Miracle-dz/UniFAR-FTUs huggingface dataset:
Federated Learning in Smart Cities: A Comprehensive Survey

Federated learning plays an important role in the process of smart cities. With the development of big data and artificial intelligence, there is a problem of data privacy protection in this process. Federated learning is capable of solving this problem. This paper starts with the current developments of federated learning and its applications in various fields. We conduct a comprehensive investigation. This paper summarize the latest research on the application of federated learning in various fields of smart cities. In-depth understanding of the current development of federated learning from the Internet of Things, transportation, communications, finance, medical and other fields. Before that, we introduce the background, definition and key technologies of federated learning. Further more, we review the key technologies and the latest results. Finally, we discuss the future applications and research directions of federated learning in smart cities.
Phase-coherent thermoelectricity and nonequilibrium Josephson current in Andreev interferometers

We develop a detailed theory describing a nontrivial interplay between nonequilibrium effects and long-range quantum coherence in superconducting hybrid nanostructures exposed to a temperature gradient. We establish a direct relation between thermoelectric and Josephson effects in such structures and demonstrate that at temperatures exceeding the Thouless energy of our device both phase-coherent thermoelectric signal and the supercurrent may be strongly enhanced due to nonequilibrium low-energy quasiparticles propagating across the system without any significant phase relaxation. By applying a temperature gradient, one can drive the system into a well-pronounced $\ensuremath{\pi}$-junction state, thereby creating novel opportunities for applications of Andreev interferometers.
OPAM: Online Purchasing-behavior Analysis using Machine learning

Customer purchasing behavior analysis plays a key role in developing insightful communication strategies between online vendors and their customers. To support the recent increase in online shopping trends, in this work, we present a customer purchasing behavior analysis system using supervised, unsupervised and semi-supervised learning methods. The proposed system analyzes session and user-journey level purchasing behaviors to identify customer categories/clusters that can be useful for targeted consumer insights at scale. We observe higher sensitivity to the design of online shopping portals for session-level purchasing prediction with accuracy/recall in range 91-98%/73-99%, respectively. The user-journey level analysis demonstrates five unique user clusters, wherein New Shoppers are most predictable and Impulsive Shoppers are most unique with low viewing and high carting behaviors for purchases. Further, cluster transformation metrics and partial label learning demonstrates the robustness of each user cluster to new/unlabelled events. Thus, customer clusters can aid strategic targeted nudge models.
Medical Datasets Collections for Artificial Intelligence-based Medical Image Analysis

We collected 32 public datasets, of which 28 for medical imaging and 4 for natural images, to conduct study. The images of these datasets are captured by different cameras, thus vary from each other in modality, frame size and capacity. Detail information is demonstrated in Table. 1. For data accessibility, we also provide the websites of most datasets and hope this will help the readers reach the datasets. For more information, please kindly visit my personal homepage: https://wenyanger.github. io/
A comparative study of Bot Detection techniques methods with an application related to Covid-19 discourse on Twitter

Bot Detection is an essential asset in a period where Online Social Networks(OSN) is a part of our lives. This task becomes more relevant in crises, as the Covid-19 pandemic, where there is an incipient risk of proliferation of social bots, producing a possible source of misinformation. In order to address this issue, it has been compared different methods to detect automatically social bots on Twitter using Data Selection. The techniques utilized to elaborate the bot detection models include the utilization of features as the tweets metadata or the Digital Fingerprint of the Twitter accounts. In addition, it was analyzed the presence of bots in tweets from different periods of the first months of the Covid-19 pandemic, using the bot detection technique which best fits the scope of the task. Moreover, this work includes also analysis over aspects regarding the discourse of bots and humans, such as sentiment or hashtag utilization.
Efficient Estimation for Staggered Rollout Designs

We study estimation of causal effects in staggered-rollout designs—that is, settings where there is staggered treatment adoption and the timing of treatment is as good as randomly assigned. We derive the most efficient estimator in a class of estimators that nests several popular generalized difference-in-differences methods. A feasible plug-in version of the efficient estimator is asymptotically unbiased, with efficiency (weakly) dominating that of existing approaches. We provide both t-based and permutation-test-based methods for inference. In an application to a training program for police officers, confidence intervals for the proposed estimator are as much as eight times shorter than those for existing approaches.
Model Selection in Local Approximation Gaussian Processes: A Markov Random Fields Approach

Local approximations are popular methods to scale Gaussian processes (GPs) to big data. Local approximations reduce time complexity by dividing the original dataset into subsets and training a local expert on each subset. Aggregating the experts' prediction is done assuming either conditional dependence or independence between the experts. Imposing the conditional independence assumption (CI) between the experts renders the aggregation of different expert predictions time efficient at the cost of poor uncertainty quantification. On the other hand, modeling dependent experts can provide precise predictions and uncertainty quantification at the expense of impractically high computational costs. By eliminating weak experts via a theory-guided expert selection step, we substantially reduce the computational cost of aggregating dependent experts while ensuring calibrated uncertainty quantification. We leverage techniques from the literature on undirected graphical models, using sparse precision matrices that encode conditional dependencies between experts to select the most important experts. Moreover, our approach also provides a solution to the poor uncertainty quantification in CI-based models.
VPT: Privacy Preserving Energy Trading and Block Mining Mechanism for Blockchain based Virtual Power Plants

The desire to overcome reliability issues of distributed energy resources (DERs) lead researchers to development of a novel concept named as virtual power plant (VPP). VPPs are supposed to carry out intelligent, secure, and smart energy trading among prosumers, buyers, and generating stations along with providing efficient energy management. Therefore, integrating blockchain in a decentralized VPP network emerged as a new paradigm, and recent experiments over this integration have shown fruitful results. However, this decentralization also suffers with energy management, trust, reliability, and efficiency issues due to the dynamic nature of DERs. In order to overcome this, in this paper, we first work over providing an efficient energy management strategy for VPP to enhance demand response, then we propose an energy oriented trading and block mining protocol and name it as proof of energy market (PoEM). To enhance it further, we integrate differential privacy in PoEM and propose a Private PoEM (PPoEM) model. Collectively, we propose a private decentralized VPP trading model and named it as Virtual Private Trading (VPT) model. We further carry out extensive theoretical analysis and derive step-by-step valuations for market race probability, market stability probability, energy trading expectation, winning state probability, and prospective leading time profit values. Afterwards, we carry out simulation-based experiments of our proposed model. The performance evaluation and theoretical analysis of our VPT model make it one of the most viable models for blockchain based VPP networks as compared to other state-of-the-art works.
Adjoint Rigid Transform Network: Self-supervised Alignment of 3D Shapes

Most learning methods for 3D data (pointclouds, meshes) suffer signiﬁcant performance drops when the data is not carefully aligned to a canonical orientation. Aligning real world 3D data collected from different sources is non-trivial and requires manual intervention. In this paper, we propose the Adjoint Rigid Transform (ART) Network, a neural module which can be integrated with existing 3D networks to signiﬁcantly boost their performance in tasks such as shape reconstruction, non-rigid registration, and latent disentanglement. ART learns to rotate input shapes to a unique canonical orientation, which is crucial for a lot of tasks. ART achieves this with self-supervision and a rotation equivariance constraint on predicted rotations. The remarkable result is that with only self-supervision (no labels), ART can discover a unique canonical orientation for both rigid and nonrigid objects, which leads to a notable boost in downstream task performance. We will release our code and pre-trained models for further research.
Tight Integration of Feature-based Relocalization in Monocular Direct Visual Odometry

In this paper we propose a framework for inte-grating map-based relocalization into online direct visual odometry. To achieve map-based relocalization for direct methods, we integrate image features into Direct Sparse Odometry (DSO) and rely on feature matching to associate online visual odometry (VO) with a previously built map. The integration of the relocalization poses is threefold. Firstly, they are incorporated as pose priors in the direct image alignment of the front-end tracking. Secondly, they are tightly integrated into the back-end bundle adjustment. Thirdly, an online fusion module is further proposed to combine relative VO poses and global relocalization poses in a pose graph to estimate keyframe-wise smooth and globally accurate poses. We evaluate our method on two multi-weather datasets showing the benefits of integrating different handcrafted and learned features and demonstrating promising improvements on camera tracking accuracy.
Tomography of the Unique Ongoing Jet in the Planetary Nebula NGC 2392

Jets (fast collimated outflows) are claimed to be the main shaping agent of the most asymmetric planetary nebulae (PNs), as they impinge on the circumstellar material at late stages of the asymptotic giant branch phase. The first jet detected in a PN was that of NGC 2392, yet there is no available image because of its low surface brightness contrast with the bright nebular emission. Here we take advantage of the tomographic capabilities of Gran Telescopio de Canarias Multi-Espectrógrafo en GTC de Alta Resolución para Astronomía high-dispersion integral field spectroscopic observations of the jet in NGC 2392 to gain unprecedented details of its morphology and kinematics. The jet of NGC 2392 is found to emanate from the central star, break through the walls of the inner shell of this iconic PN and extend outside the nebula’s outermost regions with an S-shaped morphology suggestive of precession. At odds with the fossil jets found in mature PNs, the jet in NGC 2392 is currently being collimated and launched. The high nebular excitation of NGC 2392, which implies an He++/He ionization fraction too high to be attributed to the known effective temperature of the star, has been proposed in the past to hint at the presence of a hot white dwarf companion. In conjunction with the hard X-ray emission from the central star, the present-day jet collimation would support the presence of such a double-degenerate system where one component undergoes accretion from a remnant circumbinary disk of the common envelope phase.
A Tight Bound for Stochastic Submodular Cover

We show that the Adaptive Greedy algorithm of Golovin and Krause achieves an approximation bound of (ln(Q/η)+1) for Stochastic Submodular Cover: here Q is the “goal value” and η is the minimum gap between Q and any attainable utility value Q′<Q. Although this bound was claimed by Golovin and Krause in the original version of their paper, the proof was later shown to be incorrect by Nan and Saligrama. The subsequent corrected proof of Golovin and Krause gives a quadratic bound of (ln(Q/η)+1). A bound of 56(ln(Q/η)+1) is implied by work of Im et al. Other bounds for the problem depend on quantities other than Q and η. Our bound restores the original bound claimed by Golovin and Krause, generalizing the well-known (ln m+1) approximation bound on the greedy algorithm for the classical Set Cover problem, where m is the size of the ground set.
Targeted Branching for the Maximum Independent Set Problem

Finding a maximum independent set is a fundamental NP-hard problem that is used in many real-world applications. Given an unweighted graph, this problem asks for a maximum cardinality set of pairwise non-adjacent vertices. In recent years, some of the most successful algorithms for solving this problem are based on the branch-and-bound or branch-and-reduce paradigms. In particular, branch-and-reduce algorithms, which combine branch-and-bound with reduction rules, have been able to achieve substantial results, solving many previously infeasible real-world instances. These results were to a large part achieved by developing new, more practical reduction rules. However, other components that have been shown to have a significant impact on the performance of these algorithms have not received as much attention. One of these is the branching strategy, which determines what vertex is included or excluded in a potential solution. Even now, the most commonly used strategy selects vertices solely based on their degree and does not take into account other factors that contribute to the performance of the algorithm. In this work, we develop and evaluate several novel branching strategies for both branch-andbound and branch-and-reduce algorithms. Our strategies are based on one of two approaches which are motivated by existing research. They either (1) aim to decompose the graph into two or more connected components which can then be solved independently, or (2) try to remove vertices that hinder the application of a reduction rule which can lead to smaller graphs. Our experimental evaluation on a large set of real-world instances indicates that our strategies are able to improve the performance of the state-of-the-art branch-and-reduce algorithm by Akiba and Iwata. To be more specific, our reduction-based packing branching rule is able to outperform the default branching strategy of selecting a vertex of highest degree on 65% of all instances tested. Furthermore, our decomposition-based strategy based on edge cuts is able to achieve a speedup of 2.29 on sparse networks (1.22 on all instances). 2012 ACM Subject Classification Mathematics of computing → Graph algorithms; Theory of computation → Branch-and-bound; Mathematics of computing → Combinatorial optimization
Some remarks on rotation theorems for complex polynomials

For any complex polynomial P (z) = c0+c1z+. +cnz , cn 6= 0, having all its zeros in the unit disk |z| ≤ 1, we consider the behavior of the function (argP (e))θ when the real argument θ changes. We give some sharp estimates of this function involving of the values of P (e), argP (e) or the coefficients ck, k = 0, 1, n − 1, n.
Depth separation beyond radial functions

High-dimensional depth separation results for neural networks show that certain functions can be efficiently approximated by two-hidden-layer networks but not by one-hidden-layer ones in high-dimensions $d$. Existing results of this type mainly focus on functions with an underlying radial or one-dimensional structure, which are usually not encountered in practice. The first contribution of this paper is to extend such results to a more general class of functions, namely functions with piece-wise oscillatory structure, by building on the proof strategy of (Eldan and Shamir, 2016). We complement these results by showing that, if the domain radius and the rate of oscillation of the objective function are constant, then approximation by one-hidden-layer networks holds at a $\mathrm{poly}(d)$ rate for any fixed error threshold. A common theme in the proofs of depth-separation results is the fact that one-hidden-layer networks fail to approximate high-energy functions whose Fourier representation is spread in the domain. On the other hand, existing approximation results of a function by one-hidden-layer neural networks rely on the function having a sparse Fourier representation. The choice of the domain also represents a source of gaps between upper and lower approximation bounds. Focusing on a fixed approximation domain, namely the sphere $\mathbb{S}^{d-1}$ in dimension $d$, we provide a characterisation of both functions which are efficiently approximable by one-hidden-layer networks and of functions which are provably not, in terms of their Fourier expansion.
Mean-field theory of an asset exchange model with economic growth and wealth distribution.

We develop a mean-field theory of the growth, exchange, and distribution (GED) model introduced by Liu et al. [K. K. L. Liu et al., preceding paper, Phys. Rev. E 104, 014150 (2021)10.1103/PhysRevE.104. 014150] that accurately describes the phase transition in the limit that the number of agents N approaches infinity. The GED model is a generalization of the yard-sale model in which the additional wealth added by economic growth is nonuniformly distributed to the agents according to their wealth in a way determined by the parameter λ. The model is shown numerically to have a phase transition at λ=1 and be characterized by critical exponents and critical slowing down. Our mean-field treatment of the GED model correctly predicts the existence of the phase transition, a critical slowing down, and the values of the critical exponents and introduces an energy whose probability satisfies the Boltzmann distribution for λ<1, implying that the system is in thermodynamic equilibrium in the limit that N→∞. We show that the values of the critical exponents obtained by varying λ for a fixed value of N do not satisfy the usual scaling laws, but do satisfy scaling if a combination of parameters, which we refer to as the Ginzburg parameter, is much greater than one and is held constant. We discuss possible implications of our results for understanding economic systems and the subtle nature of the mean-field limit in systems with both additive and multiplicative noise.
Human-Machine Collaborative Video Coding Through Cuboidal Partitioning

Video coding algorithms encode and decode an entire video frame while feature coding techniques only preserve and communicate the most critical information needed for a given application. This is because video coding targets human perception, while feature coding aims for machine vision tasks. Recently, attempts are being made to bridge the gap between these two domains. In this work, we propose a video coding framework by leveraging on to the commonality that exists between human vision and machine vision applications using cuboids. This is because cuboids, estimated rectangular regions over a video frame, are computationally efficient, has a compact representation and object centric. Such properties are already shown to add value to traditional video coding systems. Herein cuboidal feature descriptors are extracted from the current frame and then employed for accomplishing a machine vision task in the form of object detection. Experimental results show that a trained classifier yields superior average precision when equipped with cuboidal features oriented representation of the current test frame. Additionally, this representation costs 7% less in bit rate if the captured frames are need be communicated to a receiver.
VLT/X-shooter spectroscopy of the GRB 120327A afterglow⋆⋆⋆

Aims. We present a study of the environment of the Swift long gamma-ray burst GRB 120327A at z ≈ 2.8 through optical spectroscopy of its afterglow. Methods. We analyzed medium-resolution, multi-epoch spectroscopic observations (R ∼ 7000−12 000, corresponding to ∼15−23 km s−1, S/N = 15−30 and wavelength range 3000−25 000 Å) of the optical afterglow of GRB 120327A, taken with X-shooter at the VLT 2.13 and 27.65 hr after the GRB trigger. Results. The first epoch spectrum shows that the ISM in the GRB host galaxy at z = 2.8145 is extremely rich in absorption features, with three components contributing to the line profiles. The hydrogen column density associated with GRB 120327A has log NH/cm−2 = 22.01 ± 0.09, and the metallicity of the host galaxy is in the range [X/H] = −1.3 to −1.1. In addition to the ground state lines, we detect absorption features associated with excited states of C ii, O i, Si ii, Fe ii, and Ni ii, which we used to derive information on the distance between the host absorbing gas and the site of the GRB explosion. The variability of the Fe ii λ2396 excited line between the two epochs proves that these features are excited by the GRB UV flux. Moreover, the distance of component I is found to be dI = 200+100 −60 pc, while component II is located closer to the GRB, at dII = 100 +40 −30 pc. These values are among the lowest found in GRBs. Component III does not show excited transitions, so it should be located farther away from the GRB. The presence of H2 molecules is firmly established, with a molecular fraction f in the range f = 4 × 10−7–10−4. This particularly low value can be attributed to the small dust content. This represents the third positive detection of molecules in a GRB environment.
ASSOCIATION BETWEEN SINGLE NUCLEOTIDE POLYMORPHISMS AND BACKFAT THICKNESS IN NELLORE CATTLE USING HIGH DENSITY PANELS

The objective of this work was to analyze the genomic association between SNP markers and backfat thickness in Nelore cattle, aiming to bring information for genetic evaluation of animals for this trait. A total of 740 Nellore males born in 2008 and 2009 were used. The contemporary groups were defined as: year, farm and management group at birth, and management group at weaning and yearling. The slaughter of animals occurred in commercial slaughterhouses and the measurement of backfat thickness was performed using calipers, measuring the fat layer at an angle of 45° from the geometric center of the sample, between the 12 and 13 ribs. The DNA was extracted from 5 grams of Longissimus dorsi muscle. The genotyping was performed using the high density panel, Illumina BovineHD BeadChip, with 777,962 SNP markers throughout the genome. After the quality control, 446,986 markers were available for genomic association. For backfat thickness, 68, 38 and 35 significant SNPs (p<0.001) were found, located respectively on chromosomes 7, 8 and 10.