Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting cosmic ray antinuclei fluxes with a new coalescence model

Antideuteron and antihelium nuclei have been proposed as promising detection channels for dark matter because of the low astrophysical backgrounds expected. To estimate both potential exotic contributions and their backgrounds, one usually employs the coalescence model in momentum space. Here we use instead a newly developed coalescence model based on the Wigner function representations of the produced nuclei states. This approach includes both the process-dependent size of the formation region of antinuclei, and the momentum correlations of coalescing antinucleons in a semi-classical picture. The model contains a single universal parameter σ that we tune to experimental data on antideuteron production in electron-positron, proton-proton and proton-nucleus collisions. The obtained value σ≃ 1 fm agrees well with its physical interpretation as the size of the formation region of antinuclei in collisions of point-like particles. This model allows us therefore to calculate in a consistent frame-work the antideuteron and antihelium fluxes both from secondary production and from dark matter annihilations. We find that the antihelium-3 flux falls short by more than an order of magnitude of the detection sensitivity of the AMS-02 experiment, assuming standard cosmic ray propagation parameters, while the antideuteron flux can be comparable to the sensitivities of the AMS-02 and GAPS experiments.
A Double Q-Learning Approach for Navigation of Aerial Vehicles with Connectivity Constraint

This paper studies the trajectory optimization problem for an aerial vehicle with the mission of flying between a pair of given initial and final locations. The objective is to minimize the travel time of the aerial vehicle ensuring that the communication connectivity constraint required for the safe operation of the aerial vehicle is satisfied. We consider two different criteria for the connectivity constraint of the aerial vehicle which leads to two different scenarios. In the first scenario, we assume that the maximum continuous time duration that the aerial vehicle is out of the coverage of the ground base stations (GBSs) is limited to a given threshold. In the second scenario, however, we assume that the total time periods that the aerial vehicle is not covered by the GBSs is restricted. Based on these two constraints, we formulate two trajectory optimization problems. To solve these non-convex problems, we use an approach based on the double Q-learning method which is a model-free reinforcement learning technique and unlike the existing algorithms does not need perfect knowledge of the environment. Moreover, in contrast to the well-known Qlearning technique, our double Q-learning algorithm does not suffer from the over-estimation issue. Simulation results show that although our algorithm does not require prior information of the environment, it works well and shows near optimal performance.
Gaussian Process Regression for Probabilistic Short-term Solar Output Forecast

With increasing concerns of climate change, renewable resources such as photovoltaic (PV) have gained popularity as a means of energy generation. The smooth integration of such resources in power system operations is enabled by accurate forecasting mechanisms that address their inherent intermittency and variability. This paper proposes a probabilistic framework to predict short-term PV output taking into account the uncertainty of weather. To this end, we make use of datasets that comprise of power output and meteorological data such as irradiance, temperature, zenith, and azimuth. First, we categorise the data into four groups based on solar output and time by using k-means clustering. Next, a correlation study is performed to choose the weather features which affect solar output to a greater extent. Finally, we determine a function that relates the aforementioned selected features with solar output by using Gaussian Process Regression and Matern 5/2 as a kernel function. We validate our method with five solar generation plants in different locations and compare the results with existing methodologies. More specifically, in order to test the proposed model, two different methods are used: (i) 5-fold cross-validation; and (ii) holding out 30 random days as test data. To confirm the model accuracy, we apply our framework 30 independent times on each of the four clusters. The average error follows a normal distribution, and with 95% confidence level, it takes values between -1.6% to 1.4%.
Limitations for field-enhanced atom interferometry

We discuss the possibility to enhance the sensitivity of optical interferometric devices by increasing its open area using an external field gradient that act differently on the two arms of the interfer-ometers. The use of combined electric and magnetic field cancel non linear terms that dephases the interferometer. This is possible using well defined (typically with n $\sim$ 20 Rydberg) states, a magnetic field of few Tesla and an electric field gradient of $\sim$ 10V/cm 2. However this allows only for interaction times on the order of tens of $\mu$s leading a reachable accuracy of only 1 or 2 order of magnitude higher than standard light-pulse atom interferometers. Furthermore, the control of fields and states and 3D trajectories puts severe limits to the reachable accuracy. This idea is therefore not suitable for precision measurement but might eventually be used for gravity or neutrality in antimatter studies.
A Type Checker for a Logical Framework with Union and Intersection Types

We present the syntax, semantics, and typing rules of Bull, a prototype theorem prover based on the Delta-Framework, i.e. a fully-typed lambda-calculus decorated with union and intersection types, as described in previous papers by the authors. Bull also implements a subtyping algorithm for the Type Theory Xi of Barbanera-Dezani-de'Liguoro. Bull has a command-line interface where the user can declare axioms, terms, and perform computations and some basic terminal-style features like error pretty-printing, subexpressions highlighting, and file loading. Moreover, it can typecheck a proof or normalize it. These terms can be incomplete, therefore the typechecking algorithm uses unification to try to construct the missing subterms. Bull uses the syntax of Berardi's Pure Type Systems to improve the compactness and the modularity of the kernel. Abstract and concrete syntax are mostly aligned and similar to the concrete syntax of Coq. Bull uses a higher-order unification algorithm for terms, while typechecking and partial type inference are done by a bidirectional refinement algorithm, similar to the one found in Matita and Beluga. The refinement can be split into two parts: the essence refinement and the typing refinement. Binders are implemented using commonly-used de Bruijn indices. We have defined a concrete language syntax that will allow the user to write Delta-terms. We have defined the reduction rules and an evaluator. We have implemented from scratch a refiner which does partial typechecking and type reconstruction. We have experimented Bull with classical examples of the intersection and union literature, such as the ones formalized by Pfenning with his Refinement Types in LF. We hope that this research vein could be useful to experiment, in a proof theoretical setting, forms of polymorphism alternatives to Girard's parametric one.
A study of resting-state EEG biomarkers for depression recognition

Background: Depression has become a major health burden worldwide, and effective detection depression is a great public-health challenge. This Electroencephalography (EEG)-based research is to explore the effective biomarkers for depression recognition. Methods: Resting state EEG data was collected from 24 major depressive patients (MDD) and 29 normal controls using 128 channel HydroCel Geodesic Sensor Net (HCGSN). To better identify depression, we extracted different types of EEG features including linear features, nonlinear features and functional connectivity features phase lagging index (PLI) to comprehensively analyze the EEG signals in patients with MDD. And using different feature selection methods and classifiers to evaluate the optimal feature sets. Results: Functional connectivity feature PLI is superior to the linear features and nonlinear features. And when combining all the types of features to classify MDD patients, we can obtain the highest classification accuracy 82.31% using ReliefF feature selection method and logistic regression (LR) classifier. Analyzing the distribution of optimal feature set, it was found that intrahemispheric connection edges of PLI were much more than the interhemispheric connection edges, and the intrahemispheric connection edges had a significant differences between two groups. Conclusion: Functional connectivity feature PLI plays an important role in depression recognition. Especially, intrahemispheric connection edges of PLI might be an effective biomarker to identify depression. And statistic results suggested that MDD patients might exist functional dysfunction in left hemisphere.
Interpolating Between Gradient Descent and Exponentiated Gradient Using Reparameterized Gradient Descent

Continuous-time mirror descent (CMD) can be seen as the limit case of the discrete-time MD update when the step-size is infinitesimally small. In this paper, we focus on the geometry of the primal and dual CMD updates and introduce a general framework for reparameterizing one CMD update as another. Specifically, the reparameterized update also corresponds to a CMD, but on the composite loss w.r. t. the new variables, and the original variables are obtained via the reparameterization map. We employ these results to introduce a new family of reparameterizations that interpolate between the two commonly used updates, namely the continuous-time gradient descent (GD) and unnormalized exponentiated gradient (EGU), while extending to many other well-known updates. In particular, we show that for the underdetermined linear regression problem, these updates generalize the known behavior of GD and EGU, and provably converge to the minimum $\mathrm{L}_{2-\tau}$-norm solution for $\tau\in[0,1]$. Our new results also have implications for the regularized training of neural networks to induce sparsity.
Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE)

Diffusion MRI (dMRI) is a unique imaging technique for in vivo characterization of tissue microstructure and white matter pathways. However, its relatively long acquisition time implies greater motion artifacts when imaging, for example, infants and Parkinson's disease patients. To accelerate dMRI acquisition, we propose in this paper (i) a diffusion encoding scheme, called Slice-Interleaved Diffusion Encoding (SIDE), that interleaves each diffusion-weighted (DW) image volume with slices that are encoded with different diffusion gradients, essentially allowing the slice-undersampling of image volume associated with each diffusion gradient to significantly reduce acquisition time, and (ii) a method based on deep learning for effective reconstruction of DW images from the highly slice-undersampled data. Evaluation based on the Human Connectome Project (HCP) dataset indicates that our method can achieve a high acceleration factor of up to 6 with minimal information loss. Evaluation using dMRI data acquired with SIDE acquisition demonstrates that it is possible to accelerate the acquisition by as much as 50 folds when combined with multi-band imaging.
Forming Diverse Teams from Sequentially Arriving People

Collaborative work often benefits from having teams or organizations with heterogeneous members. In this paper, we present a method to form such diverse teams from people arriving sequentially over time. We define a monotone submodular objective function that combines the diversity and quality of a team and proposes an algorithm to maximize the objective while satisfying multiple constraints. This allows us to balance both how diverse the team is and how well it can perform the task at hand. Using crowd experiments, we show that, in practice, the algorithm leads to large gains in team diversity. Using simulations, we show how to quantify the additional cost of forming diverse teams and how to address the problem of simultaneously maximizing diversity for several attributes (e.g., country of origin and gender). Our method has applications in collaborative work ranging from team formation, the assignment of workers to teams in crowdsourcing, and reviewer allocation to journal papers arriving sequentially. Our code is publicly accessible for further research.
Tensor Network Renormalization with Fusion Charges—Applications to 3D Lattice Gauge Theory

Tensor network methods are powerful and efficient tools for studying the properties and dynamics of statistical and quantum systems, in particular in one and two dimensions. In recent years, these methods have been applied to lattice gauge theories, yet these theories remain a challenge in ( 2 + 1 ) dimensions. In this article, we present a new (decorated) tensor network algorithm, in which the tensors encode the lattice gauge amplitude expressed in the fusion basis. This has several advantages—firstly, the fusion basis does diagonalize operators measuring the magnetic fluxes and electric charges associated to a hierarchical set of regions. The algorithm allows therefore a direct access to these observables. Secondly the fusion basis is, as opposed to the previously employed spin network basis, stable under coarse-graining. Thirdly, due to the hierarchical structure of the fusion basis, the algorithm does implement predefined disentanglers. We apply this new algorithm to lattice gauge theories defined for the quantum group SU ( 2 ) k and identify a weak and a strong coupling phase for various levels k . As we increase the level k , the critical coupling g c decreases linearly, suggesting the absence of a deconfining phase for the continuous group SU ( 2 ) . Moreover, we illustrate the scaling behaviour of the Wilson loops in the two phases.
Learning the mapping $\mathbf{x}\mapsto \sum_{i=1}^d x_i^2$: the cost of finding the needle in a haystack

The task of using machine learning to approximate the mapping $\mathbf{x}\mapsto\sum_{i=1}^d x_i^2$ with $x_i\in[-1,1]$ seems to be a trivial one. Given the knowledge of the separable structure of the function, one can design a sparse network to represent the function very accurately, or even exactly. When such structural information is not available, and we may only use a dense neural network, the optimization procedure to find the sparse network embedded in the dense network is similar to finding the needle in a haystack, using a given number of samples of the function. We demonstrate that the cost (measured by sample complexity) of finding the needle is directly related to the Barron norm of the function. While only a small number of samples is needed to train a sparse network, the dense network trained with the same number of samples exhibits large test loss and a large generalization gap. In order to control the size of the generalization gap, we find that the use of explicit regularization becomes increasingly more important as $d$ increases. The numerically observed sample complexity with explicit regularization scales as $\mathcal{O}(d^{2.5})$, which is in fact better than the theoretically predicted sample complexity that scales as $\mathcal{O}(d^{4})$. Without explicit regularization (also called implicit regularization), the numerically observed sample complexity is significantly higher and is close to $\mathcal{O}(d^{4.5})$.
Phylogenomics of monitor lizards and the role of competition in dictating body size disparity

Organismal interactions drive the accumulation of diversity by influencing species ranges, morphology, and behavior. Interactions vary from agonistic to cooperative and should result in predictable patterns in trait and range evolution. However, despite a conceptual understanding of these processes, they have been difficult to model, particularly on macroevolutionary timescales and across broad geographic spaces. Here we investigate the influence of biotic interactions on trait evolution and community assembly in monitor lizards (Varanus). Monitors are an iconic radiation with a cosmopolitan distribution and the greatest size disparity of any living terrestrial vertebrate genus. Between the colossal Komodo dragon Varanus komodoensis and the smallest Australian dwarf goannas, Varanus length and mass vary by multiple orders of magnitude. To test the hypothesis that size variation in this genus was driven by character displacement, we extended existing phylogenetic comparative methods which consider lineage interactions to account for dynamic biogeographic history and apply these methods to Australian monitors and marsupial predators. We use a phylogenomic approach to estimate the relationships among living and extinct varaniform lizards, incorporating both exon-capture molecular and morphological datasets. Our results suggest that communities of Australian Varanus show high functional diversity as a result of continent-wide interspecific competition among monitors but not with faunivorous marsupials. We demonstrate that patterns of trait evolution resulting from character displacement on continental scales are recoverable from comparative data and highlight that these macroevolutionary patterns may develop in parallel across widely distributed sympatric groups.
A Framework of Applying Kelly Stationary Index to Stock Trading in Taiwan Market

Portfolio management and money management have always been important issues for investors and researchers in the financial field. The Kelly criterion is a theoretical approach of money management, and is a mathematical method for optimizing long-term expected return. Kelly criterion requires the future outcomes distribution as input, which can be predicted through the techniques of machine learning (ML). With the revolutionary growth of the amount of information, big data is the key to boost ML prediction, therefore, we introduce a general Kelly framework, including the strength of Kelly, ML, and big data.In addition, we propose the Kelly stationary index (KSI) to quantity the stationarity of the stock’s outcomes distribution, which will affect the trading period and forecasting frequency. We calculate the KSI of each constituent stock of Taiwan’s 50, and apply the Kelly criterion strategy to verify the effectiveness of KSI. The experimental results show that there is a moderate downhill relationship between the strategy performance and KSI with the -0.591 of correlation coefficient. It also indicates that the closer the estimated distribution is to the actual distribution, the higher the expected profit. In the future, we will use KSI for money management, strategy development, and apply KSI into the general Kelly framework.
Establishment of Work-Flow for Roof Damage Detection Utilizing Drones, Human and AI based on Human-in-the-Loop Framework

Once disaster occurs, we have to understand the whole picture of damage situation. However, there is not enough human resources and time to do it. In 2019, we were affected by Yamagata earthquake, and many buildings were damaged in Murakami city. Most of buildings damage were concentrated on their roofs. Local responders tried to inspect those roof damage, however they cannot do it from ground. Against this issue, we decided to take images of roof damage utilizing drones. We designed the flight plan covering over affected area, operated a drone, and got images. Aftermath, we created orthophoto mosaic from those images. We published it for local responders to inspect roof damage of each building in the web-based GIS platform. Furthermore, we detect roof damage by human and let AI learn the result of our detection of roof damage. This is followed the framework of Human-in-the-Loop. Just now, accuracy of the roof-damage detection by AI was not so high. In this paper, we introduce the work-flow of this challenge from designing flight plan for drone to roof-damage detection by AI which is educated with images of actual damage situation after disaster occurrence.
Characterizing Mobile Web Traffic: A Case Study of an Academic Web Server

With the proliferation of Internet-based technologies over the past two decades and the associated growth in the volume and diversity of Internet traffic, it becomes increasingly important to understand how these changes affect the overall workload characteristics of servers. This paper revisits the seminal work of Arlitt and Williamson [1] to determine whether or not the ten invariants they derived from server logs continue to adequately characterise modern web traffic. Furthermore, a specialised analysis is performed to determine how well these invariants model mobile web traffic in particular. Our results show that while the majority of the invariants hold, some do not. In particular, combined and mobile web traffic has dramatically changed in the file types requested, and the origin of the hosts making requests, as well as a noticeable change in response types. Furthermore, mobile web traffic demonstrated significantly fewer one-time requests as compared to the original study and the combined logs from this study.
Local Differential Privacy with K-anonymous for Frequency Estimation

Data release, such as statistics of data distribution, in many data analysis and machine learning tasks is needed, which poses significant risks of user’s privacy. Usually, to preserve privacy of every individual, frequency estimation based on LDP (Local Differential Privacy) is used to replace the real distribution of data. Unfortunately, when an individual sends values multiple times, privacy leakage, i.e., same value problems may occur, along with other performance problems such as memory usage problem. To narrow these gaps, SAnonLDP (Sample Anonymous Local Differential Privacy) is proposed in this paper. We build the SAnonLDP framework by integrating k-anonymous into LDP, which includes four blocks: random grouping; anonymous and Walsh-Fourier transforms; random response; singular value decomposition (SVD). Among them, the second block ’Anonymous and Walsh-Fourier transforms’ significantly decreases the communication cost and the memory requirements. The left blocks make up for the loss of information to achieve an acceptable frequency estimation. More important, we verify that this estimation is unbiased by the strict mathematical reasoning. Finally, the numerical experiments demonstrate that SAnonLAP achieves better KL-divergence and estimation error compared to another known privacy model: RAPPOR.
Stochastic Gastric Image Augmentation for Cancer Detection from X-ray Images

X-ray examinations are a common choice in mass screenings for gastric cancer. Compared to endoscopy and other common modalities, X-ray examinations have the significant advantage that they can be performed not only by radiologists but also by radiology technicians. However, the diagnosis of gastric X-ray images is very difficult and it has been reported that the diagnostic accuracy of these images is only 85.5%. In this study, we propose a practical diagnosis support system for gastric X-ray images. An important component of our system is the proposed on-line data augmentation strategy named stochastic gastric image augmentation (sGAIA), which stochastically generates various enhanced images of gastric folds in X-ray images. The proposed sGAIA improves the detection performance of the malignant region by 6.9% in F1-score and our system demonstrates promising screening performance for gastric cancer (recall of 92.3% with a precision of 32.4%) from X-ray images in a clinical setting based on Faster R-CNN with ResNetl01 networks.