Miracle-dz/UniFAR-FTUs huggingface dataset:
Laundry wastewater treatment using ultrafiltration under different operating conditions

The laundry wastewater of one of the hospitals in Qatar was selected and studied. It was found that hospital laundry is releasing considerable amount of wastewater i.e. 1800 – 2500 liters/cycle while washing 40 – 50 kg of laundry items. The wastewater characterization showed that the laundry effluent was polluted enough to cause environmental pollution if not treated properly. Single step membrane technology i.e. ultrafiltration membrane with lower molecular weight cut-off (MWCO) of 5kDa was used for the treatment of laundry wastewater. It was found that the one step treatment is enough to remove most of the pollutants in terms of COD, Surfactants and suspended solids could be rejected while maintaining acceptable permeates flux. The treated water was found to be complying with different standards of wastewater reuse considered in this study. Effect of different operating parameters like pressure, temperature and flow rate on membrane rejection and permeate flux was also investigated. The in-depth studies of variation in wastewater characteristics and also reuse tests (using treated water to wash clothes) were recommended for further verification of potential of proposed technology.The laundry wastewater of one of the hospitals in Qatar was selected and studied. It was found that hospital laundry is releasing considerable amount of wastewater i.e. 1800 – 2500 liters/cycle while washing 40 – 50 kg of laundry items. The wastewater characterization showed that the laundry effluent was polluted enough to cause environmental pollution if not treated properly. Single step membrane technology i.e. ultrafiltration membrane with lower molecular weight cut-off (MWCO) of 5kDa was used for the treatment of laundry wastewater. It was found that the one step treatment is enough to remove most of the pollutants in terms of COD, Surfactants and suspended solids could be rejected while maintaining acceptable permeates flux. The treated water was found to be complying with different standards of wastewater reuse considered in this study. Effect of different operating parameters like pressure, temperature and flow rate on membrane rejection and permeate flux was also investigated. The in-depth studies.
DevBench: A Comprehensive Benchmark for Software Development

Recent advancements in large language models (LLMs) have significantly enhanced their coding capabilities. However, existing benchmarks predominantly focused on simplified or isolated aspects of programming, such as single-file code generation or repository issue debugging, falling short of measuring the full spectrum of challenges raised by real-world programming activities. To this end, we propose DevBench, a comprehensive benchmark that evaluates LLMs across various stages of the software development lifecycle, including software design, environment setup, implementation, acceptance testing, and unit testing. DevBench features a wide range of programming languages and domains, high-quality data collection, and carefully designed and verified metrics for each task. Empirical studies show that current LLMs, including GPT-4-Turbo, fail to solve the challenges presented within DevBench. Analyses reveal that models struggle with understanding the complex structures in the repository, managing the compilation process, and grasping advanced programming concepts. Our findings offer actionable insights for the future development of LLMs toward real-world programming applications. Our benchmark is available at https://github.com/open-compass/DevBench
STMPL: Human Soft-Tissue Simulation

In various applications, such as virtual reality and gaming, simulating the deformation of soft tissues in the human body during interactions with external objects is essential. Traditionally, Finite Element Methods (FEM) have been employed for this purpose, but they tend to be slow and resource-intensive. In this paper, we propose a unified representation of human body shape and soft tissue with a data-driven simulator of non-rigid deformations. This approach enables rapid simulation of realistic interactions. Our method builds upon the SMPL model, which generates human body shapes considering rigid transformations. We extend SMPL by incorporating a soft tissue layer and an intuitive representation of external forces applied to the body during object interactions. Specifically, we mapped the 3D body shape and soft tissue and applied external forces to 2D UV maps. Leveraging a UNET architecture designed for 2D data, our approach achieves high-accuracy inference in real time. Our experiment shows that our method achieves plausible deformation of the soft tissue layer, even for unseen scenarios.
Do Language Models Care About Text Quality? Evaluating Web-Crawled Corpora Across 11 Languages

Large, curated, web-crawled corpora play a vital role in training language models (LMs). They form the lion's share of the training data in virtually all recent LMs, such as the well-known GPT, LLaMA and XLM-RoBERTa models. However, despite this importance, relatively little attention has been given to the quality of these corpora. In this paper, we compare four of the currently most relevant large, web-crawled corpora (CC100, MaCoCu, mC4 and OSCAR) across eleven lower-resourced European languages. Our approach is two-fold: first, we perform an intrinsic evaluation by performing a human evaluation of the quality of samples taken from different corpora; then, we assess the practical impact of the qualitative differences by training specific LMs on each of the corpora and evaluating their performance on downstream tasks. We find that there are clear differences in quality of the corpora, with MaCoCu and OSCAR obtaining the best results. However, during the extrinsic evaluation, we actually find that the CC100 corpus achieves the highest scores. We conclude that, in our experiments, the quality of the web-crawled corpora does not seem to play a significant role when training LMs.
Improving Memory Dependence Prediction with Static Analysis

This paper explores the potential of communicating information gained by static analysis from compilers to Out-of-Order (OoO) machines, focusing on the memory dependence predictor (MDP). The MDP enables loads to issue without all in-flight store addresses being known, with minimal memory order violations. We use LLVM to find loads with no dependencies and label them via their opcode. These labelled loads skip making lookups into the MDP, improving prediction accuracy by reducing false dependencies. We communicate this information in a minimally intrusive way, i.e.~without introducing additional hardware costs or instruction bandwidth, providing these improvements without any additional overhead in the CPU. We find that in select cases in Spec2017, a significant number of load instructions can skip interacting with the MDP and lead to a performance gain. These results point to greater possibilities for static analysis as a source of near zero cost performance gains in future CPU designs.
Semantic Segmentation of Solar Radio Spikes at Low Frequencies

Solar radio spikes are short lived, narrow bandwidth features in low frequency solar radio observations. The timing of their occurrence and the number of spikes in a given observation is often unpredictable. The high temporal and frequency of resolution of modern radio telescopes such as NenuFAR mean that manually identifying radio spikes is an arduous task. Machine learning approaches to data exploration in solar radio data is on the rise. Here we describe a convolutional neural network to identify the per pixel location of radio spikes as well as determine some simple characteristics of duration, spectral width and drift rate. The model, which we call SpikeNet, was trained using an Nvidia Tesla T4 16GB GPU with ~100000 sample spikes in a total time of 2.2 hours. The segmentation performs well with an intersection over union in the test set of ~0.85. The root mean squared error for predicted spike properties is of the order of 23%. Applying the algorithm to unlabelled data successfully generates segmentation masks although the accuracy of the predicted properties is less reliable, particularly when more than one spike is present in the same 64 X 64 pixel time-frequency range. We have successfully demonstrated that our convolutional neural network can locate and characterise solar radio spikes in a number of seconds compared to the weeks it would take for manual identification.
Can physical information aid the generalization ability of Neural Networks for hydraulic modeling?

Application of Neural Networks to river hydraulics is fledgling, despite the field suffering from data scarcity, a challenge for machine learning techniques. Consequently, many purely data-driven Neural Networks proved to lack predictive capabilities. In this work, we propose to mitigate such problem by introducing physical information into the training phase. The idea is borrowed from Physics-Informed Neural Networks which have been recently proposed in other contexts. Physics-Informed Neural Networks embed physical information in the form of the residual of the Partial Differential Equations (PDEs) governing the phenomenon and, as such, are conceived as neural solvers, i.e. an alternative to traditional numerical solvers. Such approach is seldom suitable for environmental hydraulics, where epistemic uncertainties are large, and computing residuals of PDEs exhibits difficulties similar to those faced by classical numerical methods. Instead, we envisaged the employment of Neural Networks as neural operators, featuring physical constraints formulated without resorting to PDEs. The proposed novel methodology shares similarities with data augmentation and regularization. We show that incorporating such soft physical information can improve predictive capabilities.
Decoupled Federated Learning on Long-Tailed and Non-IID data with Feature Statistics

Federated learning is designed to enhance data security and privacy, but faces challenges when dealing with heterogeneous data in long-tailed and non-IID distributions. This paper explores an overlooked scenario where tail classes are sparsely distributed over a few clients, causing the models trained with these classes to have a lower probability of being selected during client aggregation, leading to slower convergence rates and poorer model performance. To address this issue, we propose a two-stage Decoupled Federated learning framework using Feature Statistics (DFL-FS). In the first stage, the server estimates the client's class coverage distributions through masked local feature statistics clustering to select models for aggregation to accelerate convergence and enhance feature learning without privacy leakage. In the second stage, DFL-FS employs federated feature regeneration based on global feature statistics and utilizes resampling and weighted covariance to calibrate the global classifier to enhance the model's adaptability to long-tailed data distributions. We conducted experiments on CIFAR10-LT and CIFAR100-LT datasets with various long-tailed rates. The results demonstrate that our method outperforms state-of-the-art methods in both accuracy and convergence rate.
Enhancing Space Situational Awareness to Mitigate Risk: A Case Study in the Misidentification of Starlink Satellites as UAP in Commercial Aviation

Over the past several years, the misidentification of SpaceX Starlink satellites as Unidentified Aerial Phenomena (UAP) by pilots and laypersons has generated unnecessary aviation risk and confusion. The many deployment and orbital evolution strategies, coupled with changing sun specular reflection angles, contribute to this gap in space situational awareness. In this paper we present a case analysis of an incident that generated multiple, corroborating reports of a UAP from five pilots on two commercial airline flights over the Pacific Ocean on August 10th, 2022. This incident included two cell phone photos and a video of an unrecognizable and possibly anomalous phenomenon. We then use supplemental two-line elements (TLEs) for the Starlink train of satellites launched that same day and Automatic Dependent Surveillance Broadcast (ADS-B) data from the flight with the photographs to reconstruct a view of these satellites from the cockpit at the time and place of the sighting. The success of this work demonstrates an approach that could, in principle, warn aviators about satellites that could be visible in unusual or novel illumination configurations, thus increasing space situational awareness and supporting aviation safety. We conclude with recommendations for governments and satellite operators to provide better a-priori data that can be used to create advisories to aviators and the public. The automated simulation of known specular reflection off constellations of satellites could also support researchers investigating sightings of unfamiliar aerial or aerospace objects as likely being from normal versus novel space events.
Nonlinear dynamics of volcanic systems from geodetic data

We study the use of the "multichannel singular spectrum analysis" on GPS time series. This method allows to simultaneously analyze a set of time series in order to extract from it common modes of variability without using any a priori on the temporal or the spatial structure of geophysical fields. The extracted modes correspond either to nonlinear trends, oscillations or noise. The method is applied on a set of GPS time series recorded at Akutan, a volcano located in Aleutian arc in Alaska. Two types of signals are extracted from it. The first one corresponds to seasonal deformations and the other represents two successive cycles of inflation and subsidence of Akutan volcano. The inflations are fast and short and are followed by deflations that are slower and longer. In the second part we take benefit of the M-SSA to analyze GPS time series recorded at several volcanoes. Okmok and Shishaldin in Alaska and Piton de la Fournaise in La Reunion possess a part of their deformation history that is similar to Akutan volcano. The cyclic nature of the observed deformations leads us to make an analogy between the oscillatory regime of a simple nonlinear oscillator and the deformation cycles of these volcanoes. Geochemical, petrological and geophysical data available for Okmok and Piton de la Fournaise combined with the constraint on the qualitative dynamics bring by the nonlinear oscillator allow to propose a physical model. Two shallow reservoirs are connected by a cylindrical conduit in which the magma have a viscosity that depends on the temperature. Such system behaves like the nonlinear oscillator mentioned above. When the temperature gradient inside theconduit is large enough and the flux of magma entering the shallow system is bounded by values that are determined analytically anonlinear oscillatory regime arises.
Autoregressive Score Generation for Multi-trait Essay Scoring

Recently, encoder-only pre-trained models such as BERT have been successfully applied in automated essay scoring (AES) to predict a single overall score. However, studies have yet to explore these models in multi-trait AES, possibly due to the inefficiency of replicating BERT-based models for each trait. Breaking away from the existing sole use of *encoder*, we propose an autoregressive prediction of multi-trait scores (ArTS), incorporating a *decoding* process by leveraging the pre-trained T5. Unlike prior regression or classification methods, we redefine AES as a score-generation task, allowing a single model to predict multiple scores. During decoding, the subsequent trait prediction can benefit by conditioning on the preceding trait scores. Experimental results proved the efficacy of ArTS, showing over 5% average improvements in both prompts and traits.
Online Multi-Contact Feedback Model Predictive Control for Interactive Robotic Tasks

In this paper, we propose a model predictive control (MPC) that accomplishes interactive robotic tasks, in which multiple contacts may occur at unknown locations. To address such scenarios, we made an explicit contact feedback loop in the MPC framework. An algorithm called Multi-Contact Particle Filter with Exploration Particle (MCP-EP) is employed to establish real-time feedback of multi-contact information. Then the interaction locations and forces are accommodated in the MPC framework via a spring contact model. Moreover, we achieved real-time control for a 7 degrees of freedom robot without any simplifying assumptions by employing a Differential-Dynamic-Programming algorithm. We achieved 6.8kHz, 1.9kHz, and 1.8kHz update rates of the MPC for 0, 1, and 2 contacts, respectively. This allows the robot to handle unexpected contacts in real time. Real-world experiments show the effectiveness of the proposed method in various scenarios.
COSTREAM: Learned Cost Models for Operator Placement in Edge-Cloud Environments

In this work, we present COSTREAM, a novel learned cost model for Distributed Stream Processing Systems that provides accurate predictions of the execution costs of a streaming query in an edge-cloud environment. The cost model can be used to find an initial placement of operators across heterogeneous hardware, which is particularly important in these environments. In our evaluation, we demonstrate that COSTREAM can produce highly accurate cost estimates for the initial operator placement and even generalize to unseen placements, queries, and hardware. When using COSTREAM to optimize the placements of streaming operators, a median speed-up of around 21x can be achieved compared to baselines.
Benchmarking quantum master equations beyond ultraweak coupling

Recently, Nathan and Rudner derived a Gorini-Kossakowski-Sudarshan-Lindblad master equation from the Redfield equation. The claim is that the level of approximation is equal to that of the Redfield equation. This is ground breaking work since a quantum master equation that guarantees physical states and that is as accurate as the Redfield equation is still missing. Here we benchmark the Nathan-Rudner equation (NRE) against the exact solution of a damped harmonic oscillator and compare its performance to that of the time-dependent Redfield equation (RE). We find that which of the equations performs better depends on the regime considered. It turns out that the short-time dynamics is generally much better captured by the RE, whereas the NRE delivers similar results to the rotating-wave approximation. For the steady state, in the high-temperature limit the RE also performs better and its solution approaches the exact result for ultrahigh temperatures. Nevertheless, also here the NR equation constitutes a good approximation. In the low-temperature limit, in turn, the NRE still provides a good approximation to the steady state, while the solution of the RE becomes unphysical for too strong coupling. Moreover, we show that, like the RE, also the NRE approaches the correct steady state in the limit of vanishing system-bath coupling. However, in second-order system-bath coupling, where the RE is known to provide the steady-state coherences correctly but not the populations, the NRE generally neither reproduces the correct populations nor the coherences.
Tractable Local Equilibria in Non-Concave Games

While Online Gradient Descent and other no-regret learning procedures are known to efficiently converge to coarse correlated equilibrium in games where each agent's utility is concave in their own strategy, this is not the case when the utilities are non-concave, a situation that is common in machine learning applications where the agents' strategies are parameterized by deep neural networks, or the agents' utilities are computed by a neural network, or both. Indeed, non-concave games present a host of game-theoretic and optimization challenges: (i) Nash equilibria may fail to exist; (ii) local Nash equilibria exist but are intractable; and (iii) mixed Nash, correlated, and coarse correlated equilibria have infinite support in general, and are intractable. To sidestep these challenges we propose a new solution concept, termed $(\varepsilon, \Phi(\delta))$-local equilibrium, which generalizes local Nash equilibrium in non-concave games, as well as (coarse) correlated equilibrium in concave games. Importantly, we show that two instantiations of this solution concept capture the convergence guarantees of Online Gradient Descent and no-regret learning, which we show efficiently converge to this type of equilibrium in non-concave games with smooth utilities.
Compliant Hierarchical Control for Arbitrary Equality and Inequality Tasks with Strict and Soft Priorities

When a robotic system is redundant with respect to a given task, the remaining degrees of freedom can be used to satisfy additional objectives. With current robotic systems having more and more degrees of freedom, this can lead to an entire hierarchy of tasks that need to be solved according to given priorities. In this paper, the first compliant control strategy is presented that allows to consider an arbitrary number of equality and inequality tasks, while still preserving the natural inertia of the robot. The approach is therefore a generalization of a passivity-based controller to the case of an arbitrary number of equality and inequality tasks. The key idea of the method is to use a Weighted Hierarchical Quadratic Problem to extract the set of active tasks and use the latter to perform a coordinate transformation that inertially decouples the tasks. Thereby unifying the line of research focusing on optimization-based and passivity-based multi-task controllers. The method is validated in simulation.
Calibrating coordinate system alignment in a scanning transmission electron microscope using a digital twin

In four-dimensional scanning transmission electron microscopy (4D STEM) a focused beam is scanned over a specimen and a diffraction pattern is recorded at each position using a pixelated detector. During the experiment, it must be ensured that the scan coordinate system of the beam is correctly calibrated relative to the detector coordinate system. Various simplified and approximate models are used implicitly and explicitly for understanding and analyzing the recorded data, requiring translation between the physical reality of the instrument and the abstractions used in data interpretation. Here, we introduce a calibration method where interactive live data processing in combination with a digital twin is used to match a set of models and their parameters with the action of a real-world instrument.
Low-Complexity Beam Training for Multi-RIS-Assisted Multi-User Communications

In this paper, we investigate the beam training problem in the multi-user millimeter wave (mmWave) communication system, where multiple reconfigurable intelligent surfaces (RISs) are deployed to improve the coverage and the achievable rate. However, existing beam training techniques in mmWave systems suffer from the high complexity (i.e., exponential order) and low identification accuracy. To address these problems, we propose a novel hashing multi-arm beam (HMB) training scheme that reduces the training complexity to the logarithmic order with the high accuracy. Specifically, we first design a generation mechanism for HMB codebooks. Then, we propose a demultiplexing algorithm based on the soft decision to distinguish signals from different RIS reflective links. Finally, we utilize a multi-round voting mechanism to align the beams. Simulation results show that the proposed HMB training scheme enables simultaneous training for multiple RISs and multiple users, and reduces the beam training overhead to the logarithmic level. Moreover, it also shows that our proposed scheme can significantly improve the identification accuracy by at least 20% compared to existing beam training techniques.