Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Unsupervised Learning Strategy for Monitoring Industrial Batch Processes

Industrial production processes, especially in the pharmaceutical industry, are complex systems that require continuous monitoring to ensure efficiency, product quality, and safety. This paper presents a hybrid unsupervised learning strategy (HULS) for monitoring complex industrial processes. Addressing the limitations of traditional Self-Organizing Maps (SOMs), especially in scenarios with unbalanced data sets and highly correlated process variables, HULS combines existing unsupervised learning techniques to address these challenges. To evaluate the performance of the HULS concept, comparative experiments are performed based on a laboratory batch
An Efficient Time-frequency Algorithm for Weak Signal Acquisition of Modernized GNSS Signals

The new GPS/Galileo/Compass signals use much longer code lengths and higher code frequencies than the GPS L1 C/A signal. As a result, they can provide improved ranging and anti-jamming performances. Unfortunately, their acquisition complexity is significantly higher, especially in the high sensitivity or weak signal acquisition context. Acquisition of very weak signals requires long integration times, but the integration time is often limited because of the code phase shifting induced in the receiver by an unmatched code frequency. Therefore, for the purpose of minimizing the acquisition time, a new algorithm that we named “slope parallel frequency and code searching (SPFCS)”, has been developed and is presented in this paper. This algorithm combines the parallel frequency searching and parallel code phase searching together in a novel way, and satisfies the requirements for providing standalone high sensitivity (i.e., without any network or additional sensors’ assistance). In order to assess its performance, we consider in this paper the acquisition of the GPS L5 pilot signal. Our simulation results indicate that the proposed algorithm can successfully acquire, without assistance, L5 signals as low as 15 dB-Hz with 35 kHz Doppler shifts, and a computational burden over 2 to 4 times smaller at 15 dB-Hz and 18 dB-Hz, respectively, as compared to the traditional parallel code phase search (PCS) or double block zero padding (DBZP) methods.
A method for grasp evaluation based on disturbance force rejection

This paper presents a method for grasp evaluation. It is based on the ability of the grasp to reject disturbance forces. The procedure takes the geometry of object into account, and it is also possible to incorporate task-oriented information. The evaluation criterion is formulated as a min-max optimization problem, for which an efficient algorithm is proposed and analyzed. The result of this algorithm is independent of scale and choice of reference frame, and can easily be visualized as a surface in the force space. The method is illustrated with several examples.
A multilevel framework for accelerating uSARA in radio-interferometric imaging

This paper presents a multilevel algorithm specifically designed for radio-interferometric imaging in astronomy. The proposed algorithm is used to solve the uSARA (unconstrained Sparsity Averaging Reweighting Analysis) formulation of this image restoration problem. Multilevel algorithms rely on a hierarchy of approximations of the objective function to accelerate its optimization. In contrast to the usual multilevel approaches where this hierarchy is derived in the parameter space, here we construct the hierarchy of approximations in the observation space. The proposed approach is compared to a reweighted forward-backward procedure, which is the backbone iteration scheme for solving the uSARA problem.
The Middle East context:an introduction

This handbook provides an indepth input on a variety of topics related to human resource management (HRM) in the Middle East. It is divided into three parts: contextual and functional issues, country and regional perspectives, and emerging themes and the future of HRM in the Middle East. This introductory chapter has three main aims. First, to highlight the dynamic context of Middle East within which this handbook has been developed. Second, to analyse the key developments in the field of HRM in the region and the need for this volume. And third, to provide a summary of each chapter.
Knowing Your Nonlinearities: Shapley Interactions Reveal the Underlying Structure of Data

Measuring nonlinear feature interaction is an established approach to understanding complex patterns of attribution in many models. In this paper, we use Shapley Taylor interaction indices (STII) to analyze the impact of underlying data structure on model representations in a variety of modalities, tasks, and architectures. Considering linguistic structure in masked and auto-regressive language models (MLMs and ALMs), we find that STII increases within idiomatic expressions and that MLMs scale STII with syntactic distance, relying more on syntax in their nonlinear structure than ALMs do. Our speech model findings reflect the phonetic principal that the openness of the oral cavity determines how much a phoneme varies based on its context. Finally, we study image classifiers and illustrate that feature interactions intuitively reflect object boundaries. Our wide range of results illustrates the benefits of interdisciplinary work and domain expertise in interpretability research.
Different Tokenization Schemes Lead to Comparable Performance in Spanish Number Agreement

The relationship between language model tokenization and performance is an open area of research. Here, we investigate how different tokenization schemes impact number agreement in Spanish plurals. We find that morphologically-aligned tokenization performs similarly to other tokenization schemes, even when induced artificially for words that would not be tokenized that way during training. We then present exploratory analyses demonstrating that language model embeddings for different plural tokenizations have similar distributions along the embedding space axis that maximally distinguishes singular and plural nouns. Our results suggest that morphologically-aligned tokenization is a viable tokenization approach, and existing models already generalize some morphological patterns to new items. However, our results indicate that morphological tokenization is not strictly required for performance.
LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions

Parametric kernel methods currently dominate the literature regarding the construction of animal home ranges (HRs) and utilization distributions (UDs). These methods frequently fail to capture the kinds of hard boundaries common to many natural systems. Recently a local convex hull (LoCoH) nonparametric kernel method, which generalizes the minimum convex polygon (MCP) method, was shown to be more appropriate than parametric kernel methods for constructing HRs and UDs, because of its ability to identify hard boundaries (e.g., rivers, cliff edges) and convergence to the true distribution as sample size increases. Here we extend the LoCoH in two ways: “fixed sphere-of-influence,” or r-LoCoH (kernels constructed from all points within a fixed radius r of each reference point), and an “adaptive sphere-of-influence,” or a-LoCoH (kernels constructed from all points within a radius a such that the distances of all points within the radius to the reference point sum to a value less than or equal to a), and compare them to the original “fixed-number-of-points,” or k-LoCoH (all kernels constructed from k-1 nearest neighbors of root points). We also compare these nonparametric LoCoH to parametric kernel methods using manufactured data and data collected from GPS collars on African buffalo in the Kruger National Park, South Africa. Our results demonstrate that LoCoH methods are superior to parametric kernel methods in estimating areas used by animals, excluding unused areas (holes) and, generally, in constructing UDs and HRs arising from the movement of animals influenced by hard boundaries and irregular structures (e.g., rocky outcrops). We also demonstrate that a-LoCoH is generally superior to k- and r-LoCoH (with software for all three methods available at http://locoh.cnr.berkeley.edu).
The automated design of believable dialogues for animated presentation teams

During the last few years, an increasing number of attempts have been made to develop systems that are able to generate presentations automatically. The aim to generate presentation variants on the fly to accommodate for different purposes and user needs is a driving force behind these developments. The availability of multiple media offers the choice between media as well as the possibility to compose media combinations that convey information in a more intelligible and appealing way. The output of such presentation systems comprises mere replications of illustrated texts laid out on a computer screen, but also multimedia documents with timed media, such as video and audio clips, and hypertext-style documents. Trying to deploy the repertoire of skills of human presenters, some R&D projects have begun to use animated characters (or agents) in presentation tasks. Based on either cartoon drawings, recorded video images of persons, or 3-D body models, presentation agents enrich the repertoire of available presentation styles. For instance, consider the presentation of research results at a conference. Even though a slide show, a video clip or a poster may contain all the relevant information, the presence of a skilled speaker in addition to well prepared presentation material is usually much more appealing. A potential strength of animated characters is their ability to convey nonverbal conversational signals that are difficult to communicate in traditional media. In this paper, we investigate a new style for presenting information. We introduce the notion of presentation teams which—rather than addressing the user directly—convey information in the style of performances to be observed by the user. The paper is organized as follows. First, we report on our experience with two single animated presentation agents and explain how to evaluate their success. After that, we move to presentation teams and discuss their potential benefits for presentation tasks. In section 2, we describe the basic steps of our approach to the automated generation of performances with multiple characters. This approach has been applied to two different
Accelerated quantum search using partial oracles and Grover's algorithm

Grover's algorithm, orginally conceived as a means of searching an unordered database, can also be used as a technique for extracting solutions from the result sets generated by quantum computations. The algorithm exploits the concept of an oracle function, which abstracts the process of matching a search item (returning 1 for a match and 0 otherwise). This black-box approach hides the details of a search problem and works with the assumption that the items in the search space are completely unordered. In this case, searching for 1 target item in a search space of size $N$ scales as $\mathcal{O}(\sqrt{N})$ oracle queries (or $\mathcal{O}(\sqrt{N/M})$ oracle queries for $M$ target items in a search space of size $N$). Hidden inside the typical black-box oracle, however, the process of matching an item usually involves matching multiple data bits. In this article, we explore the idea of associating a separate oracle with each bit of the matching condition, obtaining multiple partial oracle functions which can be tested independently. Exploring this idea leads to a multi-stage hybrid search algorithm, whose performance can fall within a wide range, anywhere between $\mathcal{O}(\sqrt{N})$ (same as Grover) or $\mathcal{O}(\log(N))$ (exponentially faster). Apparently, the search acceleration works by dynamically discovering order in the search space, where the order consists of correlations between the partial oracle functions and the search index. This new algorithm is validated against the simplest kind of search scenario.
FACT: Fast and Active Coordinate Initialization for Vision-based Drone Swarms

Swarm robots have sparked remarkable developments across a range of fields. While it is necessary for various applications in swarm robots, a fast and robust coordinate initialization in vision-based drone swarms remains elusive. To this end, our paper proposes a complete system to recover a swarm's initial relative pose on platforms with size, weight, and power (SWaP) constraints. To overcome limited coverage of field-of-view (FoV), the drones rotate in place to obtain observations. To tackle the anonymous measurements, we formulate a non-convex rotation estimation problem and transform it into a semi-definite programming (SDP) problem, which can steadily obtain global optimal values. Then we utilize the Hungarian algorithm to recover relative translation and correspondences between observations and drone identities. To safely acquire complete observations, we actively search for positions and generate feasible trajectories to avoid collisions. To validate the practicability of our system, we conduct experiments on a vision-based drone swarm with only stereo cameras and inertial measurement units (IMUs) as sensors. The results demonstrate that the system can robustly get accurate relative poses in real time with limited onboard computation resources. The source code is released.
Tackling the Singularities at the Endpoints of Time Intervals in Diffusion Models

Most diffusion models assume that the reverse process adheres to a Gaussian distribution. However, this approximation has not been rigorously validated, especially at singularities, where t=0 and t=1. Improperly dealing with such singularities leads to an average brightness issue in applications, and limits the generation of images with extreme brightness or darkness. We primarily focus on tackling singularities from both theoretical and practical perspectives. Initially, we establish the error bounds for the reverse process approximation, and showcase its Gaussian characteristics at singularity time steps. Based on this theoretical insight, we confirm the singularity at t=1 is conditionally removable while it at t=0 is an inherent property. Upon these significant conclusions, we propose a novel plug-and-play method SingDiffusion to address the initial singular time step sampling, which not only effectively resolves the average brightness issue for a wide range of diffusion models without extra training efforts, but also enhances their generation capability in achieving notable lower FID scores.
Weakly supervised segmentation of intracranial aneurysms using a novel 3D focal modulation UNet

Accurate identification and quantification of unruptured intracranial aneurysms (UIAs) is crucial for the risk assessment and treatment of this cerebrovascular disorder. Current 2D manual assessment on 3D magnetic resonance angiography (MRA) is suboptimal and time-consuming. In addition, one major issue in medical image segmentation is the need for large well-annotated data, which can be expensive to obtain. Techniques that mitigate this requirement, such as weakly supervised learning with coarse labels are highly desirable. In the paper, we propose FocalSegNet, a novel 3D focal modulation UNet, to detect an aneurysm and offer an initial, coarse segmentation of it from time-of-flight MRA image patches, which is further refined with a dense conditional random field (CRF) post-processing layer to produce a final segmentation map. We trained and evaluated our model on a public dataset, and in terms of UIA detection, our model showed a low false-positive rate of 0.21 and a high sensitivity of 0.80. For voxel-wise aneurysm segmentation, we achieved a Dice score of 0.68 and a 95% Hausdorff distance of ~0.95 mm, demonstrating its strong performance. We evaluated our algorithms against the state-of-the-art 3D Residual-UNet and Swin-UNETR, and illustrated the superior performance of our proposed FocalSegNet, highlighting the advantages of employing focal modulation for this task.
RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPS

Recent advances in view synthesis and real-time rendering have achieved photorealistic quality at impressive rendering speeds. While Radiance Field-based methods achieve state-of-the-art quality in challenging scenarios such as in-the-wild captures and large-scale scenes, they often suffer from excessively high compute requirements linked to volumetric rendering. Gaussian Splatting-based methods, on the other hand, rely on rasterization and naturally achieve real-time rendering but suffer from brittle optimization heuristics that underperform on more challenging scenes. In this work, we present RadSplat, a lightweight method for robust real-time rendering of complex scenes. Our main contributions are threefold. First, we use radiance fields as a prior and supervision signal for optimizing point-based scene representations, leading to improved quality and more robust optimization. Next, we develop a novel pruning technique reducing the overall point count while maintaining high quality, leading to smaller and more compact scene representations with faster inference speeds. Finally, we propose a novel test-time filtering approach that further accelerates rendering and allows to scale to larger, house-sized scenes. We find that our method enables state-of-the-art synthesis of complex captures at 900+ FPS.
DeblurDiNAT: A Lightweight and Effective Transformer for Image Deblurring

Blurry images may contain local and global non-uniform artifacts, which complicate the deblurring process and make it more challenging to achieve satisfactory results. Recently, Transformers generate improved deblurring outcomes than existing CNN architectures. However, the large model size and long inference time are still two bothersome issues which have not been fully explored. To this end, we propose DeblurDiNAT, a compact encoder-decoder Transformer which efficiently restores clean images from real-world blurry ones. We adopt an alternating dilation factor structure with the aim of global-local feature learning. Also, we observe that simply using self-attention layers in networks does not always produce good deblurred results. To solve this problem, we propose a channel modulation self-attention (CMSA) block, where a cross-channel learner (CCL) is utilized to capture channel relationships. In addition, we present a divide and multiply feed-forward network (DMFN) allowing fast feature propagation. Moreover, we design a lightweight gated feature fusion (LGFF) module, which performs controlled feature merging. Comprehensive experimental results show that the proposed model, named DeblurDiNAT, provides a favorable performance boost without introducing noticeable computational costs over the baseline, and achieves state-of-the-art (SOTA) performance on several image deblurring datasets. Compared to nearest competitors, our space-efficient and time-saving method demonstrates a stronger generalization ability with 3%-68% fewer parameters and produces deblurred images that are visually closer to the ground truth.
Shadows of black holes in dynamical Chern-Simons modified gravity

We revisit and extend the study of null geodesics around a slowly rotating black hole in Chern-Simons modified gravity. We employ the Hamilton-Jacobi formalism to derive the equations for the shadow profile and determine its shape. We compare our results with numerical ray tracing, finding good agreement within the validity of our approximations for slow rotation and small Chern-Simons coupling. We constrain the model parameters using data from the observed shadow of SgrA$^*$.
Simple and highly efficient BAC recombineering using galK selection

Recombineering allows DNA cloned in Escherichia coli to be modified via lambda (λ) Red-mediated homologous recombination, obviating the need for restriction enzymes and DNA ligases to modify DNA. Here, we describe the construction of three new recombineering strains (SW102, SW105 and SW106) that allow bacterial artificial chromosomes (BACs) to be modified using galK positive/negative selection. This two-step selection procedure allows DNA to be modified without introducing an unwanted selectable marker at the modification site. All three strains contain an otherwise complete galactose operon, except for a precise deletion of the galK gene, and a defective temperature-sensitive λ prophage that makes recombineering possible. SW105 and SW106 cells in addition carry l-arabinose-inducible Cre or Flp genes, respectively. The galK function can be selected both for and against. This feature greatly reduces the background seen in other negative-selection schemes, and galK selection is considerably more efficient than other related selection methods published. We also show how galK selection can be used to rapidly introduce point mutations, deletions and loxP sites into BAC DNA and thus facilitate functional studies of SNP and/or disease-causing point mutations, the identification of long-range regulatory elements and the construction of conditional targeting vectors.
Safety-Aware Reinforcement Learning for Electric Vehicle Charging Station Management in Distribution Network

The increasing integration of electric vehicles (EVs) into the grid can pose a significant risk to the distribution system operation in the absence of coordination. In response to the need for effective coordination of EVs within the distribution network, this paper presents a safety-aware reinforcement learning (RL) algorithm designed to manage EV charging stations while ensuring the satisfaction of system constraints. Unlike existing methods, our proposed algorithm does not rely on explicit penalties for constraint violations, eliminating the need for penalty coefficient tuning. Furthermore, managing EV charging stations is further complicated by multiple uncertainties, notably the variability in solar energy generation and energy prices. To address this challenge, we develop an off-policy RL algorithm to efficiently utilize data to learn patterns in such uncertain environments. Our algorithm also incorporates a maximum entropy framework to enhance the RL algorithm's exploratory process, preventing convergence to local optimal solutions. Simulation results demonstrate that our algorithm outperforms traditional RL algorithms in managing EV charging in the distribution network.
Automatic Navigation Map Generation for Mobile Robots in Urban Environments

A fundamental prerequisite for safe and efficient navigation of mobile robots is the availability of reliable navigation maps upon which trajectories can be planned. With the increasing industrial interest in mobile robotics, especially in urban environments, the process of generating navigation maps has become of particular interest, being a labor intensive step of the deployment process. Automating this step is challenging and becomes even more arduous when the perception capabilities are limited by cost considerations. This paper proposes an algorithm to automatically generate navigation maps using a typical navigation-oriented sensor setup: a single top-mounted 3D LiDAR sensor. The proposed method is designed and validated with the urban environment as the main use case: it is shown to be able to produce accurate maps featuring different terrain types, positive obstacles of different heights as well as negative obstacles. The algorithm is applied to data collected in a typical urban environment with a wheeled inverted pendulum robot, showing its robustness against localization, perception and dynamic uncertainties. The generated map is validated against a human-made map.