Federated learning framework and energy disaggregation techniques for residential energy management

Residential energy use is a significant part of total power usage in developed countries. To reduce overall energy use and save funds, these countries need solutions that help them keep track of how different appliances are used at residences. Non-Intrusive Load Monitoring (NILM) or energy disaggregation is a method for calculating individual appliance power consumption from a single meter tracking the aggregated power of several appliances. To implement any NILM approach in the real world, it is necessary to collect massive amounts of data from individual residences and transfer them to centralized servers, where they will undergo extensive analysis. The centralized fashion of this procedure makes it time-consuming and costly since transferring the data from thousands of residences to the central server takes a lot of time and storage. This thesis proposes utilizing Federated Learning (FL) framework for NILM in order to make the entire system cost-effective and efficient. Rather than collecting data from all clients (residences) and sending it back to the central server, local models are generated on each client’s end and trained on local data in FL. This allows FL to respond more quickly to changes in the environment and handle data locally in a single household, increasing the system’s speed. On top of that, without any data transfer, FL prevents data leakage and preserves the clients’ privacy, leading to a safe and trustworthy system. For the first time, in this work, the performance of deploying FL in NILM was investigated with two different energy disaggregation models: Short Sequence-to-Point (Seq2Point) and Variational Auto-Encoder (VAE). Short Seq2Point with fewer samples as input window for each appliance, tries to simulate the real-time energy disaggregation for the different appliances. Despite having a light-weighted model, Short Seq2Point lacks generalizability and might confront some challenges while disaggregating multi-state appliances

Overall Complexity Certification of a Standard Branch and Bound Method for Mixed-Integer Quadratic Programming

This paper presents a method to certify the computational complexity of a standard Branch and Bound method for solving Mixed-Integer Quadratic Programming (MIQP) problems defined as instances of a multi-parametric MIQP. Beyond previous work, not only the size of the binary search tree is considered, but also the exact complexity of solving the relaxations in the nodes by using recent result from exact complexity certification of active-set QP methods. With the algorithm proposed in this paper, a total worst-case number of QP iterations to be performed in order to solve the MIQP problem can be determined as a function of the parameter in the problem. An important application of the proposed method is Model Predictive Control for hybrid systems, that can be formulated as an MIQP that has to be solved in real-time. The usefulness of the proposed method is successfully illustrated in numerical examples.Comment: Paper accepted for presentation at, and publication in the proceedings of, the 2022 American Control Conferenc

Charting the Chemical and Mechanistic Scope of Light-Triggered Protein Ligation

The creation of discrete, covalent bonds between a protein and a functional molecule like a drug, fluorophore, or radiolabeled complex is essential for making state-of-the-art tools that find applications in basic science and clinical medicine. Photochemistry offers a unique set of reactive groups that hold potential for the synthesis of protein conjugates. Previous studies have demonstrated that photoactivatable desferrioxamine B (DFO) derivatives featuring a para-substituted aryl azide ($ArN_3$) can be used to produce viable zirconium-89-radiolabeled monoclonal antibodies ($^{89}Zr-mAbs$) for applications in noninvasive diagnostic positron emission tomography (PET) imaging of cancers. Here, we report on the synthesis, $^{89}Zr$-radiochemistry, and light-triggered photoradiosynthesis of $^{89}Zr$-labeled human serum albumin (HSA) using a series of 14 different photoactivatable DFO derivatives. The photoactive groups explore a range of substituted, and isomeric $ArN_3$ reagents, as well as derivatives of benzophenone, a para-substituted trifluoromethyl phenyl diazirine, and a tetrazole species. For the compounds studied, efficient photochemical activation occurs inside the UVA-to-visible region of the electromagnetic spectrum (∼365–450 nm) and the photochemical reactions with HSA in water were complete within 15 min under ambient conditions. Under standardized experimental conditions, photoradiosynthesis with compounds 1–14 produced the corresponding $^{89}ZrDFO-PEG_{3}-HSA$ conjugates with decay-corrected isolated radiochemical yields between 18.1 ± 1.8% and 62.3 ± 3.6%. Extensive density functional theory (DFT) calculations were used to explore the reaction mechanisms and chemoselectivity of the light-induced bimolecular conjugation of compounds 1–14 to protein. The photoactivatable DFO-derivatives operate by at least five distinct mechanisms, each producing a different type of bioconjugate bond. Overall, the experimental and computational work presented here confirms that photochemistry is a viable option for making diverse, functionalized protein conjugates

Glioblastoma Following Radiosurgery for Meningioma

We report a patient who underwent gamma knife radiosurgery to treat recurrent meningioma after microsurgery and thereafter developed secondary malignancy adjacent to the original tumor. A 47-year-old woman had underwent resection of the olfactory groove meningioma. Then radiosurgery was done three times over 4 year period for the recurrent tumor. After 58 months from the initial radiosurgery, she presented with headache and progressive mental dullness. Huge tumor in bifrontal location was revealed in MRI. Subsequent operation and pathological examination confirmed diagnosis of glioblastoma. This case fits the criteria of radiation-induced tumor and the clinical implication of the issue is discussed

Letter: The Impact of the Coronavirus (COVID-19) Pandemic on Neurosurgeons Worldwide

This article is made available for unrestricted research re-use and secondary analysis in any form or be any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.The aim of our study was to explore the impact of this pandemic on neurosurgeons with the hope of improving preparedness for future crisis. We created a 20-question survey designed to explore demographics (nation, duration and scope of practice, and case-burden), knowledge (source of information), clinical impact (elective clinic/surgery cancellations), hospital preparedness (availability of personal protective equipment [PPE] and cost of the supplies), and personal factors (financial burden, workload, scientific and research activities). The survey was first piloted with 10 neurosurgeons and then revised. Surveys were distributed electronically in 7 languages (Chinese, English, French, German, Italian, Portuguese, and Spanish) between March 20 and April 3, 2020 using Google Forms, WeChat used to obtain responses, and Excel (Microsoft) and SPSS (IBM) used to analyze results. All responses were cross-verified by 2 members of our team. After obtaining results, we analyzed our data with histograms and standard statistical methods (Chi-square and Fisher's exact tests and logistic regression). Participants were first informed about the objectives of our survey and assured confidentiality after they agreed to participate (Helsinki declaration). We received 187 responses from 308 invitations (60.7%), and 474 additional responses were obtained from social media-based neurosurgery groups (total responses = 661). The respondents were from 96 countries representing 6 continents (Figure ​(Figure11A-​A-11C)

Complexity Certification Algorithms for Mixed-Integer Linear and Quadratic Programming : with Applications to Hybrid MPC

Model predictive control (MPC) generates control actions by iteratively solving optimization problems while explicitly accounting for system dynamics and constraints. Hybrid MPC extends this framework to systems involving both continuous and discrete variables, where the underlying optimization problems typically take the form of mixed-integer linear programs (MILPs) or mixed-integer quadratic programs (MIQPs), depending on the chosen performance measures. Ensuring the reliable real-time execution of hybrid MPC, particularly in safety-critical applications, requires rigorous worst-case computational complexity guarantees to meet limited time and hardware constraints. Motivated by this need, this thesis develops a comprehensive framework for certifying the worst-case computational complexity of solving MILPs and MIQPs, tailored to hybrid MPC applications. Specifically, it focuses on the branch-and-bound (B&amp;B) method, a standard approach for solving these non-convex optimization problems through solving a sequence of relaxations. The proposed framework quantifies key complexity measures, such as the total number of linear systems of equations solved in relaxations (iterations) and the number of relaxations (B&amp;B nodes) explored within B&amp;B, to provide a priori guarantees on computational effort, thereby enabling a deep understanding of the computational resources needed to solve these optimization problems in real time. To enhance practical applicability, the framework is extended to incorporate algorithmic strategies commonly used in B&amp;B, such as various branching strategies, node-selection strategies, and warm-starting of the solver of the relaxations in B&amp;B. Additionally, it is adapted to suboptimal B&amp;B algorithms, which reduce computational effort by trading off global optimality. The framework is further extended to certify certain primal heuristics, including start and improvement heuristics, which leverage feasible solutions to enhance efficiency throughout the B&amp;B process. To further improve scalability, this thesis introduces parallel complexity-certification algorithms, enabling the analysis of high-dimensional and computationally demanding problems. By providing theoretical guarantees and detailed insights, these results facilitate the reliable deployment of B&amp;B-based MILP and MIQP solvers, which are essential for real-time applications such as hybrid MPC, where computational tractability needs to be ensured a priori.Funding agencies: The Wallenberg AI, Autonomous Systems, and Software Program (WASP), funded by the Knut and AliceWallenberg Foundation</p