Maritime transportation is the backbone of global trade, making ship inspection essential for ensuring maritime safety and environmental protection. Port State Control (PSC), conducted by national ports, enforces compliance with safety regulations, with ship detention being the most severe consequence, impacting both ship schedules and company reputations. Traditional machine learning methods for ship detention prediction are limited by the capacity of representation learning and thus suffer from low accuracy. Meanwhile, autoencoder-based deep learning approaches face challenges due to the severe data imbalance in learning historical PSC detention records. To address these limitations, we propose Maritime Ship Detention with Large Language Models (MSD-LLM), integrating a dual robust subspace recovery (DSR) layer-based autoencoder with a progressive learning pipeline to handle imbalanced data and extract meaningful PSC representations. Then, a large language model groups and ranks features to identify likely detention cases, enabling dynamic thresholding for flexible detention predictions. Extensive evaluations on 31,707 PSC inspection records from the Asia-Pacific region show that
Detention ponds can mitigate flooding and improve water quality by allowing the settlement of pollutants. Typically, they are operated with fully open orifices and weirs (i.e., passive control). Active controls can improve the performance of these systems: orifices can be retrofitted with controlled valves and spillways can have controllable gates. The real-time optimal operation of its hydraulic devices can be achieved with techniques such as Model Predictive Control (MPC). A distributed quasi-2D hydrologic-hydrodynamic coupled with a reservoir flood routing model is developed and integrated with an MPC algorithm to estimate the operation of valves and movable gates. The control optimization problem is adapted to switch from a flood-related algorithm focusing on mitigating floods to a heuristic objective function that aims to increase the detention time when no inflow hydrographs are predicted. The case studies show the potential of applying the methods developed in a catchment in Sao Paulo, Brazil. The performance of MPC compared to alternatives with either fully or partially open valves and gates are tested. Comparisons with HEC-RAS 2D indicate volume and peak flow errors of app
We analyze the spread of COVID-19 cases within adult detention facilities in Los Angeles (LA) county. Throughout the analysis we review the data to explore the range of positive cases in each center and see the percentage of people who were positive for COVID-19 against the amount of people who were tested. Additionally, we see if there is any correlation between the surrounding community of each detention center and the number of positive cases in each center and explore the protocols in place at each detention center. We use the cloud visualization tool SAP Analytics Cloud (SAC) with the data from the California government website through adult detention facilities in LA County. We found that (1) the number of confirmed cases at the facilities and the surrounding communities are not related, (2) the data does not represent all positive cases at the facility, and (3) there are not enough tests at the facilities.
Storm water detention ponds are used to manage the discharge of rainfall runoff from urban areas to nearby streams. Their purpose is to reduce the hydraulic impact and sediment loads of the receiving waters. Detention ponds are currently designed based on static controls: the output flow of a pond is capped at a fixed value. This is not optimal with respect to the current infrastructure capacity and for some detention ponds it might even violate current regulations set by the European Water Framework Directive. We apply formal methods to synthesize (i.e., derive automatically) a safe and optimal active controller. We model the storm water detention pond, including the urban catchment area and the rain forecasts, as a hybrid Markov decision process. Subsequently, we use the tool Uppaal Stratego to synthesize a control strategy minimizing the cost related to pollution (optimality) while guaranteeing no emergency overflow of the detention pond (safety). Simulation results for an existing pond show that Uppaal Stratego can learn optimal strategies that prevent emergency overflows, where the current static control is not always able to prevent it. At the same time, our approach can impr
Room allocation is a challenging task in detention centers since lots of related people need to be held separately with limited rooms. It is extremely difficult and risky to allocate rooms manually, especially for organized crime groups with close connections. To tackle this problem, we develop an intelligent room allocation system for detention centers to provide optimized room allocation schemes automatically. We first formalize the detention room allocation problem as inverse graph partitioning, which can measure the quality of room allocation schemes. Then, we propose two heuristic algorithms to achieve the global optimization and local optimization of detention room allocation. Experiment results on real-world datasets show that the proposed algorithms significantly outperform manual allocation and suggest that the system is of great practical application value.
After colliding with a surface, microswimmers reside there during the detention time. They accumulate and may form complex structures such as biofilms. We introduce a general framework to calculate the distribution of detention times using the method of first-passage times and study how rotational noise and hydrodynamic interactions influence the escape from a surface. We compare generic swimmer models to the simple active Brownian particle. While the respective detention times of source dipoles are smaller, the ones of pullers are larger by up to several orders of magnitude, and pushers show both trends. We apply our results to the more realistic squirmer model, for which we use lubrication theory, and validate them by simulations with multi-particle collision dynamics.
This paper tests for discrimination against immigrant defendants in the criminal justice system in Chile using a decade of nationwide administrative records on pretrial detentions. Observational benchmark regressions show that immigrant defendants are 8.6 percentage points less likely to be released pretrial relative to Chilean defendants with similar proxies for pretrial misconduct potential. Diagnostics for omitted variable bias -- including a novel test to assess the quality of the proxy vector based on comparisons of pretrial misconduct rates among released defendants -- suggest that the discrimination estimates are not driven by omitted variable bias and that, if anything, failing to fully account for differences in misconduct potential leads to an underestimation of discrimination. Our estimates suggest that discrimination stems from an informational problem because judges do not observe criminal records in origin countries, with stereotypes and taste-based discrimination playing a role in the problem's resolution. We find that discrimination is especially large for drug offenses and that discrimination increased after a recent immigration wave.
Pretrial risk assessment tools are used on over one million U.S. defendants each year, yet their use for predicting rare violent re-offense faces a basic statistical barrier. We derive a universal precision bound -- the Likelihood Ratio Wall -- showing that when violent re-arrest rates are low (2-5%), achieving even a 50% hit rate among people labeled "high risk" (positive predictive value, or PPV) would require tools far more discriminative than current instruments appear to be. For rare outcomes, a tool can have respectable-looking performance metrics and still be wrong most of the time it flags someone as "high risk for violence." We show that post-hoc score recalibration cannot solve this problem because it does not improve the tool's underlying ability to separate true positives from false positives. We further prove a Surveillance Ceiling: when over-policing inflates recorded "risk factors" among those who would not re-offend, the maximum achievable precision is structurally lower for over-policed groups, even at equal offense rates. We translate these results into the Number Needed to Detain (how many people must be detained to prevent one violent offense), and propose that
Articulated objects used in simulation and embodied AI are typically specified by geometry and kinematic structure, but lack the fine-grained dynamical effects that govern realistic mechanical behavior, such as frictional holding, detents, soft closing, and snap latching. Existing approaches either ignore the detailed structure of dynamics entirely, or use simple models with limited expressiveness. We introduce JODA, a framework for generating joint-level dynamics as a structured three-channel field over the joint degree of freedom, capturing conservative forces, dry friction, and damping. Instantiated using shape-constrained piecewise cubic interpolation (PCHIP), this formulation defines a compact and expressive function space that is both interpretable and compatible with differentiable simulation. Building on this representation, we develop methods for inferring and refining joint dynamics from multimodal inputs. Given visual observations and joint context, a vision-language model proposes structured dynamical primitives, which are composed into a unified dynamics field. The resulting representation supports both direct manipulation and gradient-based refinement. We demonstrate
In 2025, intensified Immigration and Customs Enforcement (ICE) raids in Oxnard, California, disrupted the state's \$49 billion agricultural industry, a critical supplier of 75% of U.S. fruits and nuts and one-third of its vegetables. This paper quantifies the economic consequences of these raids on labor markets, crop production, and food prices using econometric modeling. We estimate a 20-40% reduction in the agricultural workforce, leading to \$3-7 billion in crop losses and a 5-12% increase in produce prices. The analysis draws on USDA Economic Research Service data and recent ICE detention figures, which show arrests in Southern California rising from 699 in May to nearly 2,000 in June 2025. The raids disproportionately affect labor-intensive crops like strawberries, exacerbating supply chain disruptions. Policy recommendations include expanding the H-2A visa program and legalizing undocumented workers to stabilize the sector. This study contributes to agricultural economics by providing a data-driven assessment of immigration enforcement's economic toll.
The rapid development of deep learning and generative AI technologies has profoundly transformed the digital contact landscape, creating realistic Deepfake that poses substantial challenges to public trust and digital media integrity. This paper introduces a novel Deepfake detention framework, Volume of Differences (VoD), designed to enhance detection accuracy by exploiting temporal and spatial inconsistencies between consecutive video frames. VoD employs a progressive learning approach that captures differences across multiple axes through the use of consecutive frame differences (CFD) and a network with stepwise expansions. We evaluate our approach with intra-dataset and cross-dataset testing scenarios on various well-known Deepfake datasets. Our findings demonstrate that VoD excels with the data it has been trained on and shows strong adaptability to novel, unseen data. Additionally, comprehensive ablation studies examine various configurations of segment length, sampling steps, and intervals, offering valuable insights for optimizing the framework. The code for our VoD framework is available at https://github.com/xuyingzhongguo/VoD.
This paper presents a new software framework for HRI experimentation with the sixth version of the common NAO robot produced by the United Robotics Group. Embracing the common demand of researchers for better performance and new features for NAO, the authors took advantage of the ability to run ROS2 onboard on the NAO to develop a framework independent of the APIs provided by the manufacturer. Such a system provides NAO with not only the basic skills of a humanoid robot such as walking and reproducing movements of interest but also features often used in HRI such as: speech recognition/synthesis, face and object detention, and the use of Generative Pre-trained Transformer (GPT) models for conversation. The developed code is therefore configured as a ready-to-use but also highly expandable and improvable tool thanks to the possibilities provided by the ROS community.
This paper investigates the integration of force feedback in Digital Musical Instruments (DMI), specifically evaluating the reproduction of intricate vibrato techniques using haptic feedback controllers. We introduce our system for vibrato modulation using force feedback, composed of Bend-aid (a web-based sequencer platform using pre-designed haptic feedback models) and TorqueTuner (an open-source 1 Degree-of-Freedom (DoF) rotary haptic device for generating programmable haptic effects). We designed a formal user study to assess the impact of each haptic mode on user experience in a vibrato mimicry task. Twenty musically trained participants rated their user experience for the three haptic modes (Smooth, Detent, and Spring) using four Likert-scale scores: comfort, flexibility, ease of control, and helpfulness for the task. Finally, we asked participants to share their reflections. Our research indicates that while the Spring mode can help with light vibrato, preferences for haptic modes vary based on musical training background. This emphasizes the need for adaptable task interfaces and flexible haptic feedback in DMI design.
Criminal courts across the United States handle millions of cases every year, and the scheduling of those cases must accommodate a diverse set of constraints, including the preferences and availability of courts, prosecutors, and defense teams. When criminal court schedules are formed, defendants' scheduling preferences often take the least priority, although defendants may face significant consequences (including arrest or detention) for missed court dates. Additionally, studies indicate that defendants' nonappearances impose costs on the courts and other system stakeholders. To address these issues, courts and commentators have begun to recognize that pretrial outcomes for defendants and for the system would be improved with greater attention to court processes, including \emph{court scheduling practices}. There is thus a need for fair criminal court pretrial scheduling systems that account for defendants' preferences and availability, but the collection of such data poses logistical challenges. Furthermore, optimizing schedules fairly across various parties' preferences is a complex optimization problem, even when such data is available. In an effort to construct such a fair sch
Accurate flood modeling is crucial for effective analysis and forecasting. Full momentum hydrodynamic models often require extensive computational time, sometimes exceeding the forecast horizon. In contrast, low-complexity models, like local-inertial approximations, provide accurate results in subcritical flows but may have limited skillfulness in supercritical conditions. This paper explores two main aspects: (i) the impact of urban infrastructure on 2D hydrodynamic modeling without detailed sewer and drainage data, and (ii) the accuracy of 2D local-inertial modeling using three numerical schemes (original formulation, s-centered, and s-upwind) in a dam-break scenario on complex, flat terrain. The HydroPol2D model is benchmarked against HEC-RAS 2D full momentum solver. We present one numerical case study and three real-world scenarios in São Paulo, Brazil: a detention pond with a $1$ in $100$-year inflow, a highly urbanized catchment with a $1$ in $50$-year hyetograph, and a dam-break scenario threatening a coastal city of nearly 200,000 residents. Results show that the model accurately simulates internal boundary conditions, achieving peak errors under 5\% compared to HEC-RAS 2D.
Predictive algorithms are now used to help distribute a large share of our society's resources and sanctions, such as healthcare, loans, criminal detentions, and tax audits. Under the right circumstances, these algorithms can improve the efficiency and equity of decision-making. At the same time, there is a danger that the algorithms themselves could entrench and exacerbate disparities, particularly along racial, ethnic, and gender lines. To help ensure their fairness, many researchers suggest that algorithms be subject to at least one of three constraints: (1) no use of legally protected features, such as race, ethnicity, and gender; (2) equal rates of "positive" decisions across groups; and (3) equal error rates across groups. Here we show that these constraints, while intuitively appealing, often worsen outcomes for individuals in marginalized groups, and can even leave all groups worse off. The inherent trade-off we identify between formal fairness constraints and welfare improvements -- particularly for the marginalized -- highlights the need for a more robust discussion on what it means for an algorithm to be "fair". We illustrate these ideas with examples from healthcare and
Pretrial risk assessment tools are used in jurisdictions across the country to assess the likelihood of "pretrial failure," the event where defendants either fail to appear for court or reoffend. Judicial officers, in turn, use these assessments to determine whether to release or detain defendants during trial. While algorithmic risk assessment tools were designed to predict pretrial failure with greater accuracy relative to judges, there is still concern that both risk assessment recommendations and pretrial decisions are biased against minority groups. In this paper, we develop methods to investigate the association between risk factors and pretrial failure, while simultaneously estimating misclassification rates of pretrial risk assessments and of judicial decisions as a function of defendant race. This approach adds to a growing literature that makes use of outcome misclassification methods to answer questions about fairness in pretrial decision-making. We give a detailed simulation study for our proposed methodology and apply these methods to data from the Virginia Department of Criminal Justice Services. We estimate that the VPRAI algorithm has near-perfect specificity, but i
The present paper deals with the modelling of rapid transients at partially lifted sluice gates from both a mathematical and numerical perspective in the context of the Shallow water Equations (SWE). First, an improved exact solution of the dam-break problem is presented, assuming (i) the dependence of the gate contraction coefficient on the upstream flow depth, and (ii) a physically congruent definition for the submerged flow equation. It is shown that a relevant solution always exists for any set of initial conditions, but there are also initial conditions for which the solution is multiple. In the last case, a novel disambiguation criterion based on the continuous dependence of the solution on the initial conditions is used to select the physically congruent one among the alternatives. Secondly, a one (1-d) and a two-dimensional (2-d) form of a SWE Finite Volume numerical model, equipped with an approximate Riemann solver for the sluice gate treatment at cells interfaces, are presented. It is shown that the numerical implementation of classic steady state gate equations (classic equilibrium approach) leads to unsatisfactory numerical results in the case of fast transients, while
The emergence of infectious disease COVID-19 has challenged and changed the world in an unprecedented manner. The integration of wireless networks with edge computing (namely wireless edge networks) brings opportunities to address this crisis. In this paper, we aim to investigate the prediction of the infectious probability and propose precautionary measures against COVID-19 with the assistance of wireless edge networks. Due to the availability of the recorded detention time and the density of individuals within a wireless edge network, we propose a stochastic geometry-based method to analyze the infectious probability of individuals. The proposed method can well keep the privacy of individuals in the system since it does not require to know the location or trajectory of each individual. Moreover, we also consider three types of mobility models and the static model of individuals. Numerical results show that analytical results well match with simulation results, thereby validating the accuracy of the proposed model. Moreover, numerical results also offer many insightful implications. Thereafter, we also offer a number of countermeasures against the spread of COVID-19 based on wirel
We study the detention statistics of self-propelling droplet microswimmers attaching to microfluidic pillars. These droplets show negative autochemotaxis: they shed a persistent repulsive trail of spent fuel that biases them to detach from pillars of a certain size after orbiting them just once. We have designed a microfluidic assay recording swimmers in pillar arrays of varying diameter, derived detention statistics via digital image analysis and interpreted these statistics via the Langevin dynamics of an active Brownian particle (ABP) model. By comparing data from orbits with and without residual chemical field, we can independently estimate quantities like hydrodynamic and chemorepulsive torques, chemical coupling constants and diffusion coefficients, as well as their dependence on boundary conditions like wall curvature.