Miracle-dz/UniFAR-FTUs huggingface dataset:
Group IV Mid-Infrared Thermophotovoltaic Cells on Silicon

Compound semiconductors have been the predominant building blocks for the current midinfrared thermophotovoltaic devices relevant to sub-<inline-formula><tex-math notation="LaTeX">$2000 \,\mathrm{K}$</tex-math></inline-formula> heat conversion and power beaming. However, the prohibitively high cost associated with these technologies limits their broad adoption. Herein, to alleviate this challenge we introduce an all-group IV midinfrared cell consisting of GeSn alloy directly on a silicon wafer. This emerging class of semiconductors provides strain and composition as degrees of freedom to control the bandgap energy thus covering the entire midinfrared range. The proposed thermophotovoltaic device is composed of a fully relaxed Ge<inline-formula><tex-math notation="LaTeX">$_{0.83}$</tex-math></inline-formula>Sn<inline-formula><tex-math notation="LaTeX">$_{0.17}$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula>-<inline-formula><tex-math notation="LaTeX">$i$</tex-math></inline-formula>-<inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> homojunction corresponding to a bandgap energy of <inline-formula><tex-math notation="LaTeX">$0.29 \,\mathrm{e\mathrm{V}}$</tex-math></inline-formula>. A theoretical framework is derived to evaluate cell performance under high injection. The black-body radiation absorption is investigated using the generalized transfer matrix method thereby considering the mixed coherent/incoherent layer stacking. Moreover, the intrinsic recombination mechanisms and their importance in a narrow bandgap semiconductor were also taken into account. In this regard, the parabolic band approximation and Fermi's golden rule were combined for an accurate estimation of the radiative recombination rate. Based on these analyses, power conversion efficiencies of up to 9% are predicted for Ge<inline-formula><tex-math notation="LaTeX">$_{0.83}$</tex-math></inline-formula>Sn<inline-formula><tex-math notation="LaTeX">$_{0.17}$</tex-math></inline-formula> thermophotovoltaic cells under black-body radiation at temperatures in the 500–<inline-formula><tex-math notation="LaTeX">$1500 \,\mathrm{K}$</tex-math></inline-formula> range. A slight improvement in the efficiency is observed under the frontside illumination but vanishes below <inline-formula><tex-math notation="LaTeX">$800 \,\mathrm{K}$</tex-math></inline-formula>, while the use of a backside reflector improves the efficiency across the investigated black-body temperature range. The effects of the heterostructure thickness, surface recombination velocity, and carrier lifetime are also elucidated and discussed.
UAV Path Planning Employing MPC- Reinforcement Learning Method for search and rescue mission

—In this paper, we tackle the problem of Unmanned Aerial (UAV) path planning in complex and uncertain environments by designing a Model Predictive Control (MPC), based on a Long-Short-Term Memory (LSTM) network integrated into the Deep Deterministic Policy Gradient algorithm. In the proposed solution, LSTM-MPC operates as a deterministic policy within the DDPG network, and it leverages a predicting pool to store predicted future states and actions for improved robustness and efficiency. The use of the predicting pool also enables the initialization of the critic network, leading to improved convergence speed and reduced failure rate compared to traditional reinforcement learning and deep reinforcement learning methods. The effectiveness of the proposed solution is evaluated by numerical simulations.
Creating Disasters: Recession Forecasting with GAN-Generated Synthetic Time Series Data

A common problem when forecasting rare events, such as recessions, is limited data availability. Recent advancements in deep learning and generative adversarial networks (GANs) make it possible to produce high-fidelity synthetic data in large quantities. This paper uses a model called DoppelGANger, a GAN tailored to producing synthetic time series data, to generate synthetic Treasury yield time series and associated recession indicators. It is then shown that short-range forecasting performance for Treasury yields is improved for models trained on synthetic data relative to models trained only on real data. Finally, synthetic recession conditions are produced and used to train classification models to predict the probability of a future recession. It is shown that training models on synthetic recessions can improve a model's ability to predict future recessions over a model trained only on real data.
Polarization transport in optical fibers beyond Rytov's law

We consider the propagation of light in arbitrarily curved step-index optical fibers. Using a multiple-scales approximation scheme, set-up in Fermi normal coordinates, the full vectorial Maxwell equations are solved in a perturbative manner. At leading order, this provides a rigorous derivation of Rytov's law. At next order, we obtain non-trivial dynamics of the electromagnetic field, characterized by two coupling constants, the phase and the polarization curvature moments, which describe the curvature response of the light's phase and its polarization vector, respectively. The latter can be viewed as an inverse spin Hall effect of light, where the direction of propagation is constrained along the optical fiber and the polarization evolves in a frequency-dependent way.
Diagrammatic method for many-body non-Markovian dynamics: Memory effects and entanglement transitions

We study the quantum dynamics of a many-body system subject to coherent evolution and coupled to a non-Markovian bath. We propose a technique to unravel the non-Markovian dynamics in terms of quantum jumps, a connection that was so far only understood for single-body systems. We develop a systematic method to calculate the probability of a quantum trajectory, and formulate it in a diagrammatic structure. We find that non-Markovianity renormalizes the probability of realizing a quantum trajectory, and that memory effects can be interpreted as a perturbation on top of the Markovian dynamics. We show that the diagrammatic structure is akin to that of a Dyson equation, and that the probability of the trajectories can be calculated analytically. We then apply our results to study the measurement-induced entanglement transition in random unitary circuits. We find that non-Markovianity does not significantly shift the transition, but stabilizes the volume law phase of the entanglement by shielding it from transient strong dissipation.
Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction

Real-time, accurate prediction of human steering behaviors has wide applications, from developing intelligent traffic systems to deploying autonomous driving systems in both real and simulated worlds. In this letter, we present ContextVAE, a context-aware approach for multi-modal vehicle trajectory prediction. Built upon the backbone architecture of a timewise variational autoencoder, ContextVAE observation encoding employs a dual attention mechanism that accounts for the environmental context and the dynamic agents' states, in a unified way. By utilizing features extracted from semantic maps during agent state encoding, our approach takes into account both the social features exhibited by agents on the scene and the physical environment constraints to generate map-compliant and socially-aware trajectories. We perform extensive testing on the nuScenes prediction challenge (Caesar et al., 2023), Lyft Level 5 dataset (Houston et al., 2023) and Waymo Open Motion Dataset (Ettinger et al., 2023) to show the effectiveness of our approach and its state-of-the-art performance. In all tested datasets, ContextVAE models are fast to train and provide high-quality multi-modal predictions in real-time.
Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning

We approach the problem of understanding how people interact with each other in collaborative settings, especially when individuals know little about their teammates, via Multiagent Inverse Reinforcement Learning (MIRL), where the goal is to infer the reward functions guiding the behavior of each individual given trajectories of a team's behavior during some task. Unlike current MIRL approaches, we do not assume that team members know each other's goals a priori; rather, that they collaborate by adapting to the goals of others perceived by observing their behavior, all while jointly performing a task. To address this problem, we propose a novel approach to MIRL via Theory of Mind (MIRL-ToM). For each agent, we first use ToM reasoning to estimate a posterior distribution over baseline reward profiles given their demonstrated behavior. We then perform MIRL via decentralized equilibrium by employing single-agent Maximum Entropy IRL to infer a reward function for each agent, where we simulate the behavior of other teammates according to the time-varying distribution over profiles. We evaluate our approach in a simulated 2-player search-and-rescue operation where the goal of the agents, playing different roles, is to search for and evacuate victims in the environment. Our results show that the choice of baseline profiles is paramount to the recovery of the ground-truth rewards, and that MIRL-ToM is able to recover the rewards used by agents interacting both with known and unknown teammates.
Effects of Co-channel Interference on RIS Empowered Wireless Networks amid Multiple Eavesdropping Attempts

This letter is concerned with the secrecy performance of reconfigurable intelligent surfaces (RIS)-aided wireless networks in the existence of multiple interferers towards the destination. To be more precise, we analyze three critical issues in the design of secure RIS-assisted networks: 1) How do interferers affect the performance of secure wireless networks? 2) Which of the two groups of eavesdroppers (i.e., colluding and non-colluding) is more severe? 3) How can RIS improve network confidentiality? To do so, we develop the analytical expression of secrecy outage probability in closed-form, along with asymptotic analysis at high signal-to-noise ratio regime to better understand the impacts of different system parameters on secrecy performance. Finally, we validate our analytical results using a computer based Monte-Carlo simulation.
Co-Driven Recognition of Semantic Consistency via the Fusion of Transformer and HowNet Sememes Knowledge

Semantic consistency recognition aims to detect and judge whether the semantics of two text sentences are consistent with each other. However, the existing methods usually encounter the challenges of synonyms, polysemy and difficulty to understand long text. To solve the above problems, this paper proposes a co-driven semantic consistency recognition method based on the fusion of Transformer and HowNet sememes knowledge. Multi-level encoding of internal sentence structures via data-driven is carried out firstly by Transformer, sememes knowledge base HowNet is introduced for knowledge-driven to model the semantic knowledge association among sentence pairs. Then, interactive attention calculation is carried out utilizing soft-attention and fusion the knowledge with sememes matrix. Finally, bidirectional long short-term memory network (BiLSTM) is exploited to encode the conceptual semantic information and infer the semantic consistency. Experiments are conducted on two financial text matching datasets (BQ, AFQMC) and a cross-lingual adversarial dataset (PAWSX) for paraphrase identification. Compared with lightweight models including DSSM, MwAN, DRCN, and pre-training models such as ERNIE etc., the proposed model can not only improve the accuracy of semantic consistency recognition effectively (by 2.19%, 5.57% and 6.51% compared with the DSSM, MWAN and DRCN models on the BQ dataset), but also reduce the number of model parameters (to about 16M). In addition, driven by the HowNet sememes knowledge, the proposed method is promising to adapt to scenarios with long text.
Epistemic Prediction and Planning with Implicit Coordination for Multi-Robot Teams in Communication Restricted Environments

In communication restricted environments, a multi-robot system can be deployed to either: i) maintain constant communication but potentially sacrifice operational efficiency due to proximity constraints or ii) allow disconnections to increase environmental coverage efficiency, challenges on how, when, and where to reconnect (rendezvous problem). In this work we tackle the latter problem and notice that most state-of-the-art methods assume that robots will be able to execute a predetermined plan; however system failures and changes in environmental conditions can cause the robots to deviate from the plan with cascading effects across the multi-robot system. This paper proposes a coordinated epistemic prediction and planning framework to achieve consensus without communicating for exploration and coverage, task discovery and completion, and rendezvous applications. Dynamic epistemic logic is the principal component implemented to allow robots to propagate belief states and empathize with other agents. Propagation of belief states and subsequent coverage of the environment is achieved via a frontier-based method within an artificial physics-based framework. The proposed framework is validated with both simulations and experiments with unmanned ground vehicles in various cluttered environments.
SurvLIMEpy: A Python package implementing SurvLIME

In this paper we present SurvLIMEpy, an open-source Python package that implements the SurvLIME algorithm. This method allows to compute local feature importance for machine learning algorithms designed for modelling Survival Analysis data. Our implementation takes advantage of the parallelisation paradigm as all computations are performed in a matrix-wise fashion which speeds up execution time. Additionally, SurvLIMEpy assists the user with visualization tools to better understand the result of the algorithm. The package supports a wide variety of survival models, from the Cox Proportional Hazards Model to deep learning models such as DeepHit or DeepSurv. Two types of experiments are presented in this paper. First, by means of simulated data, we study the ability of the algorithm to capture the importance of the features. Second, we use three open source survival datasets together with a set of survival algorithms in order to demonstrate how SurvLIMEpy behaves when applied to different models.
Fast and Painless Image Reconstruction in Deep Image Prior Subspaces

The deep image prior (DIP) is a state-of-the-art unsupervised approach for solving linear inverse problems in imaging. We address two key issues that have held back practical deployment of the DIP: the long computing time needed to train a separate deep network per reconstruction, and the susceptibility to overﬁtting due to a lack of robust early stopping strategies in the unsupervised setting. To this end, we restrict DIP optimisation to a sparse linear subspace of the full parameter space. We construct the subspace from the principal eigenspace of a set of parameter vectors sampled at equally spaced intervals during DIP pre-training on synthetic task-agnostic data. The low-dimensionality of the resulting subspace reduces DIP’s capacity to ﬁt noise and allows the use of fast second order optimisation meth-ods, e.g., natural gradient descent or L-BFGS. Experiments across tomographic tasks of different geometry, ill-posedness and stopping criteria consistently show that second order optimisation in a subspace is Pareto-optimal in terms of optimisation time to reconstruction ﬁdelity trade-off.
Particle transport diagnostics on CHS and LHD with tracer-encapsulated solid pellet injection

A method using a tracer-encapsulated solid pellet (TESPEL) has been developed to diagnose particle transport. TESPEL consists of polystyrene as the outer part and another material as the inner core. At first, it was confirmed at the compact helical system (CHS) that the tracer of lithium was deposited locally in the plasma. Moreover, the behaviour of the tracer particles was observed by charge exchange recombination spectroscopy. TESPEL was also injected into the large helical device (LHD). In this case, the Kα line of titanium was observed without the necessity of a neutral beam, and it was shown that this method is promising.
Electronic Laboratory Notebook on Web2py Framework

Proper experimental record-keeping is an important cornerstone in research and development for the purpose of auditing. The gold standard of record-keeping is based on the judicious use of physical, permanent notebooks. However, advances in technology had resulted in large amounts of electronic records making it virtually impossible to maintain a full set of records in physical notebooks. Electronic laboratory notebook systems aim to meet the stringency for keeping records electronically. This manuscript describes CyNote which is an electronic laboratory notebook system that is compliant with 21 CFP Part 11 controls on electronic records, requirements set by USA Food and Drug Administration for electronic records. CyNote is implemented on web2py framework and is adhering to the architectural paradigm of model-view-controller (MVC), allowing for extension modules to be built for CyNote. CyNote is available at http://cynote.sf. net.
The JWST Advanced Deep Extragalactic Survey: Discovery of an Extreme Galaxy Overdensity at z = 5.4 with JWST/NIRCam in GOODS-S

We report the discovery of an extreme galaxy overdensity at z = 5.4 in the GOODS-S field using James Webb Space Telescope (JWST)/NIRCam imaging from JADES and JEMS alongside JWST/NIRCam wide-field slitless spectroscopy from FRESCO. We identified potential members of the overdensity using Hubble Space Telescope+JWST photometry spanning λ = 0.4–5.0 μm. These data provide accurate and well-constrained photometric redshifts down to m ≈ 29–30 mag. We subsequently confirmed N = 81 galaxies at 5.2 < z < 5.5 using JWST slitless spectroscopy over λ = 3.9–5.0 μm through a targeted line search for Hα around the best-fit photometric redshift. We verified that N = 42 of these galaxies reside in the field, while N = 39 galaxies reside in a density around ∼10 times that of a random volume. Stellar populations for these galaxies were inferred from the photometry and used to construct the star-forming main sequence, where protocluster members appeared more massive and exhibited earlier star formation (and thus older stellar populations) when compared to their field galaxy counterparts. We estimate the total halo mass of this large-scale structure to be 12.6 ≲ log 10 M halo / M ⊙ ≲ 12.8 using an empirical stellar mass to halo mass relation, which is likely an underestimate as a result of incompleteness. Our discovery demonstrates the power of JWST at constraining dark matter halo assembly and galaxy formation at very early cosmic times.
Modelling lateral manoeuvres in fish

Abstract We propose a method to model manoeuvres in self-propelled flexible-bodied fish by modelling the hydrodynamics coupled to the body inertia. Flexible body motion is prescribed and the equations of motion are solved for the position of the centre of mass and rotation of the body. The governing equations are formulated by applying the conservation of linear and angular momentum. Two independent methods to model the fluid dynamics are pursued: Model 1 is an extension of elongated-body theory, modified for self-propulsion and flexible motion. Model 2 applies a numerical boundary-element formulation with the fish modelled as an infinitely thin rectangular body. The manoeuvring response to an impulsive input is first examined to understand the rigid-body characteristics of the fish. A flexible bend action is included to model C-bends of the type observed during escapes in fish. Models 1 and 2 are used to cross-verify the respective implementations as well as to develop physical insights into manoeuvring. A parameter study shows that fish of intermediate body depths are best adapted to rapid turns whereas the initial dynamic state of the fish is instrumental in affecting the sign as well as the magnitude of the turn angle, for a prescribed bend deflection. Computations for combined swimming and turning show that the initial rigid-body dynamics of the fish is much more effective than the induced effect of the prior shed wake in enhancing the turning response.
On Function-Coupled Watermarks for Deep Neural Networks

Well-performed deep neural networks (DNNs) generally require massive labelled data and computational resources for training. Various watermarking techniques are proposed to protect such intellectual properties (IPs), wherein the DNN providers implant secret information into the model so that they can later claim IP ownership by retrieving their embedded watermarks with some dedicated trigger inputs. While promising results are reported in the literature, existing solutions suffer from watermark removal attacks, such as model fine-tuning and model pruning. In this paper, we propose a novel DNN watermarking solution that can effectively defend against the above attacks. Our key insight is to enhance the coupling of the watermark and model functionalities such that removing the watermark would inevitably degrade the model's performance on normal inputs. To this end, unlike previous methods relying on secret features learnt from out-of-distribution data, our method only uses features learnt from in-distribution data. Specifically, on the one hand, we propose to sample inputs from the original training dataset and fuse them as watermark triggers. On the other hand, we randomly mask model weights during training so that the information of our embedded watermarks spreads in the network. By doing so, model fine-tuning/pruning would not forget our function-coupled watermarks. Evaluation results on various image classification tasks show a 100\% watermark authentication success rate under aggressive watermark removal attacks, significantly outperforming existing solutions. Code is available: https://github.com/cure-lab/Function-Coupled-Watermark.
Rectification and nonlinear Hall effect by fluctuating finite-momentum Cooper pairs

Nonreciprocal charge transport is attracting much attention as a novel probe and functionality of noncentrosymmetric superconductors. In this work, we show that both the longitudinal and transverse nonlinear paraconductivity are hugely enhanced in helical superconductors in moderate and high magnetic fields, which can be observed by second-harmonic resistance measurements. The discussion is based on the generalized formulation of nonlinear paraconductivity in combination with the microscopically determined Ginzburg-Landau coefficients. The enhanced nonreciprocal transport would be observable even with the cyclotron motion of fluctuating Cooper pairs, which is elucidated with a Kubo-type formula of nonlinear paraconductivity. Nonreciprocal charge transport in the fluctuation regime is thereby established as a promising probe of helical superconductivity regardless of the sample dimensionality. Implications on the other finite-momentum superconducting states are briefly discussed.
A comparative study of human inverse kinematics techniques for lower limbs

Inverse Kinematics (IK) has been an active research topic and many methods have been introduced to provide a fast and accurate solution. However, high computational cost and the generation of unrealistic positions constitute the weak points in most existing IK methods. In this paper, a comparative study was established to analyze the performance of popular IK techniques applied to the human leg. The objective is to determine the most efficient method in terms of computation time and to reach the desired position with a realistic human posture while respecting the range of motion and joint comfort zones of every joint.