Miracle-dz/UniFAR-FTUs huggingface dataset:
Program logics for ledgers

9 Distributed ledgers nowadays manage substantial monetary funds in the form of cryptocurrencies 10 such as Bitcoin, Ethereum, and Cardano. For such ledgers to be safe, operations that add new 11 entries must be cryptographically sound—but it less clear how to reason eﬀectively about such 12 ever-growing linear data structures. 13 This paper views distributed ledgers as computer programs , that, when executed, transfer funds 14 between various parties. As a result, familiar program logics, such as Hoare logic and separation 15 logic, can be deﬁned in this novel setting. Borrowing ideas from concurrent separation logic, this 16 enables modular reasoning principles over arbitrary fragments of any ledger. 17 All the results presented in this paper have been mechanised in the Agda proof assistant and 18 are publicly available. 19
A two-stream convolution architecture for environment sound classiﬁcation based on audio feature disentanglement

Abstract: ESC (Environmental Sound Classiﬁcation) is an active area of research in the ﬁeld of audio classiﬁcation that has made signiﬁcant progress in recent years. The current mainstream ESC methods are based on increasing the dimension of the extracted audio features and therefore draw on the two-dimensional convolution methods used in image processing. However, two-dimensional convolution is expensive to train and the complexity of the corresponding model is usually very high. In response to these issues, we propose a novel two-stream neural network model by the idea of disentanglement, which uses one-dimensional convolution for feature extraction to disentangle the audio features into the time and frequency domains separately. Our approach balances computational pressure with classiﬁcation accuracy well. The accuracy of our approach on the Urbansound 8k and Esc-10 datasets was 98.51% and 97.50%, respectively, which exceeds that of most models. Meanwhile, the model complexity is also lower
Structure of Nonlinear Node Embeddings in Stochastic Block Models

Nonlinear node embedding techniques such as DeepWalk and Node2Vec are used extensively in practice to uncover structure in graphs. Despite theoretical guarantees in special regimes (such as the case of high embedding dimension), the structure of the optimal low dimensional embed-dings has not been formally understood even for graphs obtained from simple generative models. We consider the stochastic block model and show that under appropriate separation conditions, the optimal embeddings can be analytically characterized. Akin to known results on eigenvector based (spectral) embeddings, we prove theoretically that solution vectors are well-clustered, up to a sublinear error.
BeautifAI - Personalised Occasion-based Makeup Recommendation

With the global metamorphosis of the beauty industry and the rising demand for beauty products worldwide, the need for a robust makeup recommendation system has never been more. Despite the significant advancements made towards personalised makeup recommendation, the current research still falls short of incorporating the context of occasion and integrating feedback for users. In this work, we propose BeautifAI , a novel recommendation system, delivering personalised occasion-oriented makeup recommendations to users. The proposed work’s novel contributions, including incorporating occasion context to makeup recommendation and a region-wise method using neural embeddings, set our system apart from the current work in makeup recommendation. We also propose real-time makeup previews and continuous makeup feedback to provide a more personalised and interactive experience to users.
Select and Optimize: Learning to aolve large-scale TSP instances

Learning-based algorithms to solve TSP are getting popular in recent years, but most existing works cannot solve very large-scale TSP instances within a limited time. To solve this problem, this paper introduces a creative and distinctive method to select and locally optimize sub-parts of a solution. Concretely, we design a novel framework to generalize a small-scale selector-and-optimizer network to large-scale TSP instances by iteratively selecting while optimizing one sub-problem. At each iteration, the running time of sub-problem sampling and selection is significantly reduced due to the full use of parallel computing. Our neural model is well-designed to exploit the characteristics of the sub-problems. Furthermore, we introduce a trick called destroy-and-repair to avoid the local minimum of the iterative algorithm from a global perspective. Extensive experiments show that our method accelerates state-of-the-art learning-based algorithm more than 2x while achieving better solution quality on large-scale TSP instances ranging in size from 200 to 20,000.
A gamified career guidance platform with the potential to motivate young people intrinsically

Digital offerings for young people's career orientation are on the rise, but it is a challenge to design digital applications that are used voluntarily outside of school. In this paper, we present a gamification and design concept of a career guidance platform and results of a field phase (n = 35) at a German school (grade 8) in which we investigated the perceived intrinsic and extrinsic motivation as well as the usefulness of the elements of the platform. Our results show that perceived intrinsic motivation is higher than perceived extrinsic motivation for all elements; badges, items, progress indicator, challenges & quests, buddy with feedback function, story, vision board, interactive graphics, todo list, and a certificate . The perceived intrinsic motivation and the usefulness of all elements correlate positively (highly significant). For almost all elements, a significant correlation between intrinsic and extrinsic motivation is also evident, except for the story .
PAC Learning of Halfspaces with Malicious Noise in Nearly Linear Time

We study the problem of efﬁcient PAC learning of halfspaces in R d in the presence of the malicious noise, where a fraction of the training samples are adversarially corrupted. A series of recent works have developed polynomial-time algorithms that enjoy near-optimal sample complexity and noise tolerance, yet leaving open whether a linear-time algorithm exists and matches these appealing statistical performance guarantees. In this work, we give an afﬁrmative answer by developing an algo-rithm that runs in time ˜ O ( md ) , where m = ˜ O ( d(cid:15) ) is the sample size and (cid:15) ∈ (0 , 1) is the target error rate. Notably, the computational complexity of all prior algorithms suffer either a high order dependence on the problem size, or is implicitly proportional to 1 (cid:15) 2 through the sample size. Our key idea is to combine localization and an approximate version of matrix multiplicative weights update method to progressively downweight the contribution of the corrupted samples while reﬁn-ing the learned halfspace.
Thresholded linear bandits

We introduce the thresholded linear bandit problem, a novel sequential decision making problem at the interface of structured stochastic multiarmed bandits and learning halfspaces. The set of arms is [0, 1], the expected Bernoulli reward is piecewise constant with a jump at a separating hyperplane, and each arm is associated with a cost that is a positive linear combination of the arm’s components. This problem is motivated by several practical applications. For instance, imagine tuning the continuous features of an offer to a consumer; higher values incur higher cost to the vendor but result in a more attractive offer. At some threshold, the offer is attractive enough for a random consumer to accept at the higher probability level. For the one-dimensional case, we present Leftist, which enjoys log T problem-dependent regret in favorable cases and has log(T ) √ T worstcase regret; we also give a lower bound suggesting this is unimprovable. We then present MD-Leftist, our extension of Leftist to the multi-dimensional case, which obtains similar regret bounds but with d log d and d log d dependence on dimension for the two types of bounds respectively. Finally, we experimentally evaluate Leftist.
Learning with Domain Knowledge to Develop Justifiable Convolutional Networks

The inherent structure of the Convolutional Neural Networks (CNN) allows them to extract features that are highly correlated with the classes while performing image classiﬁcation. However, it may happen that the extracted features are merely coincidental and may not be justiﬁable from a human perspective. For example, from a set of images of cows on grassland, CNN can erroneously extract grass as the feature of the class cow. There are two main limitations to this kind of learning: ﬁrstly, in many false-negative cases, correct features will not be used, and secondly, in false-positive cases the system will lack accountability. There is no implicit way to inform CNN to learn the features that are justiﬁable from a human perspective to resolve these issues. In this paper, we argue that if we provide domain knowledge to guide the learning process of CNN, it is possible to reliably learn the justiﬁable features. We propose a systematic yet simple mechanism to incorporate domain knowledge to guide the learning process of the CNNs to extract justiﬁable features. The ﬂip side is that it needs additional input. However, we have shown that even with minimal additional input our method can eﬀectively propagate the knowledge within a class during training. We demonstrate that justiﬁable features not only enhance accuracy but also demand less amount of data and training time. Moreover, we also show that the proposed method is more robust against perturbational changes in the input images.
Mixed Linear Regression via Approximate Message Passing

In mixed linear regression, each observation comes from one of L regression vectors (signals), but we do not know which one. The goal is to estimate the signals from the unlabeled observations. We propose a novel approximate message passing (AMP) algorithm for estimation and rig-orously characterize its performance in the high-dimensional limit. This characterization is in terms of a state evolution recursion, which allows us to precisely compute performance measures such as the asymptotic mean-squared error. This can be used to tailor the AMP algo-rithm to take advantage of any known structural information about the signals. Using state evolution, we derive an optimal choice of AMP ‘de-noising’ functions that minimizes the estimation error in each iteration. Numerical simulations are provided to validate the theoretical results, and show that AMP significantly outperforms other estimators including spectral methods, expectation maximization, and alternating minimization. Though our numerical results focus on mixed linear regression, the proposed AMP algorithm can be applied to a broader class of models including mixtures of generalized linear models and max-affine regression.
Using Sliced Mutual Information to Study Memorization and Generalization in Deep Neural Networks

In this paper, we study the memorization and generalization behaviour of deep neural networks (DNNs) using sliced mutual information (SMI), which is the average of the mutual information (MI) between one-dimensional random projections. We argue that the SMI between features in a DNN ( T ) and ground truth labels ( Y ), SI ( T ; Y ) , can be seen as a form of usable information that the features contain about the labels. We show theoretically that SI ( T ; Y ) can encode geometric properties of the feature distribution, such as its spherical soft-margin and intrinsic dimensionality, in a way that MI cannot. Additionally, we present empirical evidence showing how SI ( T ; Y ) can capture memorization and generalization in DNNs. In particular, we find that, in the presence of label noise, all layers start to memorize but the earlier layers stabilize more quickly than the deeper layers. Finally, we point out that, in the context of Bayesian Neural Networks, the SMI between the penultimate layer and the output represents the worst case uncertainty of the network’s output.
A Unified Perspective on Regularization and Perturbation in Differentiable Subset Selection

Subset selection, i.e., ﬁnding a bunch of items from a collection to achieve speciﬁc goals, has wide applications in information retrieval, statistics, and machine learning. To implement an end-to-end learning framework, different relaxed differentiable operators of subset selection are proposed. Most existing work relies on either regularization method or perturbation method . In this work, we provide a probabilistic interpretation for regularization relaxation and unify two schemes. Besides, we build some concrete examples to show the generic connection between these two relaxations. Finally, we evaluate the perturbed selector as well as the regularized selector on two tasks: the maximum entropy sampling problem and the feature selection problem. The experimental results show that these two methods can achieve competitive performance against other benchmarks.
Annotated Lexicon for Sentiment Analysis in Bosnian Language

The paper presents the first sentiment annotated lexicon of the Bosnian language. The language coverage of the lexicon was evaluated using two reference corpora. The usability of the lexicon was already proven on a Twitter-based comparison. Two approaches were observed in this experiment, the first method used a frequency list of all lemmas extracted from two relevant Bosnian language corpora, and the second method used all lemmas occurrences without using frequency as the main factor in counting. The results of the study suggest usable language coverage. The computed coverage for the first corpus was 27 . 25% , while the second corpus yields 24 . 34% . The second method yields 1 . 899% coverage for the first corpus and 6 . 05% for the second corpus.
A Case Study of Leaf Wettability Variability and the Relations with Leaf Traits and Surface Water Storage for Urban Landscape Plants

Leaf wettability, the affinity of a leaf surface to water droplets, affects the interactions between leaves and external environments. This study aimed to determine the interspecific and seasonal variabilities of leaf wettability across 30 common landscape plants, and their relationships with leaf functional traits, surface micromorphology and rainfall interception in Hefei city, China. Results indicated that leaf wettability was species-specific, and the adaxial and abaxial contact angles ranged from 63° to 134° and 66° to 134°, respectively, with the adaxial surface proving more wettable. Leaf wettability gradually increased from spring to winter. Classification of life forms revealed that there were no significant wettability differences among trees, shrubs and herbs, and between evergreen and deciduous plants, but deciduous plants’ wettability increased more significantly in winter. Leaf wettability was not significantly correlated with any leaf functional traits. Single surface microscopic parameters also had low correlations with leaf wettability. Instead, the low-wettability species were found to possess more prominent epidermis cells, dense waxy layers or trichomes on leaf surfaces. Leaf wettability was the best predictor of surface rainwater storage within all functional traits. Our results highlighted that leaf wettability was variable between different species and growth periods due to micromorphological differences, and significantly affected rainfall interception at the leaf scale, which may have great significance for evaluating plant hydrological function in urban areas.
A Discrete Model for Bootstrap Iteration

The bootstrap can be validated by considering the sequence of P values obtained by bootstrap iteration, rather than asymptotically. If this sequence converges to a random variable with the uniform U(0,1) distribution, the bootstrap is valid. Here, the model is made discrete and finite, characterised by a three-dimensional array of probabilities. This renders bootstrap iteration to any desired order feasible. A unit-root test for a process driven by a stationary MA(1) process is known to be unreliable when the MA(1) parameter is near −1. Iteration of the bootstrap P value to convergence achieves reliable inference unless the parameter value is very close to −1.
First-principles modeling of magnetic misfit interfaces

We investigate the structural and magnetic properties of interfaces with large lattice mismatch, choosing Pt/Co and Au/Co as prototypes. For our first-principles calculations, we reduce the lattice mismatch to 0.2% by constructing Moir\'e supercells. Our results show that the roughness and atomic density, and thus the magnetic properties, depend strongly on the substrate and thickness of the Co slab. An increasing thickness leads to the formation of a Co transition layer at the interface, especially for Pt/Co due to strong Pt-Co interaction. A Moir\'e supercell with a transition layer is found to reproduce the main experimental findings and thus turns out to be the appropriate model for simulating magnetic misfit interfaces.
Through the kaleidoscope: star formation the host galaxies of radio-AGN

Abstract There exist strong evidence supporting the co-evolution of central supermassive black holes and their host galaxies; however it is still under debate how such a relation comes about and whether it is relevant for all or only a subset of galaxies. A rich multi-wavelength dataset is available for the North Ecliptic Pole field, most notably surveyed by the AKARI infrared space telescope. We investigate the star-formation properties of the host galaxies of radio-AGN together with the radio feedback mechanism, potentially responsible for the eventual quenching of star formation. Using broadband SED modelling, the nuclear and host galaxy components of these sources are studied as a function of their radio luminosity. Here we present results concerning the AGN content of the radio sources in this field, while offering evidence supporting a “maintenance” type of feedback from powerful radio-jets.
Numerical Renormalization, Doped Antiferromagnets, and Statistics - Topics in Low Dimensional Physics

The first part of the thesis deals with so called numerical renormalization group methods, and in particular the density-matrix renormalization group (DMRG). This method performs extremely well for large one-dimensional quantum systems like spin-chains and Hubbard models. We discuss general features of the quantum states obtained by using this method for large systems. One finds that the method works better, i.e. faster convergence and higher accuracy, when applied to systems having an energy gap between the ground state and the first excited state. An investigation of the behavior of the DMRG for a simple gapless system is done, mainly focusing on the properties of correlation functions. The second part of the thesis deals with topics related to superconductivity. It starts with a discussion of magnetic Josephson junctions and the physical processes governing the behavior of such a system. We also introduce and discuss models of high-temperature superconductors, in particular the t-J model and its relation to stripes. Starting from the t-J model on a two dimensional square lattice, we discuss an approximation where the spin-configuration is assumed static. We discuss the properties of the fictitious magnetic field experienced by the electrons due to the static spin-configuration. Configurations being close to ferromagnetic and antiferromagnetic turn out to have very different properties. Furthermore, we derive an effective model for this system and look for different phases of this model. In particular we investigate whether the system takes advantage of a spin-generated fictitious flux. Uniform phases are discussed in a Hartree-Fock approximation and we also consider stripes between antiferromagnetically ordered regions, these being modeled in a self-consistent Hartree approximation. Finally, and inspired by the fictitious fluxes, there is a chapter discussing the statistics of particles living on a circle. It is shown that there are two free parameters in the quantization of such a system and we also sort out the relation between fermions and hard-core bosons in one dimension.
Effect of serrated trailing edge on boundary layer instability noise

Wings operating at low and moderate Reynolds number such as the ones of UAVs or the blades of small wind turbines and of compressor fans, can be the source of an aeroacoustics phenomenon called laminar boundary layer instability noise. This paper presents an investigation with time-resolved PIV and far field microphones of the dynamical flow phenomena and the acoustic emissions of a NACA 0018 airfoil, with and without serrated trailing edges, operating in a transitional regime (Reynolds number≈ 3.3− 4.7× 10). The measurements are done in an open jet wind tunnel facility with low noise and turbulence intensity. The planar PIV measurements are done on the pressure side of the airfoil in two different planes, parallel and orthogonal to the wing span at the trailing edge region, to capture the flow evolution along the airfoil chord as well as its spanwise arrangement. The results presented here show the effectiveness of the serrated geometries to reduce the noise emitted by this type of flows. Recently published literature outcomes concerning the streamwise evolution of the flow with and without serrations are confirmed. Moreover new insights related to the spanwise coherence of the traveling vortical structures are presented leading to a better understanding of the hydrodynamic and acoustic mechanisms involved in this aeroacoustics phenomenon.
Machine-Learning-Based Indoor Localization under Shadowing Condition for P-NOMA VLC Systems

The localization of agents for collaborative tasks is crucial to maintain the quality of the communication link for successful data transmission between the base station and agents. Power-domain Non-Orthogonal Multiple Access (P-NOMA) is an emerging multiplexing technique that enables the base station to accumulate signals for different agents using the same time-frequency channel. The environment information such as distance from the base station is required at the base station to calculate communication channel gains and allocate suitable signal power to each agent. The accurate estimate of the position for power allocation of P-NOMA in a dynamic environment is challenging due to the changing location of the end-agent and shadowing. In this paper, we take advantage of the two-way Visible Light Communication (VLC) link to (1) estimate the position of the end-agent in a real-time indoor environment based on the signal power received at the base station using machine learning algorithms and (2) allocate resources using the Simplified Gain Ratio Power Allocation (S-GRPA) scheme with the look-up table method. In addition, we use the Euclidean Distance Matrix (EDM) to estimate the location of the end-agent whose signal was lost due to shadowing. The simulation results show that the machine learning algorithm is able to provide an accuracy of 0.19 m and allocate power to the agent.
Comparison of the Performance-Degrading Near-Interface Traps in Commercial SiC MOSFETs

This paper presents a comparison of the density of performance-degrading near-interface traps (NITs) in the most commonly available 1200 V commercial N-channel SiC power metal–oxide–semiconductor field-effect transistors (MOSFETs). A recently developed integrated-charge technique was used to measure the density of NITs with energy levels aligned to the conduction band, which degrade MOSFET’s performance by capturing and releasing electrons from the channel biased in the strong-inversion condition. Trench MOSFETs of one manufacturer have lower densities of these NITs in comparison to MOSFETs with the planar gate structure, corresponding to observed higher channel-carrier mobility in trench MOSFETs. Different response-time distributions were also observed, corresponding to different spatial location of the measured NITs.