Miracle-dz/UniFAR-FTUs huggingface dataset:
Large-scale Measurements of Wireless Network Behavior

Meraki is a cloud-based network management system which provides centralized configuration, monitoring, and network troubleshooting tools across hundreds of thousands of sites worldwide. As part of its architecture, the Meraki system has built a database of time-series measurements of wireless link, client, and application behavior for monitoring and debugging purposes. This paper studies an anonymized subset of measurements, containing data from approximately ten thousand radio access points, tens of thousands of links, and 5.6 million clients from one-week periods in January 2014 and January 2015 to provide a deeper understanding of real-world network behavior. This paper observes the following phenomena: wireless network usage continues to grow quickly, driven most by growth in the number of devices connecting to each network. Intermediate link delivery rates are common indoors across a wide range of deployment environments. Typical access points share spectrum with dozens of nearby networks, but the presence of a network on a channel does not predict channel utilization. Most access points see 2.4 GHz channel utilization of 20% or more, with the top decile seeing greater than 50%, and the majority of the channel use contains decodable 802.11 headers.
Estimation of Staphylococcus aureus Growth Parameters from Turbidity Data: Characterization of Strain Variation and Comparison of Methods

ABSTRACT Turbidity methods offer possibilities for generating data required for addressing microorganism variability in risk modeling given that the results of these methods correspond to those of viable count methods. The objectives of this study were to identify the best approach for determining growth parameters based on turbidity data and use of a Bioscreen instrument and to characterize variability in growth parameters of 34 Staphylococcus aureus strains of different biotypes isolated from broiler carcasses. Growth parameters were estimated by fitting primary growth models to turbidity growth curves or to detection times of serially diluted cultures either directly or by using an analysis of variance (ANOVA) approach. The maximum specific growth rates in chicken broth at 17°C estimated by time to detection methods were in good agreement with viable count estimates, whereas growth models (exponential and Richards) underestimated growth rates. Time to detection methods were selected for strain characterization. The variation of growth parameters among strains was best described by either the logistic or lognormal distribution, but definitive conclusions require a larger data set. The distribution of the physiological state parameter ranged from 0.01 to 0.92 and was not significantly different from a normal distribution. Strain variability was important, and the coefficient of variation of growth parameters was up to six times larger among strains than within strains. It is suggested to apply a time to detection (ANOVA) approach using turbidity measurements for convenient and accurate estimation of growth parameters. The results emphasize the need to consider implications of strain variability for predictive modeling and risk assessment.
Enhanced Group-Based Key Management Scheme for Wireless Sensor Networks using Deployment Knowledge

Key establishment plays a central role in authentication and encryption in wireless sensor networks, especially when they are mainly deployed in hostile environments. Because of the strict constraints in power, processing and storage, designing an efficient key establishment protocol is not a trivial task. Compared with public key cryptography, symmetric key cryptographic with key predistribution mechanism is more suitable for large-scale wireless sensor networks. Most of previous solutions have some issues on performance and security capabilities. In this paper, we propose a novel key predistribution model using pre-deployment knowledge and random values in pairwise key generation to take advantage in terms of network connectivity, memory cost, energy for transmission and strong resilience against node capture attacks.
Premenstrual syndrome (PMS) among high school students

Background Premenstrual syndrome (PMS) is a common health problem among adolescents. Objective To assess the prevalence of PMS in Thai high school students. Materials and methods This was a prospective study conducted among menstruating high school students in Khon Kaen, Thailand, from September to December, 2015. Participants were asked to prospectively complete an anonymous questionnaire, which included information about demographic data, menstrual patterns, and symptoms to be recorded on a daily calendar of premenstrual experiences according to the diagnostic criteria proposed by the American College of Obstetricians and Gynecologists. All of the data were prospectively recorded for 90 consecutive days. Results Of the 399 participants, 289 (72.4%) completed the self-report questionnaire. Eighty-six participants (29.8%; 95% CI, 24.5%–35.4%) reported having PMS. The most common somatic and affective symptoms among participants with PMS were breast tenderness (74.4%) and angry outbursts (97.7%). There were significant differences between the PMS and non-PMS groups, and PMS was associated with various problems related to educational activities, including lack of concentration and motivation, poor individual work performance, poor collaborative work performance, and low scores. However, there were no significant differences regarding interpersonal relationships between the PMS and non-PMS groups. Conclusions PMS is a common menstrual disorder among Thai high school students. The most common symptoms reported in this study were angry outbursts and breast tenderness.
Deformation and mineralisation in the Scotty Creek Basin, Agnew district, Eastern Goldfields, Western Australia: evidence for D1- and D3-related gold mineralisation

Abstract Gold deposits in the Agnew district display markedly different structural styles. The Waroonga and Songvang deposits are hosted in layer-parallel extensional shears formed under highly ductile conditions. In contrast, the New Holland–Genesis deposits are shallow-dipping quartz-filled brittle fractures and breccia zones that cut across the tightly folded bedding and formed during east–west compression. It is difficult to attribute their formation to a single compressive event. The Waroonga and Songvang deposits formed during D1 extension, uplift and exhumation of the Agnew granitic complex and formation of the Scotty Creek Basin at ca 2670–2660 Ma. The New Holland–Genesis deposits formed during east–west D3 compression at about ca 2650–2630 Ma. An S1 foliation wraps around the Agnew granitic complex and L1 stretching lineations form a radial pattern around the granite, consistent with formation during D1 uplift of the composite granite body. Uplift and erosion of granite bodies in the surrounding area provide a source for the granite clasts in the upper parts of the Scotty Creek Basin. As clasts in the basin are undeformed, no significant deformation occurred prior to the uplift and erosion of the source granites in this area. Syn-tectonic emplacement of the Lawlers Tonalite during formation of the Scotty Creek Basin at ca 2665 Ma may have provided a good heat/fluid source for the mineralising systems during the first gold event. The distribution of the large deposits along the western edge of the Agnew granitic complex indicates that the extensional shear along the granite contact is a first-order control on gold deposition by providing a conduit for rising hydrothermal fluids. The northerly trend of high-grade shoots in the Waroonga deposit coincides with early north-trending growth faults, which are also likely fluid conduits.
Robust-Locomotion-by-Logic: Perturbation-Resilient Bipedal Locomotion via Signal Temporal Logic Guided Model Predictive Control

This study introduces a robust planning framework that utilizes a model predictive control (MPC) approach, enhanced by incorporating signal temporal logic (STL) specifications. This marks the first-ever study to apply STL-guided trajectory optimization for bipedal locomotion, specifically designed to handle both translational and orientational perturbations. Existing recovery strategies often struggle with reasoning complex task logic and evaluating locomotion robustness systematically, making them susceptible to failures caused by inappropriate recovery strategies or lack of robustness. To address these issues, we design an analytical robustness metric for bipedal locomotion and quantify this metric using STL specifications, which guide the generation of recovery trajectories to achieve maximum locomotion robustness. To enable safe and computational-efficient crossed-leg maneuver, we design data-driven self-leg-collision constraints that are $1000$ times faster than the traditional inverse-kinematics-based approach. Our framework outperforms a state-of-the-art locomotion controller, a standard MPC without STL, and a linear-temporal-logic-based planner in a high-fidelity dynamic simulation, especially in scenarios involving crossed-leg maneuvers. Additionally, the Cassie bipedal robot achieves robust performance under horizontal and orientational perturbations such as those observed in ship motions. These environments are validated in simulations and deployed on hardware. Furthermore, our proposed method demonstrates versatility on stepping stones and terrain-agnostic features on inclined terrains.
Real-Valued Somewhat-Pseudorandom Unitaries

We explore a very simple distribution of unitaries: random (binary) phase -- Hadamard -- random (binary) phase -- random computational-basis permutation. We show that this distribution is statistically indistinguishable from random Haar unitaries for any polynomial set of orthogonal input states (in any basis) with polynomial multiplicity. This shows that even though real-valued unitaries cannot be completely pseudorandom (Haug, Bharti, Koh, arXiv:2306.11677), we can still obtain some pseudorandom properties without giving up on the simplicity of a real-valued unitary. Our analysis shows that an even simpler construction: applying a random (binary) phase followed by a random computational-basis permutation, would suffice, assuming that the input is orthogonal and \emph{flat} (that is, has high min-entropy when measured in the computational basis). Using quantum-secure one-way functions (which imply quantum-secure pseudorandom functions and permutations), we obtain an efficient cryptographic instantiation of the above.
Efficient Frame Schedule Scheme for Real-time Video Transmission Across the Internet Using TCP

The great end-to-end delays are the major factor to influence the visual quality of real-time video across the Internet using TCP as transport layer protocol. In this paper, we present a video frame schedule scheme for rate adaptive real-time video transmission over TCP. The scheme schedules video frames between the application layer sender-buffer, the TCP sender-buffer and TCP receiver-buffer and can regulate automaticlly the video frame rate and play out buffer delays according to the network congestion level. The sheme requires only an extra buffer of application layer and can significantly cut down the end-to-end delays of real-time video without any modification to the network infrastructure or TCP protocol stack. The performance of the proposed solution is evaluated through extensive simulations using the NS-2 simulator.
Medical Image Registration and Its Application in Retinal Images: A Review

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, these surveys have not systematically summarized methodologies of existing medical image registration methods. To this end, we provide a comprehensive review of these methods from traditional and deep learning-based directions, aiming to help audiences understand the development of medical image registration quickly. In particular, we review recent advances in retinal image registration at the end of each section, which has not attracted much attention. Additionally, we also discuss the current challenges of retinal image registration and provide insights and prospects for future research.
Enhancing Visual Place Recognition via Fast and Slow Adaptive Biasing in Event Cameras

Event cameras are increasingly popular in robotics due to their beneficial features, such as low latency, energy efficiency, and high dynamic range. Nevertheless, their downstream task performance is greatly influenced by the optimization of bias parameters. These parameters, for instance, regulate the necessary change in light intensity to trigger an event, which in turn depends on factors such as the environment lighting and camera motion. This paper introduces feedback control algorithms that automatically tune the bias parameters through two interacting methods: 1) An immediate, on-the-fly fast adaptation of the refractory period, which sets the minimum interval between consecutive events, and 2) if the event rate exceeds the specified bounds even after changing the refractory period repeatedly, the controller adapts the pixel bandwidth and event thresholds, which stabilizes after a short period of noise events across all pixels (slow adaptation). Our evaluation focuses on the visual place recognition task, where incoming query images are compared to a given reference database. We conducted comprehensive evaluations of our algorithms' adaptive feedback control in real-time. To do so, we collected the QCR-Fast-and-Slow dataset that contains DAVIS346 event camera streams from 366 repeated traversals of a Scout Mini robot navigating through a 100 meter long indoor lab setting (totaling over 35km distance traveled) in varying brightness conditions with ground truth location information. Our proposed feedback controllers result in superior performance when compared to the standard bias settings and prior feedback control methods. Our findings also detail the impact of bias adjustments on task performance and feature ablation studies on the fast and slow adaptation mechanisms.
Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks

The characterization of the global maximum of energy efficiency (EE) problems in wireless networks is a challenging problem due to their nonconvex nature in interference channels. The aim of this paper is to develop a new and general framework to achieve globally optimal solutions. First, the hidden monotonic structure of the most common EE maximization problems is exploited jointly with fractional programming theory to obtain globally optimal solutions with exponential complexity in the number of network links. To overcome the high complexity, we also propose a framework to compute suboptimal power control strategies with affordable complexity. This is achieved by merging fractional programming and sequential optimization. The proposed monotonic framework is used to shed light on the ultimate performance of wireless networks in terms of EE and also to benchmark the performance of the lower-complexity framework based on sequential programming. Numerical evidence is provided to show that the sequential fractional programming framework achieves global optimality in several practical communication scenarios.
Perception and Control of Surfing in Virtual Reality using a 6-DoF Motion Platform

The paper presents a system for simulating surfing in Virtual Reality (VR), emphasizing the recreation of aquatic motions and user-initiated propulsive forces using a 6-Degree of Freedom (DoF) motion platform. We present an algorithmic approach to accurately render surfboard kinematics and interactive paddling dynamics, validated through experimental evaluation with \(N=17\) participants. Results indicate that the system effectively reproduces various acceleration levels, the perception of which is independent of users' body posture. We additionally found that the presence of ocean ripples amplifies the perception of acceleration. This system aims to enhance the realism and interactivity of VR surfing, laying a foundation for future advancements in surf therapy and interactive aquatic VR experiences.
Nanoscale Imaging of Phonons and Reconfiguration in Topologically-Engineered, Self-Assembled Nanoparticle Lattice

Topologically-engineered mechanical frames are important model constructs for architecture, machine mechanisms, and metamaterials. Despite significant advances in macroscopically fashioned frames, realization and phonon imaging of nanoframes have remained challenging. Here we extend for the first time the principles of topologically-engineered mechanical frames to lattices self-assembled from nanoparticles. Liquid-phase transmission electron microscopy images the vibrations of nanoparticles in self-assembled Maxwell and hexagonal lattices at the nanometer resolution, measuring a series of otherwise inaccessible properties such as phonon spectra and nonlinear lattice deformation paths. These properties are experimentally modulated by ionic strength, captured by our discrete mechanical model considering the complexity of nanoscale interactions and thermal fluctuations. The experiment-theory integration bridges mechanical metamaterials and colloidal self-assembly, opening new opportunities to manufacture phononic devices with solution processibility, transformability, light weight, and emergent functions, at underexplored length, frequency, and energy scales.
Blueprint for a High-Performance Biomaterial: Full-Length Spider Dragline Silk Genes

Spider dragline (major ampullate) silk outperforms virtually all other natural and manmade materials in terms of tensile strength and toughness. For this reason, the mass-production of artificial spider silks through transgenic technologies has been a major goal of biomimetics research. Although all known arthropod silk proteins are extremely large (>200 kiloDaltons), recombinant spider silks have been designed from short and incomplete cDNAs, the only available sequences. Here we describe the first full-length spider silk gene sequences and their flanking regions. These genes encode the MaSp1 and MaSp2 proteins that compose the black widow's high-performance dragline silk. Each gene includes a single enormous exon (>9000 base pairs) that translates into a highly repetitive polypeptide. Patterns of variation among sequence repeats at the amino acid and nucleotide levels indicate that the interaction of selection, intergenic recombination, and intragenic recombination governs the evolution of these highly unusual, modular proteins. Phylogenetic footprinting revealed putative regulatory elements in non-coding flanking sequences. Conservation of both upstream and downstream flanking sequences was especially striking between the two paralogous black widow major ampullate silk genes. Because these genes are co-expressed within the same silk gland, there may have been selection for similarity in regulatory regions. Our new data provide complete templates for synthesis of recombinant silk proteins that significantly improve the degree to which artificial silks mimic natural spider dragline fibers.
U-ARE-ME: Uncertainty-Aware Rotation Estimation in Manhattan Environments

Camera rotation estimation from a single image is a challenging task, often requiring depth data and/or camera intrinsics, which are generally not available for in-the-wild videos. Although external sensors such as inertial measurement units (IMUs) can help, they often suffer from drift and are not applicable in non-inertial reference frames. We present U-ARE-ME, an algorithm that estimates camera rotation along with uncertainty from uncalibrated RGB images. Using a Manhattan World assumption, our method leverages the per-pixel geometric priors encoded in single-image surface normal predictions and performs optimisation over the SO(3) manifold. Given a sequence of images, we can use the per-frame rotation estimates and their uncertainty to perform multi-frame optimisation, achieving robustness and temporal consistency. Our experiments demonstrate that U-ARE-ME performs comparably to RGB-D methods and is more robust than sparse feature-based SLAM methods. We encourage the reader to view the accompanying video at https://callum-rhodes.github. io/U-ARE-ME for a visual overview of our method.
Minimal decaying dark matter: from cosmological tensions to neutrino signatures

The invisible decay of cold dark matter into a slightly lighter dark sector particle on cosmological time-scales has been proposed as a solution to the $S_8$ tension. In this work we discuss the possible embedding of this scenario within a particle physics framework, and we investigate its phenomenology. We identify a minimal dark matter decay setup that addresses the $S_8$ tension, while avoiding the stringent constraints from indirect dark matter searches. In our scenario, the dark sector contains two singlet fermions $N_{1,2}$, quasi-degenerate in mass, and carrying lepton number so that the heaviest state ($N_2$) decays into the lightest ($N_1$) and two neutrinos via a higher-dimensional operator $N_2\to \bar N_1\nu\nu$. The conservation of lepton number, and the small phase-space available for the decay, forbids the decay channels into hadrons and strongly suppresses the decays into photons or charged leptons. We derive complementary constraints on the model parameters from neutrino detectors, freeze-in dark matter production via $\nu\nu\to N_1N_2$, collider experiments and blazar observations, and we show that the upcoming JUNO neutrino observatory could detect signals of dark matter decay for model parameters addressing the $S_8$ tension if the dark matter mass is below $\simeq 1$ GeV.
Elite360D: Towards Efficient 360 Depth Estimation via Semantic- and Distance-Aware Bi-Projection Fusion

360 depth estimation has recently received great attention for 3D reconstruction owing to its omnidirectional field of view (FoV). Recent approaches are predominantly focused on cross-projection fusion with geometry-based re-projection: they fuse 360 images with equirectangular projection (ERP) and another projection type, e.g., cubemap projection to estimate depth with the ERP format. However, these methods suffer from 1) limited local receptive fields, making it hardly possible to capture large FoV scenes, and 2) prohibitive computational cost, caused by the complex cross-projection fusion module design. In this paper, we propose Elite360D, a novel framework that inputs the ERP image and icosahedron projection (ICOSAP) point set, which is undistorted and spatially continuous. Elite360D is superior in its capacity in learning a representation from a local-with-global perspective. With a flexible ERP image encoder, it includes an ICOSAP point encoder, and a Bi-projection Bi-attention Fusion (B2F) module (totally ~1M parameters). Specifically, the ERP image encoder can take various perspective image-trained backbones (e.g., ResNet, Transformer) to extract local features. The point encoder extracts the global features from the ICOSAP. Then, the B2F module captures the semantic- and distance-aware dependencies between each pixel of the ERP feature and the entire ICOSAP feature set. Without specific backbone design and obvious computational cost increase, Elite360D outperforms the prior arts on several benchmark datasets.