Miracle-dz/UniFAR-FTUs huggingface dataset:
Application and Parameter Sensitivities of a State-Space Cold Load Pickup Model for a Synthetic Restoration Test Case

Following a partial or system-wide blackout, the demand needed to restore power is often significantly higher than normal conditions. This phenomenon is known as cold load and its restoration is described as a cold load pickup. Modeling cold load is highly complex and dependent on various factors such as load type, cause of outage, duration, and weather conditions. In this paper an end-use load model focusing on determining cold load demand after an outage is introduced. By using a state-space model, independent load characteristics present when interruption occurs can be captured for unpredictable outages based on system status and historical demand under normal conditions. Through the use of a load accumulation state variable, excess demand is determined based on outage duration where local limitations can be set by utilities to fine tune predictions to fit their areas serviced. Characteristic parameters were determined through sensitivity studies based on data from recorded blackstart events. The model is applied in this paper for integration into synthetic grids for blackout studies. Initial results remain consistent with prior work and available data, and show the effects of some of the factors affecting demand. With the ability to provide accurate load predictions, cold load data can be integrated into synthetic grids to simulate blackout restorations that reflect impacts of outages on the grid.
A Study on Improvement of Traffic Sign Detection Accuracy for Blurred Images

In recent years, the demand for image recognition has been increasing. For example, face recognition can be used to unlock lock screens in smartphones, and it can also be combined with a temperature sensor to detect body temperature. It can also be applied to automatic driving, where it can recognize people, traffic lights, and signs using image recognition to increase safety. In automatic driving, images captured by on-board cameras may become blurred due to the shaking of cars. To ensure safety, it is necessary to be able to detect objects stably even when those during driving are blurred. This paper attempts to achieve stable detection.
Gender and age differences in mind perception of robots

The mind perception of robots is an essential topic in studying people’s perceptions and emotions towards robots. This study revealed that participants’ age and gender significantly influenced the mind perceptions of robots through the mind perception questionnaire for three types of robots (robot, service robot, and security robot). We found that female participants showed higher expectations of the robot’s agency ability than male participants. The younger (compared to the elder) participants scored significantly higher on the experience dimension of the robot and the security robot. Participants rated agency significantly higher than experience for all three types of robots. This study provides a helpful indicator for the study and design of robots.
Towards Semi-Autonomous Manipulation of an Electromagnetically Actuated Soft-Tethered Colonoscope Based on Visual Servo Control

Conventional colonoscopy using a flexible colono-scope remains two major limitations, including patient discomfort and nonintuitive manipulations for surgeons. To address these limitations, robotic colonoscopes have been developed to provide new approaches for conducting colon diagnosis and therapy. However, low autonomy level of most robotic colonoscopes leads to nonintuitive and difficult manipulations, which limits their applications in clinical practice. In this paper, we demonstrate semi-autonomous manipulations of an electro-magnetic actuated soft-tethered (EAST) colonoscope based on visual servo control, which aims to reduce the difficulties of colonoscope manipulation and lower the workload of surgeons. A visual servo controller is established with the magnetic actuation model and kinematic model of the tethered colono-scope. Two semi-autonomous manipulations, including region-of-interest automatic tracking and autonomous navigation with automatic polyp detection, are proposed based on visual servo control, with several image feature extraction methods developed. Experimental validation is conducted with a proof-of-concept EAST colonoscope prototype and a colon phantom. The results show that the system successfully performs visual servoing and semi-autonomous manipulations, which demon-strates the potential of applications in clinical practice.
Influence of the sample length and profile of the magnetoimpedance effect in FeCrSiBCuNb ultrasoft magnetic wires

The magnetoimpedance (MI) effect has been investigateded in Fe63.5Cr10Si13. 5B9Cu1Nb3 alloy wire exhibiting an ultrasoft magnetic character after suitable thermal treatment at 525 °C. It has been measured as a function of the sample length and of the distance to the ends of the wire (MI profile). The observed dependence on both parameters is correlated with the local particularities of the magnetization process: complex domain structures emerging near the ends to decrease the magnetostatic energy modify the local magnetic susceptibility and consequently the MI response. The present study points out the importance of geometrical dimensions of samples and measuring conditions when reporting numerical values in MI studies. This wire, showing large magnetoimpedance together with optimized soft magnetic behavior and improved corrosion properties seems to be indicated as a sensing element in GMI based sensors.
Calculation of thermodynamic properties and transport coefficients of SF6-PTFE-Cu mixtures

The nozzle ablation and contact corrosion of power switchgear (such as high voltage SF6 circuit breaker) will lead to the generation of PTFE and copper vapor, which will affect the physical parameters of arc plasma and the breaking characteristics of SF6 circuit breaker. Therefore, based on the Gibbs free energy minimization method, the local thermodynamic equilibrium (LTE) composition of SF6-PTFE-Cu gas mixtures at different temperatures is obtained. We calculated the thermodynamic properties and transport coefficients of SF6-PTFE-Cu gas mixtures in the temperature range of 300K ~ 30000k and the pressure range of 0.1~0.8MPa. Different gas pressures, SF6/PTFE mixing ratios, copper proportions have certain influence on the thermodynamic properties and transport coefficient of the gas mixtures. The addition of PTFE has a great influence on these physical parameters. The addition of a small amount of copper will not lead to obvious changes in properties. However, when the mole fraction of copper exceeds 10%, the thermodynamic properties and transport coefficients will change significantly. Increasing the gas pressure will increase the transport coefficient, particularly in high temperature areas. The calculated results provide essential statistics for further researches of plasma behavior by using MHD arc model.
H ∞ state estimation for discrete‑time stochastic memristive BAM neural networks with mixed time‑delays

In this paper, the H ∞ state estimation problem is investigated for a class of discrete-time stochastic memristive bidirectional associative memory (DSMBAM) neural networks with mixed time delays. The mixed time delays comprise both discrete and distributed time-delays. A series of novel switching functions are proposed to reflect the state-dependent characteristics of the memristive connection weights in the discrete-time setting, which facilitates the dynamics analysis of the addressed memristive neural networks (MNNs). By means of the introduced series of switching functions, an H ∞ state estimator is designed such that the estimation error is exponentially mean-square stable and the prescribed H ∞ performance requirement is achieved. The gain matrices of the desired estimator are parameterized by utilizing the semi-definite programming method. Finally, a simulation example is employed to demonstrate the usefulness and effectiveness of the proposed theoretical results.
Tailor: Altering Skip Connections for Resource-Efficient Inference

Deep neural networks use skip connections to improve training convergence. However, these skip connections are costly in hardware, requiring extra buffers and increasing on- and off-chip memory utilization and bandwidth requirements. In this paper, we show that skip connections can be optimized for hardware when tackled with a hardware-software codesign approach. We argue that while a network’s skip connections are needed for the network to learn, they can later be removed or shortened to provide a more hardware efficient implementation with minimal to no accuracy loss. We introduce Tailor, a codesign tool whose hardware-aware training algorithm gradually removes or shortens a fully trained network’s skip connections to lower their hardware cost. Tailor improves resource utilization by up to 34% for BRAMs, 13% for FFs, and 16% for LUTs for on-chip, dataflow-style architectures. Tailor increases performance by 30% and reduces memory bandwidth by 45% for a 2D processing element array architecture.
DDS: Decoupled Dynamic Scene-Graph Generation Network

—Scene-graph generation involves creating a struc- tural representation of the relationships between objects in a scene by predicting subject-object-relation triplets from input data. However, existing methods show poor performance in detecting triplets outside of a predeﬁned set, primarily due to their reliance on dependent feature learning. To address this issue we propose DDS– a decoupled dynamic scene-graph generation network– that consists of two independent branches that can disentangle extracted features. The key innovation of the current paper is the decoupling of the features representing the relationships from those of the objects, which enables the detection of novel object-relationship combinations. The DDS model is evaluated on three datasets and outperforms previous methods by a signiﬁcant margin, especially in detecting previously unseen triplets.
Pseudorandom Generators for Sliding-Window Algorithms

A sliding-window algorithm of window size $t$ is an algorithm whose current operation depends solely on the last $t$ symbols read. We construct pseudorandom generators (PRGs) for low-space randomized sliding-window algorithms that have access to a binary randomness source. More specifically, we lift these algorithms to the non-uniform setting of branching programs and study them as a subclass thereof that we call sliding-window branching programs (SWBPs), accordingly. For general SWBPs, given a base PRG $G_\mathrm{base}$ with seed length $d_\mathrm{base}$ that $\varepsilon_\mathrm{base}$-fools width-$w$, length-$t$ (general) branching programs, we give two PRG constructions for fooling any same-width SWBP of length $n$ and window size $t$ (where we assume $w \ge n$). The first uses an additional $d_\mathrm{base} + O(\log(n/t) \log(1/\varepsilon_\mathrm{base}))$ random bits, whereas the second has a seed length of $O((d_\mathrm{base} + \log\log(n/t) + \log(1/\varepsilon_\mathrm{base})) \log(d_\mathrm{base} + \log(1/\varepsilon_\mathrm{base})))$. Both PRGs incur only a $(n/2t)^{O(1)}$ multiplicative loss in the error parameter. As an application, we show how to decide the language of a sublinear-time probabilistic cellular automaton using small space. More specifically, these results target the model of PACAs, which are probabilistic cellular automata that accept if and only if all cells are simultaneously accepting. For (sublinear) $T(n) = \Omega(\log n)^{1.01}$, we prove that every language accepted by a $T$-time one-sided error PACA (the PACA equivalent of $\mathsf{RP}$) can be decided using only $O(T)$ space. Meanwhile, forgoing the previous requirement on $T$, we show the same holds for $T$-time two-sided error PACA (the PACA equivalent of $\mathsf{BPP}$) if we use $\tilde{O}(T) + O(\log n)$ space instead (where the $\tilde{O}$ notation hides only $\mathsf{polylog}(T)$ factors).
Meta variance reduction for Monte Carlo estimation of energetic particle confinement during stellarator optimization

This work introduces meta estimators that combine multiple multifidelity techniques based on control variates, importance sampling, and information reuse to yield a quasi-multiplicative amount of variance reduction. The proposed meta estimators are particularly efficient within outer-loop applications when the input distribution of the uncertainties changes during the outer loop, which is often the case in reliability-based design and shape optimization. We derive asymptotic bounds of the variance reduction of the meta estimators in the limit of convergence of the outer-loop results. We demonstrate the meta estimators, using data-driven surrogate models and biasing densities, on a design problem under uncertainty motivated by magnetic confinement fusion, namely the optimization of stellarator coil designs to maximize the estimated confinement of energetic particles. The meta estimators outperform all of their constituent variance reduction techniques alone, ultimately yielding two orders of magnitude speedup compared to standard Monte Carlo estimation at the same computational budget.
Optimistically Tempered Online Learning

Optimistic Online Learning algorithms have been developed to exploit expert advices, assumed optimistically to be always useful. However, it is legitimate to question the relevance of such advices \emph{w.r. t. } the learning information provided by gradient-based online algorithms. In this work, we challenge the confidence assumption on the expert and develop the \emph{optimistically tempered} (OT) online learning framework as well as OT adaptations of online algorithms. Our algorithms come with sound theoretical guarantees in the form of dynamic regret bounds, and we eventually provide experimental validation of the usefulness of the OT approach.
Temporal Perceiving Video-Language Pre-training

Video-Language Pre-training models have recently significantly improved various multi-modal downstream tasks. Previous dominant works mainly adopt contrastive learning to achieve global feature alignment across modalities. However, the local associations between videos and texts are not modeled, restricting the pre-training models' generality, especially for tasks requiring the temporal video boundary for certain query texts. This work introduces a novel text-video localization pre-text task to enable fine-grained temporal and semantic alignment such that the trained model can accurately perceive temporal boundaries in videos given the text description. Specifically, text-video localization consists of moment retrieval, which predicts start and end boundaries in videos given the text description, and text localization which matches the subset of texts with the video features. To produce temporal boundaries, frame features in several videos are manually merged into a long video sequence that interacts with a text sequence. With the localization task, our method connects the fine-grained frame representations with the word representations and implicitly distinguishes representations of different instances in the single modality. Notably, comprehensive experimental results show that our method significantly improves the state-of-the-art performance on various benchmarks, covering text-to-video retrieval, video question answering, video captioning, temporal action localization and temporal moment retrieval. The code will be released soon.
KILDST: Effective Knowledge-Integrated Learning for Dialogue State Tracking using Gazetteer and Speaker Information

Dialogue State Tracking (DST) is core research in dialogue systems and has received much attention. In addition, it is necessary to define a new problem that can deal with dialogue between users as a step toward the conversational AI that extracts and recommends information from the dialogue between users. So, we introduce a new task - DST from dialogue between users about scheduling an event (DST-USERS). The DST-USERS task is much more challenging since it requires the model to understand and track dialogue states in the dialogue between users, as well as to understand who suggested the schedule and who agreed to the proposed schedule. To facilitate DST-USERS research, we develop dialogue datasets between users that plan a schedule. The annotated slot values which need to be extracted in the dialogue are date, time, and location. Previous approaches, such as Machine Reading Comprehension (MRC) and traditional DST techniques, have not achieved good results in our extensive evaluations. By adopting the knowledge-integrated learning method, we achieve exceptional results. The proposed model architecture combines gazetteer features and speaker information efficiently. Our evaluations of the dialogue datasets between users that plan a schedule show that our model outperforms the baseline model.
Effective End-to-End Vision Language Pretraining With Semantic Visual Loss

Current vision language pretraining models are dominated by methods using region visual features extracted from object detectors. Given their good performance, the extract-then-process pipeline significantly restricts the inference speed and therefore limits their real-world use cases. However, training vision language models from raw image pixels is difficult, as the raw image pixels give much less prior knowledge than region features. In this paper, we systematically study how to leverage auxiliary visual pretraining tasks to help training end-to-end vision language models. We introduce three types of visual losses that enable much faster convergence and better finetuning accuracy. Compared with region feature models, our end-to-end models could achieve similar or better performance on down-stream tasks and run more than 10 times faster during inference. Compared with other end-to-end models, our proposed method could achieve similar or better performance when pretrained for only 10% of the pretraining GPU hours.
MultiCalib4DEB: A toolbox exploiting multimodal optimisation in Dynamic Energy Budget parameters calibration

Calibration is a crucial step for the validation of computational models and a challenging task to accomplish. Dynamic Energy Budget (DEB) theory has experienced an exponential rise in the number of published papers, which in large part has been made possible by the DEBtool toolbox. Multimodal evolutionary optimisation could provide DEBtool with new capabilities, particularly relevant on the provisioning of equally optimal and diverse solutions. In this paper we present MultiCalib4DEB, a MATLAB toolbox directly integrated into the existing DEBtool toolbox, which uses multimodal evolutionary optimisation algorithms to find multiple global and local optimal and diverse calibration solutions for DEB models. MultiCalib4DEB adds powerful calibration mechanisms, statistical analysis, and visualisation methods to the DEBtool toolbox and provides a wide range of outputs, different calibration alternatives, and specific tools to strengthen the DEBtool calibration module and to aid DEBtool users to evaluate the performance of the calibration results.
The Neon Gap: Probing Ionization with Dwarf Galaxies at z ∼ 1

We present measurements of [Neiii]λ3869 emission in z ∼ 1 low-mass galaxies taken from the Keck/DEIMOS spectroscopic surveys HALO7D and DEEPwinds. We identify 167 individual galaxies with significant [Neiii] emission lines, including 112 “dwarf” galaxies with log(M⋆/M⊙)<9.5 , with 0.3 < z < 1.4. We also measure [Neiii] emission from composite spectra derived from all [Oii]λλ3727,3729 line emitters in this range. This provides a unique sample of [Neiii] emitters between well-studied emitters at z = 0 and 2 < z < 3. To study evolution in ionization conditions in the interstellar medium (ISM) over this time, we analyze the log([Neiii]λ3869/[Oii]λλ3727,3729) ratio (Ne3O2) as a function of the stellar mass and of the log([Oiii]λλ4959,5007/[Oii]λλ3727,3729) ratio (O32). We find that the typical star-forming dwarf galaxy at this redshift, as measured from the composite spectra, shares the Ne3O2–M ⋆ relation with local galaxies but has higher O32 at a given Ne3O2. This finding implies that the ionization and metallicity characteristics of the z ∼ 1 dwarf population do not evolve substantially from z ∼ 1 to z = 0, suggesting that the known evolution in those parameters from z ∼ 2 has largely taken place by z ∼ 1. Individual [Neiii]-detected galaxies have emission characteristics situated between local and z ∼ 2 galaxies, with elevated Ne3O2 and O32 emission potentially explained by variations in stellar and nebular metallicity. We also compare our dwarf sample to similarly low-mass z > 7 galaxies identified in JWST Early Release Observations, finding four HALO7D dwarfs with similar size, metallicity, and star formation properties.
Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant

Semi-Supervised Semantic Segmentation aims at training the segmentation model with limited labeled data and a large amount of unlabeled data. To effectively leverage the unlabeled data, pseudo labeling, along with the teacher-student framework, is widely adopted in semi-supervised semantic segmentation. Though proved to be effective, this paradigm suffers from incorrect pseudo labels which inevitably exist and are taken as auxiliary training data. To alleviate the negative impact of incorrect pseudo labels, we delve into the current Semi-Supervised Semantic Segmentation frameworks. We argue that the unlabeled data with pseudo labels can facilitate the learning of representative features in the feature extractor, but it is unreliable to supervise the mask predictor. Motivated by this consideration, we propose a novel framework, Gentle Teaching Assistant (GTA-Seg) to disentangle the effects of pseudo labels on feature extractor and mask predictor of the student model. Specifically, in addition to the original teacher-student framework, our method introduces a teaching assistant network which directly learns from pseudo labels generated by the teacher network. The gentle teaching assistant (GTA) is coined gentle since it only transfers the beneficial feature representation knowledge in the feature extractor to the student model in an Exponential Moving Average (EMA) manner, protecting the student model from the negative influences caused by unreliable pseudo labels in the mask predictor. The student model is also supervised by reliable labeled data to train an accurate mask predictor, further facilitating feature representation. Extensive experiment results on benchmark datasets validate that our method shows competitive performance against previous methods. Code is available at https://github.com/Jin-Ying/GTA-Seg.
HMDO: Markerless Multi-view Hand Manipulation Capture with Deformable Objects

We construct the first markerless deformable interaction dataset recording interactive motions of the hands and deformable objects, called HMDO (Hand Manipulation with Deformable Objects). With our built multi-view capture system, it captures the deformable interactions with multiple perspectives, various object shapes, and diverse interactive forms. Our motivation is the current lack of hand and deformable object interaction datasets, as 3D hand and deformable object reconstruction is challenging. Mainly due to mutual occlusion, the interaction area is difficult to observe, the visual features between the hand and the object are entangled, and the reconstruction of the interaction area deformation is difficult. To tackle this challenge, we propose a method to annotate our captured data. Our key idea is to collaborate with estimated hand features to guide the object global pose estimation, and then optimize the deformation process of the object by analyzing the relationship between the hand and the object. Through comprehensive evaluation, the proposed method can reconstruct interactive motions of hands and deformable objects with high quality. HMDO currently consists of 21600 frames over 12 sequences. In the future, this dataset could boost the research of learning-based reconstruction of deformable interaction scenes.