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Hydroelectricity accounted for roughly 66% of the total generation in Brazil in 2023 and addressed most of the intermittency of wind and solar generation. Thus, one of the most important steps in the operation planning of this country is the forecast of the natural inflow energy (NIE) time series, an approximation of the energetic value of the water inflows. To manage water resources over time, the Brazilian system operator performs long-term forecasts for the NIE to assess the water values through long-term hydrothermal planning models, which are then used to define the short-term merit order in day-ahead scheduling. Therefore, monitoring optimistic bias in NIE forecasts is crucial to prevent an optimistic view of future system conditions and subsequent riskier storage policies. In this article, we investigate and showcase strong evidence of an optimistic bias in the official NIE forecasts, with predicted values consistently exceeding the observations over the past 12 years in the two main subsystems (Southeast and Northeast). Rolling window out-of-sample tests conducted with real data demonstrate that the official forecast model exhibits a statistically significant bias of 6%, 13%, 18%, and 23% for 1, 6, 12, and 24 steps ahead in the Southeast subsystem, and 19%, 57%, 80%, and 108% in the Northeast.

The hybrid organic-inorganic halide perovskite (HOIP), for example MAPbBr3, exhibits extended spin lifetime and apparent spin lifetime anisotropy in experiments. The underlying mechanisms of these phenomena remain illusive. By utilizing our first-principles densitymatrix dynamics approach with quantum scatterings including electron-phonon and electronelectron interactions and self-consistent spinorbit coupling, we present temperature- and magnetic field-dependent spin lifetimes in hybrid perovskites, in agreement with experimental observations. For centrosymmetric hybrid perovskite MAPbBr3, the experimentally observed spin lifetime anisotropy is mainly attributed to the dynamical Rashba effect arising from the interaction between organic and inorganic components and the rotation of the organic cation. For noncentrosymmetric perovskite, such as MPSnBr3, we found persistent spin helix texture at the conduction band minimum, which significantly enhances the spin lifetime anisotropy. Our study provides theoretical insight to spin dynamics in HOIP and strategies for controlling and optimizing spin transport.

We propose a mathematical walk around the gcd of the values $A(n)$ and $B(n)$ of two polynomials evaluated at an integer $n$. This is an opportunity to use a very powerful tool: the resultant., Comment: The final version will appear in the American Mathematical Monthly

Sampling from generative models has become a crucial tool for applications like data synthesis and augmentation. Diffusion, Flow Matching and Continuous Normalizing Flows have shown effectiveness across various modalities, and rely on Gaussian latent variables for generation. For search-based or creative applications that require additional control over the generation process, it has become common to manipulate the latent variable directly. However, existing approaches for performing such manipulations (e.g. interpolation or forming low-dimensional representations) only work well in special cases or are network or data-modality specific. We propose Combination of Gaussian variables (COG) as a general purpose method to form linear combinations of latent variables while adhering to the assumptions of the generative model. COG is easy to implement yet outperforms recent sophisticated methods for interpolation. As COG naturally addresses the broader task of forming linear combinations, new capabilities are afforded, including the construction of subspaces of the latent space, dramatically simplifying the creation of expressive low-dimensional spaces of high-dimensional objects.

While the blockchain-based smart contract has become a hot topic of research over the last decade, not the least in the context of Industry 4.0, it now has well-known legal and technical shortcomings that currently prohibit its real-world application. These shortcomings come from (1) that a smart contract is a computer program, not a document describing legal obligations, and (2) that blockchain-based systems are complicated to use and operate. In this paper, we present a refined and extended summary of our work taking key technologies from the blockchain sphere and applying them to the ricardian contract, which is a traditional contract in digital form with machine-readable parameters. By putting the ricardian contract in the context of our contract network architecture, we facilitate the infrastructure required for contracts to be offered, negotiated, performed, renegotiated and terminated in a completely digital and automatable fashion. Our architecture circumvents the legal issues of blockchains by facilitating an artifact very much alike a traditional contract, as well as its operational complexity by requiring consensus only between nodes representing directly involved parties. To demonstrate its utility, we also present how it could be used for (1) private data purchasing, (2) treasury management, (3) order-driven manufacturing and (4) automated device on-boarding., Comment: 11 pages, 9 figures

In this paper we study the localization of a derived category of a graded gentle algebra by a subcategory generated by a spherical band object. This object corresponds to a simple closed curve under the equivalence between the perfect derived category of the graded gentle algebra and the partially wrapped Fukaya category of the associated graded marked surface, as established by Haiden, Katzarkov and Kontsevich. We describe this localization as a recollement that involves the derived category of a new graded algebra given by quiver and relations. This leads us to the introduction of the class of graded pinched gentle algebras, a generalization of graded gentle algebras. We then show that these algebras are in bijection with graded marked surfaces with conical singularities. Moreover, under this correspondence the localization process amounts to the contraction of the closed curve., Comment: 25 pages

The short-loading cycle is a repetitive task performed in high quantities, making it a great alternative for automation. In the short-loading cycle, an expert operator navigates towards a pile, fills the bucket with material, navigates to a dump truck, and dumps the material into the tipping body. The operator has to balance the productivity goal while minimising the fuel usage, to maximise the overall efficiency of the cycle. In addition, difficult interactions, such as the tyre-to-surface interaction further complicate the cycle. These types of hard-to-model interactions that can be difficult to address with rule-based systems, together with the efficiency requirements, motivate us to examine the potential of data-driven approaches. In this paper, the possibility of teaching an agent through reinforcement learning to approach a dump truck's tipping body and get in position to dump material in the tipping body is examined. The agent is trained in a 3D simulated environment to perform a simplified navigation task. The trained agent is directly transferred to a real vehicle, to perform the same task, with no additional training. The results indicate that the agent can successfully learn to navigate towards the dump truck with a limited amount of control signals in simulation and when transferred to a real vehicle, exhibits the correct behaviour.

Distributed ledger technology such as blockchain is considered essential for supporting large numbers of micro-transactions in the Machine Economy, which is envisioned to involve billions of connected heterogeneous and decentralized cyber-physical systems. This stresses the need for performance and scalability of distributed ledger technologies. Sharding divides the blockchain network into multiple committees and is a common approach to improve scalability. However, with current sharding approaches, costly cross-shard verification is needed to prevent double-spending. This paper proposes a novel and more scalable distributed ledger method named ScaleGraph that implements dynamic sharding by using routing and logical proximity concepts from distributed hash tables. ScaleGraph addresses cyber security in terms of integrity, availability, and trust, to support frequent micro-transactions between autonomous devices. Benefits of ScaleGraph include a total storage space complexity of O(t), where t is the global number of transactions (assuming a constant replication degree). This space is sharded over n nodes so that each node needs O(t/n) storage, which provides a high level of concurrency and data localization as compared to other delegated consensus proposals. ScaleGraph allows for a dynamic grouping of validators which are selected based on a distance metric. We analyze the consensus requirements in such a dynamic setting and show that a synchronous consensus protocol allows shards to be smaller than an asynchronous one, and likely yields better performance. Moreover, we provide an experimental analysis of security aspects regarding the required size of the consensus groups with ScaleGraph. Our analysis shows that dynamic sharding based on proximity concepts brings attractive scalability properties in general, especially when the fraction of corrupt nodes is small., Comment: 19 pages, 6 figures, submitted to ACM DLT

Breast implants are widely used after breast cancer resection and must be changed regularly to avoid a rupture. To date, there are no quantitative criteria to help this decision. The mechanical evolution of the gels and membranes of the implants is still under investigated, although it can lead to early rupture. In this study, 35 breast explants having been implanted in patients for up to 17 years were characterized by ex vivo measurements of their mechanical properties. Using Acoustic Radiation Force Impulse (ARFI) ultrasound elastography, an imaging method for non-destructive mechanical characterization, an increase in the stiffness of the explants has been observed. This increase was correlated with the implantation duration, primarily after 8 years of implantation. With an increase of the shear modulus of up to a factor of nearly 3, the loss of flexibility of the implants is likely to lead to a significant increase of their risk of rupture. A complementary analysis of the gel from the explants by mass spectrometry imaging (MSI) and liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) confirms the presence of metabolites of cholesterol originating from the breast tissues, which most likely crossed the membrane of the implants and most likely degrades the gel. By observing the consequences of the physical-chemical mechanisms at work within patients, this study shows that ultrasound elastography could be used in vivo as a quantitative indicator of the risk of breast implant rupture and help diagnose their replacement.

Ensembles of independently trained deep neural networks yield uncertainty estimates that rival Bayesian networks in performance. They also offer sizable improvements in terms of predictive performance over single models. However, deep ensembles are not commonly used in environments with limited computational budget -- such as autonomous driving -- since the complexity grows linearly with the number of ensemble members. An important observation that can be made for robotics applications, such as autonomous driving, is that data is typically sequential. For instance, when an object is to be recognized, an autonomous vehicle typically observes a sequence of images, rather than a single image. This raises the question, could the deep ensemble be spread over time? In this work, we propose and analyze Deep Ensembles Spread Over Time (DESOT). The idea is to apply only a single ensemble member to each data point in the sequence, and fuse the predictions over a sequence of data points. We implement and experiment with DESOT for traffic sign classification, where sequences of tracked image patches are to be classified. We find that DESOT obtains the benefits of deep ensembles, in terms of predictive and uncertainty estimation performance, while avoiding the added computational cost. Moreover, DESOT is simple to implement and does not require sequences during training. Finally, we find that DESOT, like deep ensembles, outperform single models for out-of-distribution detection.

We study a broad class of morsifications of germs of univariate real analytic functions. We characterize the combinatorial types of the resulting Morse functions, via planar contact trees constructed from Newton-Puiseux roots of the polar curves of the morsifications., Comment: 23 pages, 16 figures. Compared to the first version, we clarified some statements as well as Figure 1 and we added Example 6.4 and Remarks 6.8, 7.1. To appear in Mathematische Nachrichten

In this technical report, we compare multiple reformulation techniques and solvers that can be used with the Julia package BilevelJuMP. We focus on the special case of Hyperparameter Tuning for Support Vector Regression. We describe a bilevel model for the problem in question. Then we present code for generating data and models that solve the problem. Finally, we present results and a brief analysis., Comment: arXiv admin note: substantial text overlap with arXiv:2205.02307

Docker allows for the packaging of applications and dependencies, and its instructions are described in Dockerfiles. Nowadays, version pinning is recommended to avoid unexpected changes in the latest version of a package. However, version pinning in Dockerfiles is not yet fully realized (only 17k of the 141k Dockerfiles we analyzed), because of the difficulties caused by version pinning. To maintain Dockerfiles with version-pinned packages, it is important to update package versions, not only for improved functionality, but also for software supply chain security, as packages are changed to address vulnerabilities and bug fixes. However, when updating multiple version-pinned packages, it is necessary to understand the dependencies between packages and ensure version compatibility, which is not easy. To address this issue, we explore the applicability of the meta-maintenance approach, which aims to distribute the successful updates in a part of a group that independently maintains a common artifact. We conduct an exploratory analysis of 7,914 repositories on GitHub that hold Dockerfiles, which retrieve packages on GitHub by URLs. There were 385 repository groups with the same multiple package combinations, and 208 groups had Dockerfiles with newer version combinations compared to others, which are considered meta-maintenance applicable. Our findings support the potential of meta-maintenance for updating multiple version-pinned packages and also reveal future challenges., Comment: 10 pages

In this work, we present a new approach to analyze the gradient flow for a positive semi-definite matrix denoising problem in an extensive-rank and high-dimensional regime. We use recent linear pencil techniques of random matrix theory to derive fixed point equations which track the complete time evolution of the matrix-mean-square-error of the problem. The predictions of the resulting fixed point equations are validated by numerical experiments. In this short note we briefly illustrate a few predictions of our formalism by way of examples, and in particular we uncover continuous phase transitions in the extensive-rank and high-dimensional regime, which connect to the classical phase transitions of the low-rank problem in the appropriate limit. The formalism has much wider applicability than shown in this communication.

A recent line of work has shown remarkable behaviors of the generalization error curves in simple learning models. Even the least-squares regression has shown atypical features such as the model-wise double descent, and further works have observed triple or multiple descents. Another important characteristic are the epoch-wise descent structures which emerge during training. The observations of model-wise and epoch-wise descents have been analytically derived in limited theoretical settings (such as the random feature model) and are otherwise experimental. In this work, we provide a full and unified analysis of the whole time-evolution of the generalization curve, in the asymptotic large-dimensional regime and under gradient-flow, within a wider theoretical setting stemming from a gaussian covariate model. In particular, we cover most cases already disparately observed in the literature, and also provide examples of the existence of multiple descent structures as a function of a model parameter or time. Furthermore, we show that our theoretical predictions adequately match the learning curves obtained by gradient descent over realistic datasets. Technically we compute averages of rational expressions involving random matrices using recent developments in random matrix theory based on "linear pencils". Another contribution, which is also of independent interest in random matrix theory, is a new derivation of related fixed point equations (and an extension there-off) using Dyson brownian motions.

The package manager (PM) is crucial to most technology stacks, acting as a broker to ensure that a verified dependency package is correctly installed, configured, or removed from an application. Diversity in technology stacks has led to dozens of PMs with various features. While our recent study indicates that package management features of PM are related to end-user experiences, it is unclear what those issues are and what information is required to resolve them. In this paper, we have investigated PM issues faced by end-users through an empirical study of content on Stack Overflow (SO). We carried out a qualitative analysis of 1,131 questions and their accepted answer posts for three popular PMs (i.e., Maven, npm, and NuGet) to identify issue types, underlying causes, and their resolutions. Our results confirm that end-users struggle with PM tool usage (approximately 64-72%). We observe that most issues are raised by end-users due to lack of instructions and errors messages from PM tools. In terms of issue resolution, we find that external link sharing is the most common practice to resolve PM issues. Additionally, we observe that links pointing to useful resources (i.e., official documentation websites, tutorials, etc.) are most frequently shared, indicating the potential for tool support and the ability to provide relevant information for PM end-users.

Increasing complexity and data-generation rates in cyber-physical systems and the industrial Internet of things are calling for a corresponding increase in AI capabilities at the resource-constrained edges of the Internet. Meanwhile, the resource requirements of digital computing and deep learning are growing exponentially, in an unsustainable manner. One possible way to bridge this gap is the adoption of resource-efficient brain-inspired "neuromorphic" processing and sensing devices, which use event-driven, asynchronous, dynamic neurosynaptic elements with colocated memory for distributed processing and machine learning. However, since neuromorphic systems are fundamentally different from conventional von Neumann computers and clock-driven sensor systems, several challenges are posed to large-scale adoption and integration of neuromorphic devices into the existing distributed digital-computational infrastructure. Here, we describe the current landscape of neuromorphic computing, focusing on characteristics that pose integration challenges. Based on this analysis, we propose a microservice-based framework for neuromorphic systems integration, consisting of a neuromorphic-system proxy, which provides virtualization and communication capabilities required in distributed systems of systems, in combination with a declarative programming approach offering engineering-process abstraction. We also present concepts that could serve as a basis for the realization of this framework, and identify directions for further research required to enable large-scale system integration of neuromorphic devices.

AlphaCode is a code generation system for assisting software developers in solving competitive programming problems using natural language problem descriptions. Despite the advantages of the code generating system, the open source community expressed concerns about practicality and data licensing. However, there is no research investigating generated codes in terms of code clone and performance. In this paper, we conduct an empirical study to find code similarities and performance differences between AlphaCode-generated codes and human codes. The results show that (i) the generated codes from AlphaCode are similar to human codes (i.e., the average maximum similarity score is 0.56) and (ii) the generated code performs on par with or worse than the human code in terms of execution time and memory usage. Moreover, AlphaCode tends to generate more similar codes to humans for low-difficulty problems (i.e., four cases have the exact same codes). It also employs excessive nested loops and unnecessary variable declarations for high-difficulty problems, which cause low performance regarding our manual investigation. The replication package is available at https:/doi.org/10.5281/zenodo.6820681, Comment: Accepted to the 16th International Workshop on Software Clones (IWSC 2022)

An algebraic domain is a closed topological subsurface of a real affine plane whose boundary consists of disjoint smooth connected components of real algebraic plane curves. We study the geometric shape of an algebraic domain by collapsing all vertical segments contained in it: this yields a Poincar\'e-Reeb graph, which is naturally transversal to the foliation by vertical lines. We show that any transversal graph whose vertices have only valencies 1 and 3 and are situated on distinct vertical lines can be realized as a Poincar\'e-Reeb graph., Comment: 31 pages, 37 figures. We improved the exposition, according to the referee's suggestions. We made small changes, added references and a question from the reviewers. To appear in Revista Matem\'atica Complutense

We consider polynomial equations, or systems of polynomial equations, with integer coefficients, modulo prime numbers $p$. We offer an elementary approach based on a counting method. The outcome is a weak form of the Lang-Weil lower bound for the number of solutions modulo $p$, only differing from Lang-Weil by an asymptotic $p^\epsilon$ multiplicative factor. Our second contribution is a reduction lemma to the case of a single equation which we use to extend our results to systems of equations. We show further how to use this reduction to prove the full Lang-Weil estimate for varieties, assuming it for hypersurfaces, in a version using a variant of the classical degree in the error term., Comment: The spirit of the paper was to give simple proofs around the Lang-Weil formula. However, the formula at the second line of paragraph 2.4 was pointed out as incorrect by Mirko Torresani. As a result, we no longer have an elementary proof for formula (3)

Third-party library dependencies are commonplace in today's software development. With the growing threat of security vulnerabilities, applying security fixes in a timely manner is important to protect software systems. As such, the community developed a list of software and hardware weakness known as Common Weakness Enumeration (CWE) to assess vulnerabilities. Prior work has revealed that maintenance activities such as refactoring code potentially correlate with security-related aspects in the source code. In this work, we explore the relationship between refactoring and security by analyzing refactoring actions performed jointly with vulnerability fixes in practice. We conducted a case study to analyze 143 maven libraries in which 351 known vulnerabilities had been detected and fixed. Surprisingly, our exploratory results show that developers incorporate refactoring operations in their fixes, with 31.9% (112 out of 351) of the vulnerabilities paired with refactoring actions. We envision this short paper to open up potential new directions to motivate automated tool support, allowing developers to deliver fixes faster, while maintaining their code., Comment: Accepted as ERA paper to EASE2022

In this paper we present BilevelJuMP, a new Julia package to support bilevel optimization within the JuMP framework. The package is a Julia library that enables the user to describe both upper and lower-level optimization problems using the JuMP algebraic syntax. Due to the generality and flexibility our library inherits from JuMP's syntax, our package allows users to model bilevel optimization problems with conic constraints in the lower level and all JuMP supported constraints in the upper level (Conic, Quadratic, Non-Linear, Integer, etc.). Moreover, the user-defined problem can be subsequently solved by various techniques relying on mathematical program with equilibrium constraints (MPEC) reformulations. Manipulations on the original problem data are possible due to MathOptInterface.jl's structures and Dualization.jl features. Hence, the proposed package allows quickly model, deploy, and thereby experiment bilevel models based on off-the-shelf mixed integer linear programming and nonlinear solvers.

In the field of data science, and for academics in general, the Python programming language is a popular choice, mainly because of its libraries for storing, manipulating, and gaining insight from data. Evidence includes the versatile set of machine learning, data visualization, and manipulation packages used for the ever-growing size of available data. The Zen of Python is a set of guiding design principles that developers use to write acceptable and elegant Python code. Most principles revolve around simplicity. However, as the need to compute large amounts of data, performance has become a necessity for the Python programmer. The new idea in this paper is to confirm whether writing the Pythonic way peaks performance at scale. As a starting point, we conduct a set of preliminary experiments to evaluate nine Pythonic code examples by comparing the performance of both Pythonic and Non-Pythonic code snippets. Our results reveal that writing in Pythonic idioms may save memory and time. We show that incorporating list comprehension, generator expression, zip, and itertools.zip_longest idioms can save up to 7,000 MB and up to 32.25 seconds. The results open more questions on how they could be utilized in a real-world setting. The replication package includes all scripts, and the results are available at https://doi.org/10.5281/zenodo.5712349, Comment: To be published in the proceedings of the 30th IEEE/ACM International Conference on Program Comprehension (ICPC)

Recent evidence has shown the existence of a so-called double-descent and even triple-descent behavior for the generalization error of deep-learning models. This important phenomenon commonly appears in implemented neural network architectures, and also seems to emerge in epoch-wise curves during the training process. A recent line of research has highlighted that random matrix tools can be used to obtain precise analytical asymptotics of the generalization (and training) errors of the random feature model. In this contribution, we analyze the whole temporal behavior of the generalization and training errors under gradient flow for the random feature model. We show that in the asymptotic limit of large system size the full time-evolution path of both errors can be calculated analytically. This allows us to observe how the double and triple descents develop over time, if and when early stopping is an option, and also observe time-wise descent structures. Our techniques are based on Cauchy complex integral representations of the errors together with recent random matrix methods based on linear pencils.

It has become common practice for software projects to adopt third-party dependencies. Developers are encouraged to update any outdated dependency to remain safe from potential threats of vulnerabilities. In this study, we present an approach to aid developers show whether or not a vulnerable code is reachable for JavaScript projects. Our prototype, S\=ojiTantei, is evaluated in two ways (i) the accuracy when compared to a manual approach and (ii) a larger-scale analysis of 780 clients from 78 security vulnerability cases. The first evaluation shows that S\=ojiTantei has a high accuracy of 83.3%, with a speed of less than a second analysis per client. The second evaluation reveals that 68 out of the studied 78 vulnerabilities reported having at least one clean client. The study proves that automation is promising with the potential for further improvement., Comment: To be published in IEICE Transactions on Information and Systems (Special Section on Empirical Software Engineering)

The management of third-party package dependencies is crucial to most technology stacks, with package managers acting as brokers to ensure that a verified package is correctly installed, configured, or removed from an application. Diversity in technology stacks has led to dozens of package ecosystems with their own management features. While recent studies have shown that developers struggle to migrate their dependencies, the common assumption is that package ecosystems are used without any issue. In this study, we explore 13 package ecosystems to understand whether their features correlate with the experience of their users. By studying experience through the questions that developers ask on the question-and-answer site Stack Overflow, we find that developer questions are grouped into three themes (i.e., Package management, Input-Output, and Package Usage). Our preliminary analysis indicates that specific features are correlated with the user experience. Our work lays out future directions to investigate the trade-offs involved in designing the ideal package ecosystem.