 4D parameter space, and analytically demonstrate irrelevance of the geometry parameter space for physical observables. Along with these topics, we demonstrate the equivalence of two sets of criteria for compositeness of a quantum system, and show uniqueness of the limit of various algebras leading to $SU(\infty)$.
Tighter Bounds for Local Differentially Private Core Decomposition and Densest Subgraph

Computing the core decomposition of a graph is a fundamental problem that has recently been studied in the differentially private setting, motivated by practical applications in data mining. In particular, Dhulipala et al. [FOCS 2022] gave the first mechanism for approximate core decomposition in the challenging and practically relevant setting of local differential privacy. One of the main open problems left by their work is whether the accuracy, i.e., the approximation ratio and additive error, of their mechanism can be improved. We show the first lower bounds on the additive error of approximate and exact core decomposition mechanisms in the centralized and local model of differential privacy, respectively. We also give mechanisms for exact and approximate core decomposition in the local model, with almost matching additive error bounds. Our mechanisms are based on a black-box application of continual counting. They also yield improved mechanisms for the approximate densest subgraph problem in the local model.
ConvDTW-ACS: Audio Segmentation for Track Type Detection During Car Manufacturing

This paper proposes a method for Acoustic Constrained Segmentation (ACS) in audio recordings of vehicles driven through a production test track, delimiting the boundaries of surface types in the track. ACS is a variant of classical acoustic segmentation where the sequence of labels is known, contiguous and invariable, which is especially useful in this work as the test track has a standard configuration of surface types. The proposed ConvDTW-ACS method utilizes a Convolutional Neural Network for classifying overlapping image chunks extracted from the full audio spectrogram. Then, our custom Dynamic Time Warping algorithm aligns the sequence of predicted probabilities to the sequence of surface types in the track, from which timestamps of the surface type boundaries can be extracted. The method was evaluated on a real-world dataset collected from the Ford Manufacturing Plant in Valencia (Spain), achieving a mean error of 166 milliseconds when delimiting, within the audio, the boundaries of the surfaces in the track. The results demonstrate the effectiveness of the proposed method in accurately segmenting different surface types, which could enable the development of more specialized AI systems to improve the quality inspection process.
Event Detection for Non-intrusive Load Monitoring using Tukey s Fences

The primary objective of non-intrusive load monitoring (NILM) techniques is to monitor and track power consumption within residential buildings. This is achieved by approximating the consumption of each individual appliance from the aggregate energy measurements. Event-based NILM solutions are generally more accurate than other methods. Our paper introduces a novel event detection algorithm called Tukey's Fences-based event detector (TFED). This algorithm uses the fast Fourier transform in conjunction with the Tukey fences rule to detect variations in the aggregated current signal. The primary benefit of TFED is its superior ability to accurately pinpoint the start times of events, as demonstrated through simulations. Our findings reveal that the proposed algorithm boasts an impressive 99% accuracy rate, surpassing the accuracy of other recent works in the literature such as the Cepstrum and $\chi ^2$ GOF statistic-based analyses, which only achieved 98% accuracy.
Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging

Logs produced by extensive software systems are integral to monitoring system behaviors. Advanced log analysis facilitates the detection, alerting, and diagnosis of system faults. Log parsing, which entails transforming raw log messages into structured templates, constitutes a critical phase in the automation of log analytics. Existing log parsers fail to identify the correct templates due to reliance on human-made rules. Besides, These methods focus on statistical features while ignoring semantic information in log messages. To address these challenges, we introduce a cutting-edge \textbf{L}og parsing framework with \textbf{E}ntropy sampling and Chain-of-Thought \textbf{M}erging (Lemur). Specifically, to discard the tedious manual rules. We propose a novel sampling method inspired by information entropy, which efficiently clusters typical logs. Furthermore, to enhance the merging of log templates, we design a chain-of-thought method for large language models (LLMs). LLMs exhibit exceptional semantic comprehension, deftly distinguishing between parameters and invariant tokens. We have conducted experiments on large-scale public datasets. Extensive evaluation demonstrates that Lemur achieves the state-of-the-art performance and impressive efficiency.
Conservation status assessment of native vascular flora of Kalam Valley, Swat District, Northern Pakistan

In the present study, conservation status of important vascular flora found in Kalam valley was assessed. Kalam Valley represents the extreme northern part of Swat District in KPK Province of Pakistan. The valley contains some of the precious medicinal plants. 245 plant species which were assessed for conservation studies revealed that 10.20% (25 species) were found to be endangered, 28.16% (69 species) appeared to be vulnerable. Similarly, 50.6% (124 species) were rare, 8.16% (20 species) were infrequent and 2.9% (7 species) were recognized as dominant. It was concluded that Kalam Valley inhabits most important plants majority of which are used in medicines; but due to anthropogenic activities including unplanned tourism, deforestation, uprooting of medicinal plants and over grazing, majority of these plant species are rapidly heading towards regional extinction in the near future. To maintain the biodiversity of the study area, some in-situ and ex-situ conservation measures in the form of protected areas, sustainable grazing, supplying alternative energy sources to the native population, seeds preservation and growing precious medicinal plant species in nurseries established under the supervision of forest department are urgently needed. Key words: Preserving, vascular flora, regional extinction.
A network-constrain Weibull AFT model for biomarkers discovery

We propose AFTNet, a novel network-constraint survival analysis method based on the Weibull accelerated failure time (AFT) model solved by a penalized likelihood approach for variable selection and estimation. When using the log-linear representation, the inference problem becomes a structured sparse regression problem for which we explicitly incorporate the correlation patterns among predictors using a double penalty that promotes both sparsity and grouping effect. Moreover, we establish the theoretical consistency for the AFTNet estimator and present an efficient iterative computational algorithm based on the proximal gradient descent method. Finally, we evaluate AFTNet performance both on synthetic and real data examples.
UKF-Based Sensor Fusion for Joint-Torque Sensorless Humanoid Robots

This paper proposes a novel sensor fusion based on Unscented Kalman Filtering for the online estimation of joint-torques of humanoid robots without joint-torque sensors. At the feature level, the proposed approach considers multimodal measurements (e.g. currents, accelerations, etc.) and non-directly measurable effects, such as external contacts, thus leading to joint torques readily usable in control architectures for human-robot interaction. The proposed sensor fusion can also integrate distributed, non-collocated force/torque sensors, thus being a flexible framework with respect to the underlying robot sensor suit. To validate the approach, we show how the proposed sensor fusion can be integrated into a twolevel torque control architecture aiming at task-space torquecontrol. The performances of the proposed approach are shown through extensive tests on the new humanoid robot ergoCub, currently being developed at Istituto Italiano di Tecnologia. We also compare our strategy with the existing state-of-theart approach based on the recursive Newton-Euler algorithm. Results demonstrate that our method achieves low root mean square errors in torque tracking, ranging from 0.05 Nm to 2.5 Nm, even in the presence of external contacts.
Using Graph Neural Networks to Predict Local Culture

Urban research has long recognized that neighbourhoods are dynamic and relational. However, lack of data, methodologies, and computer processing power have hampered a formal quantitative examination of neighbourhood relational dynamics. To make progress on this issue, this study proposes a graph neural network (GNN) approach that permits combining and evaluating multiple sources of information about internal characteristics of neighbourhoods, their past characteristics, and flows of groups among them, potentially providing greater expressive power in predictive models. By exploring a public large-scale dataset from Yelp, we show the potential of our approach for considering structural connectedness in predicting neighbourhood attributes, specifically to predict local culture. Results are promising from a substantive and methodologically point of view. Substantively, we find that either local area information (e.g. area demographics) or group profiles (tastes of Yelp reviewers) give the best results in predicting local culture, and they are nearly equivalent in all studied cases. Methodologically, exploring group profiles could be a helpful alternative where finding local information for specific areas is challenging, since they can be extracted automatically from many forms of online data. Thus, our approach could empower researchers and policy-makers to use a range of data sources when other local area information is lacking.
Human Shape and Clothing Estimation

Human shape and clothing estimation has gained significant prominence in various domains, including online shopping, fashion retail, augmented reality (AR), virtual reality (VR), and gaming. The visual representation of human shape and clothing has become a focal point for computer vision researchers in recent years. This paper presents a comprehensive survey of the major works in the field, focusing on four key aspects: human shape estimation, fashion generation, landmark detection, and attribute recognition. For each of these tasks, the survey paper examines recent advancements, discusses their strengths and limitations, and qualitative differences in approaches and outcomes. By exploring the latest developments in human shape and clothing estimation, this survey aims to provide a comprehensive understanding of the field and inspire future research in this rapidly evolving domain.
Data-driven nonlinear turbulent flow scaling with Buckingham Pi variables

Nonlinear machine learning for turbulent flows can exhibit robust performance even outside the range of training data. This is achieved when machine-learning models can accommodate scale-invariant characteristics of turbulent flow structures. This study presents a data-driven approach to reveal scale-invariant vortical structures across Reynolds numbers that provide insights for supporting nonlinear machine-learning-based studies of turbulent flows. To uncover conditions for which nonlinear models are likely to perform well, we use a Buckingham Pi-based sparse nonlinear scaling to find the influence of the Pi groups on the turbulent flow data. We consider nonlinear scalings of the invariants of the velocity gradient tensor for an example of three-dimensional decaying isotropic turbulence. The present scaling not only enables the identification of vortical structures that are interpolatory and extrapolatory for the given flow field data but also captures non-equilibrium effects of the energy cascade. As a demonstration, the present findings are applied to machine-learning-based super-resolution analysis of three-dimensional isotropic turbulence. We show that machine-learning models reconstruct vortical structures well in the interpolatory space with reduced performance in the extrapolatory space revealed by the nonlinearly scaled invariants. The present approach enables us to depart from labeling turbulent flow data with a single parameter of Reynolds number and comprehensively examine the flow field to support training and testing of nonlinear machine-learning techniques.