Miracle-dz/UniFAR-FTUs huggingface dataset:
Trajectory Optimization and NMPC Tracking for a Fixed–Wing UAV in Deep Stall with Perch Landing*

This paper presents a novel recovery technique for a fixed-wing UAV (Unmanned Aerial Vehicle) based on constrained optimization: i) we propose a trajectory generation for landing the UAV where it first reduces its altitude by deep stalling, then perches on a recovery net, ii) we design an NMPC (Nonlinear Model Predictive Control) tracking controller with terminal constraints for the optimal generated trajectory under disturbances. Compared to nominal net recovery procedures, this technique greatly reduces the landing time and the final airspeed of the UAV. Simulation results for various wind conditions demonstrate the feasibility of the idea.
Concrete one complex dimensional moduli spaces of hyperbolic manifolds and orbifolds

The Riley slice is arguably the simplest example of a moduli space of Kleinian groups; it is naturally embedded in C , and has a natural coordinate system (introduced by Linda Keen and Caroline Series in the early 1990s) which reﬂects the geometry of the underlying 3-manifold deformations. The Riley slice arises in the study of arithmetic Kleinian groups, the theory of two-bridge knots, the theory of Schottky groups, and the theory of hyperbolic 3-manifolds; because of its simplicity it provides an easy source of examples and deep questions related to these subjects. We give an introduction for the non-expert to the Riley slice and much of the related background material, assuming only graduate level complex analysis and topology; we review the history of and literature surrounding the Riley slice; and we announce some results of our own, extending the work of Keen and Series to the one complex dimensional moduli spaces of Kleinian groups isomorphic to Z p ∗ Z q acting on the Riemann sphere, 2 ≤ p , q ≤ ∞ . The Riley slice is the case p = q = ∞ (i.e. two parabolic generators). Informally, in Teichm¨uller space, we pay attention not just to what metric a surface is wear- ing, but also to how it is worn. In moduli space, all surfaces wearing the same metric are equivalent. The importance of the distinction will be clear to anybody who, after putting a pajama suit on an infant, has found one leg to be twisted.
SegDiscover: Visual Concept Discovery via Unsupervised Semantic Segmentation

Visual concept discovery has long been deemed important to improve interpretability of neural networks, because a bank of semantically meaningful concepts would provide us with a starting point for building machine learning models that exhibit intelligible reasoning process. Previous methods have disadvantages: either they rely on labelled support sets that incorporate human biases for objects that are"useful,"or they fail to identify multiple concepts that occur within a single image. We reframe the concept discovery task as an unsupervised semantic segmentation problem, and present SegDiscover, a novel framework that discovers semantically meaningful visual concepts from imagery datasets with complex scenes without supervision. Our method contains three important pieces: generating concept primitives from raw images, discovering concepts by clustering in the latent space of a self-supervised pretrained encoder, and concept refinement via neural network smoothing. Experimental results provide evidence that our method can discover multiple concepts within a single image and outperforms state-of-the-art unsupervised methods on complex datasets such as Cityscapes and COCO-Stuff. Our method can be further used as a neural network explanation tool by comparing results obtained by different encoders.
Fiber-optic multimode interference sensing: comprehensive characterization and its potential for strain-insensitive temperature sensing

A strain-insensitive temperature sensor based on multimode interference using standard multimode ﬁbers (MMFs) is proposed according to the comprehensive study of the characteristics of the MMFs. The temperature and strain dependences on the core diameter, numerical aperture (NA), and the length of the MMF section in the single-mode–multimode– single-mode (SMS) ﬁber structure are investigated experimentally. The results indicate that the larger core diameter of the MMF leads to higher temperature sensitivity but lower strain sensitivity (absolute values); the higher NA does not inﬂuence the temperature sensitivity but results in higher absolute value of strain sensitivity; the longer MMF section brings lower temperature sensitivity but does not have an impact on strain sensitivity. These ﬁnd-ings also contribute to the theoretical analysis of the length dependence in the SMS ﬁber sensors. Besides, the results of the characterization study show that the strain sensitivity is relatively low, which brings a possibility to develop a strain-insensitive temperature sensor. The proposed sensor is used for temperature sensing while the strain is constantly applied from 0 to 1100 µε with steps of 100 µε . The measured results are consistent with the comprehensive study. The mean temperature sensitivity is 6.14 pm/ ◦ C with a standard deviation of 0.39 pm/ ◦ C, which proves that the proposed temperature sensor exhibits good stability and is insensitive to strain. We expect that these results will provide a profound guideline to ﬁber sensors based on multimode interference.
Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction

Speech emotion recognition systems have high prediction latency because of the high computational requirements for deep learning models and low generalizability mainly because of the poor reliability of emotional measurements across multiple corpora. To solve these problems, we present a speech emotion recognition system based on a reductionist approach of decomposing and analyzing syllable-level features. Mel-spectrogram of an audio stream is decomposed into syllable-level components, which are then analyzed to extract statistical features. The proposed method uses formant attention, noise-gate filtering, and rolling normalization contexts to increase feature processing speed and tolerance to adversity. A set of syllable-level formant features is extracted and fed into a single hidden layer neural network that makes predictions for each syllable as opposed to the conventional approach of using a sophisticated deep learner to make sentence-wide predictions. The syllable level predictions help to achieve the real-time latency and lower the aggregated error in utterance level cross-corpus predictions. The experiments on IEMOCAP (IE), MSP-Improv (MI), and RAVDESS (RA) databases show that the method archives real-time latency while predicting with state-of-the-art cross-corpus unweighted accuracy of 47.6% for IE to MI and 56.2% for MI to IE.
A Multi -level Alignment Training Scheme for Video-and-Language Grounding

To solve video-and-Ianguage grounding tasks, the key is for the network to understand the connection between the two modalities. For a pair of video and language description, their semantic relation is reflected by their encodings ‘ similarity. A good multi-modality encoder should be able to well capture both inputs' semantics and encode them in the shared feature space where embedding distance gets properly translated into their semantic similarity. In this work, we focused on this semantic connection between video and language, and developed a multi- level alignment training scheme to directly shape the encoding process. Global and segment levels of video-language alignment pairs were designed, based on the information similarity ranging from high-level context to fine-grained semantics. The contrastive loss was used to contrast the encodings' similarities between the positive and negative alignment pairs, and to ensure the network is trained in such a way that similar information is encoded closely in the shared feature space while information of different semantics is kept apart. Our multi-level alignment training can be applied to various video-and-Ianguage grounding tasks. Together with the task-specific training loss, our framework achieved comparable performance as previous state-of-the-arts on multiple video QA and retrieval datasets.
Aspect-based Analysis of Advertising Appeals for Search Engine Advertising

Writing an ad text that attracts people and persuades them to click or act is essential for the success of search engine advertising. Therefore, ad creators must consider various aspects of advertising appeals (A^3) such as the price, product features, and quality. However, products and services exhibit unique effective A^3 for different industries. In this work, we focus on exploring the effective A^3 for different industries with the aim of assisting the ad creation process. To this end, we created a dataset of advertising appeals and used an existing model that detects various aspects for ad texts. Our experiments demonstrated %through correlation analysis that different industries have their own effective A^3 and that the identification of the A^3 contributes to the estimation of advertising performance.
Towards Data-Free Model Stealing in a Hard Label Setting

Machine learning models deployed as a service (MLaaS) are susceptible to model stealing attacks, where an adversary attempts to steal the model within a restricted access framework. While existing attacks demonstrate near-perfect clone-model performance using softmax predictions of the classification network, most of the APIs allow access to only the top-1 labels. In this work, we show that it is indeed possible to steal Machine Learning models by accessing only top-1 predictions (Hard Label setting) as well, without access to model gradients (Black-Box setting) or even the training dataset (Data-Free setting) within a low query budget. We propose a novel GAN-based framework11Project Page: https://sites.google. com/view/dfms-hl that trains the student and generator in tandem to steal the model effectively while overcoming the challenge of the hard label setting by utilizing gradients of the clone network as a proxy to the victim's gradients. We propose to overcome the large query costs associated with a typical Data-Free setting by utilizing publicly available (potentially unrelated) datasets as a weak image prior. We additionally show that even in the absence of such data, it is possible to achieve state-of-the-art results within a low query budget using synthetically crafted samples. We are the first to demonstrate the scalability of Model Stealing in a restricted access setting on a 100 class dataset as well.
Terahertz waveform synthesis from integrated lithium niobate circuits

Bridging the “terahertz (THz) gap” relies upon synthesizing arbitrary waveforms in the THz domain enabling applications that require both narrow band sources for sensing and few-cycle drives for classical and quantum objects. However, realization of custom-tailored waveforms needed for these applications is currently hindered due to limited ﬂexibility for optical rectiﬁcation of femtosecond pulses in bulk crystals. Here, we experimentally demonstrate that thin-ﬁlm lithium niobate (TFLN) circuits provide a versatile solution for such waveform synthesis through combining the merits of complex integrated architectures, low-loss distribution of pump pulses on-chip, and an eﬃcient optical rectiﬁcation. Our distributed pulse phase-matching scheme grants shaping the temporal, spectral, phase, amplitude, and farﬁeld characteristics of the emitted THz ﬁeld through designer on-chip components. This strictly circumvents prior limitations caused by the phase-delay mismatch in conventional systems and relaxes the requirement for cumbersome spectral pre-engineering of the pumping light. We provide a toolbox of basic blocks that produce broadband emission up to 680 GHz with adaptable phase and coherence properties by using near-infrared pump pulse energies below 100 pJ.
Time Series Forecasting (TSF) Using Various Deep Learning Models

— Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed length window in the past as an explicit input. In this paper, we study how the performance of predictive models change as a function of different look-back window sizes and different amounts of time to predict into the future. We also consider the performance of the recent attention-based Transformer models, which has had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website , which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.
LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback

Pseudo-relevance feedback (PRF) has proven to be an effective query reformulation technique to improve retrieval accuracy. It aims to alleviate the mismatch of linguistic expressions between a query and its potential relevant documents. Existing PRF methods independently treat revised queries originating from the same query but using different numbers of feedback documents, resulting in severe query drift. Without comparing the effects of two different revisions from the same query, a PRF model may incorrectly focus on the additional irrelevant information increased in the more feedback, and thus reformulate a query that is less effective than the revision using the less feedback. Ideally, if a PRF model can distinguish between irrelevant and relevant information in the feedback, the more feedback documents there are, the better the revised query will be. To bridge this gap, we propose the Loss-over-Loss (LoL) framework to compare the reformulation losses between different revisions of the same query during training. Concretely, we revise an original query multiple times in parallel using different amounts of feedback and compute their reformulation losses. Then, we introduce an additional regularization loss on these reformulation losses to penalize revisions that use more feedback but gain larger losses. With such comparative regularization, the PRF model is expected to learn to suppress the extra increased irrelevant information by comparing the effects of different revised queries. Further, we present a differentiable query reformulation method to implement this framework. This method revises queries in the vector space and directly optimizes the retrieval performance of query vectors, applicable for both sparse and dense retrieval models. Empirical evaluation demonstrates the effectiveness and robustness of our method for two typical sparse and dense retrieval models.
Bi-Objective Optimization of Information Rate and Harvested Power in RIS-Aided SWIPT Systems

The problem of simultaneously optimizing the information rate and the harvested power in a reconfigurable intelligent surface (RIS)-aided multiple-input single-output downlink multiuser wireless network with simultaneous wireless information and power transfer (SWIPT) is addressed. The beamforming vectors, RIS reflection coefficients, and power split ratios are jointly optimized subject to maximum power constraints, minimum harvested power constraints, and realistic constraints on the RIS reflection coefficients. A practical algorithm is developed through an interplay of alternating optimization, sequential optimization, and pricing-based methods. Numerical results show that the deployment of RISs jointly improves the information rate and the amount of harvested power.
Forecasting Electricity Prices

Forecasting electricity prices is a challenging task and an active area of research since the 1990s and the deregulation of the traditionally monopolistic and government-controlled power sectors. Although it aims at predicting both spot and forward prices, the vast majority of research is focused on short-term horizons which exhibit dynamics unlike in any other market. The reason is that power system stability calls for a constant balance between production and consumption, while being weather (both demand and supply) and business activity (demand only) dependent. The recent market innovations do not help in this respect. The rapid expansion of intermittent renewable energy sources is not offset by the costly increase of electricity storage capacities and modernization of the grid infrastructure. On the methodological side, this leads to three visible trends in electricity price forecasting research as of 2022. Firstly, there is a slow, but more noticeable with every year, tendency to consider not only point but also probabilistic (interval, density) or even path (also called ensemble) forecasts. Secondly, there is a clear shift from the relatively parsimonious econometric (or statistical) models towards more complex and harder to comprehend, but more versatile and eventually more accurate statistical/machine learning approaches. Thirdly, statistical error measures are nowadays regarded as only the first evaluation step. Since they may not necessarily reflect the economic value of reducing prediction errors, more and more often, they are complemented by case studies comparing profits from scheduling or trading strategies based on price forecasts obtained from different models.
Localisation of perturbations of a constant state in a traffic flow model

We consider, in the Aw-Rascle-Zhang traffic flow model, the problem of the asymptotic stability of constant flows. By using a perturbative approach, we show the stability in a larger space of perturbation than previous results. Furthermore, we are able to compute where the perturbation is mainly localised in space for a given time, based on the localisation of the perturbation initially. These new ideas can be applied to various other models of hyperbolic conservation laws with relaxations.
Progressive Learning for Image Retrieval with Hybrid-Modality Queries

Image retrieval with hybrid-modality queries, also known as composing text and image for image retrieval (CTI-IR), is a retrieval task where the search intention is expressed in a more complex query format, involving both vision and text modalities. For example, a target product image is searched using a reference product image along with text about changing certain attributes of the reference image as the query. It is a more challenging image retrieval task that requires both semantic space learning and cross-modal fusion. Previous approaches that attempt to deal with both aspects achieve unsatisfactory performance. In this paper, we decompose the CTI-IR task into a three-stage learning problem to progressively learn the complex knowledge for image retrieval with hybrid-modality queries. We first leverage the semantic embedding space for open-domain image-text retrieval, and then transfer the learned knowledge to the fashion-domain with fashion-related pre-training tasks. Finally, we enhance the pre-trained model from single-query to hybrid-modality query for the CTI-IR task. Furthermore, as the contribution of individual modality in the hybrid-modality query varies for different retrieval scenarios, we propose a self-supervised adaptive weighting strategy to dynamically determine the importance of image and text in the hybrid-modality query for better retrieval. Extensive experiments show that our proposed model significantly outperforms state-of-the-art methods in the mean of Recall@K by 24.9% and 9.5% on the Fashion-IQ and Shoes benchmark datasets respectively.
The sound of the udu

The udu is a traditional Nigerian percussion instrument that can be thought of as a Helmholtz resonator with two apertures. We use a simple two-particle mechanical analogue to study the behaviour of such a resonator and find that the model describes quite well the main features of udu acoustics.
Neural Annealing and Visualization of Autoregressive Neural Networks in the Newman–Moore Model

Artificial neural networks have been widely adopted as ansatzes to study classical and quantum systems. However, for some notably hard systems, such as those exhibiting glassiness and frustration, they have mainly achieved unsatisfactory results, despite their representational power and entanglement content, thus suggesting a potential conservation of computational complexity in the learning process. We explore this possibility by implementing the neural annealing method with autoregressive neural networks on a model that exhibits glassy and fractal dynamics: the two-dimensional Newman–Moore model on a triangular lattice. We find that the annealing dynamics is globally unstable because of highly chaotic loss landscapes. Furthermore, even when the correct ground-state energy is found, the neural network generally cannot find degenerate ground-state configurations due to mode collapse. These findings indicate that the glassy dynamics exhibited by the Newman–Moore model caused by the presence of fracton excitations in the configurational space likely manifests itself through trainability issues and mode collapse in the optimization landscape.
Development of a dexterity assessment method

This research was aimed at contributing to the understanding of dexterity through a scientific analysis of some of its more important features, as well as the development of viable methods to quantify these characteristics. The methods and procedures developed during this research were applied to evaluate traditional dexterity assessment methods, comparing dexterity features among tests and hand daily living tasks in order to characterise their reliability and robustness. The study makes use of visual, mathematical, and experimental methods to obtain, process, and analyse a series of hand function parameters that account for some of the main features that affect dexterity in modern daily living. Furthermore, the designed methods and analysis techniques provide fundamental insights into our understanding of the relationships between motor coordination, movement, and hand function. More importantly, the data and conclusions derived from this research have the potential to aid in the development of improved health care practice, assistive technologies, and quality of life research, by providing practitioners and researchers with updated knowledge on human movement analysis, hand function, and dexterity. The overall conclusion of this research is that the broad range of movements and patterns of the human hand, along with the infinite number of possible coordination strategies result in the need for the identification of movement patterns in order to accurately assess dexterity and hand function. Furthermore, although timed tests are time-efficient and cost-effective methods to measure dexterity, a truly objective and robust measurement of dexterity most cover all the factors and parameters that play a role in this phenomenon.