Miracle-dz/UniFAR-FTUs huggingface dataset:
ARMS: Automated rules management system for fraud detection

Fraud detection is essential in financial services, with the potential of greatly reducing criminal activities and saving considerable resources for businesses and customers. We address online fraud detection, which consists of classifying incoming transactions as either legitimate or fraudulent in real-time. Modern fraud detection systems consist of a machine learning model and rules defined by human experts. Often, the rules performance degrades over time due to concept drift, especially of adversarial nature. Furthermore, they can be costly to maintain, either because they are computationally expensive or because they send transactions for manual review. We propose ARMS, an automated rules management system that evaluates the contribution of individual rules and optimizes the set of active rules using heuristic search and a user-defined loss-function. It complies with critical domain-specific requirements, such as handling different actions (e.g., accept, alert, and decline), priorities, blacklists, and large datasets (i.e., hundreds of rules and millions of transactions). We use ARMS to optimize the rule-based systems of two real-world clients. Results show that it can maintain the original systems' performance (e.g., recall, or false-positive rate) using only a fraction of the original rules (~ 50% in one case, and ~ 20% in the other).
A 5° × 5° deep H i survey of the M81 group − II. H i distribution and kinematics of IC 2574 and HIJASS J1021+68

We analyse the eastern region of a $5^\circ\times5^\circ$ deep HI survey of the M81 group containing the dwarf galaxy IC 2574 and the HI complex HIJASS J1021+68, located between the dwarf and the M81 system. The data show that IC 2574 has an extended HI envelope that connects to HIJASS J1021+68 in the form of a collection of small clouds, but no evident connection has been found between IC 2574 and the central members of the M81 group. We argue, based on the morphology of the clouds forming HIJASS J1021+68 and its velocity distribution, that the complex is not a dark galaxy as previously suggested, but is instead a complex of clouds either stripped from, or falling onto the primordial HI envelope of IC 2574. We also use the deep HI observations to map the extended HI envelope around IC 2574 and, using a 3D tilted-ring model, we derive the rotation curve of the galaxy to a larger extent than has been done before. Combining the obtained rotation curve to higher resolution curves from the literature, we constrain the galaxy's dark matter halo parameters.
Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States

Portfolio management (PM) is a fundamental financial planning task that aims to achieve investment goals such as maximal profits or minimal risks. Its decision process involves continuous derivation of valuable information from various data sources and sequential decision optimization, which is a prospective research direction for reinforcement learning (RL). In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity – the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty – the financial market is versatile and non-stationary. To incorporate heterogeneous data and enhance robustness against environment uncertainty, our SARL augments the asset information with their price movement prediction as additional states, where the prediction can be solely based on financial data (e.g., asset prices) or derived from alternative sources such as news. Experiments on two real-world datasets, (i) Bitcoin market and (ii) HighTech stock market with 7-year Reuters news articles, validate the effectiveness of SARL over existing PM approaches, both in terms of accumulated profits and risk-adjusted profits. Moreover, extensive simulations are conducted to demonstrate the importance of our proposed state augmentation, providing new insights and boosting performance significantly over standard RL-based PM method and other baselines.
Dialogue History Integration into End-to-End Signal-to-Concept Spoken Language Understanding Systems

This work investigates the embeddings for representing dialog history in spoken language understanding (SLU) systems. We focus on the scenario when the semantic information is extracted directly from the speech signal by means of a single end-to-end neural network model. We proposed to integrate dialogue history into an end-to-end signal-to-concept SLU system. The dialog history is represented in the form of dialog history embedding vectors (so-called h-vectors) and is provided as an additional information to end-to-end SLU models in order to improve the system performance. Three following types of h-vectors are proposed and experimentally evaluated in this paper: (1) supervised-all embeddings predicting bag-of-concepts expected in the answer of the user from the last dialog system response; (2) supervised-freq embeddings focusing on predicting only a selected set of semantic concept (corresponding to the most frequent errors in our experiments); and (3) unsupervised embeddings. Experiments on the MEDIA corpus for the semantic slot filling task demonstrate that the proposed h-vectors improve the model performance.
SEMI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data

Although convolutional neural networks have been proven to be an effective tool to generate high quality maps from remote sensing images, their performance significantly deteriorates when there exists a large domain shift between training and test data. To address this issue, we propose a new data augmentation approach that transfers the style of test data to training data using generative adversarial networks. Our semantic segmentation framework consists in first training a U-net from the real training data and then fine-tuning it on the test stylized fake training data generated by the proposed approach. Our experimental results prove that our framework outperforms the existing domain adaptation methods.
Learning to Rank for Uplift Modeling

Causal classification concerns the estimation of the net effect of a treatment on an outcome of interest at the instance level, i.e., of the individual treatment effect (ITE). For binary treatment and outcome variables, causal classification models produce ITE estimates that essentially allow one to rank instances from a large positive effect to a large negative effect. Often, as in uplift modeling (UM), one is merely interested in this ranking, rather than in the ITE estimates themselves. In this regard, we investigate the potential of learning to rank (L2R) techniques to learn a ranking of the instances directly. We propose a unified formalization of different binary causal classification performance measures from the UM literature and explore how these can be integrated into the L2R framework. Additionally, we introduce a new metric for UM with L2R called the <italic>promoted cumulative gain</italic> (PCG). We employ the L2R technique LambdaMART to optimize the ranking according to PCG and show improved results over the use of standard L2R metrics and equal to improved results when compared with state-of-the-art UM. Finally, we show how L2R techniques can be used to specifically optimize for the top-<inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="verbeke-ieq1-3048510.gif"/></alternatives></inline-formula> fraction of the ranking in a UM context, however, these results do not generalize to the test set.
Bacatá: Notebooks for DSLs, Almost for Free

Context: Computational notebooks are a contemporary style of literate programming, in which users can communicate and transfer knowledge by interleaving executable code, output, and prose in a single rich document. A Domain-Specific Language (DSL) is an artificial software language tailored for a particular application domain. Usually, DSL users are domain experts that may not have a software engineering background. As a consequence, they might not be familiar with Integrated Development Environments (IDEs). Thus, the development of tools that offer different interfaces for interacting with a DSL is relevant. Inquiry: However, resources available to DSL designers are limited. We would like to leverage tools used to interact with general purpose languages in the context of DSLs. Computational notebooks are an example of such tools. Then, our main question is: What is an efficient and effective method of designing and implementing notebook interfaces for DSLs? By addressing this question we might be able to speed up the development of DSL tools, and ease the interaction between end-users and DSLs. Approach: In this paper, we present Bacat\'a, a mechanism for generating notebook interfaces for DSLs in a language parametric fashion. We designed this mechanism in a way in which language engineers can reuse as many language components (e.g., language processors, type checkers, code generators) as possible. Knowledge: Our results show that notebook interfaces generated by Bacat\'a can be automatically generated with little manual configuration. There are few considerations and caveats that should be addressed by language engineers that rely on language design aspects. The creation of a notebook for a DSL with Bacat\'a becomes a matter of writing the code that wires existing language components in the Rascal language workbench with the Jupyter platform. Grounding: We evaluate Bacat\'a by generating functional computational notebook interfaces for three different non-trivial DSLs, namely: a small subset of Halide (a DSL for digital image processing), SweeterJS (an extended version of JavaScript), and QL (a DSL for questionnaires). Additionally, it is relevant to generate notebook implementations rather than implementing them manually. We measured and compared the number of Source Lines of Code (SLOCs) that we reused from existing implementations of those languages. Importance: The adoption of notebooks by novice-programmers and end-users has made them very popular in several domains such as exploratory programming, data science, data journalism, and machine learning. Why are they popular? In (data) science, it is essential to make results reproducible as well as understandable. However, notebooks are only available for GPLs. This paper opens up the notebook metaphor for DSLs to improve the end-user experience when interacting with code and to increase DSLs adoption.
Dense monocular Simultaneous Localization and Mapping by direct surfel optimization

This work presents a novel approach for monocular dense Simultaneous Localization and Mapping. The surface to be estimated is represented as a piecewise planar surface, defined as a group of surfels each having as parameters its position and normal. These parameters are then directly estimated from the raw camera pixels measurements, by a Gauss-Newton iterative process. The representation of the surface as a group of surfels has several advantages. It allows the recovery of robust and accurate pixel depths, without the need to use a computationally demanding depth regularization schema. This has the further advantage of avoiding the use of a physically unlikely surface smoothness prior. New surfels can be correctly initialized from the information present in nearby surfels, avoiding also the need to use an expensive initialization routine commonly needed in Gauss-Newton methods. The method was written in the GLSL shading language, allowing the usage of GPU thus achieving real-time. The method was tested against several datasets, showing both its depth and normal estimation correctness, and its scene reconstruction quality. The results presented here showcase the usefulness of the more physically grounded piecewise planar scene depth prior, instead of the more commonly pixel depth independence and smoothness prior.
Constrained Dominant sets and Its applications in computer vision

In this thesis, we present new schemes which leverage a constrained clustering method to solve several computer vision tasks ranging from image retrieval, image segmentation and co-segmentation, to person re-identification. In the last decades clustering methods have played a vital role in computer vision applications; herein, we focus on the extension, reformulation, and integration of a well-known graph and game theoretic clustering method known as Dominant Sets. Thus, we have demonstrated the validity of the proposed methods with extensive experiments which are conducted on several benchmark datasets.
COSMOGRAIL XIX: Time delays in 18 strongly lensed quasars from 15 years of optical monitoring

We present the results of 15 years of monitoring of lensed quasars conducted by the COSMOGRAIL program at the Leonhard Euler 1.2m Swiss Telescope. The decade-long light curves of 23 lensed QSOs are presented for the first time. We complement our data set with other monitoring data available in the literature to measure the time delays in 18 systems, among which 9 reach a relative precision better than 15% for at least one time delay. To achieve this, we develop an automated version of the curve-shifting toolbox PyCS to ensure robust estimation of the time delay in the presence of microlensing while accounting for the errors due to imperfect representation of microlensing. We also reanalyze the previously published time delays of RX J1131$-$1231 and HE 0435$-$1223, adding respectively 6 and 2 new seasons of monitoring and confirming the previous time-delay measurements. When the time delay measurement is possible, we correct the light curves of the lensed images from their time delay and present the difference curves to highlight the microlensing signal contained in the data. This is to date the largest sample of decade-long lens monitoring data, useful to measure $H_0$, to measure the size of quasar accretion disks with microlensing, and to study quasar variability.
Fragmentation of star-forming filaments in the X-shaped nebula of the California molecular cloud

Dense molecular filaments are central to the star formation process, but the detailed manner in which they fragment into prestellar cores is not yet well understood. Here, we investigate the fragmentation properties and dynamical state of several star-forming filaments in the X-shape Nebula region of the California MC, in an effort to shed some light on this issue. We used multi-wavelength far-infrared images from Herschel and the getsources and getfilaments extraction methods to identify dense cores and filaments and derive their basic properties. We also used a map of $\rm ^{13}CO (2-1)$ emission from SMT 10m submillimeter telescope to constrain the dynamical state of the filaments. We identified 10 filaments, as well as 57 dense cores. Two star-forming filaments (# 8 and # 10) stand out in that they harbor quasi-periodic chains of dense cores with a typical projected core spacing of $\sim$0.15 pc. These two filaments have thermally supercritical line masses and are not static. Filament~8 exhibits a prominent transverse velocity gradient, suggesting that it is accreting gas from the parent cloud gas reservoir. In both cases, the observed (projected) core spacing is similar to the filament width and significantly shorter than the canonical separation of $\sim \,$4 times the filament width predicted by classical cylinder fragmentation theory. We suggest that continuous accretion of gas onto the two star-forming filaments, as well as geometrical bending of the filaments, may account for the observed core spacing. Our findings suggest that the characteristic fragmentation lengthscale of molecular filaments is quite sensitive to external perturbations from the parent cloud, such as gravitational accretion of ambient material.
Bayesian Learning of Causal Relationships for System Reliability

Causal theory is now widely developed with many applications to medicine and public health. However within the discipline of reliability, although causation is a key concept in this field, there has been much less theoretical attention. In this paper, we will demonstrate how some aspects of established causal methodology can be translated via trees, and more specifically chain event graphs, into domain of reliability theory to help the probability modeling of failures. We further show how various domain specific concepts of causality particular to reliability can be imported into more generic causal algebras and so demonstrate how these disciplines can inform each other. This paper is informed by a detailed analysis of maintenance records associated with a large electrical distribution company. Causal hypotheses embedded within these natural language texts are extracted and analyzed using the new graphical framework we introduced here.
Variational Conditional-Dependence Hidden Markov Models for Human Action Recognition

Hidden Markov Models (HMMs) are a powerful generative approach for modeling sequential data and time-series in general. However, the commonly employed assumption of the dependence of the current time frame to a single or multiple immediately preceding frames is unrealistic; more complicated dynamics potentially exist in real world scenarios. Human Action Recognition constitutes such a scenario, and has attracted increased attention with the advent of low-cost 3D sensors. The naturally arising variations and complex temporal dependencies have established this task as a challenging problem in the community. This paper revisits conventional sequential modeling approaches, aiming to address the problem of capturing time-varying temporal dependency patterns. To this end, we propose a different formulation of HMMs, whereby the dependence on past frames is dynamically inferred from the data. Specifically, we introduce a hierarchical extension by postulating an additional latent variable layer; therein, the (time-varying) temporal dependence patterns are treated as latent variables over which inference is performed. We leverage solid arguments from the Variational Bayes framework and derive a tractable inference algorithm based on the forward-backward algorithm. As we experimentally show using benchmark datasets, our approach yields competitive recognition accuracy and can effectively handle data with missing values.
2-Dimensional Categories

2-Dimensional Categories provides an introduction to 2-categories and bicategories, assuming only the most elementary aspects of category theory. A review of basic category theory is followed by a systematic discussion of 2-/bicategories; pasting diagrams; lax functors; 2-/bilimits; the Duskin nerve; the 2-nerve; internal adjunctions; monads in bicategories; 2-monads; biequivalences; the Bicategorical Yoneda Lemma; and the Coherence Theorem for bicategories. Grothendieck fibrations and the Grothendieck construction are discussed next, followed by tricategories, monoidal bicategories, the Gray tensor product, and double categories. Completely detailed proofs of several fundamental but hard-to-find results are presented for the first time. With exercises and plenty of motivation and explanation, this book is useful for both beginners and experts.
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms

Variance-reduced algorithms, although achieve great theoretical performance, can run slowly in practice due to the periodic gradient estimation with a large batch of data. Batch-size adaptation thus arises as a promising approach to accelerate such algorithms. However, existing schemes either apply prescribed batch-size adaption rule or exploit the information along optimization path via additional backtracking and condition verification steps. In this paper, we propose a novel scheme, which eliminates backtracking line search but still exploits the information along optimization path by adapting the batch size via history stochastic gradients. We further theoretically show that such a scheme substantially reduces the overall complexity for popular variance-reduced algorithms SVRG and SARAH/SPIDER for both conventional nonconvex optimization and reinforcement learning problems. To this end, we develop a new convergence analysis framework to handle the dependence of the batch size on history stochastic gradients. Extensive experiments validate the effectiveness of the proposed batch-size adaptation scheme.
Spacetime Tomography Using the Event Horizon Telescope

We have now entered a new era of high-resolution imaging astronomy with the beginning of the Event Horizon Telescope (EHT). The EHT can resolve the dynamics of matter in the immediate vicinity around black holes at and below the horizon scale. One of the candidate black holes, Sagittarius A*, flares 1–4 times a day depending on the wavelength. A possible interpretation of these flares could be hotspots generated through magnetic-reconnection events in the accretion flow. In this paper, we construct a semi-analytical model for hotspots that includes the effects of shearing as a spot moves along the accretion flow. We then explore the ability of the EHT to recover these hotspots. Even including significant systematic uncertainties, such as thermal noise, diffractive scattering, and background emission due to an accretion disk, we were able to recover the hotspots and spacetime structure to sub-percent precision. Moreover, by observing multiple flaring events we show how the EHT could be used to tomographically map spacetime. This provides new avenues for testing relativistic fluid dynamics and general relativity near the event horizon of supermassive black holes.
Heterologous Expression of Ethanol Synthesis Pathway in Glycogen Deficient Synechococcus elongatus PCC 7942 Resulted in Enhanced Production of Ethanol and Exopolysaccharides

In this study, the Synechococcus elongatus PCC 7942 (hereafter S. elongatus) was engineered by the glgC knockout as well as the insertion of the pdc-adh genes from two different microorganisms. The insertion of pdc-adh genes increased the ethanol synthesis with further improvement in the productivity upon the destruction of glycogen synthesis pathway and the supplementation of cofactor. The abolition of glycogen synthesis pathway led to a considerable increase of the engineered S. elongatus metabolites involved in the ethanol synthesis pathway. Moreover, the studies on cofactor addition highlighted the importance of Mg+2, Zn+2, thiamine pyrophosphate, and NADP+ in ethanol synthesis. The yields of 3856 mg/L ethanol and 109.5 µg/108 cells exopolysaccharides were obtained in the engineered S. elongatus using a photo-bioreactor under optimized conditions. This enhanced production in ethanol and exopolysaccharides are attributed to the flux of carbon from glycogen synthesis pathway and proper availability of essential components.