Miracle-dz/UniFAR-FTUs huggingface dataset:
Paved2Paradise: Cost-Effective and Scalable LiDAR Simulation by Factoring the Real World

To achieve strong real world performance, neural networks must be trained on large, diverse datasets; however, obtaining and annotating such datasets is costly and time-consuming, particularly for 3D point clouds. In this paper, we describe Paved2Paradise, a simple, cost-effective approach for generating fully labeled, diverse, and realistic lidar datasets from scratch, all while requiring minimal human annotation. Our key insight is that, by deliberately collecting separate"background"and"object"datasets (i.e.,"factoring the real world"), we can intelligently combine them to produce a combinatorially large and diverse training set. The Paved2Paradise pipeline thus consists of four steps: (1) collecting copious background data, (2) recording individuals from the desired object class(es) performing different behaviors in an isolated environment (like a parking lot), (3) bootstrapping labels for the object dataset, and (4) generating samples by placing objects at arbitrary locations in backgrounds. To demonstrate the utility of Paved2Paradise, we generated synthetic datasets for two tasks: (1) human detection in orchards (a task for which no public data exists) and (2) pedestrian detection in urban environments. Qualitatively, we find that a model trained exclusively on Paved2Paradise synthetic data is highly effective at detecting humans in orchards, including when individuals are heavily occluded by tree branches. Quantitatively, a model trained on Paved2Paradise data that sources backgrounds from KITTI performs comparably to a model trained on the actual dataset. These results suggest the Paved2Paradise synthetic data pipeline can help accelerate point cloud model development in sectors where acquiring lidar datasets has previously been cost-prohibitive.
STREAM: Software Tool for Routing Efficiently Advanced Macrofluidics

The current fabrication and assembly of fluidic circuits for soft robots relies heavily on manual processes; as the complexity of fluidic circuits increases, manual assembly becomes increasingly arduous, error-prone, and timeconsuming. We introduce a software tool that generates printable fluidic networks automatically. We provide a library of fluidic logic elements that are easily 3D printed from thermoplastic polyurethanes using Fused Deposition Modeling only. Our software tool and component library allow the development of arbitrary soft digital circuits. We demonstrate a variable frequency ring oscillator and a full adder. The simplicity of our approach using FDM printers only, democratizes fluidic circuit implementation beyond specialized laboratories. Our software is available on GitHub (https://github.com/roboticmaterialsgroup/FluidLogic).
EVALUACIÓN DE DOS SUSTRATOS Y DOS DOSIS DE FERTILIZACIÓN EN CONDICIONES HIDROPÓNICAS BAJO INVERNADERO EN LECHUGA Lactuca sativa L. EVALUATION OF TWO SUSTRATOS AND TWO DOSE OF FERTILIZATION UNDER CONDITIONS HIDROPONICS LOW HOTHOUSE IN LETTUCE Lactuca sativa L.

In the greenhouse at the University of Narino Colombia, an assessment was conducted in order to determine the effect of two types of substrates (grazing Sand and crushed brick) and the application of two doses of fertilizer in hydroponic conditions for development Lettuce cultivation. As farming units were used wooden benches, with a total of 12 plants per bed. Variables were evaluated: leaf length (LH) blade diameter (DH), Plant height (PH), number of leaves (NH), Diameter of head lettuce (DC), Weight Plant (PP) and Performance (RTO). We used an unrestricted random design (DIA), with a factorial arrangement with three replications combinatorial, where the factor A corresponded to the substrate with two levels, grazing sand and crushed brick, the factor B, the fertilization rate, Dose 1: 3 g + 3 g major elements and trace elements Dose 2: 6 g + 6 g major elements of minor elements. The results showed that treatment with doses of crushed brick 3 + 3, showed the best values for all variables. As the crushed brick substrates showed better conditions for growing hydroponic lettuce, as well as the lowest dose of fertilizer used.
Seasonal variation of CDOM in the Middle Atlantic Bight: terrestrial inputs and photooxidation

Surveys of the fluorescence and absorption of chromophore- containing dissolved organic matter (CDOM) were made along a cruise line extending from the mount of Delaware Bay southeast to the Sargasso Sea. With shallow stratification in August, photobleaching dramatically altered the optical properties of the surface waters, with approximately 70 percent of the CDOM absorption and fluorescence lost through photo-oxidation in the waters at the outer shelf. S, the slope of the log-linearized absorption spectrum of CDOM, increased offshore and appeared to increase with photodegradation. The increase in S underscores the difficulty in developing ocean color algorithms to predict Chl concentrations in highly absorbing coastal waters. The seasonal variation in the CDOM fluorescence-absorption relationship and fluorescence quantum yields was less than 15 percent, making the airborne lidar approach for remote determination of CDOM absorption coefficients a robust technique. The photo-oxidation of CDOM in August also affected the relationship between CDOM and DOC, which can be described by a simple model.
Exploring the Viability of Synthetic Audio Data for Audio-Based Dialogue State Tracking

Dialogue state tracking plays a crucial role in extracting information in task-oriented dialogue systems. However, preceding research are limited to textual modalities, primarily due to the shortage of authentic human audio datasets. We address this by investigating synthetic audio data for audio-based DST. To this end, we develop cascading and end-to-end models, train them with our synthetic audio dataset, and test them on actual human speech data. To facilitate evaluation tailored to audio modalities, we introduce a novel PhonemeF1 to capture pronunciation similarity. Experimental results showed that models trained solely on synthetic datasets can generalize their performance to human voice data. By eliminating the dependency on human speech data collection, these insights pave the way for significant practical advancements in audio-based DST. Data and code are available at https://github.com/JihyunLee1/E2E-DST.1
Prior-Aware Robust Beam Alignment for Low-SNR Millimeter-Wave Communications

This paper presents a robust beam alignment technique for millimeter-wave communications in low signal-to-noise ratio (SNR) environments. The core strategy of our technique is to repeatedly transmit the most probable beam candidates to reduce beam misalignment probability induced by noise. Specifically, for a given beam training overhead, both the selection of candidates and the number of repetitions for each beam candidate are optimized based on channel prior information. To achieve this, a deep neural network is employed to learn the prior probability of the optimal beam at each location. The beam misalignment probability is then analyzed based on the channel prior, forming the basis for an optimization problem aimed at minimizing the analyzed beam misalignment probability. A closed-form solution is derived for a special case with two beam candidates, and an efficient algorithm is developed for general cases with multiple beam candidates. Simulation results using the DeepMIMO dataset demonstrate the superior performance of our technique in dynamic low-SNR communication environments when compared to existing beam alignment techniques.
Variational Self-Supervised Contrastive Learning Using Beta Divergence

Learning a discriminative semantic space using unlabelled and noisy data remains unaddressed in a multi-label setting. We present a contrastive self-supervised learning method which is robust to data noise, grounded in the domain of variational methods. The method (VCL) utilizes variational contrastive learning with beta-divergence to learn robustly from unlabelled datasets, including uncurated and noisy datasets. We demonstrate the effectiveness of the proposed method through rigorous experiments including linear evaluation and fine-tuning scenarios with multi-label datasets in the face understanding domain. In almost all tested scenarios, VCL surpasses the performance of state-of-the-art self-supervised methods, achieving a noteworthy increase in accuracy.
On the Contact Process with Modified Border

We study a one-dimensional contact process with two infection parameters. One giving the infection rates at the boundaries of a finite infected region and the other one the rates within that region. We prove that the critical value of each of these parameters is a strictly monotone continuous function of the other parameter. We also show that if one of these parameters is equal to the critical value of the standard contact process and the other parameter is strictly larger, then the infection starting from a single point has positive probability of surviving.
Single-sample versus case-control sampling scheme for Positive Unlabeled data: the story of two scenarios

In the paper we argue that performance of the classifiers based on Empirical Risk Minimization (ERM) for positive unlabeled data, which are designed for case-control sampling scheme may significantly deteriorate when applied to a single-sample scenario. We reveal why their behavior depends, in all but very specific cases, on the scenario. Also, we introduce a single-sample case analogue of the popular non-negative risk classifier designed for case-control data and compare its performance with the original proposal. We show that the significant differences occur between them, especiall when half or more positive of observations are labeled. The opposite case when ERM minimizer designed for the case-control case is applied for single-sample data is also considered and similar conclusions are drawn. Taking into account difference of scenarios requires a sole, but crucial, change in the definition of the Empirical Risk.
Convergences for Minimax Optimization Problems over Infinite-Dimensional Spaces Towards Stability in Adversarial Training

Training neural networks that require adversarial optimization, such as generative adversarial networks (GANs) and unsupervised domain adaptations (UDAs), suffers from instability. This instability problem comes from the difficulty of the minimax optimization, and there have been various approaches in GANs and UDAs to overcome this problem. In this study, we tackle this problem theoretically through a functional analysis. Specifically, we show the convergence property of the minimax problem by the gradient descent over the infinite-dimensional spaces of continuous functions and probability measures under certain conditions. Using this setting, we can discuss GANs and UDAs comprehensively, which have been studied independently. In addition, we show that the conditions necessary for the convergence property are interpreted as stabilization techniques of adversarial training such as the spectral normalization and the gradient penalty.
Optimal Dual-Polarized Planar Arrays for Massive Capacity Over Point-to-Point MIMO Channels

Future wireless networks must provide ever higher data rates. The available bandwidth increases roughly linearly as we increase the carrier frequency, but the range shrinks drastically. This paper explores if we can instead reach massive capacities using spatial multiplexing over multiple-input multiple-output (MIMO) channels. In line-of-sight (LOS) scenarios, therank of the MIMO channel matrix depends on the polarization and antenna arrangement. We optimize the rank and condition number by identifying the optimal antenna spacing in dual-polarized planar antenna arrays with imperfect isolation. The result is sparely spaced antenna arrays that exploit radiative near-field properties. We further optimize the array geometry for minimum aperture length and aperture area, which leads to different configurations. Moreover, we prove analytically that for fixed-sized arrays, the MIMO rank grows quadratically with the carrier frequency in LOS scenarios, if the antennas are appropriately designed. Hence, MIMO technology contributes more to the capacity growth than the bandwidth. The numerical results show that massive data rates, far beyond 1 Tbps, can be reached both over fixed point-to-point links. It is also possible for a large base station to serve a practically-sized mobile device.
Terrain-Based Coverage Manifold Estimation: Machine Learning, Stochastic Geometry, or Simulation?

Given the necessity of connecting the unconnected, covering blind spots has emerged as a critical task in the next-generation wireless communication network. A direct solution involves obtaining a coverage manifold that visually showcases network coverage performance at each position. Our goal is to devise different methods that minimize the absolute error between the estimated coverage manifold and the actual coverage manifold (referred to as accuracy), while simultaneously maximizing the reduction in computational complexity (measured by computational latency). Simulation is a common method for acquiring coverage manifolds. Although accurate, it is computationally expensive, making it challenging to extend to large-scale networks. In this paper, we expedite traditional simulation methods by introducing a statistical model termed line-of-sight probability-based accelerated simulation. Stochastic geometry is suitable for evaluating the performance of large-scale networks, albeit in a coarse-grained manner. Therefore, we propose a second method wherein a model training approach is applied to the stochastic geometry framework to enhance accuracy and reduce complexity. Additionally, we propose a machine learning-based method that ensures both low complexity and high accuracy, albeit with a significant demand for the size and quality of the dataset. Furthermore, we describe the relationships between these three methods, compare their complexity and accuracy as performance verification, and discuss their application scenarios.
ActiveClean: Generating Line-Level Vulnerability Data via Active Learning

Deep learning vulnerability detection tools are increasing in popularity and have been shown to be effective. These tools rely on large volume of high quality training data, which are very hard to get. Most of the currently available datasets provide function-level labels, reporting whether a function is vulnerable or not vulnerable. However, for a vulnerability detection to be useful, we need to also know the lines that are relevant to the vulnerability. This paper makes efforts towards developing systematic tools and proposes. ActiveClean to generate the large volume of line-level vulnerability data from commits. That is, in addition to function-level labels, it also reports which lines in the function are likely responsible for vulnerability detection. In the past, static analysis has been applied to clean commits to generate line-level data. Our approach based on active learning, which is easy to use and scalable, provide a complementary approach to static analysis. We designed semantic and syntactic properties from commit lines and use them to train the model. We evaluated our approach on both Java and C datasets processing more than 4.3K commits and 119K commit lines. AcitveClean achieved an F1 score between 70-74. Further, we also show that active learning is effective by using just 400 training data to reach F1 score of 70.23. Using ActiveClean, we generate the line-level labels for the entire FFMpeg project in the Devign dataset, including 5K functions, and also detected incorrect function-level labels. We demonstrated that using our cleaned data, LineVul, a SOTA line-level vulnerability detection tool, detected 70 more vulnerable lines and 18 more vulnerable functions, and improved Top 10 accuracy from 66% to 73%.
SANeRF-HQ: Segment Anything for NeRF in High Quality

Recently, the Segment Anything Model (SAM) has showcased remarkable capabilities of zero-shot segmentation, while NeRF (Neural Radiance Fields) has gained popularity as a method for various 3D problems beyond novel view synthesis. Though there exist initial attempts to incorporate these two methods into 3D segmentation, they face the challenge of accurately and consistently segmenting objects in complex scenarios. In this paper, we introduce the Segment Anything for NeRF in High Quality (SANeRF-HQ) to achieve high quality 3D segmentation of any object in a given scene. SANeRF-HQ utilizes SAM for open-world object segmentation guided by user-supplied prompts, while leveraging NeRF to aggregate information from different viewpoints. To overcome the aforementioned challenges, we employ density field and RGB similarity to enhance the accuracy of segmentation boundary during the aggregation. Emphasizing on segmentation accuracy, we evaluate our method quantitatively on multiple NeRF datasets where high-quality ground-truths are available or manually annotated. SANeRF-HQ shows a significant quality improvement over previous state-of-the-art methods in NeRF object segmentation, provides higher flexibility for object localization, and enables more consistent object segmentation across multiple views. Additional information can be found at https://lyclyc52.github. io/SANeRF-HQ/.
A Comparative Analysis Towards Melanoma Classification Using Transfer Learning by Analyzing Dermoscopic Images

Melanoma is a sort of skin cancer that starts in the cells known as melanocytes. It is more dangerous than other types of skin cancer because it can spread to other organs. Melanoma can be fatal if it spreads to other parts of the body. Early detection is the key to cure, but it requires the skills of skilled doctors to diagnose it. This paper presents a system that combines deep learning techniques with established transfer learning methods to enable skin lesions classification and diagnosis of melanoma skin lesions. Using Convolutional Neural Networks, it presents a method for categorizing melanoma images into benign and malignant images in this research (CNNs). Researchers used 'Deep Learning' techniques to train an expansive number of photos&essentially to get the expected result deep neural networks to need to be trained with a huge number of parameters as dermoscopic images are sensitive&very hard to classify. This paper, has been emphasized building models with less complexity and comparatively better accuracy with limited datasets&partially fewer deep networks so that the system can predict Melanoma at ease from input dermoscopic images as correctly as possible within devices with less computational power. The dataset has been obtained from ISIC Archive. Multiple pre-trained models ResNet101, DenseNet, EfficientNet, InceptionV3 have been implemented using transfer learning techniques to complete the comparative analysis&every model achieved good accuracy. Before training the models, the data has been augmented by multiple parameters to improve the accuracy. Moreover, the results are better than the previous state-of-the-art approaches&adequate to predict melanoma. Among these architectures, DenseNet performed better than the others which gives a validation accuracy of 96.64%, validation loss of 9.43%&test set accuracy of 99.63%.
Privacy Preserving Event Detection

This paper presents a privacy-preserving event detection scheme based on measurements made by a network of sensors. A diameter-like decision statistic made up of the marginal types of the measurements observed by the sensors is employed. The proposed detection scheme can achieve the best type-I error exponent as the type-II error rate is required to be negligible. Detection performance with finite-length observations is also demonstrated through a simulation example of spectrum sensing. Privacy protection is achieved by obfuscating the type data with random zero-modulo-sum numbers that are generated and distributed via the exchange of encrypted messages among the sensors. The privacy-preserving performance against ``honest but curious'' adversaries, including colluding sensors, the fusion center, and external eavesdroppers, is analyzed through a series of cryptographic games. It is shown that the probability that any probabilistic polynomial time adversary successfully estimates the sensors' measured types can not be much better than independent guessing, when there are at least two non-colluding sensors.
Moduli Space of Dihedral Spherical Surfaces and Measured Foliations

Cone spherical surfaces are Riemannian surfaces with constant curvature one and a finite number of conical singularities. Dihedral surfaces, a subset of these spherical surfaces, possess monodromy groups that preserve a pair of antipodal points on the unit two-sphere within three-dimensional Euclidean space. On each dihedral surface, we naturally define a pair of transverse measured foliations, which, conversely, fully characterize the original dihedral surface. Additionally, we introduce several geometric decompositions and deformations for dihedral surfaces. As a practical application, we determine the dimension of the moduli space for dihedral surfaces with prescribed cone angles and topological types. This dimension serves as a measure of the independent geometric parameters determining the isometric classes of such surfaces.
WATonoBus: An All Weather Autonomous Shuttle

Autonomous vehicle all-weather operation poses significant challenges, encompassing modules from perception and decision-making to path planning and control. The complexity arises from the need to address adverse weather conditions like rain, snow, and fog across the autonomy stack. Conventional model-based and single-module approaches often lack holistic integration with upstream or downstream tasks. We tackle this problem by proposing a multi-module and modular system architecture with considerations for adverse weather across the perception level, through features such as snow covered curb detection, to decision-making and safety monitoring. Through daily weekday service on the WATonoBus platform for almost a year, we demonstrate that our proposed approach is capable of addressing adverse weather conditions and provide valuable learning from edge cases observed during operation.
A Review of Link Prediction Applications in Network Biology

In the domain of network biology, the interactions among heterogeneous genomic and molecular entities are represented through networks. Link prediction (LP) methodologies are instrumental in inferring missing or prospective associations within these biological networks. In this review, we systematically dissect the attributes of local, centrality, and embedding-based LP approaches, applied to static and dynamic biological networks. We undertake an examination of the current applications of LP metrics for predicting links between diseases, genes, proteins, RNA, microbiomes, drugs, and neurons. We carry out comprehensive performance evaluations on established biological network datasets to show the practical applications of standard LP models. Moreover, we compare the similarity in prediction trends among the models and the specific network attributes that contribute to effective link prediction, before underscoring the role of LP in addressing the formidable challenges prevalent in biological systems, ranging from noise, bias, and data sparseness to interpretability. We conclude the review with an exploration of the essential characteristics expected from future LP models, poised to advance our comprehension of the intricate interactions governing biological systems.