Miracle-dz/UniFAR-FTUs huggingface dataset:
GEV-PWM Model for Distribution of Minimum Flows

Equations for the probability weighted moments (PWMs) of the generalized extreme value (GEV) distribution for minima are derived. The GEV-PWM model and five other models are fitted to the low flow data of 16 streams. The suitability of the models is checked by the probability plot correlation coefficient test. Weibull distribution fits the data better than lognormal. GEV-PWM has the best performance among the three-parameter models, but it frequently leads to infeasible lower bounds. The 10-year minimum flow estimates based on various models are determined. In the majority of cases (11 stations), differences between the largest and the smallest estimates are <20%. However, in a few cases, rather large differences (71%) are found. The estimates from the GEV-PWM and W3-MSO models are usually either the largest or smallest at a station.
Stabilization of circular Rydberg atoms by circularly polarized infrared laser fields

The ionization dynamics of circular Rydberg states in strong circularly polarized infrared (800 nm) laser fields is studied by means of numerical simulations with the time-dependent Schroedinger equation. We find that at certain intensities, related to the radius of the Rydberg states, atomic stabilization sets in, and the ionization probability decreases as the intensity is further increased. Moreover, there is a strong dependence of the ionization probability on the rotational direction of the applied laser field, which can be understood from a simple classical analogy.
Action of the Mazur pattern up to topological concordance

In the '80s, Freedman showed that the Whitehead doubling operator acts trivally up to topological concordance. On the other hand, Akbulut showed that the Whitehead doubling operator acts nontrivially up to smooth concordance. The Mazur pattern is a natural candidate for a satellite operator which acts by the identity up to topological but not smooth concordance. Recently there has been a resurgence of study of the action of the Mazur pattern up to concordance in the smooth and topological categories. Examples showing that the Mazur pattern does not act by the identity up to smooth concordance have been given by Cochran-Franklin-Hedden-Horn and Collins. In this paper, we give evidence that the Mazur pattern acts by the identity up to topological concordance. In particular, we show that two satellite operators $P_{K_0,\eta_0}$ and $P_{K_1,\eta_1}$ with $\eta_0$ and $\eta_1$ freely homotopic have the same action on the topological concordance group modulo the subgroup of $(1)$-solvable knots, which gives evidence that they act in the same way up to topological concordance. In particular, the Mazur pattern and the identity operator are related in this way, and so this is evidence for the topological side of the analogy to the Whitehead doubling operator. We give additional evidence that they have the same action on the full topological concordance group by showing that up to topological concordance they cannot be distinguished by Casson-Gordon signatures or metabelian $\rho$-invariants.
Determination of the Resultant Dipole of the Heart from Measurements on the Body Surface

By means of the vector calculus, it is proved that the magnitude, orientation, and location of the resultant dipole of a system of sources and sinks inside a finite volume conductor is given by an integration over the bounding surface. The method is applied to finding the ``heart vector,'' or the resultant dipole moment of the human heart. The theory was checked in two‐ and three‐dimensional electrolytic tank models of the human thorax.
Demystifying Advertising Campaign Bid Recommendation: A Constraint target CPA Goal Optimization

In cost-per-click (CPC) or cost-per-impression (CPM) advertising campaigns, advertisers always run the risk of spending the budget without getting enough conversions. Moreover, the bidding on advertising inventory has few connections with propensity one can reach to target cost-per-acquisition (tCPA) goals. To address this problem, this paper presents a bid optimization scenario to achieve the desired CPA goals for advertisers. In particular, we build the optimization engine to make a decision by solving the rigorously formalized constrained optimization problem, which leverages the bid landscape model learned from rich historical auction data using non-parametric learning. The proposed model can naturally recommend the bid that meets the advertisers’ expectations by making inference over advertisers’ history auction behaviors, which essentially deals with the data challenges commonly faced by bid landscape modeling: incomplete logs in auctions, and uncertainty due to the variation and fluctuations in advertising bidding behaviors. The bid optimization model outperforms the baseline methods on real-world campaigns, and has been applied into a wide range of scenarios for performance improvement and revenue liftup.
Why adiabatic quantum annealing is unlikely to yield speed-up

We study quantum annealing for combinatorial optimization with Hamiltonian H=H0+zHf where Hf is diagonal, H0=−|ϕ⟩⟨ϕ| is the equal superposition state projector and z the annealing parameter. We analytically compute the minimal spectral gap, which is O1/N with N the total number of states, and its location z∗ . We show that quantum speed-up requires an annealing schedule which demands a precise knowledge of z∗ , which can be computed only if the density of states of the optimization problem is known. However, in general the density of states is intractable to compute, making quadratic speed-up unfeasible for any practical combinatorial optimization problems. We conjecture that it is likely that this negative result also applies for any other instance independent transverse Hamiltonians such as H0=−∑i=1nσix .
S2S-WTV: Seismic Data Noise Attenuation Using Weighted Total Variation Regularized Self-Supervised Learning

Seismic data often undergo severe noise due to environmental factors, which seriously affect subsequent applications. Traditional hand-crafted denoisers such as filters and regularizations utilize interpretable domain knowledge to design generalizable denoising techniques, while their representation capacities may be inferior to deep learning denoisers, which can learn complex and representative denoising mappings from abundant training pairs. However, due to the scarcity of high-quality training pairs, deep learning denoisers may sustain some generalization issues over various scenarios. In this work, we propose a self-supervised method that combines the capacities of the deep denoiser and the generalization abilities of the hand-crafted regularization for seismic data noise attenuation. Specifically, we leverage the Self2Self (S2S) learning framework with a trace-wise masking strategy for seismic data denoising by solely using the observed noisy data. Parallelly, we suggest the weighted total variation (WTV) to further capture the horizontal local smooth structure of seismic data. Our method, dubbed as S2S-WTV, enjoys both high representation abilities brought from the self-supervised deep network and good generalization abilities of the hand-crafted WTV regularizer and the self-supervised nature. Therefore, our method can effectively remove noise and preserve the fine details of the seismic signal. To tackle the S2S-WTV optimization model, we introduce an alternating direction multiplier method (ADMM)-based algorithm. Extensive experiments on synthetic and field noisy seismic data demonstrate the effectiveness of our method as compared with the state-of-the-art traditional and deep learning-based seismic data denoising methods. Code is available at https://github.com/XuZitai/S2S-WTV.
Fast and energy-efficient derivatives risk analysis: Streaming option Greeks on Xilinx and Intel FPGAs

Whilst FPGAs have enjoyed success in accelerating high-frequency financial workloads for some time, their use for quantitative finance, which is the use of mathematical models to analyse financial markets and securities, has been far more limited to-date. Currently, CPUs are the most common architecture for such workloads, and an important question is whether FPGAs can ameliorate some of the bottlenecks encountered on those architectures. In this paper we extend our previous work accelerating the industry standard Securities Technology Analysis Center's (STAC®) derivatives risk analysis benchmark STAC-A2™, by first porting this from our previous Xilinx implementation to an Intel Stratix-10 FPGA, exploring the challenges encountered when moving from one FPGA architecture to another and suitability of techniques. We then present a host-data-streaming approach that ultimately outperforms our previous version on a Xilinx Alveo U280 FPGA by up to 4.6 times and requiring 9 times less energy at the largest problem size, while outperforming the CPU and GPU versions by up to 8.2 and 5.2 times respectively. The result of this work is a significant enhancement in FPGA performance against the previous version for this industry standard benchmark running on both Xilinx and Intel FPGAs, and furthermore an exploration of optimisation and porting techniques that can be applied to other HPC workloads.
Distributed Active Noise Control System Based on a Block Diffusion FxLMS Algorithm with Bidirectional Communication

—Recently, distributed active noise control systems based on diffusion adaptation have attracted significant research interest due to their balance between computational complexity and stability compared to conventional centralized and decentralized adaptation schemes. However, the existing diffusion F x LMS algorithm employs node-specific adaptation and neighborhood-wide combination, and assumes that the control filters of neighbor nodes are similar to each other. This assumption is not true in practical applications, and it leads to inferior performance to the centralized controller approach. In contrast, this paper proposes a Block Diffusion F x LMS algorithm with bidirectional communication, which uses neighborhood-wide adaptation and node-specific combination to update the control filters. Simulation results validate that the proposed algorithm converges to the solution of the centralized controller with reduced computational burden.
A Compositional Approach to Creating Architecture Frameworks with an Application to Distributed AI Systems

Artificial intelligence (AI) in its various forms finds more and more its way into complex distributed systems. For instance, it is used locally, as part of a sensor system, on the edge for low-latency high-performance inference, or in the cloud, e.g. for data mining. Modern complex systems, such as connected vehicles, are often part of an Internet of Things (IoT). To manage complexity, architectures are described with architecture frameworks, which are composed of a number of architectural views connected through correspondence rules. Despite some attempts, the definition of a mathematical foundation for architecture frameworks that are suitable for the development of distributed AI systems still requires investigation and study. In this paper, we propose to extend the state of the art on architecture framework by providing a mathematical model for system architectures, which is scalable and supports co-evolution of different aspects for example of an AI system. Based on Design Science Research, this study starts by identifying the challenges with architectural frameworks. Then, we derive from the identified challenges four rules and we formulate them by exploiting concepts from category theory. We show how compositional thinking can provide rules for the creation and management of architectural frameworks for complex systems, for example distributed systems with AI. The aim of the paper is not to provide viewpoints or architecture models specific to AI systems, but instead to provide guidelines based on a mathematical formulation on how a consistent framework can be built up with existing, or newly created, viewpoints. To put in practice and test the approach, the identified and formulated rules are applied to derive an architectural framework for the EU Horizon 2020 project ``Very efficient deep learning in the IoT"(VEDLIoT) in the form of a case study.
Explainable AI for Bioinformatics: Methods, Tools, and Applications

Artificial intelligence (AI) systems utilizing deep neural networks and machine learning (ML) algorithms are widely used for solving critical problems in bioinformatics, biomedical informatics and precision medicine. However, complex ML models that are often perceived as opaque and black-box methods make it difficult to understand the reasoning behind their decisions. This lack of transparency can be a challenge for both end-users and decision-makers, as well as AI developers. In sensitive areas such as healthcare, explainability and accountability are not only desirable properties but also legally required for AI systems that can have a significant impact on human lives. Fairness is another growing concern, as algorithmic decisions should not show bias or discrimination towards certain groups or individuals based on sensitive attributes. Explainable AI (XAI) aims to overcome the opaqueness of black-box models and to provide transparency in how AI systems make decisions. Interpretable ML models can explain how they make predictions and identify factors that influence their outcomes. However, the majority of the state-of-the-art interpretable ML methods are domain-agnostic and have evolved from fields such as computer vision, automated reasoning or statistics, making direct application to bioinformatics problems challenging without customization and domain adaptation. In this paper, we discuss the importance of explainability and algorithmic transparency in the context of bioinformatics. We provide an overview of model-specific and model-agnostic interpretable ML methods and tools and outline their potential limitations. We discuss how existing interpretable ML methods can be customized and fit to bioinformatics research problems. Further, through case studies in bioimaging, cancer genomics and text mining, we demonstrate how XAI methods can improve transparency and decision fairness. Our review aims at providing valuable insights and serving as a starting point for researchers wanting to enhance explainability and decision transparency while solving bioinformatics problems. GitHub: https://github.com/rezacsedu/XAI-for-bioinformatics.
Exploring unsupervised top tagging using Bayesian inference

Recognizing hadronically decaying top-quark jets in a sample of jets, or even its total fraction in the sample, is an important step in many LHC searches for Standard Model and Beyond Standard Model physics as well. Although there exists outstanding top-tagger algorithms, their construction and their expected performance rely on Montecarlo simulations, which may induce potential biases. For these reasons we develop two simple unsupervised top-tagger algorithms based on performing Bayesian inference on a mixture model. In one of them we use as the observed variable a new geometrically-based observable \tilde{A}_{3}Ã3, and in the other we consider the more traditional \tau_{3}/\tau_{2}τ3/τ2NN-subjettiness ratio, which yields a better performance. As expected, we find that the unsupervised tagger performance is below existing supervised taggers, reaching expected Area Under Curve AUC \sim 0.80-0.81∼0.80−0.81 and accuracies of about 69% -− 75% in a full range of sample purity. However, these performances are more robust to possible biases in the Montecarlo that their supervised counterparts. Our findings are a step towards exploring and considering simpler and unbiased taggers.
A Bayesian Framework for Automated Debugging

Debugging takes up a significant portion of developer time. As a result, automated debugging techniques including Fault Localization (FL) and Automated Program Repair (APR) have garnered significant attention due to their potential to aid developers in debugging tasks. With the recent advance in techniques that treat the two tasks as closely coupled, such as Unified Debugging, a framework to formally express these two tasks together would heighten our understanding of automated debugging and provide a way to formally analyze techniques and approaches. To this end, we propose a Bayesian framework of understanding automated debugging. We find that the Bayesian framework, along with a concrete statement of the objective of automated debugging, can recover maximal fault localization formulae from prior work, as well as analyze existing APR techniques and their underlying assumptions. As a means of empirically demonstrating our framework, we further propose BAPP, a Bayesian Patch Prioritization technique that incorporates intermediate program values to analyze likely patch locations and repair actions, with its core equations being derived by our Bayesian framework. We find that incorporating program values allows BAPP to identify correct patches more precisely: the rankings produced by BAPP reduced the number of required patch evaluations by 68% and consequently reduced the repair time by 34 minutes on average. Further, our Bayesian framework suggests a number of changes to the way fault localization information is used in program repair, which we validate is useful for BAPP. These results highlight the potential of value-cognizant automated debugging techniques, and further verifies our theoretical framework.
Optimal algorithms for group distributionally robust optimization and beyond

Distributionally robust optimization (DRO) can improve the robustness and fairness of learning methods. In this paper, we devise stochastic algorithms for a class of DRO problems including group DRO, subpopulation fairness, and empirical conditional value at risk (CVaR) optimization. Our new algorithms achieve faster convergence rates than existing algorithms for multiple DRO settings. We also provide a new information-theoretic lower bound that implies our bounds are tight for group DRO. Empirically, too, our algorithms outperform known methods
Python FPGA Programming with Data-Centric Multi-Level Design

Although high-level synthesis (HLS) tools have significantly improved programmer productivity over hardware description languages, developing for FPGAs remains tedious and error prone. Programmers must learn and implement a large set of vendor-specific syntax, patterns, and tricks to optimize (or even successfully compile) their applications, while dealing with ever-changing toolflows from the FPGA vendors. We propose a new way to develop, optimize, and compile FPGA programs. The Data-Centric parallel programming (DaCe) framework allows applications to be defined by their dataflow and control flow through the Stateful DataFlow multiGraph (SDFG) representation, capturing the abstract program characteristics, and exposing a plethora of optimization opportunities. In this work, we show how extending SDFGs with multi-level Library Nodes incorporates both domain-specific and platform-specific optimizations into the design flow, enabling knowledge transfer across application domains and FPGA vendors. We present the HLS-based FPGA code generation backend of DaCe, and show how SDFGs are code generated for either FPGA vendor, emitting efficient HLS code that is structured and annotated to implement the desired architecture.
Robust cross-vendor mammographic texture models using augmentation-based domain adaptation for long-term breast cancer risk

Abstract. Purpose Risk-stratified breast cancer screening might improve early detection and efficiency without comprising quality. However, modern mammography-based risk models do not ensure adaptation across vendor-domains and rely on cancer precursors, associated with short-term risk, which might limit long-term risk assessment. We report a cross-vendor mammographic texture model for long-term risk. Approach The texture model was robustly trained using two systematically designed case-control datasets. Textural features, indicative of future breast cancer, were learned by excluding samples with diagnosed/potential malignancies from training. An augmentation-based domain adaption technique, based on flavorization of mammographic views, ensured generalization across vendor-domains. The model was validated in 66,607 consecutively screened Danish women with flavorized Siemens views and 25,706 Dutch women with Hologic-processed views. Performances were evaluated for interval cancers (IC) within 2 years from screening and long-term cancers (LTC) from 2 years after screening. The texture model was combined with established risk factors to flag 10% of women with the highest risk. Results In Danish women, the texture model achieved an area under the receiver operating characteristic curve (AUC) of 0.71 and 0.65 for ICs and LTCs, respectively. In Dutch women with Hologic-processed views, the AUCs were not different from AUCs in Danish women with flavorized views. The AUC for texture combined with established risk factors increased to 0.68 for LTCs. The 10% of women flagged as high-risk accounted for 25.5% of ICs and 24.8% of LTCs. Conclusions The texture model robustly estimated long-term breast cancer risk while adapting to an unseen processed vendor-domain and identified a clinically relevant high-risk subgroup.
Does Big Data Require Complex Systems? A Performance Comparison Between Spark and Unicage Shell Scripts

The paradigm of big data is characterized by the need to collect and process data sets of great volume, arriving at the systems with great velocity, in a variety of formats. Spark is a widely used big data processing system that can be integrated with Hadoop to provide powerful abstractions to developers, such as distributed storage through HDFS and resource management through YARN. When all the required configurations are made, Spark can also provide quality attributes, such as scalability, fault tolerance, and security. However, all of these benefits come at the cost of complexity, with high memory requirements, and additional latency in processing. An alternative approach is to use a lean software stack, like Unicage, that delegates most control back to the developer. In this work we evaluated the performance of big data processing with Spark versus Unicage, in a cluster environment hosted in the IBM Cloud. Two sets of experiments were performed: batch processing of unstructured data sets, and query processing of structured data sets. The input data sets were of significant size, ranging from 64 GB to 8192 GB in volume. The results show that the performance of Unicage scripts is superior to Spark for search workloads like grep and select, but that the abstractions of distributed storage and resource management from the Hadoop stack enable Spark to execute workloads with inter-record dependencies, such as sort and join, with correct outputs.