Miracle-dz/UniFAR-FTUs huggingface dataset:
PayPlace: Secure and Flexible Operator-Mediated Payments in Blockchain Marketplaces at Scale

Decentralized marketplace applications demand fast, cheap and easy-to-use cryptocurrency payment mechanisms to facilitate high transaction volumes. The standard solution for off-chain payments, state channels, are optimized for frequent transactions between two entities and impose prohibitive liquidity and capital requirements on payment senders for marketplace transactions. We propose PayPlace, a scalable off-chain protocol for payments between consumers and sellers. Using PayPlace, consumers establish a virtual unidirectional payment channel with an intermediary operator to pay for their transactions. Unlike state channels, however, the PayPlace operator can reference the custodial funds accrued off-chain in these channels to in-turn make tamper-proof off-chain payments to merchants, without locking up corresponding capital in channels with merchants. Our design ensures that new payments made to merchants are guaranteed to be safe once notarized and provably mitigates well-known drawbacks in previous constructions like the data availability attack and ensures that neither consumers nor merchants need to be online to ensure continued safety of their notarized funds. We show that the on-chain monetary and computational costs for PayPlace is O(1) in the number of payment transactions processed, and is near-constant in other parameters in most scenarios. PayPlace can hence scale the payment throughput for large-scale marketplaces at no marginal cost and is orders of magnitude cheaper than the state-of-art solution for non-pairwise off-chain payments, Zero Knowledge Rollups.
Incorporating spatial autocorrelation and settlement type segregation to improve the performance of an urban growth model

As rapid urbanization and population growth have become global issues, urban growth modeling has become an essential tool for decision-makers to understand how urban growth works in overall dense environments and to assess the sustainability of current urban forms. However, in urban growth models (particularly when incorporating quantitative approaches to include driving factors of urban growth), spatial autocorrelation may influence the overall model performance. In this paper, an empirical study was conducted in the region of Munich, and an integrated urban growth model was tested to explain current urban growth. The modeling contributes to advances in the state of the art by combining a range of driving factors using autologistic regression with a transition probability matrix from the Markov chain method in a cellular automata model simulation. The autologistic regression employed here addresses the impact of spatial autocorrelation compared to ordinary logistic regression. Furthermore, this study compared modeling of overall settlement growth with modeling high- and low-density settlement types separately. Incorporating spatial dependency into the model through application of autologistic regression showed improvements when compared to the ordinary logistic regression model. The Kappa indexes were higher when separately modeling the two types of settlement density compared to modeling overall settlement growth since the driving factors of settlement growth of different densities might be different. From an urban planning perspective, this novel autologistic regression-Markov chain-based cellular automata model is a powerful tool that offers an opportunity for planners and government authorities to gain a more precise understanding of the different urban growth processes that might occur in an urban region similar to the one tested here. It should allow for a better assessment of the potential costs, benefits, and risks of corresponding planning strategies.
Occupational Hazards in Dentistry and Preventing Them

Every profession has its own unavoidable occupational risks. Dentists, being in a highly professional occupation today, encounter many hazards related to their habitations. Some of the risks they face are within the scope of knowledge of dental surgeons, while others are somehow skipped. It is mandatory to have a thorough knowledge of professional hazards one may encounter while working, and proper measures must be taken to prevent them. A dental surgeon works around many perils in the business environment while being a prosthodontist, periodontist, endodontist, orthodontist, etc. There are many risk factors specific to any particular specialty. Hazards most commonly encountered are musculoskeletal disorders (MSDs), latex hypersensitivity, and professional burnout. Dental surgeons are also exposed to many bloodborne pathogens, sharp instrument injuries, ocular and hearing problems, neuromuscular disorders, cross-contamination, dental material allergies, mercury poisoning, radiation hazards, chemical injuries, etc. Some problems may cause the death of dentistry personnel if proper care is not taken. Thus, it is crucial to conduct awareness programs on early diagnosis, health education, and prevention modalities.
Damage evaluation by means of cyclostationarity

Our study focuses on the estimation of 316L stainless steel specimens lifetime, subjected to Low Cycle Fatigue (LCF) and High Cycle Fatigue (HCF). So we developed a fatigue damage test rig using alternative bending. The LCF is defined by repetitive cyclic stress in a short period. The material behaviour subjected to LCF and HCF can provide information linked to the fatigue damage. The purpose of this study is to analyze and caracterize the fatigue of a material subjected to LCF and HCF using signal processing tools. We observe that the specimen vibration signal is a coupling of periodic phenomenon (cycle of sollicitaion) with random stationary phenomenon (random amplitude due to the fatigue damage). For this reason spectral analysis and cyclostationary study are carry out during the manipulation setup. Our observations show that the fatigue damage of the material produces a periodic stochastic processes (cyclostationarity of order 2 ) appear in vibration signal. Accordingly we propose a new indicator for fatigue damage : the amplitude of cyclic frequencies.
A Study of Edge Computing Offloading Based on Security

Abstract With the widespread use of IOT scenario and the development of mobile network technology, the access to many devices at the edge of the network has generated an exponential increase in the volume of data, bringing higher than ever before the demand for data transmission bandwidth, traditional centralized cloud computing can no longer meet the demand, the need to adopt edge computing distributed collaborative approach to data processing. In this paper, we study a mobile edge computing model based on energy consumption optimization management under a certain delay constraint, focus on an edge computing offload scheme based on security management, and design an offload decision scheme based on a multi-objective optimization hybrid quantum evolution algorithm (MHQEA) to ensure the security of user equipments during computing offload in the edge network and reduce total system energy consumption.
To predict transitional and fully developed turbulent flow in a backward facing step geometry

This paper concerns the use of Computational Fluid Dynamics (CFD) for the prediction of room air movement and provide a guide on selecting of the proper turbulence model. The benchmark which developed here for the first time is a backward-facing step flow problem. The measurements is performed in a small-scale model and the velocity filed is measured by Particle Image Velocimetry (PIV). The measurements focus on transitional flow and fully developed turbulent flow at isothermal condition. The PIV measurements and different CFD predictions are compared. The CFD predictions are generally slightly diverse, and this is, among other things, the result of using different turbulence models as well as using different software codes, grid distribution and boundary values etc. The k-ε family of turbulence models is an option for fully developed flow, and the k-ω family does work for transitional low turbulent flow in this backward-facing step flow.
Learning transferable skills in large lecture halls: Implementing a POGIL approach in biochemistry

As research‐based, active learning approaches become more common in biochemistry classrooms, the large lecture course remains the most challenging to transform. Here, we provide a case study demonstrating how process oriented guided inquiry learning (POGIL) can be implemented in a large class taught in a traditional lecture hall. Course structure and multiple strategies to support student learning and encourage engagement are described in detail. Therefore, this case study could act as a model for others wishing to transform their own courses from lecture to a more student‐centered format. Student feedback about the course format was overwhelmingly positive and preliminary assessment data demonstrated student learning gains in several important areas. BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION, 2012
Noise Contrastive Estimation for Autoencoding-based One-Class Collaborative Filtering

One-class collaborative filtering (OC-CF) is a common class of recommendation problem where only the positive class is explicitly observed (e.g., purchases, clicks). Autoencoder based recommenders such as AutoRec and variants demonstrate strong performance on many OC-CF benchmarks, but also empirically suffer from a strong popularity bias. While a careful choice of negative samples in the OC-CF setting can mitigate popularity bias, Negative Sampling (NS) is often better for training embeddings than for the end task itself. To address this, we propose a two-headed AutoRec to first train an embedding layer via one head using Negative Sampling then to train for the final task via the second head. While this NS-AutoRec improves results for AutoRec and outperforms many state-of-the-art baselines on OC-CF problems, we notice that Negative Sampling can still take a large amount of time to train. Since Negative Sampling is known to be a special case of Noise Contrastive Estimation (NCE), we adapt a recently proposed closed-form NCE solution for collaborative filtering to AutoRec yielding NCE-AutoRec. Overall, we show that our novel two-headed AutoRec models (NCE-AutoRec and NS-AutoRec) successfully mitigate the popularity bias issue and maintain competitive performance in comparison to state-of-the-art recommenders on multiple real-world datasets.
Weakly Supervised Construction of ASR Systems from Massive Video Data

Despite the rapid development of deep learning models, for real-world applications, building large-scale Automatic Speech Recognition (ASR) systems from scratch is still signiﬁcantly challenging, mostly due to the time-consuming and ﬁnancially-expensive process of annotating a large amount of audio data with transcripts. Although several self-supervised pre-training models have been proposed to learn speech representations, applying such models directly might be sub-optimal if more labeled, training data could be obtained without a large cost. In this paper, we present VideoASR, a weakly supervised framework for constructing ASR systems from massive video data. As user-generated videos often contain human-speech audio roughly aligned with subtitles, we consider videos as an im-portant knowledge source, and propose an effective approach to extract high-quality audio aligned with transcripts from videos based on text detection and Optical Character Recognition. The underlying ASR models can be ﬁne-tuned to ﬁt any domain-speciﬁc target training datasets after weakly supervised pre-training on automatically generated datasets. Extensive experiments show that VideoASR can easily produce state-of-the-art results on six public datasets for Mandarin speech recognition. In addition, the VideoASR framework has been deployed on the cloud to support various industrial-scale applications.
Light Perturbed by Gravitational Waves

Light undergoes perturbation as gravitational waves (GWs) pass by. This can be shown by solving Maxwell's equations in a spacetime perturbed by GWs. It can be further shown that a perturbation of light due to GWs leads to a delay of the photon transit time. A simple application of this principle is presented with regard to the detection of GWs via a pulsar timing array (PTA).
DPLink: User Identity Linkage via Deep Neural Network From Heterogeneous Mobility Data

Online services are playing critical roles in almost all aspects of users' life. Users usually have multiple online identities (IDs) in different online services. In order to fuse the separated user data in multiple services for better business intelligence, it is critical for service providers to link online IDs belonging to the same user. On the other hand, the popularity of mobile networks and GPS-equipped smart devices have provided a generic way to link IDs, i.e., utilizing the mobility traces of IDs. However, linking IDs based on their mobility traces has been a challenging problem due to the highly heterogeneous, incomplete and noisy mobility data across services. In this paper, we propose DPLink, an end-to-end deep learning based framework, to complete the user identity linkage task for heterogeneous mobility data collected from different services with different properties. DPLink is made up by a feature extractor including a location encoder and a trajectory encoder to extract representative features from trajectory and a comparator to compare and decide whether to link two trajectories as the same user. Particularly, we propose a pre-training strategy with a simple task to train the DPLink model to overcome the training difficulties introduced by the highly heterogeneous nature of different source mobility data. Besides, we introduce a multi-modal embedding network and a co-attention mechanism in DPLink to deal with the low-quality problem of mobility data. By conducting extensive experiments on two real-life ground-truth mobility datasets with eight baselines, we demonstrate that DPLink outperforms the state-of-the-art solutions by more than 15% in terms of hit-precision. Moreover, it is expandable to add external geographical context data and works stably with heterogeneous noisy mobility traces. Our code is publicly available1.
Bound state scattering simplified

In the description of the AdS5/CFT4 duality by an integrable system the scattering matrix for bound states plays a crucial role: it was initially constructed for the evaluation of finite size corrections to the planar spectrum of energy levels/anomalous dimensions by the thermodynamic Bethe ansatz, and more recently it re-appeared in the context of the glueing prescription of the hexagon approach to higher-point functions. In this work we present a simplified form of this scattering matrix and we make its pole structure manifest. We find some new relations between its matrix elements and also present an explicit form for its inverse. We finally discuss some of its properties including crossing symmetry. Our results will hopefully be useful for computing finite-size effects such as the ones given by the complicated sum-integrals arising from the glueing of hexagons, as well as help towards understanding universal features of the AdS5/CFT4 scattering matrix.
Traffic distributions and independence II: Universal constructions for traffic spaces

We investigate questions related to the notion of \traffics introduced by the third author as a non-commutative probability space with additional operations and equipped with the notion of traffic independence. We prove that any sequence of unitarily invariant random matrices that converges in non-commutative distribution converges as well in traffic distribution whenever it fulfils some factorisation property. We provide an explicit description of the limit which allows to recover and extend some applications (a result by Mingo and Popa on the asymptotic freeness from the transposed ensembles, and of Accardi, Lenczewski and Salapata on the freeness of infinite transitive graphs). We also improve the theory of traffic spaces by considering a positivity axiom related to the notion of state in non-commutative probability. We construct the free product of traffic spaces and prove that it preserves the positivity condition. This analysis leads to our main result stating that every non-commutative probability space endowed with a tracial state can be enlarged and equipped with a structure of traffic space.
Diagonal degenerations of matrix Schubert varieties

Knutson and Miller (2005) established a connection between the anti-diagonal Grobner degenerations of matrix Schubert varieties and the pre-existing combinatorics of pipe dreams. They used this correspondence to give a geometrically-natural explanation for the appearance of the combinatorially defined Schubert polynomials as representatives of Schubert classes. Recently, Hamaker, Pechenik, and Weigandt proposed a similar connection between diagonal degenerations of matrix Schubert varieties and bumpless pipe dreams, newer combinatorial objects introduced by Lam, Lee, and Shimozono. Hamaker, Pechenik, and Weigandt described new generating sets of the defining ideals of matrix Schubert varieties and conjectured a characterization of permutations for which these generating sets are diagonal Grobner bases. They proved special cases of this conjecture and described diagonal degenerations of matrix Schubert varieties in terms of bumpless pipe dreams in these cases. The purpose of this paper is to prove the general conjecture. The proof uses a connection between liaison and geometric vertex decomposition established in earlier work with Rajchgot.
Lyman-
α
 constraints on cosmic heating from dark matter annihilation and decay

We derive new constraints on models of decaying and annihilating dark matter (DM) by requiring that the energy injected into the intergalactic medium (IGM) not overheat it at late times, when measurements of the Lyman-$\alpha$ forest constrain the IGM temperature. We improve upon previous analyses by using the recently developed $\texttt{DarkHistory}$ code package, which self-consistently takes into account additional photoionization and photoheating processes due to reionization and DM sources. Our constraints are robust to the uncertainties of reionization and competitive with leading limits on sub-GeV DM that decays preferentially to electrons.
On the emergence of science in the frame of realistic observer models

In this essay I describe how we can use autonomous agents (AAs) from Embodied Cognitive Science (ECS) as models for real observers in order to understand how science may emerge for observers. I model science as the ability of observers to use symbolic systems to perform experiments. The methodology consists in designing specific experiments with AAs and application of these experiments to show how we can interpret such experiments as observers perform experiments using symbolic systems . According to an idea of Albert Einstein all human-observer notions (in science, daily life etc.) are arbitrarily creations of thinking and cannot be derived inductively from experience [4]. In other words, symbolic systems which are created by observers are only labels for stable sensor input patterns which may originate from environment objects as well as from patterns created by observers themselves. I will introduce a simple experiment with an AA which shows how we can formalize these ideas in an ECS-experiment. For this purpose I introduce various models elements like classifications processes, internal/external categories, correlation of categories and artificial/natural pattern source based categories. I speculate that if such experiments would be performed systematically in ECS with increasing complexity in the long run we will be able to obtain basic propositions for foundational sciences like physics or mathematics from such experiments. This would allow for an experimentally based understanding of how an observer creates a picture of reality.
Incorrect by Construction: Fine Tuning Neural Networks for Guaranteed Performance on Finite Sets of Examples

There is great interest in using formal methods to guarantee the reliability of deep neural networks. However, these techniques may also be used to implant carefully selected input-output pairs. We present initial results on a novel technique for using SMT solvers to fine tune the weights of a ReLU neural network to guarantee outcomes on a finite set of particular examples. This procedure can be used to ensure performance on key examples, but it could also be used to insert difficult-to-find incorrect examples that trigger unexpected performance. We demonstrate this approach by fine tuning an MNIST network to incorrectly classify a particular image and discuss the potential for the approach to compromise reliability of freely-shared machine learning models.
Efficient Urdu Caption Generation using Attention based LSTMs

Recent advancements in deep learning has created a lot of opportunities to solve those real world problems which remained unsolved for more than a decade. Automatic caption generation is a major research field, and research community has done a lot of work on this problem on most common languages like English. Urdu is the national language of Pakistan and also much spoken and understood in the sub-continent region of Pakistan-India, and yet no work has been done for Urdu language caption generation. Our research aims to fill this gap by developing an attention-based deep learning model using techniques of sequence modelling specialized for Urdu language. We have prepared a dataset in Urdu language by translating a subset of "Flickr8k" dataset containing 700 'man' images. We evaluate our proposed technique on this dataset and show that it is able to achieve a BLEU score of 0.83 on Urdu language. We improve on the previously proposed techniques by using better CNN architectures and optimization techniques. Furthermore, we also tried adding a grammar loss to the model in order to make the predictions grammatically correct.
Moduli relations between l-adic representations and the regular inverse Galois problem

There are two famous Abel Theorems. Most well-known is his description of abelian (analytic) functions on a one dimensional compact complex torus. The other collects together those complex tori, with their prime degree isogenies, into one space. Riemann's generalization of the first features his famous theta functions. His deepest work aimed at extending Abel's second theorem; he died before he fulfilled this. That extension is often pictured on complex higher dimension torii. For Riemann, though, it was to spaces of Jacobians of compact Riemann surfaces, W, of genus g, toward studying functions \phi: W -> P^1_z, on them. Data for such pairs (W,\phi) starts with a monodromy group, G, and conjugacy classes C in G. Many applications come from putting all such covers attached to (G,C) in natural -- Hurwitz -- families. We connect two such applications: The Regular Inverse Galois Problem (RIGP) and Serre's Open Image Theorem (OIT). We call the connecting device Modular Towers (MT s). Backdrop for the OIT and RIGP uses Serre's books Abelian l-adic representations and elliptic curves (1968) and Topics in Galois theory (1992). Serre's OIT example is the case where MT levels identify as modular curves. With an example that isn't modular curves, we explain conjectured MT properties -- generalizing a Theorem of Hilbert's -- that would conclude an OIT for all MTs. Solutions of pieces on both ends of these connections are known in significant cases.