Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Automation on Aircrew Workload and Situation Awareness in Tactical Airlift Missions

Abstract : In tactical aviation, decision superiority brought upon by high situation awareness remains the arbiter of combat effectiveness. The advancement of sophisticated avionics and highly automated cockpits has allowed for the reduction of aircrew size, and in certain platforms, removal of the crew from the aircraft entirely. However, these developments have not reduced the complex and dynamic interaction between situation awareness and crew workload. While many predictive and experimental methods of evaluating workload exist, situation awareness can only be measured by conducting trials with human operators in a functional prototype. This thesis proposes an innovative methodology to predicatively determine situation awareness potential with discrete-event simulation software. This methodology measures situation awareness as both a function of task accomplishment and workload experienced. Utilizing two common but complex tactical scenarios, this method and existing workload measurement techniques can derive a direct comparison between a reduced-crew highly automated cockpit and a less automated legacy aircraft. Finally, conclusions regarding the effectiveness of replacing human operators with automation in tactical events can be made and tested in future experiments with actual aircraft and aircrews.
Emission-line Outflows from the Circumnuclear to Circumgalactic Scales in a Partially Obscured Quasar

We present a detailed analysis of the rest-frame UV and optical emission-line spectrum of the partially obscured quasar J121704.70+023417.1 (hereafter J1217+0234). Here the obscuring material, very likely the dusty torus invoked by the AGN unification models, acts as a natural coronagraph, which greatly suppresses both the continuum and broad-line emission in the UV and enables a clear detection of three emission-line components at and beyond the dusty torus scale: (1) The component, with a blueshift of v ≈ 1200 km s−1 and a line width of FWHM ≈ 2600 km s−1, shows exceptionally large intensity ratios, such as N v/Lyα ≈ 2.3 and O vi/Lyα ≈ 1.4, indicating that the emitting gas is highly ionized and has a very high density up to n H ∼ 1013 cm−3, possibly associated with the dusty torus. (2) The largely unshifted narrow-line component, with FWHM ≈ 510 km s−1, is completely absent in all UV lines but Lyα and is significantly detected in the forbidden lines of [O iii], [O ii], and [Ne iii] in the optical, implying massive low-density (n H ∼ 102 cm−3) gas ∼40 kpc from the galactic center. (3) The intermediate component is only detected in [O iii] with a blueshift and line width between (1) and (2), which might bridge the gases from the circumnuclear to the circumgalactic scales. Follow-up observations with high spatial resolution and high sensitivity are needed to confirm the speculation and are helpful to reveal outflows at multiscales in J1217+0234.
Reliability verification of stress data from extracted specimens using LCR wave stress data from full-section rail specimens

Non-destructive stress measurement using ultrasonic techniques is based on calculation of the acoustoelastic coefficient obtained from the relationship between material stress and sound wave velocity. Longitudinal critically refracted (LCR) waves are the most suitable for ultrasonic stress measurement testing. This is because these waves exhibit a relatively large change in flight time in relation to a change in stress. In general, to calculate the acoustoelastic coefficient, tensile tests are performed with specimens extracted from the test objects to be inspected, and the difference in the speed of ultrasonic waves is measured in relation to the stress applied. However, train rails are usually subjected to compressive loads and no research has been conducted to confirm whether the results obtained using this method match the coefficients obtained from test objects in full cross-section. Therefore, results from compression tests on full cross-section specimens are needed as reference data to determine the reliability of the results obtained using extracted specimens. In this study, experiments were conducted to compare the result from applying compressive loads to full cross-section specimens to the experimental result obtained from specimens extracted from each rail section. For this purpose, ultrasonic probes capable of generating LCR waves were designed and manufactured to apply the technique to measure the applied stress. Based on the results obtained using this probe, the acoustoelastic coefficient values obtained from each specimen were compared. Then, the feasibility of calculating the internal stresses in railway rails using the acoustoelastic constants obtained from the extracted specimens was determined, and the reliability of the method was verified. It was concluded that there was no significant difference in the acoustoelastic coefficients calculated using the data from extracted specimens and those using full cross-section specimens.
Regulating nanoparticles : a complex balancing act

Regulation is a key part of product commercialisation, where different stakeholders must continually negotiate what are often conflicting regulatory drivers. High technology regulation is particularly problematic as is found in nanotechnology, and nanoparticle products, where there is much misunderstanding about what these products are and how they work. Nanotechnology is the application of small products, ranging between one hundred million and one billion times smaller than a metre, considered as the next 'industrial revolution'. At the vanguard of nanotechnology products, nanoparticles are examined in this study, where rapid technological advances are creating much debate within the discipline of law for how to best regulate the nuanced physicality of these products. Extant arguments have focused on how to regulate the R&D, production, sale, consumption and end-of-life of these products, with varying considerations of physicality which is pivotal to this endeavour. Critically, and fundamental to any discussion about regulating nanotechnology is whether these products sit inside of current regulations, or whether they require new regulatory approaches to more adequately capture their physicality. Confusingly, there has often been an erroneous presupposition that nanotechnology will function as a direct mirror of larger products, which is often not the case. On this basis, this study engages with the physicality of nanoparticles to build a foundation of knowledge, asking pivotal questions about regulation, to better inform academic and industrial regulatory discourses. Attention is given to regulatory frameworks including the Precautionary Principle, Regulation, Evaluation, Authorisation and Restriction of CHemicals (REACH), and potential for nanotechnology registries for monitoring nanoparticle physicality. Importantly, for any collection of highly nuanced novel physical products as found within nanotechnology, there can be no 'one-size-fits-all' with in depth examinations being made with different specific sectors to draw out the major challenges related to the physicality of this wide ranging collection of products.
MIMO Channel Estimation Using Score-Based Generative Models

Channel estimation is a critical task in multiple-input multiple-output (MIMO) digital communications that substantially affects end-to-end system performance. In this work, we introduce a novel approach for channel estimation using deep score-based generative models. A model is trained to estimate the gradient of the logarithm of a distribution and is used to iteratively refine estimates given measurements of a signal. We introduce a framework for training score-based generative models for wireless MIMO channels and performing channel estimation based on posterior sampling at test time. We derive theoretical robustness guarantees for channel estimation with posterior sampling in single-input single-output scenarios, and experimentally verify performance in the MIMO setting. Our results in simulated channels show competitive in-distribution performance, and robust out-of-distribution performance, with gains of up to 5 dB in end-to-end coded communication performance compared to supervised deep learning methods. Simulations on the number of pilots show that high fidelity channel estimation with 25% pilot density is possible for MIMO channel sizes of up to $64 \times 256$ . Complexity analysis reveals that model size can efficiently trade performance for estimation latency, and that the proposed approach is competitive with compressed sensing in terms of floating-point operation (FLOP) count.
ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision

A cost-effective alternative to manual data labeling is weak supervision (WS), where data samples are automatically annotated using a predefined set of labeling functions (LFs), rule-based mechanisms that generate artificial labels for the associated classes. In this work, we investigate noise reduction techniques for WS based on the principle of k-fold cross-validation. We introduce a new algorithm ULF for Unsupervised Labeling Function correction, which denoises WS data by leveraging models trained on all but some LFs to identify and correct biases specific to the held-out LFs. Specifically, ULF refines the allocation of LFs to classes by re-estimating this assignment on highly reliable cross-validated samples. Evaluation on multiple datasets confirms ULF's effectiveness in enhancing WS learning without the need for manual labeling.
Adaptive Modulation for Wobbling UAV Air-to-Ground Links in Millimeter-wave Bands

The emerging millimeter-wave (mm-wave) unmanned aerial vehicle (UAV) air-to-ground (A2G) communications are facing the Doppler effect problem that arises from the inevitable wobbling of the UAV. The fast time-varying channel for UAV A2G communications may lead to the outdated channel state information (CSI) from the channel estimation. In this paper, we introduce two detectors to demodulate the received signal and get the instantaneous bit error probability (BEP) of a mm-wave UAV A2G link under imperfect CSI. Based on the designed detectors, we propose an adaptive modulation scheme to maximize the average transmission rate under imperfect CSI by optimizing the data transmission time subject to the maximum tolerable BEP. A power control policy is in conjunction with adaptive modulation to minimize the transmission power while maintaining both the BEP under the threshold and the maximized average transmission rate. Numerical results show that the proposed adaptive modulation scheme in conjunction with the power control policy could maximize the temporally averaged transmission rate, while saves as much as 50% energy.
A derivative-free approach to mixed integer constrained multiobjective nonsmooth optimization

In this work, we consider multiobjective optimization problems with both bound constraints on the variables and general nonlinear constraints, where objective and constraint function values can only be obtained by querying a black box. Furthermore, we consider the case where a subset of the variables can only take integer values. We propose a new linesearch-based solution method and show that it converges to a set of stationary points for the problem. For what concerns the continuous variables, we employ a strategy for the estimation of the Pareto frontier recently proposed in the literature and which takes advantage of dense seuqnces of search directions. The subset of variables that must assume dicrete values are dealt with using primitive directions appropriately modiﬁed to take into account the presence of more than one objective functions. Numerical results obtained with the proposed method on a set of test problems and comparison with other solution methods show the viability and eﬃciency of the proposed approach.
Adaptive Memory Management for Video Object Segmentation

Matching-based networks have achieved state-of-the-art performance for video object segmentation (VOS) tasks by storing every-k frames in an external memory bank for future inference. Storing the intermediate frames' predictions provides the network with richer cues for segmenting an object in the current frame. However, the size of the memory bank gradually increases with the length of the video, which slows down inference speed and makes it impractical to handle arbitrary length videos. This paper proposes an adaptive memory bank strategy for matching-based networks for semi-supervised video object segmentation (VOS) that can handle videos of arbitrary length by discarding obsolete features. Features are indexed based on their importance in the segmentation of the objects in previous frames. Based on the index, we discard unimportant features to accommodate new features. We present our experiments on DAVIS 2016, DAVIS 2017, and Youtube-VOS that demonstrate that our method outperforms state-of-the-art that employ first-and-latest strategy with fixed-sized memory banks and achieves comparable performance to the every-k strategy with increasing-sized memory banks. Furthermore, experiments show that our method increases inference speed by up to 80% over the every-k and 35% over first-and-latest strategies.
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks

The most significant barrier to the advancement of Neural Architecture Search (NAS) is its demand for large computational resources, which hinders scientifically sound empirical evaluations of NAS methods. Tabular NAS benchmarks have alleviated this problem substantially, making it possible to properly evaluate NAS methods in seconds on commodity machines. However, an unintended consequence of tabular NAS benchmarks has been a focus on extremely small architectural search spaces since their construction relies on exhaustive evaluations of the space. This leads to unrealistic results that do not transfer to larger spaces. To overcome this fundamental limitation, we propose a methodology to create cheap NAS surrogate benchmarks for arbitrary search spaces. We exemplify this approach by creating surrogate NAS benchmarks on the existing tabular NAS-Bench-101 and on two widely used NAS search spaces with up to $10^{21}$ architectures ($10^{13}$ times larger than any previous tabular NAS benchmark). We show that surrogate NAS benchmarks can model the true performance of architectures better than tabular benchmarks (at a small fraction of the cost), that they lead to faithful estimates of how well different NAS methods work on the original non-surrogate benchmark, and that they can generate new scientific insight. We open-source all our code and believe that surrogate NAS benchmarks are an indispensable tool to extend scientifically sound work on NAS to large and exciting search spaces.
Stability of China's Stock Market: Measure and Forecast by Ricci Curvature on Network

The systemic stability of a stock market is one of the core issues in the financial field. The market can be regarded as a complex network whose nodes are stocks connected by edges that signify their correlation strength. Since the market is a strongly nonlinear system, it is difficult to measure the macroscopic stability and depict market fluctuations in time. In this article, we use a geometric measure derived from discrete Ricci curvature to capture the higher-order nonlinear architecture of financial networks. In order to confirm the effectiveness of our method, we use it to analyze the CSI 300 constituents of China’s stock market from 2005 to 2020 and the systemic stability of the market is quantified through the network’s Ricci-type curvatures. Furthermore, we use a hybrid model to analyze the curvature time series and predict the future trends of the market accurately. As far as we know, this is the first article to apply Ricci curvature to forecast the systemic stability of China’s stock market, and our results show that Ricci curvature has good explanatory power for the market stability and can be a good indicator to judge the future risk and volatility of China’s stock market.
On Measuring Model Complexity in Heteroscedastic Linear Regression

Heteroscedasticity is common in real world applications and is often handled by incorporating case weights into a modeling procedure. Intuitively, models fitted with different weight schemes would have a different level of complexity depending on how well the weights match the inverse of error variances. However, existing statistical theories on model complexity, also known as model degrees of freedom, were primarily established under the assumption of equal error variances. In this work, we focus on linear regression procedures and seek to extend the existing measures to a heteroscedastic setting. Our analysis of the weighted least squares method reveals some interesting properties of the extended measures. In particular, we find that they depend on both the weights used for model fitting and those for model evaluation. Moreover, modeling heteroscedastic data with optimal weights generally results in fewer degrees of freedom than with equal weights, and the size of reduction depends on the unevenness of error variance. This provides additional insights into weighted modeling procedures that are useful in risk estimation and model selection.
Self-organised quantum dots in marginally twisted MoSe 2 /WSe 2 and MoS 2 /WS 2 bilayers

Moir´e superlattices in twistronic heterostruc-tures are a powerful tool for materials engineering. In marginally twisted (small misalignment angle, θ ) bilayers of nearly lattice-matched two-dimensional (2D) crystals moir´e patterns take the form of domains of commensurate stacking, separated by a network of domain walls (NoDW) with strain hot spots at the NoDW nodes. Here, we show that, for type-II transition metal dichalcogenide bilayers MoX 2 /WX 2 (X=S, Se), the hydrostatic strain component in these hot spots cre-ates quantum dots for electrons and holes. We in-vestigate the electron/hole states bound by such objects, discussing their manifestations via the intralayer intraband infrared transitions. The electron/hole conﬁnement, which is strongest for θ < 0 . 5 ◦ , leads to a red-shift of their recombination line producing single photon emitters (SPE) broadly tuneable around 1 eV by misalignment angle. These self-organised dots can form in bilayers with both aligned and inverted MoX 2 and WX 2 unit cells, emitting photons with diﬀerent polarizations. We also ﬁnd that the hot spots of strain reduce the intralayer MoX 2 A-exciton energy, enabling selective population of the quantum dot states.
Using machine learning for particle identification in ALICE

Particle identification (PID) is one of the main strengths of the ALICE experiment at the LHC. It is a crucial ingredient for detailed studies of the strongly interacting matter formed in ultrarelativistic heavy-ion collisions. ALICE provides PID information via various experimental techniques, allowing for the identification of particles over a broad momentum range (from around 100 MeV/c to around 50 GeV/c). The main challenge is how to combine the information from various detectors effectively. Therefore, PID represents a model classification problem, which can be addressed using Machine Learning (ML) solutions. Moreover, the complexity of the detector and richness of the detection techniques make PID an interesting area of research also for the computer science community. In this work, we show the current status of the ML approach to PID in ALICE. We discuss the preliminary work with the Random Forest approach for the LHC Run 2 and a more advanced solution based on Domain Adaptation Neural Networks, including a proposal for its future implementation within the ALICE computing software for the upcoming LHC Run 3.
Recent Advances and New Frontiers in Spiking Neural Networks

In recent years, spiking neural networks (SNNs) have received extensive attention in brain-inspired intelligence due to their rich spatially-temporal dynamics, various encoding methods, and event-driven characteristics that naturally fit the neuromorphic hardware. With the development of SNNs, brain-inspired intelligence, an emerging research field inspired by brain science achievements and aiming at artificial general intelligence, is becoming hot. This paper reviews recent advances and discusses new frontiers in SNNs from five major research topics, including essential elements (i.e., spiking neuron models, encoding methods, and topology structures), neuromorphic datasets, optimization algorithms, software, and hardware frameworks. We hope our survey can help researchers understand SNNs better and inspire new works to advance this field.
Double spending prevention of digital Euros using a web-of-trust

In order to provide more security on double-spending, we have implemented a system allowing for a web-of-trust. In this paper, we explore different approaches taken against double-spending and implement our own version to avoid this within TrustChain as part of the ecosystem of EuroToken, the digital version of the euro. We have used the EVA protocol as a means to transfer data between users, building on the existing functionality of transferring money between users. This allows the sender of EuroTokens to leave recommendations of users based on their previous interactions with other users. This dissemination of trust through the network allows users to make more trustworthy decisions. Although this provides an upgrade in terms of usability, the mathematical details of our implementation can be explored further in other research.
Unsupervised Temporal Learning on Monocular Videos for 3D Human Pose Estimation

—In this paper we propose an unsupervised learning method to extract temporal information on monocular videos, where we detect and encode subject of interest in each frame and leverage contrastive self-supervised (CSS) learning to extract rich latent vectors. Instead of simply treating the latent features of nearby frames as positive pairs and those of temporally-distant ones as negative pairs as in other CSS approaches, we explicitly disentangle each latent vector into a time-variant component and a time-invariant one. We then show that applying CSS only to the time-variant features and encouraging a gradual transition on them between nearby and away frames while also reconstructing the input, extract rich temporal features into the time-variant component, well-suited for human pose estimation. Our approach reduces error by about 50% compared to the standard CSS strategies, outperforms other unsupervised single-view methods and matches the performance of multi-view techniques.
A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes

We introduce ChemDisGene, a new dataset for training and evaluating multi-class multi-label biomedical relation extraction models. Our dataset contains 80k biomedical research abstracts labeled with mentions of chemicals, diseases, and genes, portions of which human experts labeled with 18 types of biomedical relationships between these entities (intended for evaluation), and the remainder of which (intended for training) has been distantly labeled via the CTD database with approximately 78% accuracy. In comparison to similar preexisting datasets, ours is both substantially larger and cleaner; it also includes annotations linking mentions to their entities. We also provide three baseline deep neural network relation extraction models trained and evaluated on our new dataset.