Miracle-dz/UniFAR-FTUs huggingface dataset:
TREATMENT OF MUNICIPAL WASTEWATER BY A MEMBRANE BIOREACTOR: RESULTS OF A SEMI-INDUSTRIAL PILOT-SCALE STUDY

Membrane bioreactors (MBR) represent a new generation of processes that can be applied to the treatment of municipal and industrial wastewater. Their main advantage is their ability to keep all biomass in the bioreactor, thus removing all suspended solids from the treated water and disinfecting it according to the membrane cut-off threshold. Perfect control as well as separation of hydraulic (HRT) and biomass retention times (SRT) also means better control of biological activity. Treatment of municipal wastewater on a semi-industrial aerobic pilot-scale MBR (HRT: 24 hr; SRT: 25 days) resulted in complete nitrification and from 93 to 99.9% removal of COD, N-NH 3 and suspended solids. The COD removed loading rate was equal to 0.2 kg/kg VSS.day and the average sludge production was around 0.2 kgSS/kgCOD. Filtration through 0.1 µm ceramic hollow fibres (Surface = 1.1 m 2 ) under moderate conditions (1.5 to 3.5 m/s; TMP
Bias of the Hubble Constant Value Caused by Errors in Galactic Distance Indicators

The bias in the determination of the Hubble parameter and the Hubble constant in the modern Universe is discussed. It could appear due to the statistical processing of data on the redshifts of galaxies and the estimated distances based on some statistical relations with limited accuracy. This causes a number of effects leading to either underestimation or overestimation of the Hubble parameter when using any methods of statistical processing, primarily the least squares method (LSM). The value of the Hubble constant is underestimated when processing a whole sample; when the sample is constrained by distance, especially when constrained from above. Moreover, it is significantly overestimated due to the data selection. The bias significantly exceeds the values of the erro ofr the Hubble constant calculated by the LSM formulae. These effects are demonstrated both analytically and using Monte Carlo simulations, which introduce deviations in the velocities and estimated distances to the original dataset described by the Hubble law. The characteristics of the deviations are similar to real observations. Errors in the estimated distances are up to 20%. They lead to the fact that, when processing the same mock sample using LSM, it is possible to obtain an estimate of the Hubble constant from 96% of the true value when processing the entire sample to 110% when processing the subsample with distances limited from above. The impact of these effects can lead to a bias in the Hubble constant obtained from real data and an overestimation of the accuracy of determining this value. This may call into question the accuracy of determining the Hubble constant and can significantly reduce the tension between the values obtained from the observations in the early and modern Universes, which were actively discussed during the last year.
Learning quantum phase transitions through topological data analysis

We implement a computational pipeline based on a recent machine learning technique, namely the Topological Data Analysis (TDA), that has the capability of extracting powerful informationcarrying topological features. We apply such a method to the study quantum phase transitions and, to showcase its validity and potential, we exploit such a method for the investigation of two paramount important quantum systems: the 2D periodic Anderson model and the Hubbard model on the honeycomb lattice, both cases on the half-filling. To this end, we have performed unbiased auxiliary field quantum Monte Carlo simulations, feeding the TDA with snapshots of the HubbardStratonovich fields through the course of the simulations. The quantum critical points obtained from TDA agree quantitatively well with the existing literature, therefore suggesting that this technique could be used to investigate quantum systems where the analysis of the phase transitions is still a challenge.
TOI-1201 b: a mini-Neptune transiting a bright and moderately young M dwarf

We present the discovery of a transiting mini-Neptune around TOI-1201, a relatively bright and moderately young early M dwarf ( J ≈ 9.5mag, ∼ 600–800Myr) in an equal-mass ∼ 8arcsecond-wide binary system, using data from the Transiting Exoplanet Survey Satel- lite, along with follow-up transit observations. With an orbital period of 2.49d, TOI-1201 b is a warm mini-Neptune with a radius of R b = 2 . 415 ± 0 . 090 R ⊕ . This signal is also present in the precise radial velocity measurements from CARMENES, conﬁrming the existence of the planet and providing a planetary mass of M b = 6 . 28 ± 0 . 88 M ⊕ and, thus, an estimated bulk density of 2 . 45 + 0 . 48 − 0 . 42 gcm − 3 . The spectroscopic observations additionally show evidence of a signal with a period of 19d and a long periodic variation of unde-termined origin. In combination with ground-based photometric monitoring from WASP-South and ASAS-SN, we attribute the 19d signal to the stellar rotation period ( P rot = 19–23d), although we cannot rule out that the variation seen in photometry belongs to the visually close binary companion. We calculate precise stellar parameters for both TOI-1201 and its companion. The transiting planet is an excellent target for atmosphere characterization (the transmission spectroscopy metric is 97 + 21 − 16 ) with the upcoming James Webb Space Telescope. It is also feasible to measure its spin-orbit alignment via the Rossiter-McLaughlin effect using current state-of-the-art spectrographs with submeter per second radial velocity precision.
Formal derivation of quantum drift-diffusion equations with spin-orbit interaction

Quantum drift-diffusion equations for a two-dimensional electron gas with spin-orbit interactions of Rashba type are formally derived from a collisional Wigner equation. The collisions are modeled by a Bhatnagar–Gross–Krook-type operator describing the relaxation of the electron gas to a local equilibrium that is given by the quantum maximum entropy principle. Because of non-commutativity properties of the operators, the standard diffusion scaling cannot be used in this context, and a hydrodynamic time scaling is required. A Chapman–Enskog procedure leads, up to first order in the relaxation time, to a system of nonlocal quantum drift-diffusion equations for the charge density and spin vector densities. Local equations including the Bohm potential are obtained in the semiclassical expansion up to second order in the scaled Planck constant. The main novelty of this work is that all spin components are considered, while previous models only consider special spin directions.
Distributed Detection and Mitigation of Biasing Attacks Over Multi-Agent Networks

This paper proposes a distributed attack detection and mitigation technique based on distributed estimation over a multi-agent network, where the agents take partial system measurements susceptible to (possible) biasing attacks. In particular, we assume that the system is not locally observable via the measurements in the direct neighborhood of any agent. First, for performance analysis in the attack-free case, we show that the proposed distributed estimation is unbiased with bounded mean-square deviation in steady-state. Then, we propose a residual-based strategy to locally detect possible attacks at agents. In contrast to the deterministic thresholds in the literature assuming an upper bound on the noise support, we define the thresholds on the residuals in a probabilistic sense. After detecting and isolating the attacked agent, a system-digraph-based mitigation strategy is proposed to replace the attacked measurement with a new observationally-equivalent one to recover potential observability loss. We adopt a graph-theoretic method to classify the agents based on their measurements, to distinguish between the agents recovering the system rank-deficiency and the ones recovering output-connectivity of the system digraph. The attack detection/mitigation strategy is specifically described for each type, which is of polynomial-order complexity for large-scale applications. Illustrative simulations support our theoretical results.
Four New Eclipsing Binary Systems in the TESS field: CD-34 13220, HD 295082, TYC 6484–426–1 and TYC 6527–2310–1

We present the first evidence for the binarity of four targets in the TESS field. The temperatures are estimated by SED analysis and the orbital periods are determined. The TESS light curves of the systems are analyzed and the orbital and the absolute parameters are derived. The targets are also compared to well-studied binary systems with the same morphological type and their evolutionary states are discussed. Our results indicate that the stars belong to the class of eclipsing detached binary systems.
A Comprehensive Overview of Recommender System and Sentiment Analysis

Recommender system has been proven to be significantly crucial in many fields and is widely used by various domains. Most of the conventional recommender systems rely on the numeric rating given by a user to reflect his opinion about a consumed item; however, these ratings are not available in many domains. As a result, a new source of information represented by the user-generated reviews is incorporated in the recommendation process to compensate for the lack of these ratings. The reviews contain prosperous and numerous information related to the whole item or a specific feature that can be extracted using the sentiment analysis field. This paper gives a comprehensive overview to help researchers who aim to work with recommender system and sentiment analysis. It includes a background of the recommender system concept, including phases, approaches, and performance metrics used in recommender systems. Then, it discusses the sentiment analysis concept and highlights the main points in the sentiment analysis, including level, approaches, and focuses on aspect-based sentiment analysis.
Fast Online Optimization for Terrain-Blind Bipedal Robot Walking With a Decoupled Actuated SLIP Model

We present an online optimization algorithm which enables bipedal robots to blindly walk over various kinds of uneven terrains while resisting pushes. The proposed optimization algorithm performs high-level motion planning of footstep locations and center-of-mass height variations using the decoupled actuated spring-loaded inverted pendulum (aSLIP) model. The decoupled aSLIP model simplifies the original aSLIP with linear inverted pendulum (LIP) dynamics in horizontal states and spring dynamics in the vertical state. The motion planning can be formulated as a discrete-time model predictive control (MPC) problem and solved at a frequency of 1 kHz. The output of the motion planner is fed into an inverse-dynamics–based whole body controller for execution on the robot. A key result of this controller is that the feet of the robot are compliant, which further extends the robot’s ability to be robust to unobserved terrain variations. We evaluate our method in simulation with the bipedal robot SLIDER. The results show that the robot can blindly walk over various uneven terrains including slopes, wave fields, and stairs. It can also resist pushes of up to 40 N for a duration of 0.1 s while walking on uneven terrains.
Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs

Complex Query Answering (CQA) is an important reasoning task on knowledge graphs. Current CQA learning models have been shown to be able to generalize from atomic operators to more complex formulas, which can be regarded as the combinatorial generalizability. In this paper, we present EFO-1-QA, a new dataset to benchmark the combinatorial generalizability of CQA models by including 301 different queries types, which is 20 times larger than existing datasets. Besides, our work, for the first time, provides a benchmark to evaluate and analyze the impact of different operators and normal forms by using (a) 7 choices of the operator systems and (b) 9 forms of complex queries. Specifically, we provide the detailed study of the combinatorial generalizability of two commonly used operators, i.e., projection and intersection, and justify the impact of the forms of queries given the canonical choice of operators. Our code and data can provide an effective pipeline to benchmark CQA models.
A Tighter Upper Bound of the Expansion Factor for Universal Coding of Integers and Its Code Constructions

In entropy coding, universal coding of integers (UCI) is a binary universal prefix code, such that the ratio of the expected codeword length to <inline-formula> <tex-math notation="LaTeX">$\max \{1, H(P)\}$ </tex-math></inline-formula> is less than or equal to a constant expansion factor <inline-formula> <tex-math notation="LaTeX">$K_{\mathcal {C}}$ </tex-math></inline-formula> for any probability distribution <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$H(P)$ </tex-math></inline-formula> is the Shannon entropy of <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula>. <inline-formula> <tex-math notation="LaTeX">$K_{\mathcal {C}}^{*}$ </tex-math></inline-formula> is the infimum of the set of expansion factors. The optimal UCI is defined as a class of UCI possessing the smallest <inline-formula> <tex-math notation="LaTeX">$K_{\mathcal {C}}^{*}$ </tex-math></inline-formula>. Based on prior research, the range of <inline-formula> <tex-math notation="LaTeX">$K_{\mathcal {C}}^{*}$ </tex-math></inline-formula> for the optimal UCI is <inline-formula> <tex-math notation="LaTeX">$2\leq K_{\mathcal {C}}^{*}\leq 2.75$ </tex-math></inline-formula>. Currently, the code constructions achieve <inline-formula> <tex-math notation="LaTeX">$K_{\mathcal {C}}=2.75$ </tex-math></inline-formula> for UCI and <inline-formula> <tex-math notation="LaTeX">$K_{\mathcal {C}}=3.5$ </tex-math></inline-formula> for asymptotically optimal UCI. In this paper, we construct a class of UCI, termed <inline-formula> <tex-math notation="LaTeX">$\iota $ </tex-math></inline-formula> code, to achieve <inline-formula> <tex-math notation="LaTeX">$K_{\mathcal {C}}=2.5$ </tex-math></inline-formula>. This further narrows the range of <inline-formula> <tex-math notation="LaTeX">$K_{\mathcal {C}}^{*}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$2\leq K_{\mathcal {C}}^{*}\leq 2.5$ </tex-math></inline-formula>. Next, a family of asymptotically optimal UCIs is presented, where their expansion factor infinitely approaches 2.5. Then, tighter upper bounds of the expansion factors are derived for several classic UCIs. Finally, we prove that the length of the first codeword of optimal UCI is one.
Fast Obstacle Avoidance Motion in Small Quadcopter operation in a Cluttered Environment

The autonomous operation of small quadcopters moving at high speed in an unknown cluttered environment is a challenging task. Current works in the literature formulate it as a Sense-And-Avoid (SAA) problem and address it by either developing new sensing capabilities or small form-factor processors. However, the SAA, with the high-speed operation, remains an open problem. The significant complexity arises due to the computational latency, which is critical for fast-moving quadcopters. In this paper, a novel Fast Obstacle Avoidance Motion (FOAM) algorithm is proposed to perform SAA operations. FOAM is a low-latency perception-based algorithm that uses multi-sensor fusion of a monocular camera and a 2-D LIDAR. A 2-D probabilistic occupancy map of the sensing region is generated to estimate a free space for avoiding obstacles. Also, a local planner is used to navigate the high-speed quadcopter towards a given target location while avoiding obstacles. The performance evaluation of FOAM is evaluated in simulated environments in Gazebo and AIRSIM. Real-time implementation of the same has been presented in outdoor environments using a custom-designed quadcopter operating at a speed of 4.5 m/s. The FOAM algorithm is implemented on a low-cost computing device to demonstrate its efficacy. The results indicate that FOAM enables a small quadcopter to operate at high speed in a cluttered environment efficiently.
Non-linear resonant torus oscillations as a model of Keplerian disc warp dynamics

Observations of distorted discs have highlighted the ubiquity of warps in a variety of astrophysical contexts. This has been complemented by theoretical efforts to understand the dynamics of warp evolution. Despite significant efforts to understand the dynamics of warped discs, previous work fails to address arguably the most prevalent regime – non-linear warps in Keplerian discs for which there is a resonance between the orbital, epicyclic and vertical oscillation frequencies. In this work, we implement a novel non-linear ring model, developed recently by Fairbairn and Ogilvie, as a framework for understanding such resonant warp dynamics. Here, we uncover two distinct non-linear regimes as the warp amplitude is increased. Initially, we find a smooth modulation theory that describes warp evolution in terms of the averaged Lagrangian of the oscillatory vertical motions of the disc. This hints towards the possibility of connecting previous warp theory under a generalized secular framework. Upon the warp amplitude exceeding a critical value, which scales as the square root of the aspect-ratio of our ring, the disc enters into a bouncing regime with extreme vertical compressions twice per orbit. We develop an impulsive theory that predicts special retrograde and prograde precessing warped solutions, which are identified numerically using our full equation set. Such solutions emphasize the essential activation of non-linear vertical oscillations within the disc and may have important implications for energy and warp dissipation. Future work should search for this behaviour in detailed numerical studies of the internal flow structure of warped discs.
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning

This paper introduces Greedy UnMix (GUM) for cooperative multi-agent reinforcement learning (MARL). Greedy UnMix aims to avoid scenarios where MARL methods fail due to overestimation of values as part of the large joint state-action space. It aims to address this through a conservative Q-learning approach through restricting the state-marginal in the dataset to avoid unobserved joint state action spaces, whilst concurrently attempting to unmix or simplify the problem space under the centralized training with decentralized execution paradigm. We demonstrate the adherence to Q-function lower bounds in the Q-learning for MARL scenarios, and demonstrate superior performance to existing Q-learning MARL approaches as well as more general MARL algorithms over a set of benchmark MARL tasks, despite its relative simplicity compared with state-of-the-art approaches.
Cluster assembly times as a cosmological test

The abundance of galaxy clusters in the low-redshift Universe provides an important cosmological test, constraining a product of the initial amplitude of fluctuations and the amount by which they have grown since early times. The degeneracy of the test with respect to these two factors remains a limitation of abundance studies. Clusters will have different mean assembly times, however, depending on the relative importance of initial fluctuation amplitude and subsequent growth. Thus, structural probes of cluster age such as concentration, shape, or substructure may provide a new cosmological test that breaks the main degeneracy in number counts. We review analytical predictions for how mean assembly time should depend on cosmological parameters, and test these predictions using cosmological simulations. Given the overall sensitivity expected, we estimate the cosmological parameter constraints that could be derived from the cluster catalogues of forthcoming surveys such as Euclid, the Nancy Grace Roman Space Telescope, eROSITA, or CMB-S4. We show that by considering the structural properties of their cluster samples, such surveys could easily achieve errors of Δσ8 = 0.01 or better.
Real-Time Trash Detection for Modern Societies using CCTV to Identifying Trash by utilizing Deep Convolutional Neural Network

To protect the environment from trash pollution, especially in societies, and to take strict action against the red-handed people who throws the trash. As modern societies are developing and these societies need a modern solution to make the environment clean. Artificial intelligence (AI) evolution, especially in Deep Learning, gives an excellent opportunity to develop real-time trash detection using CCTV cameras. The inclusion of this project is real-time trash detection using a deep model of Convolutional Neural Network (CNN). It is used to obtain eight classes mask, tissue papers, shoppers, boxes, automobile parts, pampers, bottles, and juices boxes. After detecting the trash, the camera records the video of that person for ten seconds who throw trash in society. The challenging part of this paper is preparing a complex custom dataset that took too much time. The dataset consists of more than 2100 images. The CNN model was created, labeled, and trained. The detection time accuracy and average mean precision (mAP) benchmark both models' performance. In experimental phase the mAP performance and accuracy of the improved CNN model was superior in all aspects. The model is used on a CCTV camera to detect trash in real-time.
On the analyticity of the nonlinear Fourier transform of the Benjamin-Ono equation on $\mathbb{T}$

We prove that the nonlinear Fourier transform of the Benjamin-Ono equation on T, also referred to as Birkhoff map, is a real analytic diffeomorphism from the scale of Sobolev spaces H 0(T,R), s > −1/2, to the scale of weighted l−sequence spaces, h s+1/2 r,0 (N,C), s > −1/2. As an application we show that for any −1/2 < s < 0, the flow map of the Benjamin-Ono equation S 0 : H s 0(T,R) → H s 0(T,R) is nowhere locally uniformly continuous in H 0(T,R).
Interpolation Variable Rate Image Compression

Compression standards have been used to reduce the cost of image storage and transmission for decades. In recent years, learned image compression methods have been proposed and achieved compelling performance to the traditional standards. However, in these methods, a set of different networks are used for various compression rates, resulting in a high cost in model storage and training. Although some variable-rate approaches have been proposed to reduce the cost by using a single network, most of them brought some performance degradation when applying fine rate control. To enable variable-rate control without sacrificing the performance, we propose an efficient Interpolation Variable-Rate (IVR) network, by introducing a handy Interpolation Channel Attention (InterpCA) module in the compression network. With the use of two hyperparameters for rate control and linear interpolation, the InterpCA achieves a fine PSNR interval of 0.001 dB and a fine rate interval of 0.0001 Bits-Per-Pixel (BPP) with 9000 rates in the IVR network. Experimental results demonstrate that the IVR network is the first variable-rate learned method that outperforms VTM 9.0 (intra) in PSNR and Multiscale Structural Similarity (MS-SSIM).