Miracle-dz/UniFAR-FTUs huggingface dataset:
Origins and solutions of proportional unfairness in IEEE 802.11 multi-hop wireless networks

Proportional unfairness exists in multi-hop wireless networks where IEEE 802.11 is employed as the medium access control protocol. This thesis investigates the origins of unfairness and proposes solutions to improve fairness in such networks. This thesis first studies multi-hop topologies consisting of two disjoint flows and four nodes. All possible topologies depending on whether the different nodes are within range of each other are systematically identified and further classified into three groups. We show that in the first group the two senders are in radio range of each other and contend fairly. However, long-term unfairness arises in the second group where the two senders do not have complete information of the channel and the topology is asymmetric. In the third group where the channel information each flow obtains is incomplete but the topology is symmetric, the system achieves long-term fairness yet incurs short-term unfairness. We develop analytical models to characterize the MAC performance in the second and third group. We then analyze the compounding effect of MAC and congestion control on fairness. We identify an important topology that is necessarily embedded in mesh networks, in which a one-hop TCP flow contends with a two-hop TCP flow for gateway access. We describe how the congestion control and the MAC jointly lead to unfairness between the two flows, and analytically model the system with a six-dimensional Markov chain model. Motivated by the model, we devise and evaluate a simple Contention Window Policy that only requires the one-hop mesh nodes to increase their minimum contention window. Extensive experiments and simulations show that regardless of its simplicity, this window policy has a powerful effect in improving fairness of the network. To improve fairness in mobile ad-hoc networks, we devise a distributed multi-channel medium access protocol, Asynchronous Multichannel Coordination Protocol (AMCP), that not only increases aggregate throughput, but more importantly, addresses the fundamental coordination problems that arise in a multi-hop network. We analytically derive and experimentally validate a lower throughput bound for any flow in an arbitrary topology under the operation of AMCP.
Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses

With the increased penetration of wind energy into the power grid, it has become increasingly important to be able to predict the expected power production for larger wind farms. Deep learning (DL) models can learn complex patterns in the data and have found wide success in predicting wake losses and expected power production. This paper proposes a modular framework for attention-based graph neural networks (GNN), where attention can be applied to any desired component of a graph block. The results show that the model significantly outperforms a multilayer perceptron (MLP) and a bidirectional LSTM (BLSTM) model, while delivering performance on-par with a vanilla GNN model. Moreover, we argue that the proposed graph attention architecture can easily adapt to different applications by offering flexibility into the desired attention operations to be used, which might depend on the specific application. Through analysis of the attention weights, it was showed that employing attention-based GNNs can provide insights into what the models learn. In particular, the attention networks seemed to realise turbine dependencies that aligned with some physical intuition about wake losses.
Perspective: Ultrafast Imaging of Molecular Dynamics Using Ultrafast Low-Frequency Lasers, X-ray Free Electron Laser and Electron Pulses

The requirement of high space-time resolution and brightness is a great challenge for imaging atomic motion and making molecular movies. Important breakthroughs in ultrabright tabletop laser, x-ray and electron sources have enabled the direct imaging of evolving molecular structures in chemical processes. And recent experimental advances in preparing ultrafast laser and electron pulses equipped molecular imaging with femtosecond time resolution. This Perspectives present an overview of versatile imaging methods of molecular dynamics. High-order harmonic generation imaging maps the harmonic order of photons to the time delay for nuclear motion and can reach attosecond time resolution. Coulomb explosion imaging retrieves molecular structural information by detecting the momentum vectors of fragmented ions. Diffraction imaging encodes molecular structural and electronic information in reciprocal space, achieving high spatial resolution by using x-ray or electrons with short wavelengths. The enhancement of x-ray and electron source brightness pushes diffraction into single molecular limit without the restriction of crystalline materials. Laser-induced electron diffraction is a self-imaging method based on coherent electron scattering. We also present various applications of these ultrafast imaging methods in resolving laser-induced nuclear and electronic dynamics, as well as dynamic symmetry evolution of molecules. ∗ zheng.li@pku.edu. cn † yunquan.liu@pku.edu. cn
Deployment and evaluation of an aerosol time-of-flight mass spectrometer

Abstract Aerosol particles play a significant role in the pollution of the Earth's atmosphere as well as contributing to climate change and having strong influences on health Understanding aerosol particle physical and chemical properties is important in assessing their effects. A number of instruments have been developed that allow the real time analysis of single particles. This thesis presents research using a commercial aerosol time-of-flight mass spectrometer (ATOFMS TSI 3800), which was deployed in the laboratory to determine fundamental aspects of ion formation as well as to develop scaling factors to improve the number and chemical quantification of the ATOFMS instrument and to characterise pharmaceutical drug products. Additionally, data from field campaigns both in Ireland and Scotland were evaluated.Aerosol particles play a significant role in the pollution of the Earth's atmosphere as well as contributing to climate change and having strong influences on health Understanding aerosol particle physical and chemical properties is important in assessing their effects. A number of instruments have been developed that allow the real time analysis of single particles. This thesis presents research using a commercial aerosol time-of-flight mass spectrometer (ATOFMS TSI 3800), which was deployed in the laboratory to determine fundamental aspects of ion formation as well as to develop scaling factors to improve the number and chemical quantification of the ATOFMS instrument and to characterise pharmaceutical drug products. Additionally, data from field campaigns both in Ireland and Scotland were evaluated. The ATOFMS uses pulsed UV Laser Desorption lonisation (LDI) at 266 nm to ablate, desorb and ionise the particle material. Little work has been carried out to investigate the mechanisms for formation of negative ions in single particle LDI instruments. In this thesis, the ATOFMS was applied to micron-sized aerosols consisting of alkali halides, nitrates and sulphates as models for atmosphericallyrelevant particles. Processes that can lead to negative ion formation and removal are considered in some detail and it is concluded that their formation is dependent on collisional processes and the thermodynamics of reactions in the post-irradiation ablation plume rather than the result of direct ejection from the lattice. Data from the deployment of the ATOFMS instrument at Mace Head, Galway, Ireland were used to determine the size and chemical composition of individual particles in the atmosphere as part of the North Atlantic Marine Boundary Layer Experiment (NAMBLEX) during August 2002. A total of 191,504 particles were detected. Rapid changes in number density, size and chemical composition of the atmospheric aerosol were observed. These data were analysed using a combination of statistical clustering procedures and manual classification, yielding three broad categories of particles; sea salt, dust and carbon-containing. Applications and evaluation of an aerosol time-of-flight mass spectrometer ii
The Scientific Evidence Indicator for Popular Science News

To what extent can news media help in providing more credible information about science? This is the core challenge for the Science Evidence Indicator (SEI) project, a collaboration between the Danish popular news website videnskab.dk and the authors of this paper. Looking specifically at medical science news, we aim to provide a transparent assessment of the scientific sources behind a story. This entails identifying some of the criteria that scientists use to assess research, and making it accessible and understandable for readers. We address the following research question: How can we communicate the quality of scientific publications in health science to a non-expert audience? Our goal is to make the assessments understandable for the youngest part of the website's target audience: high school students from age 16 and upwards.
RARA: Zero-shot Sim2Real Visual Navigation with Following Foreground Cues

The gap between simulation and the real-world restrains many machine learning breakthroughs in computer vision and reinforcement learning from being applicable in the real world. In this work, we tackle this gap for the specific case of camera-based navigation, formulating it as following a visual cue in the foreground with arbitrary backgrounds. The visual cue in the foreground can often be simulated realistically, such as a line, gate or cone. The challenge then lies in coping with the unknown backgrounds and integrating both. As such, the goal is to train a visual agent on data captured in an empty simulated environment except for this foreground cue and test this model directly in a visually diverse real world. In order to bridge this big gap, we show it's crucial to combine following techniques namely: Randomized augmentation of the fore- and background, regularization with both deep supervision and triplet loss and finally abstraction of the dynamics by using waypoints rather than direct velocity commands. The various techniques are ablated in our experimental results both qualitatively and quantitatively finally demonstrating a successful transfer from simulation to the real world. Code will be made available on publication22Project page: github.com/kkelchte/tgbg.
The effects of saffron (Crocus sativus L.) in conjunction with concurrent training on body composition, glycaemic status, and inflammatory markers in obese men with type 2 diabetes mellitus: A randomized double‐blind clinical trial

Chronic inflammation is one of the major challenges in the management of obesity and type 2 diabetes mellitus (T2DM). Our primary aim was to assess the anti‐inflammatory effects of Saffron (Crocus sativus L.) supplementation and concurrent training in obese men with T2DM.
IoTGAN: GAN Powered Camouflage Against Machine Learning Based IoT Device Identification

With the proliferation of IoT devices, researchers have developed a variety of IoT device identification methods with the assistance of machine learning. Nevertheless, the security of these identification methods mostly depends on collected training data. In this research, we propose a novel attack strategy named IoTGAN to manipulate an IoT device’s traffic such that it can evade machine learning based IoT device identification. In the development of IoTGAN, we have two major technical challenges: (i) How to obtain the discriminative model in a black-box setting, and (ii) How to add perturbations to IoT traffic through the manipulative model, so as to evade the identification while not influencing the functionality of IoT devices. To address these challenges, a neural network based substitute model is used to fit the target model in black-box settings, it works as a discriminative model in IoTGAN. A manipulative model is trained to add adversarial perturbations into the IoT device’s traffic to evade the substitute model. Experimental results show that IoTGAN can successfully achieve the attack goals. We also develop efficient countermeasures to protect machine learning based IoT device identification from been undermined by IoTGAN.
Smooth Nested Simulation: Bridging Cubic and Square Root Convergence Rates in High Dimensions

Nested simulation concerns estimating functionals of a conditional expectation via simulation. In this paper, we propose a new method based on kernel ridge regression to exploit the smoothness of the conditional expectation as a function of the multidimensional conditioning variable. Asymptotic analysis shows that the proposed method can effectively alleviate the curse of dimensionality on the convergence rate as the simulation budget increases, provided that the conditional expectation is sufficiently smooth. The smoothness bridges the gap between the cubic root convergence rate (that is, the optimal rate for the standard nested simulation) and the square root convergence rate (that is, the canonical rate for the standard Monte Carlo simulation). We demonstrate the performance of the proposed method via numerical examples from portfolio risk management and input uncertainty quantification. This paper was accepted by Baris Ata, stochastic models and simulation. Funding: The authors acknowledge financial support from the National Natural Science Foundation of China [Grant NSFC 12101149] and the Hong Kong Research Grants Council [Grants GRF 17201520 and GRF 17206821]. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2022. 00204 .
To Trust or to Stockpile: Modeling Human-Simulation Interaction in Supply Chain Shortages

Understanding decision-making in dynamic and complex settings is a challenge yet essential for preventing, mitigating, and responding to adverse events (e.g., disasters, financial crises). Simulation games have shown promise to advance our understanding of decision-making in such settings. However, an open question remains on how we extract useful information from these games. We contribute an approach to model human-simulation interaction by leveraging existing methods to characterize: (1) system states of dynamic simulation environments (with Principal Component Analysis), (2) behavioral responses from human interaction with simulation (with Hidden Markov Models), and (3) behavioral responses across system states (with Sequence Analysis). We demonstrate this approach with our game simulating drug shortages in a supply chain context. Results from our experimental study with 135 participants show different player types (hoarders, reactors, followers), how behavior changes in different system states, and how sharing information impacts behavior. We discuss how our findings challenge existing literature.
Weak Supervision for Affordable Modeling of Electrocardiogram Data

Analysing electrocardiograms (ECGs) is an inexpensive and non-invasive, yet powerful way to diagnose heart disease. ECG studies using Machine Learning to automatically detect abnormal heartbeats so far depend on large, manually annotated datasets. While collecting vast amounts of unlabeled data can be straightforward, the point-by-point annotation of abnormal heartbeats is tedious and expensive. We explore the use of multiple weak supervision sources to learn diagnostic models of abnormal heartbeats via human designed heuristics, without using ground truth labels on individual data points. Our work is among the first to define weak supervision sources directly on time series data. Results show that with as few as six intuitive time series heuristics, we are able to infer high quality probabilistic label estimates for over 100,000 heartbeats with little human effort, and use the estimated labels to train competitive classifiers evaluated on held out test data.
A Survey on Face Recognition Systems

Face Recognition has proven to be one of the most successful technology and has impacted heterogeneous domains. Deep learning has proven to be the most successful at computer vision tasks because of its convolution-based architecture. Since the advent of deep learning, face recognition technology has had a substantial increase in its accuracy. In this paper, some of the most impactful face recognition systems were surveyed. Firstly, the paper gives an overview of a general face recognition system. Secondly, the survey covers various network architectures and training losses that have had a substantial impact. Finally, the paper talks about various databases that are used to evaluate the capabilities of a face recognition system.
ECG Detection Device Design and Manufacture with Interactive Interface and Portable Applicability for Massive Screening of Cardiovascular Diseases

Physical and psychological problems, and economic costs generated by Cardiovascular Diseases are affecting an important number of people around the world, and mainly in developing countries. Access to early detection and treatment is not common because of the scarcity of specialists and equipment, especially in rural areas. In order to address this problematic, an inexpensive portable Electrocardiograph device with Smartphone connectivity is proposed in this document. Firstly, an electronic circuit will be in charge of amplifying, filtering and sampling the Electrocardiogram (ECG) signal of a subject acquired by means of the use of Ag/AgCl electrodes. Then, the sampled data is transferred to an Android based system in order to be processed, displayed and analyzed on the application. Finally, the ECG time series can be printed or stored into a file which can be sent to a cardiologist. The resulting device is a less costly and easier to access gadget, which also provides a reliable signal resolution for a primary analysis of different diseases. Keywords— Electrocardiograph, ECG, DSP, Microcontroller, Smartphone, Heart Rate, 3D Printing, Biomedical Instrumentation, Portable Devices, Cardiovascular Diseases.
Impact of NOMA and CoMP Implementation Order on the Performance of Ultra-Dense Networks

Non-orthogonal multiple access (NOMA) is a next-generation multiple access technology to improve users’ throughput and spectral efficiency for 5G and beyond cellular networks. Similarly, coordinated multi-point transmission and reception (CoMP) is an existing technology to improve the coverage of cell-edge users. Hence, NOMA with CoMP can potentially enhance the throughput and coverage of the users. However, the order of implementation of CoMP and NOMA can significantly impact the system performance of Ultra-dense networks (UDNs). Motivated by this, we study the performance of the CoMP and NOMA-based UDN by proposing two kinds of user grouping and pairing schemes that differ in the order in which CoMP and NOMA are performed for a group of users. Detailed simulation results are presented, comparing the proposed schemes with the state-of-the-art systems with varying user and base station densities. Through numerical results, we show that the proposed schemes can be used to achieve a suitable coverage-throughout trade-off in UDNs.
Simultaneous High Dynamic Range Algorithm, Testing, and Instrument Simulation

Within an imaging instrument’s field of view, there may be many observational targets of interest. Similarly, within a spectrograph’s bandpass, there may be many emission lines of interest. The brightness of these targets and lines can be orders of magnitude different, which poses a challenge to instrument and mission design. A single exposure can saturate the bright emission and/or have a low signal-to-noise ratio (S/N) for faint emission. Traditional high dynamic range (HDR) techniques solve this problem by either combining multiple sequential exposures of varied duration or splitting the light to different sensors. These methods, however, can result in the loss of science capability, reduced observational efficiency, or increased complexity and cost. The simultaneous HDR method described in this paper avoids these issues by utilizing a special type of detector whose rows can be read independently to define zones that are then composited, resulting in areas with short or long exposure measured simultaneously. We demonstrate this technique for the Sun, which is bright on disk and faint off disk. We emulated these conditions in the lab to validate the method. We built an instrument simulator to demonstrate the method for a realistic solar imager and input. We then calculated S/Ns, finding a value of 45 for a faint coronal mass ejection and 200 for a bright one, both at 3.5 ⊙N —meeting or far exceeding the international standard for digital photography that defines an S/N of 10 as acceptable and 40 as excellent. Future missions should consider this type of hardware and technique in their trade studies for instrument design.
Dipolar physics: a review of experiments with magnetic quantum gases

Since the achievement of quantum degeneracy in gases of chromium atoms in 2004, the experimental investigation of ultracold gases made of highly magnetic atoms has blossomed. The field has yielded the observation of many unprecedented phenomena, in particular those in which long-range and anisotropic dipole–dipole interactions (DDIs) play a crucial role. In this review, we aim to present the aspects of the magnetic quantum-gas platform that make it unique for exploring ultracold and quantum physics as well as to give a thorough overview of experimental achievements. Highly magnetic atoms distinguish themselves by the fact that their electronic ground-state configuration possesses a large electronic total angular momentum. This results in a large magnetic moment and a rich electronic transition spectrum. Such transitions are useful for cooling, trapping, and manipulating these atoms. The complex atomic structure and large dipolar moments of these atoms also lead to a dense spectrum of resonances in their two-body scattering behaviour. These resonances can be used to control the interatomic interactions and, in particular, the relative importance of contact over dipolar interactions. These features provide exquisite control knobs for exploring the few- and many-body physics of dipolar quantum gases. The study of dipolar effects in magnetic quantum gases has covered various few-body phenomena that are based on elastic and inelastic anisotropic scattering. Various many-body effects have also been demonstrated. These affect both the shape, stability, dynamics, and excitations of fully polarised repulsive Bose or Fermi gases. Beyond the mean-field instability, strong dipolar interactions competing with slightly weaker contact interactions between magnetic bosons yield new quantum-stabilised states, among which are self-bound droplets, droplet assemblies, and supersolids. Dipolar interactions also deeply affect the physics of atomic gases with an internal degree of freedom as these interactions intrinsically couple spin and atomic motion. Finally, long-range dipolar interactions can stabilise strongly correlated excited states of 1D gases and also impact the physics of lattice-confined systems, both at the spin-polarised level (Hubbard models with off-site interactions) and at the spinful level (XYZ models). In the present manuscript, we aim to provide an extensive overview of the various related experimental achievements up to the present.