Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure from motion using optical flow probability distributions

Structure from motion is a technique that attempts to reconstruct the 3D structure of a scene from a sequence of images taken from a camera moving within the scene. Structure from motion can be used on an Unmanned Aerial Vehicle or Unmanned Ground Vehicle for obstacle detection as well as for path-planning and navigation. The 3D structure of the scene is estimated using the optical flow values found at a set of feature points on the image. Typically, this is done under the assumption that all of the optical flow values have the same level of accuracy and that the accuracy of each optical flow value in the horizontal and vertical directions is the same. These assumptions are not entirely correct. The accuracy of different optical flow values are not the same and the accuracy can be measured and quantified using an optical flow probability distribution. We will present a novel structure from motion algorithm that is more accurate and more robust than other methods because it uses optical flow probability distributions. We will present one method that is designed to work on only two frames and another method designed to work on any number of frames.
Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing

contains a few good case studies directly related to quality engineering, many of the examples have nothing to do with industry (e.g., measurements on the shapes and sizes of painted turtle carapaces, weight of middle-aged men in fitness clubs, diagnosis of liver disease). These examples are used to illustrate the methodologies as they are being developed and make up the bulk of the results given in the book. On the bright side, the case studies are engaging. The data analysis objectives of the case studies are clearly described, and the presentations of results show how multivariate analysis can address the objectives. One could argue that the goal of a book describing application of multivariate methods to specific types of problems should aim to provide reads with a “feel” for how these methods work for their problems. For example, I have spent years encouraging microbiologists to use “industrial statistics,” such as response surface methods or control charts. I have found that once they see indepth case studies within their area of science that address the nuances of their data, they are almost completely on board. In this sense, the authors moderately succeed using their case studies. However, I would suggest that they make the data available to the public, because many people learn by reproducing examples that they study. To enable quality organizations to better use multivariate methods, this text should be supplemented with others. For example, the chapter on discrimination hits the major points for classical multivariate methods; however, a vast array of tools for discrimination currently exist, and the classical methods may not fit the problem well and may be far from optimal. For instance, many on-line measurement systems produce large amounts of data on each sample, such as optical coordinate measurement machines, so there may be more variables than observations. Classical methods cannot handle this situation well (e.g., LDA with more variables than observations). I suggest the text by Hastie, Tibshirani, and Friedman (2001) to supplement this book if more expertise is required for discrimination, clustering, and principal components analysis, and the text by Mason and Young (2001) for a more in-depth resource for multivariate control charting. All in all, the authors meet their intended goals somewhat. This text provides the motivation to use multivariate analysis, but could do a better job providing the means.
On-Demand Solution to Minimize I-Cache Leakage Energy with Maintaining Performance

This paper describes a new on-demand wake-up prediction policy for reducing leakage power. The key insight is that branch prediction can be used to selectively wake up only the needed cache line. This achieves better leakage savings than the best prior policies while avoiding the performance overheads of those policies, without needing an extra prediction structure. The proposed policy reduces leakage energy by 92.7 percent with only 0.08 percent performance overhead on average. The branch-prediction-based approach requires an extra pipeline stage for wake up, which adds to the branch misprediction penalty. Fortunately, this cost is mitigated because the extra wake-up stage is overlapped with misprediction recovery. This paper assumes the superdrowsy leakage control technique using reduced supply voltage because it is well suited to the instruction cache's criticality. However, the proposed policy can be also applied to other leakage-saving circuit techniques.
Gaussian Process Dynamical Models for Human Motion Gaussian Process Dynamical Models for Human Motion

2005 This thesis introduces Gaussian process dynamical models (GPDMs) for nonlinear time series analysis. A GPDM comprises a low-dimensional latent space with associated dynamics , and a map from the latent space to an observation space. We marginalize out the model parameters in closed-form, which leads to modeling both dynamics and observation mappings as Gaussian processes. This results in a nonparametric model for dynamical systems that accounts for uncertainty in the model. We train the model on human motion capture data in which each pose is 62-dimensional, and synthesize new motions by sampling from the posterior distribution. A comparison of forecasting results between different covariance functions and sampling methods is provided, and we demonstrate a simple application of GPDM on filling in missing data. Finally, to account for latent space uncertainty, we explore different priors settings on hyperparameters and show some preliminary GPDM learning results using a Monte Carlo expectation-maximization algorithm. ii Acknowledgements
Architecture and Robust Control of a Digital Frequency-Locked Loop for Fine-Grain Dynamic Voltage and Frequency Scaling in Globally Asynchronous Locally Synchronous Structures

A small area fast-reprogrammable Digital Frequency-Locked Loop (DFLL) engine is presented as a solution for the Dynamic Voltage and Frequency Scaling (DVFS) circuitry in Globally Asynchronous Locally Synchronous (GALS) architectures implemented in 32 nm CMOS technology. The DFLL control is designed so that the closed-loop system is able to cope with process variability while it rejects temperature changes and supply voltage slow variations. Therefore the DFLL is made of three main blocks, namely a Digitally Controlled Oscillator (DCO), a "sensor" that measures the frequency of the signal at the output of the DCO and a controller. A strong emphasis is set on the loop filter architecture choice and the tuning of its parameters. An analytical model of the DCO is deduced from accurate Spice simulations. The delay introduced by the sensor is also taken into account to design. From these models, an optimal and robust controller with a minimum implementation area is developed. Here, "optimal" means that the controller is computed via the minimization of a given criterion while the "robustness" capability ensures that the closed-loop system is tolerant to process and temperature variations in a given range. Therefore, performances of the closed-loop system are ensured whatever the system characteristics are in a given range.
DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution*

Recent research has demonstrated the vulnerability of fingerprint recognition systems to dictionary attacks based on MasterPrints. MasterPrints are real or synthetic fingerprints that can fortuitously match with a large number of fingerprints thereby undermining the security afforded by fingerprint systems. Previous work by Roy et al. generated synthetic MasterPrints at the feature-level. In this work we generate complete image-level MasterPrints known as DeepMasterPrints, whose attack accuracy is found to be much superior than that of previous methods. The proposed method, referred to as Latent Variable Evolution, is based on training a Generative Adversarial Network on a set of real fingerprint images. Stochastic search in the form of the Covariance Matrix Adaptation Evolution Strategy is then used to search for latent input variables to the generator network that can maximize the number of impostor matches as assessed by a fingerprint recognizer. Experiments convey the efficacy of the proposed method in generating DeepMasterPrints. The underlying method is likely to have broad applications in fingerprint security as well as fingerprint synthesis.
Tracking of fresh-water plumes in Dutch coastal waters by means of HF radar

Progress in electronics and computer techniques allowed the design of a new high-frequency (HF) radar system at the University of Hamburg (Germany). WERA (Wellen Radar) is a frequency-modulated continuous-wave (FMCW) radar which, when linked to a linear array of receive antennae, simultaneously measures surface currents and ocean waves. Operating at 27 MHz, the highest spatial resolution is 0.3 km. The area covered in this mode is about 40 km/spl times/40 km. Results from an experiment at the Dutch coast in boreal fall 1996 are presented. Occasionally, a high-velocity jet has been observed which travels northwards along the coast. At the edge of the jet, some 15 km to 30 km off the coast, the high-resolution mode of WERA revealed current shears of up to 0.002 s/sup -1/. It is assumed that the jet carries fresh water plumes which originate from the Rhine and Ijssel outflow into the North Sea. In addition to the surface currents, the spatial distribution of backscatter strength is investigated. Some evidence is found that, during phases of low wind, fresh water plumes become visible as areas of reduced backscatter. This finding is in accordance with the dependence of HF ground-wave attenuation on sea-water conductivity.
Using LLM-assisted Annotation for Corpus Linguistics: A Case Study of Local Grammar Analysis

Chatbots based on Large Language Models (LLMs) have shown strong capabilities in language understanding. In this study, we explore the potential of LLMs in assisting corpus-based linguistic studies through automatic annotation of texts with specific categories of linguistic information. Specifically, we examined to what extent LLMs understand the functional elements constituting the speech act of apology from a local grammar perspective, by comparing the performance of ChatGPT (powered by GPT-3.5), the Bing chatbot (powered by GPT-4), and a human coder in the annotation task. The results demonstrate that the Bing chatbot significantly outperformed ChatGPT in the task. Compared to human annotator, the overall performance of the Bing chatbot was slightly less satisfactory. However, it already achieved high F1 scores: 99.95% for the tag of APOLOGISING, 91.91% for REASON, 95.35% for APOLOGISER, 89.74% for APOLOGISEE, and 96.47% for INTENSIFIER. This suggests that it is feasible to use LLM-assisted annotation for local grammar analysis, together with human intervention on tags that are less accurately recognized by machine. We strongly advocate conducting future studies to evaluate the performance of LLMs in annotating other linguistic phenomena. These studies have the potential to offer valuable insights into the advancement of theories developed in corpus linguistics, as well into the linguistic capabilities of LLMs.
Characterizing Community Changing Users using Text Mining and Graph Machine Learning on Twitter

Even though the Internet and social media have increased the amount of news and information people can consume, most users are only exposed to content that reinforces their positions and isolates them from other ideological communities. This environment has real consequences with great impact on our lives like severe political polarization, easy spread of fake news, political extremism, hate groups and the lack of enriching debates, among others. Therefore, encouraging conversations between different groups of users and breaking the closed community is of importance for healthy societies. In this paper, we characterize and study users who change their community on Twitter using natural language processing techniques and graph machine learning algorithms. In particular, we collected 9 million Twitter messages from 1 . 5 million users and constructed retweet networks. We identified their communities and topics of discussion associated with them. With this data, we present a machine learning framework for social media users classification which detects users that swing from their closed community to another one. A feature importance analysis in three Twitter polarized political datasets showed that these users have low values of PageRank, suggesting that changes in community are driven because their messages have no resonance in their original communities.
Production Optimisation under Uncertainty in Fractured Reservoirs

Uncertainty in the distribution of fractures has a high impact on the fluid flow in oil reservoirs. The challenge is to propagate the uncertainty in the fracture distribution patterns into the reservoir flow response. Optimisation reservoir production under this geological uncertainty would result in to more robust operational decisions to maximise recovery and minimise production costs. Commonly the uncertainty in fracture distribution is described by multiple discrete fracture network realisations (DFN) that represent a range of geologically plausible scenarios. The range of fracture distribution scenarios is captured by spatially varying properties such as facture density distribution, orientation, length etc. Fracture characteristics depend on both geomechanical factors and rock properties, which, therefore, have a high impact on the flow response. The corresponding flow response is also subject to upscaling errors introduced by the choice of the upscaling approach. Therefore, production optimisation (well placements, perforation etc.) becomes a computationally challenging task to perform over a range of possible realisations, modelling choices and upscaling methods required to account for the associated uncertainties. We propose an approach that performs well placement optimisation over a selected sub-set of the reservoir realisations, which would represent the range of uncertainties introduced by geological and upscaling factors. The sub-set of the DFN scenarios is obtained through clustering the exhaustive set of flow response realisations in a flow metric space using a multi-dimensional scaling. The obtained clusters define a limited set of flow scenarios that can be represented by a much smaller number of selected realisations, which still adequately characterise the spread of uncertainty associated with the exhaustive set. Optimisation over a limited set of selected realisations corresponding to the range of the flow response scenarios provides a set of well configurations that maximise oil recovery and minimise the costs (produced water and the number of wells). Optimisation over multiple geological scenarios with respect to the geological uncertainty identifies the most robust development decisions than the one based on the optimisation over a single scenario. Use of multi-objective optimisations provides a greater potential variability of possible solutions, which increases the confidence in the uncertainty prediction.
Towards Effective Tutorial Feedback for Explanation Questions: A Dataset and Baselines

We propose a new shared task on grading student answers with the goal of enabling well-targeted and flexible feedback in a tutorial dialogue setting. We provide an annotated corpus designed for the purpose, a precise specification for a prediction task and an associated evaluation methodology. The task is feasible but non-trivial, which is demonstrated by creating and comparing three alternative baseline systems. We believe that this corpus will be of interest to the researchers working in textual entailment and will stimulate new developments both in natural language processing in tutorial dialogue systems and textual entailment, contradiction detection and other techniques of interest for a variety of computational linguistics tasks.
Caracterización de la vegetación en la Reserva de Biósfera Laguna Blanca (Catamarca, Argentina)

Vegetation characterization of Laguna Blanca (Catamarca, Argentina) Biosphere Reserve: In this work we present the first characterization and classification of plant communities and habitats in a typical area of the Argentinean Dry Puna sub-region, corresponding to Laguna Blanca (Catamarca, Argentina) Biosphere Reserve. A floristic list of 75 plant species was conducted. Six types of environments were characterized based on field work, satellite images using GIS: grass steppe, shrub steppe, mixed steppe, “vegas”, “salt marsh” and “peladares”. Three types of shrub steppes were recognized, based on dominant shrub: “rica-rical” (Acantholippia sp.), “tolillal alto” (Fabiana densa) and “tolillar bajo” (Fabiana spp.). Also three types of grass steppes were defined depending on dominant grass: Stipa spp. , Festuca spp. and Panicum chloroleucum steppes. Two mixed steppes were described, based on grass species present: Stipa spp. and Panicum chloroleucum steppes. This classification was validated with Kappa index using independent control samplings. Four physiographic patterns or sub-landscapes were identified using visual image Recibido: 29 de octubre de 2004; Fin de arbitraje: 5 de octubre de 2005; Revision recibida: 15 de febrero de 2006; Aceptado: 31 de mayo de 2006 Dpto. de Ciencias Basicas, Universidad Nacional de Lujan. Rutas 5 y 7 cc221, B6700ZBA. Lujan, Buenos Aires, Argentina. mborgnia@mail.unlu. edu.ar 30 M BORGNIA ET AL Ecologia Austral 16:29-45 interpretation: low mountains, glacis, alluvial cones, “pedemonte”, and valley. These physiographic patterns were associated with the vegetation description. Diversity and specific richness were calculated. This vegetation characterization and mapping allowed the comparison with other areas of Puna and provided basic information for reserve management. [
FreeAnchor: Learning to Match Anchors for Visual Object Detection

Modern CNN-based object detectors assign anchors for ground-truth objects under the restriction of object-anchor Intersection-over-Unit (IoU). In this study, we propose a learning-to-match approach to break IoU restriction, allowing objects to match anchors in a flexible manner. Our approach, referred to as FreeAnchor, updates hand-crafted anchor assignment to "free" anchor matching by formulating detector training as a maximum likelihood estimation (MLE) procedure. FreeAnchor targets at learning features which best explain a class of objects in terms of both classification and localization. FreeAnchor is implemented by optimizing detection customized likelihood and can be fused with CNN-based detectors in a plug-and-play manner. Experiments on MS-COCO demonstrate that FreeAnchor consistently outperforms the counterparts with significant margins.
Unsupervised Hashing via Similarity Distribution Calibration

Existing unsupervised hashing methods typically adopt a feature similarity preservation paradigm. As a result, they overlook the intrinsic similarity capacity discrepancy between the continuous feature and discrete hash code spaces. Speciﬁcally, since the feature similarity distribution is intrinsically biased ( e.g ., moderately positive similarity scores on negative pairs), the hash code similarities of positive and negative pairs often become inseparable ( i.e ., the similarity collapse problem). To solve this problem, in this paper a novel Similarity Distribution Calibration (SDC) method is introduced. Instead of matching individual pairwise similarity scores, SDC aligns the hash code similarity distribution towards a calibration distribution ( e.g ., beta distribution) with sufﬁcient spread across the entire similarity capacity/range, to alleviate the similarity collapse problem. Extensive experiments show that our SDC outperforms the state-of-the-art alternatives on both coarse category-level and instance-level image retrieval tasks, often by a large margin. Code is available at https://github.com/kamwoh/sdc .
3.3GHz DCO with a frequency resolution of 150Hz for All-digital PLL

In all-digital phase-locked loops (ADPLLs), the quantization noise introduced by the frequency discretization in the digitally controlled oscillator (DCO) can affect the performance in terms of out-of-band phase noise. In particular, the additional quantization noise has to be kept significantly lower than the intrinsic oscillator phase-noise, mandating a very fine frequency resolution (e.g. less than one kHz in GSM) [1]. Typically, in LC oscillators, the digital tuning is realized using two (or more) capacitor banks for coarse and fine tuning. The first bank is used to compensate process and temperature variation and to select the channel while the second is required for the DCO modulation inside the PLL. Since the coarse tuning range can be several hundred MHz (e.g. 800MHz in GSM [1]), a frequency resolution in the range of kHz can result in unitary elements for the capacitor banks of the order of atto-Farads. Although such values can be achieved by means of capacitive divider networks [2], the sensitivity to mismatches and parasitics of these solutions limit the robustness of the design. Staszewski et al. solved this problem by introducing a dithering of the 3 less significant bits of the DCO frequency control word [1]. This approach reduces considerably the equivalent DCO frequency resolution (from 12kHz to 30Hz) but, as occurs in any ΔΣ data converter, the quantization noise is moved to higher frequencies where generally the phase-noise specifications are more challenging. Due to this problem, the frequency of dithering must be significantly increased (225MHz) to satisfy the emission mask requirements far away from the carrier [1].