Miracle-dz/UniFAR-FTUs huggingface dataset:
ViT-BEVSeg: A Hierarchical Transformer Network for Monocular Birds-Eye-View Segmentation

Generating a detailed near-field perceptual model of the environment is an important and challenging problem in both self-driving vehicles and autonomous mobile robotics. A Bird's Eye View (BEV) map, providing a panoptic representation, is a commonly used approach that provides a simplified 2D representation of the vehicle's surroundings with accurate semantic level segmentation for many downstream tasks. Current state-of-the art approaches to generate BEV-maps employ a Convolutional Neural Network (CNN) backbone to create feature-maps which are passed through a spatial transformer to project the derived features onto the BEV coordinate frame. In this paper, we evaluate the use of vision transformers (ViT) as a backbone architecture to generate BEV maps. Our network architecture, ViT-BEVSeg, employs standard vision transformers to generate a multi-scale representation of the input image. The resulting representation is then provided as an input to a spatial transformer decoder module which outputs segmentation maps in the BEV grid. We evaluate our approach on the nuScenes dataset demonstrating a considerable improvement in the performance relative to state-of-the-art approaches. Code is available at https://github.com/robotvisionmu/ViT-BEVSeg.
Dataset Bias in Android Malware Detection

Researchers have proposed kinds of malware detection methods to solve the explosive mobile security threats. We argue that the experiment results are inflated due to the research bias introduced by the variability of malware dataset. We explore the impact of bias in Android malware detection in three aspects, the method used to flag the ground truth, the distribution of malware families in the dataset, and the methods to use the dataset. We implement a set of experiments of different VT thresholds and find that the methods used to flag the malware data affect the malware detection performance directly. We further compare the impact of malware family types and composition on malware detection in detail. The superiority of each approach is different under various combinations of malware families. Through our extensive experiments, we showed that the methods to use the dataset can have a misleading impact on evaluation, and the performance difference can be up to over 40%. We argue that these research biases observed in this paper should be carefully controlled/eliminated to enforce a fair comparison of malware detection techniques. Providing reasonable and explainable results is better than only reporting a high detection accuracy with vague dataset and experimental settings.
Feature Learning in L2-regularized DNNs: Attraction/Repulsion and Sparsity

We study the loss surface of DNNs with $L_{2}$ regularization. We show that the loss in terms of the parameters can be reformulated into a loss in terms of the layerwise activations $Z_{\ell}$ of the training set. This reformulation reveals the dynamics behind feature learning: each hidden representations $Z_{\ell}$ are optimal w.r. t. to an attraction/repulsion problem and interpolate between the input and output representations, keeping as little information from the input as necessary to construct the activation of the next layer. For positively homogeneous non-linearities, the loss can be further reformulated in terms of the covariances of the hidden representations, which takes the form of a partially convex optimization over a convex cone. This second reformulation allows us to prove a sparsity result for homogeneous DNNs: any local minimum of the $L_{2}$-regularized loss can be achieved with at most $N(N+1)$ neurons in each hidden layer (where $N$ is the size of the training set). We show that this bound is tight by giving an example of a local minimum that requires $N^{2}/4$ hidden neurons. But we also observe numerically that in more traditional settings much less than $N^{2}$ neurons are required to reach the minima.
Knowledge Enhanced Neural Networks for relational domains

In the recent past, there has been a growing interest in Neural-Symbolic Integration frameworks, i.e., hybrid systems that integrate connectionist and symbolic approaches to obtain the best of both worlds. In this work we focus on a specific method, KENN (Knowledge Enhanced Neural Networks), a Neural-Symbolic architecture that injects prior logical knowledge into a neural network by adding on its top a residual layer that modifies the initial predictions accordingly to the knowledge. Among the advantages of this strategy, there is the inclusion of clause weights, learnable parameters that represent the strength of the clauses, meaning that the model can learn the impact of each rule on the final predictions. As a special case, if the training data contradicts a constraint, KENN learns to ignore it, making the system robust to the presence of wrong knowledge. In this paper, we propose an extension of KENN for relational data. One of the main advantages of KENN resides in its scalability, thanks to a flexible treatment of dependencies between the rules obtained by stacking multiple logical layers. We show experimentally the efficacy of this strategy. The results show that KENN is capable of increasing the performances of the underlying neural network, obtaining better or comparable accuracies in respect to other two related methods that combine learning with logic, requiring significantly less time for learning.
Galactic Chemical Evolution of Exoplanet Hosting Stars: Are High-mass Planetary Systems Young?

The imprints of stellar nucleosynthesis and chemical evolution of the galaxy can be seen in different stellar populations, with older generation stars showing higher α-element abundances and the later generations becoming enriched with iron-peak elements. The evolutionary connections and chemical characteristics of circumstellar disks, stars, and their planetary companions can be inferred by studying the interdependence of planetary and host star properties. Numerous studies in the past have confirmed that high-mass giant planets are commonly found around metal-rich stars, while the stellar hosts of low-mass planets have a wide range of metallicity. In this work, we analyzed the detailed chemical abundances for a sample of >900 exoplanet hosting stars drawn from different radial velocity and transit surveys. We correlate the stellar abundance trends for α- and iron-peak elements with the planets’ mass. We find the planet mass–abundance correlation to be primarily negative for α-elements and marginally positive or zero for the iron-peak elements, indicating that stars hosting giant planets are relatively younger. This is further validated by the age of the host stars obtained from isochrone fitting. The later enrichment of protoplanetary material with iron and iron-peak elements is also consistent with the formation of the giant planets via the core accretion process. A higher metal fraction in the protoplanetary disk is conducive to rapid core growth, thus providing a plausible route for the formation of giant planets. This study, therefore, indicates that the observed trends in stellar abundances and planet mass are most likely a natural consequence of Galactic chemical evolution.
Cascade Luminance and Chrominance for Image Retouching: More Like Artist

Photo retouching aims to adjust the luminance, contrast, and saturation of the image to make it more human aesthetically desirable. However, artists' actions in photo retouching are difficult to quantitatively analyze. By investigating their retouching behaviors, we propose a two-stage network that brightens images first and then enriches them in the chrominance plane. Six pieces of useful information from image EXIF are picked as the network's condition input. Additionally, hue palette loss is added to make the image more vibrant. Based on the above three aspects, Luminance-Chrominance Cascading Net(LCCNet) makes the machine learning problem of mimicking artists in photo retouching more reasonable. Experiments show that our method is effective on the benchmark MIT-Adobe FiveK dataset, and achieves state-of-the-art performance for both quantitative and qualitative evaluation.
CTR: Checkpoint, Transfer, and Restore for Secure Enclaves

Hardware-based Trusted Execution Environments (TEEs) are becoming increasingly prevalent in cloud computing, forming the basis for confidential computing. However, the security goals of TEEs sometimes conflict with existing cloud functionality, such as VM or process migration, because TEE memory cannot be read by the hypervisor, OS, or other software on the platform. Whilst some newer TEE architectures support migration of entire protected VMs, there is currently no practical solution for migrating individual processes containing in-process TEEs. The inability to migrate such processes leads to operational inefficiencies or even data loss if the host platform must be urgently restarted. We present CTR, a software-only design to retrofit migration functionality into existing TEE architectures, whilst maintaining their expected security guarantees. Our design allows TEEs to be interrupted and migrated at arbitrary points in their execution, thus maintaining compatibility with existing VM and process migration techniques. By cooperatively involving the TEE in the migration process, our design also allows application developers to specify stateful migration-related policies, such as limiting the number of times a particular TEE may be migrated. Our prototype implementation for Intel SGX demonstrates that migration latency increases linearly with the size of the TEE memory and is dominated by TEE system operations.
Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification

Deep learning based medical imaging classiﬁcation models usually suﬀer from the domain shift problem, where the classiﬁcation performance drops when training data and real world data diﬀer in imaging equipment manufacturer, image acquisition protocol, patient popu-lations, etc. We propose Feature Centroid Contrast Learning (FCCL), which can improve target domain classiﬁcation performance by extra supervision during training with contrastive loss between instance and class centroid. Compared with current unsupervised domain adaptation and domain generalization methods, FCCL performs better while only requires labeled image data from a single source domain and no target domain. We verify through extensive experiments that FCCL can achieve superior performance on at least three imaging modalities, i.e. fundus photographs, dermatoscopic images, and H&E tissue images.
Standardizing reverberation-measured C iv time-lag quasars, and using them with standardized Mg ii quasars to constrain cosmological parameters

We use 38 C iv quasar (QSO) reverberation-mapped (RM) observations, which span eight orders of magnitude in luminosity and the redshift range 0.001064 ≤ z ≤ 3.368, to simultaneously constrain cosmological-model and QSO radius-luminosity (R − L) relation parameters in six cosmological models, using an improved technique that more correctly accounts for the asymmetric errors bars of the time-lag measurements. We find that R − L relation parameters are independent of the cosmological models used in the analysis and so the R − L relation can be used to standardize the C iv QSOs. The C iv QSO cosmological constraints are consistent with those from Mg ii QSOs, allowing us to derive joint C iv + Mg ii QSO cosmological constraints which are consistent with currently accelerated cosmological expansion, as well as consistent with cosmological constraints derived using better-established baryon acoustic oscillation (BAO) and Hubble parameter [H(z)] measurements. When jointly analyzed with H(z) + BAO data, current C iv + Mg ii QSO data mildly tighten current H(z) + BAO data cosmological constraints.
Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation

Generalizability is seen as one of the major challenges in deep learning, in particular in the domain of medical imaging, where a change of hospital or in imaging routines can lead to a complete failure of a model. To tackle this, we introduce Consistency Training, a training procedure and alternative to data augmentation based on maximizing models’ prediction consistency across augmented and unaugmented data in order to facilitate better out-of-distribution generalization. To this end, we develop a novel auxiliary region-based segmentation loss function called Segmentation Inconsistency Loss (SIL), which considers the differences between pairs of augmented and unaugmented predictions and labels. We demonstrate that Consistency Training outperforms conventional data augmentation on several out-of-distribution datasets on polyp segmentation, a popular medical task.
The NEWS-G detector at SNOLAB

The New Experiments With Spheres-Gas (NEWS-G) collaboration intends to achieve sub-GeV/c2 Weakly Interacting Massive Particles (WIMPs) detection using Spherical Proportional Counters (SPCs). SPCs are gaseous detectors relying on ionisation with a single ionization electron energy threshold. The latest generation of SPC for direct dark matter searches has been installed at SNOLAB in Canada in 2021. This article details the different processes involved in the fabrication of the NEWS-G experiment. Also outlined in this paper are the mitigation strategies, measurements of radioactivity of the different components, and estimations of induced background event rates that were used to quantify and address detector backgrounds.
Near-future discovery of point sources of ultra-high-energy neutrinos

Upcoming neutrino telescopes may discover ultra-high-energy (UHE) cosmic neutrinos, with energies beyond 100 PeV, in the next 10–20 years. Finding their sources would identify guaranteed sites of interaction of UHE cosmic rays, whose origin is unknown. We search for sources by looking for multiplets of UHE neutrinos arriving from similar directions. Our forecasts are state-of-the-art, geared at neutrino radio-detection in IceCube-Gen2. They account for detector energy and angular response, and for critical, but uncertain backgrounds. Sources at declination of -45° to 0° will be easiest to discover. Discovering even one steady-state source in 10 years would imply that the source has an UHE neutrino luminosity at least larger than about 1043 erg/s (depending on the source redshift evolution). Discovering no transient source would disfavor transient sources brighter than 1053 erg as dominant. Our results aim to inform the design of upcoming detectors.
A Data Mining Service to Assist Instructors Involved in Virtual Education

In this chapter we present a BI application delivered as a service on-demand. In particular, it is a data mining service that aims to help instructors involved in distance education to discover their students’ behavior profiles and models about how they navigate and work in their virtual courses offered in Learning Content Management Systems such as Blackboard or Moodle. The main characteristic is that the users do not require data mining knowledge to use the service; they only have to send a data file according to one of the templates provided by the system and request the results. The service carries out the KDD process itself. Furthermore, the service provides an interface based on Web services, which can be called by external software. In short, the chapter talks about the necessity of a service with these characteristics and includes the description of its architecture and its method of operation as well as a discussion about some of the patterns it offers and how these provide instructors valuable knowledge to make decisions.
AN ABSTRACT OF THE THESIS OF Brett A. Valenti for the degree of Master of Science in Mechanical Engineering presented on March 12, 2010. Title: Condensing Observation of Locale and Agents: A State Representation Abstract approved:

approved: Kagan Tumer Computing agents require state information to make coherent and useful decisions. A state representation is a numerical translation of the environment and conditions that are pertinent factors in an agent’s decision making. Although many representations, when paired with clever learning algorithms, are able to coordinate and capture prey in specific domain types, an inherent problem is that they rarely scale well with domain changes. This is due to much of their information being of a form that is dependent upon factors such as the size of the world and number of agents. When the state information is instead a scaled and condensed view of surrounding agents, mapped to an action list from which the agent is able to choose, the state is then in a form that is independent of both world size and number of agents. Coupled with a simple, 1-step Q-Learning algorithm, this representation proves to be quite robust, outperforming a hand-coded policy and two other state-space representations. Using several 1v1 simulations, we will show that a predatory agent is capable of tracking and capturing a moving target in a simple gridworld domain. It is also able to transfer its experience to new domains that have larger gridworlds, more dynamic environments, and even sensor noise and failure.
Heavy Flavour at the LHC

Heavy ﬂavour physics provides a crucial role in the validation of the Standard Model of particle physics and in the search for new phenomena beyond. This review provides a personal summary of the headline results as of May 2013 from the LHC heavy ﬂavour community in the subject areas of heavy ﬂavour production and spectroscopy, mixing and CP violation and rare decays.
Quadratic Programming Algorithm of Approximate Nonlinear Model Predictive Control for Tracking Trajectory of Wheeled Mobile Robots

Nonlinear model predictive control (NMPC) has proved its competency in controlling constrained nonlinear processes. Although NMPC can achieve exemplary tracking performance, the related computation effort as well as guaranteeing tracking convergence are its main drawbacks. Indeed, constrained NMPC is a nonlinear and nonconvex optimization problem where it is difficult to find a feasible solution within a reasonable time. Motivated by these difficulties, this paper proposes a procedure, using Euler approximation, to transform the nonlinear optimization problem of NMPC into a constrained quadratic optimization problem. The proposed tracking controller is applied to the autonomous navigation problem of a wheeled mobile robot (WMR) in a constrained space. Under certain assumptions, we prove the closed-loop system stability and the boundedness of the tracking error. Further, we show the recursive feasibility of the solution. Simulations are performed, first to determine the adequate control parameters, and secondly to show the effectiveness of the proposed algorithm, while its real-time implementation is experimentally verified using a differential drive mobile robot.
Impact of economic forces and fundamental variables on REIT returns

ABSTRACT We examine the impacts of economic forces and fundamental variables on REIT returns. Our models endogenously select breakpoints to distinguish the heterogenous nature of the underlying properties with varying risk-return characteristics. Default risk premium and unanticipated inflation had an adverse effect, while GDP and federal funds rate had a positive effect on REITs. Market, size and value risk-premiums are significant for time periods that include the GFC but not for subsequent periods. Momentum is negative and significant for extreme events, and insignificant during calm periods. Higher beta values during the GFC followed by lower beta values confirm ‘leveraging’ and ‘deleveraging’ effects.
Influence of Excipient Composition on Survival of Vaginal Lactobacilli in Electrospun Nanofibers

The lack of appropriate delivery systems hinders the use of probiotics in the treatment of vaginal infections. Therefore, the development of a new delivery system for the local administration of vaginal probiotics is necessary. In this study, we selected three vaginal lactobacilli, i.e., Lactobacillus crispatus, Lactobacillus gasseri, and Lactobacillus jensenii, and incorporated them into nanofibers using electrospinning. Polyethylene oxide (PEO) was used as a carrier polymer to produce nanofibers. It was supplemented with alginate and sucrose selected from a group of carbohydrates for their growth-promoting effect on lactobacilli. The interaction between excipients and lactobacilli was evaluated thermally and spectroscopically. Bacterial survival in polymer solutions and in nanofibers immediately after electrospinning and after storage varied among species and was dependent on the formulation. Sucrose improved the survival in polymer solutions and preserved the viability of L. crispatus and L. jensenii immediately after electrospinning, and L. gasseri and L. jensenii during storage. Blending PEO with alginate did not improve species viability. However, the three lactobacilli in the nanofibers retained some viability after 56 days, indicating that composite multifunctional nanofibers can maintain the viability of vaginal lactobacilli and can be used as a potential solid delivery system for vaginal administration of probiotics.
Deep learning pipeline for image classification on mobile phones

This article proposes and documents a machine-learning framework and tutorial for classifying images using mobile phones. Compared to computers, the performance of deep learning model performance degrades when deployed on a mobile phone and requires a systematic approach to find a model that performs optimally on both computers and mobile phones. By following the proposed pipeline, which consists of various computational tools, simple procedural recipes, and technical considerations, one can bring the power of deep learning medical image classification to mobile devices, potentially unlocking new domains of applications. The pipeline is demonstrated on four different publicly available datasets: COVID X-rays, COVID CT scans, leaves, and colorectal cancer. We used two application development frameworks: TensorFlow Lite (real-time testing) and Flutter (digital image testing) to test the proposed pipeline. We found that transferring deep learning models to a mobile phone is limited by hardware and classification accuracy drops. To address this issue, we proposed this pipeline to find an optimized model for mobile phones. Finally, we discuss additional applications and computational concerns related to deploying deep-learning models on phones, including real-time analysis and image preprocessing. We believe the associated documentation and code can help physicians and medical experts develop medical image classification applications for distribution.