Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Benefits of GPU Sample-Based Stochastic Predictive Controllers for Legged Locomotion

Quadrupedal robots excel in mobility, navigating complex terrains with agility. However, their complex control systems present challenges that are still far from being fully addressed. In this paper, we introduce the use of Sample-Based Stochastic control strategies for quadrupedal robots, as an alternative to traditional optimal control laws. We show that Sample-Based Stochastic methods, supported by GPU acceleration, can be effectively applied to real quadruped robots. In particular, in this work, we focus on achieving gait frequency adaptation, a notable challenge in quadrupedal locomotion for gradient-based methods. To validate the effectiveness of Sample-Based Stochastic controllers we test two distinct approaches for quadrupedal robots and compare them against a conventional gradient-based Model Predictive Control system. Our findings, validated both in simulation and on a real 21Kg Aliengo quadruped, demonstrate that our method is on par with a traditional Model Predictive Control strategy when the robot is subject to zero or moderate disturbance, while it surpasses gradient-based methods in handling sustained external disturbances, thanks to the straightforward gait adaptation strategy that is possible to achieve within their formulation.
ADMIRE: a locally adaptive single-image, non-uniformity correction and denoising algorithm: application to uncooled IR camera

We propose a new way to correct for the non-uniformity (NU) and the noise in uncooled infrared-type images. This method works on static images, needs no registration, no camera motion and no model for the non uniformity. The proposed method uses an hybrid scheme including an automatic locally-adaptive contrast adjustment and a state-of-the-art image denoising method. It permits to correct for a fully non-linear NU and the noise efficiently using only one image. We compared it with total variation on real raw and simulated NU infrared images. The strength of this approach lies in its simplicity, low computational cost. It needs no test-pattern or calibration and produces no "ghost-artefact".
Risk Quadrangle and Robust Optimization Based on $\varphi$-Divergence

This paper studies robust and distributionally robust optimization based on the extended $\varphi$-divergence under the Fundamental Risk Quadrangle framework. We present the primal and dual representations of the quadrangle elements: risk, deviation, regret, error, and statistic. The framework provides an interpretation of portfolio optimization, classification and regression as robust optimization. We furnish illustrative examples demonstrating that many common problems are included in this framework. The $\varphi$-divergence risk measure used in distributionally robust optimization is a special case. We conduct a case study to visualize the risk envelope.
Exploiting Topological Prior for Boosting Point Cloud Generation

This paper presents an innovative enhancement to the Sphere as Prior Generative Adversarial Network (SP-GAN) model, a state-of-the-art GAN designed for point cloud generation. A novel method is introduced for point cloud generation that elevates the structural integrity and overall quality of the generated point clouds by incorporating topological priors into the training process of the generator. Specifically, this work utilizes the K-means algorithm to segment a point cloud from the repository into clusters and extract centroids, which are then used as priors in the generation process of the SP-GAN. Furthermore, the discriminator component of the SP-GAN utilizes the identical point cloud that contributed the centroids, ensuring a coherent and consistent learning environment. This strategic use of centroids as intuitive guides not only boosts the efficiency of global feature learning but also substantially improves the structural coherence and fidelity of the generated point clouds. By applying the K-means algorithm to generate centroids as the prior, the work intuitively and experimentally demonstrates that such a prior enhances the quality of generated point clouds.
A constant time complexity algorithm for the unbounded knapsack problem with bounded coefficients

Benchmark instances for the unbounded knapsack problem are typically generated according to specific criteria within a given constant range $R$, and these instances can be referred to as the unbounded knapsack problem with bounded coefficients (UKPB). In order to increase the difficulty of solving these instances, the knapsack capacity $C$ is usually set to a very large value. Therefore, an exact algorithm that neither time complexity nor space complexity includes the capacity coefficient $C$ is highly anticipated. In this paper, we propose an exact algorithm with time complexity of $O(R^4)$ and space complexity of $O(R^3)$. The algorithm initially divides the multiset $N$ into two multisubsets, $N_1$ and $N_2$, based on the profit density of their types. For the multisubset $N_2$ composed of types with profit density lower than the maximum profit density type, we utilize a recent branch and bound (B\&B) result by Dey et al. (Math. Prog., pp 569-587, 2023) to determine the maximum selection number for types in $N_2$. We then employ the Unbounded-DP algorithm to exactly solve for the types in $N_2$. For the multisubset $N_1$ composed of the maximum profit density type and its counterparts with the same profit density, we transform it into a linear Diophantine equation and leverage relevant conclusions from the Frobenius problem to solve it efficiently. In particular, the proof techniques required by the algorithm are primarily covered in the first-year mathematics curriculum, which is convenient for subsequent researchers to grasp.
CBR -- Boosting Adaptive Classification By Retrieval of Encrypted Network Traffic with Out-of-distribution

Encrypted network traffic Classification tackles the problem from different approaches and with different goals. One of the common approaches is using Machine learning or Deep Learning-based solutions on a fixed number of classes, leading to misclassification when an unknown class is given as input. One of the solutions for handling unknown classes is to retrain the model, however, retraining models every time they become obsolete is both resource and time-consuming. Therefore, there is a growing need to allow classification models to detect and adapt to new classes dynamically, without retraining, but instead able to detect new classes using few shots learning [1]. In this paper, we introduce Adaptive Classification By Retrieval CBR, a novel approach for encrypted network traffic classification. Our new approach is based on an ANN-based method, which allows us to effectively identify new and existing classes without retraining the model. The novel approach is simple, yet effective and achieved similar results to RF with up to 5% difference (usually less than that) in the classification tasks while having a slight decrease in the case of new samples (from new classes) without retraining. To summarize, the new method is a real-time classification, which can classify new classes without retraining. Furthermore, our solution can be used as a complementary solution alongside RF or any other machine/deep learning classification method, as an aggregated solution.
Table-Lookup MAC: Scalable Processing of Quantised Neural Networks in FPGA Soft Logic

Recent advancements in neural network quantisation have yielded remarkable outcomes, with three-bit networks reaching state-of-the-art full-precision accuracy in complex tasks. These achievements present valuable opportunities for accelerating neural networks by computing in reduced precision. Implementing it on FPGAs can take advantage of bit-level reconfigurability, which is not available on conventional CPUs and GPUs. Simultaneously, the high data intensity of neural network processing has inspired computing-in-memory paradigms, including on FPGA platforms. By programming the effects of trained model weights as lookup operations in soft logic, the transfer of weight data from memory units can be avoided, alleviating the memory bottleneck. However, previous methods face poor scalability - the high logic utilisation limiting them to small networks/sub-networks of binary models with low accuracy. In this paper, we introduce Table Lookup Multiply-Accumulate (TLMAC) as a framework to compile and optimise quantised neural networks for scalable lookup-based processing. TLMAC clusters and maps unique groups of weights to lookup-based processing elements, enabling highly parallel computation while taking advantage of parameter redundancy. Further place and route algorithms are proposed to reduce LUT utilisation and routing congestion. We demonstrate that TLMAC significantly improves the scalability of previous related works. Our efficient logic mapping and high degree of reuse enables entire ImageNet-scale quantised models with full-precision accuracy to be implemented using lookup-based computing on one commercially available FPGA.
Quantum Scalar Field on Fuzzy de Sitter Space I. Field Modes and Vacua

We study a scalar field on a noncommutative model of spacetime, the fuzzy de Sitter space, which is based on the algebra of the de Sitter group $SO(1,d)$ and its unitary irreducible representations. We solve the Klein-Gordon equation in $d=2,4$ and show, using a specific choice of coordinates and operator ordering, that all commutative field modes can be promoted to solutions of the fuzzy Klein-Gordon equation. To explore completeness of this set of modes, we specify a Hilbert space representation and study the matrix elements (integral kernels) of a scalar field: in this way the complete set of solutions of the fuzzy Klein-Gordon equation is found. The space of noncommutative solutions has more degrees of freedom than the commutative one, whenever spacetime dimension is $d>2$. In four dimensions, the new non-geometric, internal modes are parametrised by $S^2\times W$, where $W$ is a discrete matrix space. Our results pave the way to analysis of quantum field theory on the fuzzy de~Sitter space.
Electoral Competition with Rationally Inattentive Voters

This paper studies how voters’ selective ignorance interacts with policy design by political candidates. It shows that the selectivity empowers voters with extreme preferences and small groups, that divisive issues attract most attention, and that public goods are underfunded. Finer granularity of information increases these inefficiencies. Rational inattention can also explain why competing opportunistic candidates do not always converge on the same policy issues.
A Simple Mixture Policy Parameterization for Improving Sample Efficiency of CVaR Optimization

Reinforcement learning algorithms utilizing policy gradients (PG) to optimize Conditional Value at Risk (CVaR) face significant challenges with sample inefficiency, hindering their practical applications. This inefficiency stems from two main facts: a focus on tail-end performance that overlooks many sampled trajectories, and the potential of gradient vanishing when the lower tail of the return distribution is overly flat. To address these challenges, we propose a simple mixture policy parameterization. This method integrates a risk-neutral policy with an adjustable policy to form a risk-averse policy. By employing this strategy, all collected trajectories can be utilized for policy updating, and the issue of vanishing gradients is counteracted by stimulating higher returns through the risk-neutral component, thus lifting the tail and preventing flatness. Our empirical study reveals that this mixture parameterization is uniquely effective across a variety of benchmark domains. Specifically, it excels in identifying risk-averse CVaR policies in some Mujoco environments where the traditional CVaR-PG fails to learn a reasonable policy.
Person-centered Web support to women with type 1 diabetes in pregnancy and early motherhood--the development process.

BACKGROUND Pregnancy and early motherhood are extraordinarily demanding periods for women with type 1 diabetes, who therefore need optimal support. This article describes the process of developing person-centered Web-based support for women with type 1 diabetes during the period of pregnancy through early motherhood. Important aspects of person-centeredness are a broader scope of medicine, viewing the patient as a person, shared decision-making to accomplish a therapeutic alliance, and the role of documentation. MATERIALS AND METHODS A participatory design was used in the development process to capture the target group's knowledge, experiences, and needs, and a systematic process map for Web-based support was used to describe the process. RESULTS Content and layout in the Web support were developed collaboratively by project managers, advisory and scientific reference groups, technical producers, and representatives for the target group. Based on needs assessment and evidence synthesis, three main components of complementary Web-based support were identified: (1) specific information about pregnancy, childbirth, and early motherhood in relation to type 1 diabetes; (2) a self-care diary, including a device for documenting and evaluating blood glucose levels, insulin doses, food intake, physical activities, and overall well-being; and (3) a forum for communication between women with type 1 diabetes in the childbearing period. CONCLUSIONS Using a perspective of person-centered care, a participatory design and the process map were fruitful for developing person-centered Web support for self-care and self-learning. The developed Web support product will be evaluated in a randomized controlled trial and further developed based on this result.
GeoGaussian: Geometry-aware Gaussian Splatting for Scene Rendering

During the Gaussian Splatting optimization process, the scene's geometry can gradually deteriorate if its structure is not deliberately preserved, especially in non-textured regions such as walls, ceilings, and furniture surfaces. This degradation significantly affects the rendering quality of novel views that deviate significantly from the viewpoints in the training data. To mitigate this issue, we propose a novel approach called GeoGaussian. Based on the smoothly connected areas observed from point clouds, this method introduces a novel pipeline to initialize thin Gaussians aligned with the surfaces, where the characteristic can be transferred to new generations through a carefully designed densification strategy. Finally, the pipeline ensures that the scene's geometry and texture are maintained through constrained optimization processes with explicit geometry constraints. Benefiting from the proposed architecture, the generative ability of 3D Gaussians is enhanced, especially in structured regions. Our proposed pipeline achieves state-of-the-art performance in novel view synthesis and geometric reconstruction, as evaluated qualitatively and quantitatively on public datasets.
Bilateral Propagation Network for Depth Completion

Depth completion aims to derive a dense depth map from sparse depth measurements with a synchronized color image. Current state-of-the-art (SOTA) methods are predominantly propagation-based, which work as an iterative refinement on the initial estimated dense depth. However, the initial depth estimations mostly result from direct applications of convolutional layers on the sparse depth map. In this paper, we present a Bilateral Propagation Network (BP-Net), that propagates depth at the earliest stage to avoid directly convolving on sparse data. Specifically, our approach propagates the target depth from nearby depth measurements via a non-linear model, whose coefficients are generated through a multi-layer perceptron conditioned on both \emph{radiometric difference} and \emph{spatial distance}. By integrating bilateral propagation with multi-modal fusion and depth refinement in a multi-scale framework, our BP-Net demonstrates outstanding performance on both indoor and outdoor scenes. It achieves SOTA on the NYUv2 dataset and ranks 1st on the KITTI depth completion benchmark at the time of submission. Experimental results not only show the effectiveness of bilateral propagation but also emphasize the significance of early-stage propagation in contrast to the refinement stage. Our code and trained models will be available on the project page.
Probability Modeling of Autonomous Unmanned Combat Aerial Vehicles (UCAVs)

Abstract : Unmanned Combat Aerial Vehicles (UCAVs) are advanced weapon systems that can loiter autonomously in a pack over a target area, detect and acquire the targets, and then engage them. Modeling these capabilities in a specific hostile operational setting is necessary for addressing weapons' design and operational issues. In this paper we develop several analytic probability models, which range from a simple regenerative formula to a large-scale continuous-time Markov chain, with the objective to address the aforementioned issues. While these models capture key individual aspects of the weapon such as detection, recognition, memory and survivability, special attention is given to pack related aspects such as simultaneous targeting, multiple kills due to imperfect battle damage assessment, and the effect of attack coordination. From implementing the models we gain some insights on design and operational considerations regarding the employment of a pack of UCAVs in a strike scenario.
Tokensome: Towards a Genetic Vision-Language GPT for Explainable and Cognitive Karyotyping

Automatic karyotype analysis is often defined as a visual perception task focused solely on chromosomal object-level modeling. This definition has led most existing methods to overlook componential and holistic information, significantly constraining model performance. Moreover, the lack of interpretability in current technologies hinders clinical adoption. In this paper, we introduce Tokensome, a novel vision-language model based on chromosome tokenization for explainable and cognitive karyotyping. Tokensome elevates the method from the conventional visual perception layer to the cognitive decision-making layer. This elevation enables the integration of domain knowledge and cognitive reasoning via knowledge graphs and LLMs, markedly enhancing model's explainability and facilitating abnormality detection.
An Empirical Study on JIT Defect Prediction Based on BERT-style Model

Previous works on Just-In-Time (JIT) defect prediction tasks have primarily applied pre-trained models directly, neglecting the configurations of their fine-tuning process. In this study, we perform a systematic empirical study to understand the impact of the settings of the fine-tuning process on BERT-style pre-trained model for JIT defect prediction. Specifically, we explore the impact of different parameter freezing settings, parameter initialization settings, and optimizer strategies on the performance of BERT-style models for JIT defect prediction. Our findings reveal the crucial role of the first encoder layer in the BERT-style model and the project sensitivity to parameter initialization settings. Another notable finding is that the addition of a weight decay strategy in the Adam optimizer can slightly improve model performance. Additionally, we compare performance using different feature extractors (FCN, CNN, LSTM, transformer) and find that a simple network can achieve great performance. These results offer new insights for fine-tuning pre-trained models for JIT defect prediction. We combine these findings to find a cost-effective fine-tuning method based on LoRA, which achieve a comparable performance with only one-third memory consumption than original fine-tuning process.
Latent Object Characteristics Recognition with Visual to Haptic-Audio Cross-modal Transfer Learning

Recognising the characteristics of objects while a robot handles them is crucial for adjusting motions that ensure stable and efficient interactions with containers. Ahead of realising stable and efficient robot motions for handling/transferring the containers, this work aims to recognise the latent unobservable object characteristics. While vision is commonly used for object recognition by robots, it is ineffective for detecting hidden objects. However, recognising objects indirectly using other sensors is a challenging task. To address this challenge, we propose a cross-modal transfer learning approach from vision to haptic-audio. We initially train the model with vision, directly observing the target object. Subsequently, we transfer the latent space learned from vision to a second module, trained only with haptic-audio and motor data. This transfer learning framework facilitates the representation of object characteristics using indirect sensor data, thereby improving recognition accuracy. For evaluating the recognition accuracy of our proposed learning framework we selected shape, position, and orientation as the object characteristics. Finally, we demonstrate online recognition of both trained and untrained objects using the humanoid robot Nextage Open.
Causality from Bottom to Top: A Survey

Causality has become a fundamental approach for explaining the relationships between events, phenomena, and outcomes in various fields of study. It has invaded various fields and applications, such as medicine, healthcare, economics, finance, fraud detection, cybersecurity, education, public policy, recommender systems, anomaly detection, robotics, control, sociology, marketing, and advertising. In this paper, we survey its development over the past five decades, shedding light on the differences between causality and other approaches, as well as the preconditions for using it. Furthermore, the paper illustrates how causality interacts with new approaches such as Artificial Intelligence (AI), Generative AI (GAI), Machine and Deep Learning, Reinforcement Learning (RL), and Fuzzy Logic. We study the impact of causality on various fields, its contribution, and its interaction with state-of-the-art approaches. Additionally, the paper exemplifies the trustworthiness and explainability of causality models. We offer several ways to evaluate causality models and discuss future directions.
Characterization of rapid intervascular transport of cadmium in rice stem by radioisotope imaging

Participation of the intervascular transport system within the rice stem during cadmium (Cd) partitioning was investigated by characterizing 109Cd behaviour in the shoot. In addition, 45Ca, 32P, and 35S partitioning patterns were analysed for comparison with that of 109Cd. Each tracer was applied to the seedling roots for 15min, and the shoots were harvested either at 15min (i.e. immediately after tracer application) or at 48h. Distribution patterns of each element at 15min were studied to identify the primary transport pathway before remobilization was initiated. 32P was preferentially transported to completely expanded leaf blades having the highest transpiration rate. The newest leaf received minimal amounts of 32P. In contrast, the amount of 35S transported to the newest leaf was similar to that transported to the other mature leaf blades. Preferential movement towards the newest leaf was evident for 109Cd and 45Ca. These results directly indicate that elemental transport is differentially regulated in the vegetative stem as early as 15min before the elements are transported to leaves. Cd behaviour in the stem was investigated in detail by obtaining serial section images from the bottom part of shoots after 109Cd was applied to a single crown root. At 30min, the maximum amount of 109Cd was distributed in the peripheral cylinder of the longitudinal vascular bundles (PV) and, interestingly, some amount of 109Cd was transported downwards along the PV. This transport manner of 109Cd provides evidence that Cd can be loaded on the phloem at the stem immediately after Cd is transported from the root.