Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulating and classifying behavior in adversarial environments based on action-state traces: an application to money laundering

Many business applications involve adversarial relationships in which both sides adapt their strategies to optimize their opposing benefits. One of the key characteristics of these applications is the wide range of strategies that an adversary may choose as they adapt their strategy dynamically to sustain benefits and evade authorities. In this paper, we present a novel way of approaching these types of applications, in particular in the context of Anti-Money Laundering. We provide a mechanism through which diverse, realistic and new unobserved behavior may be generated to discover potential unobserved adversarial actions to enable organizations to preemptively mitigate these risks. In this regard, we make three main contributions. (a) Propose a novel behavior-based model as opposed to individual transactions-based models currently used by financial institutions. We introduce behavior traces as enriched relational representation to represent observed human behavior. (b) A modelling approach that observes these traces and is able to accurately infer the goals of actors by classifying the behavior into money laundering or standard behavior despite significant unobserved activity. And (c) a synthetic behavior simulator that can generate new previously unseen traces. The simulator incorporates a high level of flexibility in the behavioral parameters so that we can challenge the detection algorithm. Finally, we provide experimental results that show that the learning module (automated investigator) that has only partial observability can still successfully infer the type of behavior, and thus the simulated goals, followed by customers based on traces - a key aspiration for many applications today.
Single Shot Reversible GAN for BCG artifact removal in simultaneous EEG-fMRI

Simultaneous EEG-fMRI acquisition and analysis technology has been widely used in various research fields of brain science. However, how to remove the ballistocardiogram (BCG) artifacts in this scenario remains a huge challenge. Because it is impossible to obtain clean and BCG-contaminated EEG signals at the same time, BCG artifact removal is a typical unpaired signal-to-signal problem. To solve this problem, this paper proposed a new GAN training model - Single Shot Reversible GAN (SSRGAN). The model is allowing bidirectional input to better combine the characteristics of the two types of signals, instead of using two independent models for bidirectional conversion as in the past. Furthermore, the model is decomposed into multiple independent convolutional blocks with specific functions. Through additional training of the blocks, the local representation ability of the model is improved, thereby improving the overall model performance. Experimental results show that, compared with existing methods, the method proposed in this paper can remove BCG artifacts more effectively and retain the useful EEG information.
Weakly- and Semi-supervised Evidence Extraction

For many prediction tasks, stakeholders desire not only predictions but also supporting evidence that a human can use to verify its correctness. However, in practice, evidence annotations may only be available for a minority of training examples (if available at all). In this paper, we propose new methods to combine few evidence annotations (strong semi-supervision) with abundant document-level labels (weak supervision) for the task of evidence extraction. Evaluating on two classification tasks that feature evidence annotations, we find that our methods outperform baselines adapted from the interpretability literature to our task. Our approach yields gains with as few as hundred evidence annotations.
Semi-invariants of binary forms pertaining to a unimodality theorem of Reiner and Stanton

The strange symmetric difference of the $q$-binomial coefficients $F_{n,k}(q)={n+k\brack k}-q^{n}{n+k-2\brack k-2}$ as called by Stanley, was introduced by Reiner and Stanton. They proved that $F_{n,k}(q)$ is symmetric and unimodal for $k\geq 2$ and any even nonnegative integer $n$ by using the representation theory for Lie algebras. Inspired by the connection between the Gaussian coefficients, or the $q$-binomial coefficients, and semi-invariants of binary forms established by Sylvester in his proof of the unimodality of the Gaussian coefficients as conjectured by Cayley, we find an interpretation of the unimodality of $F_{n,k}(q)$ in terms of semi-invariants. In the spirit of the strict unimodality of the Gaussian coefficients due to Pak and Panova, we prove the strict unimodality of $G_{n,k,r}(q)={n+k\brack k}-q^{nr/2}{n+k-r\brack k-r}$, where $n,r\geq8$, $k\geq r$ and either $n$ or $r$ is even.
Optimal Policies for the Homogeneous Selective Labels Problem

Selective labels are a common feature of consequential decision-making applications, referring to the lack of observed outcomes under one of the possible decisions. This paper reports work in progress on learning decision policies in the face of selective labels. The setting considered is both a simplified homogeneous one, disregarding individuals' features to facilitate determination of optimal policies, and an online one, to balance costs incurred in learning with future utility. For maximizing discounted total reward, the optimal policy is shown to be a threshold policy, and the problem is one of optimal stopping. In contrast, for undiscounted infinite-horizon average reward, optimal policies have positive acceptance probability in all states. Future work stemming from these results is discussed.
Bayesian spline-based hidden Markov models with applications to actimetry data and sleep analysis

B-spline-based hidden Markov models employ B-splines to specify the emission distributions, offering a more flexible modelling approach to data than conventional parametric HMMs. We introduce a Bayesian framework for inference, enabling the simultaneous estimation of all unknown model parameters including the number of states. A parsimonious knot configuration of the B-splines is identified by the use of a trans-dimensional Markov chain sampling algorithm, while model selection regarding the number of states can be performed based on the marginal likelihood within a parallel sampling framework. Using extensive simulation studies, we demonstrate the superiority of our methodology over alternative approaches as well as its robustness and scalability. We illustrate the explorative use of our methods for data on activity in animals, i.e. whitetip-sharks. The flexibility of our Bayesian approach also facilitates the incorporation of more realistic assumptions and we demonstrate this by developing a novel hierarchical conditional HMM to analyse human activity for circadian and sleep modelling.
New insights into the formation and growth of boson stars in dark matter halos

This work studies the formation and growth of boson stars and their surrounding miniclusters by gravitational condensation using non-linear dynamical numerical methods. Fully dynamical attractive and repulsive self-interactions are also considered for the first time. In the case of pure gravity, we numerically prove that the growth of boson stars inside halos slows down and saturates as has been previously conjectured, and detail its conditions. Self-interactions are included using the Gross-Pitaevskii-Poisson equations. We find that in the case of strong attractive self-interactions the boson stars can become unstable and collapse, in agreement with previous stationary computations. At even stronger coupling, the condensate fragments. Repulsive self-interactions, as expected, inhibit boson star formation, and lead to solutions with larger radii.
Information transfer between turbulent boundary layers and porous media

Abstract The interaction between the flow above and below a permeable wall is a central topic in the study of porous media. While previous investigations have provided compelling evidence of the strong coupling between the two regions, few studies have quantitatively measured the directionality, i.e. cause-and-effect relations, of this interaction. To shed light on the problem, interface-resolved direct numerical simulations of channel flow over a cylinder array for porosity $\varphi =0.4$–$0.9$ are performed, and the friction Reynolds number of the top smooth wall is controlled to be $Re_{\tau }\approx 180$. We use transfer entropy as a marker to evaluate the causal interaction between the free turbulent flow and the porous medium. Correlation analysis and linear coherence spectra are also leveraged to complete the study. Our results show that the permeability of the porous medium has a profound impact on the intensity, time scale and spatial extent of surface–subsurface interactions. The interaction of the free flow and porous medium is further decomposed into two coupling directions, namely, top-down and bottom-up. For low-porosity cases, top-down and bottom-up interactions are strongly asymmetric, the former being mostly influenced by small near-wall eddies, and the latter reflecting the onset of Kelvin–Helmholtz type instabilities due to the perturbation from the porous medium. As the porosity increases, both top-down and bottom-up interactions are dominated by shear-flow instabilities.
Local Relay Selection in Presence of Dynamic Obstacles in Millimeter Wave D2D Communication

Blockage due to obstacles in millimeter wave (mmWave) device to device (D2D) communication is a prominent problem due to their severe penetration losses. Potential user equipments (UEs) in vicinity of the source UE must be explored in order to select a new relay when the current link gets blocked. However, dynamic obstacles are not known in advance and thus may cause unpredictable fluctuations to D2D channel quality causing newly selected relay link also to be susceptible to blockage. This might cause frequent relay switching leading to call drops and high energy consumption. We have proposed the idea of reducing frequency in relay exploration and switching and thus average end-to-end delay (in seconds) at the expense of additional exploration time units (few milliseconds) during beam alignment. We seek to learn the uncertainty in D2D link qualities by modeling the problem as finite horizon partially observable Markov decision process (POMDP) framework locally at each UE. We have derived an optimal threshold policy which maps the state to set of actions. We then give a simplified and easy to implement stationary threshold policy which counts the number of successive acknowledgment successes/failures for making decisions of selecting or not selecting a given relay locally. Through extensive simulation, we validate our theoretical findings and demonstrate that our approach captures the tradeoff between average exploration time and average end-to-end (E2E) delay in presence of dynamic obstacles.
functions: algorithm and benchmarks

We present an all-electron, periodic G 0 W 0 implementation within the numerical atomic orbital (NAO) basis framework. A localized variant of the resolution-of-the-identity (RI) approximation is employed to signiﬁcantly reduce the computational cost of evaluating and storing the two-electron Coulomb repulsion integrals. We demonstrate that the error arising from localized RI approximation can be reduced to an insigniﬁcant level by enhancing the set of auxiliary basis functions, used to expand the products of two single-particle NAOs. An eﬃcient algorithm is introduced to deal with the Coulomb singularity in the Brillouin zone sampling that is suitable for the NAO framework. We perform systematic convergence tests and identify a set of computational parameters, which can serve as the default choice for most practical purposes. Benchmark calculations are carried out for a set of prototypical semiconductors and insulators, and compared to independent reference values obtained from an independent G 0 W 0 implementation based on linearized augmented plane waves (LAPW) plus high-energy localized orbitals (HLOs) basis set, as well as experimental results. With a moderate (FHI-aims tier 2) NAO basis set, our G 0 W 0 calculations produce band gaps that typically lie in between the standard LAPW and the LAPW+HLO results. Complementing tier 2 with highly localized Slater-type orbitals (STOs), we ﬁnd that the obtained band gaps show an overall convergence towards the LAPW+HLO results. The algorithms and techniques developed in this work pave the way for eﬃcient implementations of correlated methods within the NAO framework.
Predicting Terrain Mechanical Properties in Sight for Planetary Rovers with Semantic Clues

Non-geometric mobility hazards such as rover slippage and sinkage posing great challenges to costly planetary missions are closely related to the mechanical properties of terrain. In-situ proprioceptive processes for rovers to estimate terrain mechanical properties need to experience different slip as well as sinkage and are helpless to untraversed regions. This paper proposes to predict terrain mechanical properties with vision in the distance, which expands the sensing range to the whole view and can partly halt potential slippage and sinkage hazards in the planning stage. A semantic-based method is designed to predict bearing and shearing properties of terrain in two stages connected with semantic clues. The former segmentation phase segments terrain with a light-weighted network promising to be applied onboard with competitive 93% accuracy and high recall rate over 96%, while the latter inference phase predicts terrain properties in a quantitative manner based on human-like inference principles. The prediction results in several test routes are 12.5% and 10.8% in full-scale error and help to plan appropriate strategies to avoid suffering non-geometric hazards.
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, the concerns about how to standardize open source implementation of recommendation algorithms continually increase in the research community. In the light of this challenge, we propose a unified, comprehensive and efficient recommender system library called RecBole (pronounced as [rEk'boUl@r]), which provides a unified framework to develop and reproduce recommendation algorithms for research purpose. In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation. We implement the RecBole library based on PyTorch, which is one of the most popular deep learning frameworks. Our library is featured in many aspects, including general and extensible data structures, comprehensive benchmark models and datasets, efficient GPU-accelerated execution, and extensive and standard evaluation protocols. We provide a series of auxiliary functions, tools, and scripts to facilitate the use of this library, such as automatic parameter tuning and break-point resume. Such a framework is useful to standardize the implementation and evaluation of recommender systems. The project and documents are released at https://recbole.io/.
An approach to measure route quality and refine the route during the voyage using characteristic coefficients

The paper presents a method to validate and refine the ship's route during the voyage. The method is based on computing several characteristic coefficients that represent and measure route properties. Thru the analysis of the values of these coefficient, one can analyse the overall route quality and detect possibly dangerous discrepancies between the actual route and the planned route.The paper describes the proposed characteristic coefficients, the process of route refinement and the method for prediction and validation of the route's future changes.
Layer-Wise Multi-View Learning for Neural Machine Translation

Traditional neural machine translation is limited to the topmost encoder layer’s context representation and cannot directly perceive the lower encoder layers. Existing solutions usually rely on the adjustment of network architecture, making the calculation more complicated or introducing additional structural restrictions. In this work, we propose layer-wise multi-view learning to solve this problem, circumventing the necessity to change the model structure. We regard each encoder layer’s off-the-shelf output, a by-product in layer-by-layer encoding, as the redundant view for the input sentence. In this way, in addition to the topmost encoder layer (referred to as the primary view), we also incorporate an intermediate encoder layer as the auxiliary view. We feed the two views to a partially shared decoder to maintain independent prediction. Consistency regularization based on KL divergence is used to encourage the two views to learn from each other. Extensive experimental results on five translation tasks show that our approach yields stable improvements over multiple strong baselines. As another bonus, our method is agnostic to network architectures and can maintain the same inference speed as the original model.
Asymptotic PN Approximation in Radiative Transfer Problems

Abstract We study the validity of the time-dependent asymptotic PN approximation in radiative transfer of photons. The time-dependent asymptotic PN is an approximation which uses the standard PN equations with a closure that is based on the asymptotic solution of the exact Boltzmann equation for a homogeneous problem, in space and time. The asymptotic PN approximation for radiative transfer requires careful treatment regarding the closure equation. Specifically, the mean number of particles that are emitted per collision ( ) can be larger than one due to inner or outer radiation sources and the coefficients of the closure must be extended for these cases. Our approximation is tested against a well-known radiative transfer benchmark. It yields excellent results, with almost correct particle velocity that controls the radiative heat-wave fronts.
Black holes and WIMPs: all or nothing or something else

We consider constraints on primordial black holes (PBHs) in the mass range $(10^{-18}\! -\! 10^{15})\, \mathrm{M}_{\odot }$ if the dark matter (DM) comprises weakly interacting massive particles (WIMPs) that form haloes around them and generate γ-rays by annihilations. We first study the formation of the haloes and find that their density profile prior to WIMP annihilations evolves to a characteristic power-law form. Because of the wide range of PBH masses considered, our analysis forges an interesting link between previous approaches to this problem. We then consider the effect of the WIMP annihilations on the halo profile and the associated generation of γ-rays. The observed extragalactic γ-ray background implies that the PBH DM fraction is $f^{}_{\rm PBH} \lesssim 2 \times 10^{-9}\, (m_{\chi } / {\rm TeV})^{1.1}$ in the mass range $2 \times 10^{-12}\, \mathrm{M}_{\odot }\, (m_{\chi } / {\rm TeV})^{-3.2} \lesssim M \lesssim 5 \times 10^{12}\, \mathrm{M}_{\odot }\, (m_{\chi } / {\rm TeV})^{1.1}$, where mχ and M are the WIMP and PBH masses, respectively. This limit is independent of M and therefore applies for any PBH mass function. For $M \lesssim 2\times 10^{-12}\, \mathrm{M}_{\odot }\, (m_{\chi }/ {\rm TeV})^{-3.2}$, the constraint on $f^{}_{\rm PBH}$ is a decreasing function of M and PBHs could still make a significant DM contribution at very low masses. We also consider constraints on WIMPs if the DM is mostly PBHs. If the merging black holes recently discovered by LIGO/Virgo are of primordial origin, this would rule out the standard WIMP DM scenario. More generally, the WIMP DM fraction cannot exceed 10−4 for $M \gt 10^{-9}\, \mathrm{M}_{\odot }$ and $m_{\chi } \gt 10\,$ GeV. There is a region of parameter space, with $M \lesssim 10^{-11}\, \mathrm{M}_{\odot }$ and $m_{\chi } \lesssim 100\,$ GeV, in which WIMPs and PBHs can both provide some but not all of the DM, so that one requires a third DM candidate.
A position paper on GDPR compliance in sharded blockchains: rehash of old ideas or new interesting challenges?

Sharding has emerged as one of the common techniques to address the scalability problems of blockchain systems. To this end, various sharding techniques for blockchain systems have been proposed in the literature. When sharded blockchains process personal data, the data controllers and the data processors associated with the sharded blockchains need to be compliant with the General Data Protection Regulation (GDPR). To this end, this article makes the first attempt to address the following key question: to what extent the existing techniques developed by different communities such as the distributed computing community, the distributed systems community, the database community, identity and access control community and the dependability community can be used by the data controllers and data processors for complying with the GDPR requirements of data subject rights in sharded blockchains? As part of answering this question, this article argues that there is a need for cross-disciplinary research towards finding optimal solutions for implementing the data subject rights in sharded blockchains.
Evidence for Radio Loud to Radio Quiet Evolution from Red and Blue Quasars

Recent work on red and blue quasi-stellar objects (QSOs) has identified peculiar number distributions as a function of radio-loudness that we explore and attempt to explain from the perspective of a picture in which a subset of the population of active galaxies evolves from the radio-loud to the radio-quiet state. Because the time evolution is slowed down by an order of magnitude or more for the radio-quiet phase, the numbers of red and blue QSOs approach each other at the extreme end of the radio-quiet range of radio-loudness with larger numbers. The rapid time evolution of most radio-loud phases, instead, makes the numbers similar but lower at the far radio-loud end. At the midpoint of radio-loudness, instead, the differences between red and blue QSOs experience their largest values which results from accretion rapidly spinning black holes down but subsequently spinning them up more slowly. Recovering these basic features of the observations is evidence for rapid evolution away from radio-loudness and slow evolution in radio-quiet states.
An Algorithm for Out-Of-Distribution Attack to Neural Network Encoder.

Deep neural network (DNN), especially convolutional neural network, has achieved superior performance on image classification tasks. However, such performance is only guaranteed if the input to a trained model is similar to the training samples, i.e., the input follows the probability distribution of the training set. Out-Of-Distribution (OOD) samples do not follow the distribution of training set, and therefore the predicted class labels on OOD samples become meaningless. Classification-based methods have been proposed for OOD detection; however, in this study we show that this type of method is theoretically ineffective and practically breakable because of dimensionality reduction in the model. We also show that Glow likelihood-based OOD detection is ineffective as well. Our analysis is demonstrated on five open datasets, including a COVID-19 CT dataset. At last, we present a simple theoretical solution with guaranteed performance for OOD detection.