Miracle-dz/UniFAR-FTUs huggingface dataset:
Phase-controlled multilevel converters based on dual structure associations

Multilevel converters, like neutral-point-clamped inverters or multilevel choppers, are particularly attractive in high-power applications. Nevertheless, in these structures, all switches are confronted to commutation stresses caused by their turn-on and turn-off control. Furthermore, the methods to balance the capacitor voltages or to control the neutral point voltage are complex enough. In this paper, the authors propose new multilevel converters based on series connection of zero-current-source (ZCS) inverter cells and parallel connection of zero-voltage-source (ZVS) inverters. These dual structure associations give soft-switching operation for all switches and allow the use of semiconductors, normally destined for medium-power applications, in high-power converters (up to 1 MW). The authors consider the structure design for several topologies to achieve DC-DC or DC-AC converters. The simulation results validate the simplicity of phase control techniques and give out the principal features of different topologies.
A Generalized Primal-Dual Algorithm with Improved Convergence Condition for Saddle Point Problems

We generalize the well-known primal-dual algorithm proposed by Chambolle and Pock for saddle point problems, and improve the condition for ensuring its convergence. The improved convergence-guaranteeing condition is effective for the generic setting, and it is shown to be optimal. It also allows us to discern larger step sizes for the resulting subproblems, and thus provides a simple and universal way to improve numerical performance of the original primal-dual algorithm. In addition, we present a structure-exploring heuristic to further relax the convergence-guaranteeing condition for some specific saddle point problems, which could yield much larger step sizes and hence significantly better performance. Effectiveness of this heuristic is numerically illustrated by the classic assignment problem.
Methods for measuring noise, purity changes, and entanglement entropy in quantum devices and systems

We present methods for evaluating the rate of change in quantities during quantum evolution due to coupling to the environment (dissipation hereafter). The protocol is based on repeating a given quantum circuit (or quantum operation) twice, thrice, and so on, and measuring an expectation value after each number of repetitions. We start by applying this method for measuring the rate of purity changes in quantum circuits. This provides direct information on the quality of the circuit. Furthermore, the presented scheme enables to distill the dissipative contribution in the changes of quantities such as energies and coherence. In particular, this can be applied to the local Hamiltonians of specific qubits. Thus, our approach can be used to locate"hotspots"where the dissipation takes place. A variant of this method can be used to measure the entanglement buildup in quantum circuits. These methods are scalable as they involve only a few observables which are relatively easy to measure in NISQ devices.
Global Convergence of Inexact Augmented Lagrangian Method for Zero-One Composite Optimization

We consider the problem of minimizing the sum of a smooth function and a composition of a zero-one loss function with a linear operator, namely zero-one composite optimization problem (0/1-COP). It is a versatile model including the support vector machine (SVM), multi-label classification (MLC), maximum rank correlation (MRC) and so on. However, due to the nonconvexity, discontinuity and NP-hardness of the 0/1-COP, it is intractable to design a globally convergent algorithm and the work attempting to solve it directly is scarce. In this paper, we first define and characterize the proximal stationarity to derive the minimum and the strongly exact penalization of the Lyapunov function, which is a variant of the augmented Lagrangian function for the 0/1-COP. Based on this, we propose an inexact augmented Lagrangian method (IALM) for solving 0/1-COP, where the subproblem is solved by the zero-one Bregman alternating linearized minimization (0/1-BALM) algorithm with low computational complexity. Under some suitable assumptions, we prove that the whole sequence generated by the IALM converges to the local minimizer of 0/1-COP. As a direct application, we obtain the global convergence of IALM under the assumption that the data matrix is full row rank for solving the SVM, MLC and MRC.
Leveraging Intrinsic Gradient Information for Machine Learning Model Training

Designing models that produce accurate predictions is the fundamental objective of machine learning. This work presents methods demonstrating that when the derivatives of target variables with respect to inputs can be extracted from processes of interest, they can be leveraged to improve the accuracy of differentiable machine learning models. Four key ideas are explored: (1) Improving the predictive accuracy of linear regression models and feed-forward neural networks (NNs); (2) Using the difference between the performance of feedforward NNs trained with and without gradient information to tune NN complexity (in the form of hidden node number); (3) Using gradient information to regularise linear regression; and (4) Using gradient information to improve generative image models. Across this variety of applications, gradient information is shown to enhance each predictive model, demonstrating its value for a variety of applications.
A derivative-free approach to optimal control problems with piecewise constant Mayer cost function

A piecewise constant Mayer cost function is used to model optimal control problems in which the state space is partitioned into several regions, each having its own Mayer cost value. In such a context, the standard numerical methods used in optimal control theory naturally fail, due to the discontinuities and the null gradients associated with the Mayer cost function. In this paper an hybrid numerical method, based on both derivative-free optimization and smooth optimization techniques, is proposed to solve this class of problems. Numerical simulations are performed on some standard control systems to show the efficiency of the hybrid method, where Nomad and Ipopt are used as, respectively, derivative-free optimization and smooth optimization solvers.
Trusted And Confidential Program Analysis

We develop the concept of Trusted and Confidential Program Analysis (TCPA) which enables program certification to be used where previously there was insufficient trust. Imagine a scenario where a producer may not be trusted to certify its own software (perhaps by a foreign regulator), and the producer is unwilling to release its sources and detailed design to any external body. We present a protocol that can, using trusted computing based on encrypted sources, create certification via which all can trust the delivered object code without revealing the unencrypted sources to any party. Furthermore, we describe a realization of TCPA with trusted execution environments (TEE) that enables general and efficient computation. We have implemented the TCPA protocol in a system called TCWasm for web assembly architectures. In our evaluation with 33 benchmark cases, TCWasm managed to finish the analysis with relatively slight overheads.
MOMO - Deep Learning-driven classification of external DICOM studies for PACS archivation

Patients regularly continue assessment or treatment in other facilities than they began them in, receiving their previous imaging studies as a CD-ROM and requiring clinical staff at the new hospital to import these studies into their local database. However, between different facilities, standards for nomenclature, contents, or even medical procedures may vary, often requiring human intervention to accurately classify the received studies in the context of the recipient hospital’s standards. In this study, the authors present MOMO (MOdality Mapping and Orchestration), a deep learning-based approach to automate this mapping process utilizing metadata substring matching and a neural network ensemble, which is trained to recognize the 76 most common imaging studies across seven different modalities. A retrospective study is performed to measure the accuracy that this algorithm can provide. To this end, a set of 11,934 imaging series with existing labels was retrieved from the local hospital’s PACS database to train the neural networks. A set of 843 completely anonymized external studies was hand-labeled to assess the performance of our algorithm. Additionally, an ablation study was performed to measure the performance impact of the network ensemble in the algorithm, and a comparative performance test with a commercial product was conducted. In comparison to a commercial product (96.20% predictive power, 82.86% accuracy, 1.36% minor errors), a neural network ensemble alone performs the classification task with less accuracy (99.05% predictive power, 72.69% accuracy, 10.3% minor errors). However, MOMO outperforms either by a large margin in accuracy and with increased predictive power (99.29% predictive power, 92.71% accuracy, 2.63% minor errors). Here we show that deep learning combined with metadata matching is a promising and flexible approach for the automated classification of external DICOM studies for PACS archivation.
TrollMagnifier: Detecting State-Sponsored Troll Accounts on Reddit

Growing evidence points to recurring influence campaigns on social media, often sponsored by state actors aiming to manipulate public opinion on sensitive political topics. Typically, campaigns are performed through instrumented accounts, known as troll accounts; despite their prominence, however, little work has been done to detect these accounts in the wild. In this paper, we present TROLLMAGNIFIER, a detection system for troll accounts. Our key observation, based on analysis of known Russian-sponsored troll accounts identified by Reddit, is that they show loose coordination, often interacting with each other to further specific narratives. Therefore, troll accounts controlled by the same actor often show similarities that can be leveraged for detection. TROLLMAGNIFIER learns the typical behavior of known troll accounts and identifies more that behave similarly. We train TROLLMAGNIFIER on a set of 335 known troll accounts and run it on a large dataset of Reddit accounts. Our system identifies 1,248 potential troll accounts; we then provide a multi-faceted analysis to corroborate the correctness of our classification. In particular, 66% of the detected accounts show signs of being instrumented by malicious actors (e.g., they were created on the same exact day as a known troll, they have since been suspended by Reddit, etc.). They also discuss similar topics as the known troll accounts and exhibit temporal synchronization in their activity. Overall, we show that using TROLLMAGNIFIER, one can grow the initial knowledge of potential trolls provided by Reddit by over 300%.
Predictive control of three-phase inverter

A new method for current control based on a predictive strategy is presented. This uses a discrete-time model of the load to predict the future value of the current for each of the possible voltage vectors generated by the inverter. The vector which minimises the current error at the next sampling time is selected. Experimental results that confirm the feasibility of the method are given.
Noisy Bayesian optimization for variational quantum eigensolvers

Giovanni Iannellia,b,c,d,∗ and Karl Jansen Deutsches Elektronen-Synchrotron DESY, Platanenallee 6, 15738 Zeuthen, Germany Department of Physics, University of Cyprus, Panepistimiou Street 1, 2109 Aglantzia, Nicosia, Cyprus Institut für Physik, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany Dipartimento di Fisica, Università degli Studi di Roma “Tor Vergata”, Via della Ricerca Scientifica 1, 00133 Rome, Italy E-mail: giovanni.iannelli@desy.de, karl.jansen@desy.de
Cross-Correlated Motions in Azidolysozyme

The changes in the local and global dynamics of azide-labelled lysozyme compared with that of the wild type protein are quantitatively assessed for all alanine residues along the polypeptide chain. Although attaching -N3 to alanine residues has been considered to be a minimally invasive change in the protein it is found that depending on the location of the alanine residue, the local and global changes in the dynamics differ. For Ala92, the change in the cross-correlated motions are minimal, whereas attaching -N3 to Ala90 leads to pronounced differences in the local and global correlations as quantified by the cross-correlation coefficients of the Cα atoms. We also demonstrate that the spectral region of the asymmetric azide stretch distinguishes between alanine attachment sites, whereas changes in the low frequency, far-infrared region are less characteristic.
Molecular Contrastive Learning with Chemical Element Knowledge Graph

Molecular representation learning contributes to multiple downstream tasks such as molecular property prediction and drug design. To properly represent molecules, graph contrastive learning is a promising paradigm as it utilizes self-supervision signals and has no requirements for human annotations. However, prior works fail to incorporate fundamental domain knowledge into graph semantics and thus ignore the correlations between atoms that have common attributes but are not directly connected by bonds. To address these issues, we construct a Chemical Element Knowledge Graph (KG) to summarize microscopic associations between elements and propose a novel Knowledge-enhanced Contrastive Learning (KCL) framework for molecular representation learning. KCL framework consists of three modules. The first module, knowledge-guided graph augmentation, augments the original molecular graph based on the Chemical Element KG. The second module, knowledge-aware graph representation, extracts molecular representations with a common graph encoder for the original molecular graph and a Knowledge-aware Message Passing Neural Network (KMPNN) to encode complex information in the augmented molecular graph. The final module is a contrastive objective, where we maximize agreement between these two views of molecular graphs. Extensive experiments demonstrated that KCL obtained superior performances against state-of-the-art baselines on eight molecular datasets. Visualization experiments properly interpret what KCL has learned from atoms and attributes in the augmented molecular graphs.
Wiki to Automotive: Understanding the Distribution Shift and its impact on Named Entity Recognition

While transfer learning has become a ubiquitous technique used across Natural Language Processing (NLP) tasks, it is often unable to replicate the performance of pre-trained models on text of niche domains like Automotive. In this paper we aim to understand the main characteristics of the distribution shift with automotive domain text (describing technical functionalities such as Cruise Control) and attempt to explain the potential reasons for the gap in performance. We focus on performing the Named Entity Recognition (NER) task as it requires strong lexical, syntactic and semantic understanding by the model. Our experiments with 2 different encoders, namely BERT-Base-Uncased and SciBERT-Base-Scivocab-Uncased have lead to interesting findings that showed: 1) The performance of SciBERT is better than BERT when used for automotive domain, 2) Fine-tuning the language models with automotive domain text did not make significant improvements to the NER performance, 3) The distribution shift is challenging as it is characterized by lack of repeating contexts, sparseness of entities, large number of Out-Of-Vocabulary (OOV) words and class overlap due to domain specific nuances.
Slicing Scheduling for Supporting Critical Traffic in Beyond 5G

One of the most challenging services fifth-generation (5G) mobile network is designed to support, is the critical services in-need of very low latency, and/or high reliability. It is now clear that such critical services will also be at the core of beyond 5G (B5G) networks. While 5G radio design accommodates such supports by introducing more flexibility in timing, how efficiently those services could be scheduled over a shared network with other broadband services remains as a challenge. In this paper, we use network slicing as an enabler for network sharing and propose an optimization framework to schedule resources to critical services via puncturing technique with minimal impact on the regular broadband services. We then thoroughly examine the performance of the framework in terms of throughput and reliability through simulation.
A symmetrized parametric finite element method for anisotropic surface diffusion of closed curves via a Cahn-Hoffman ξ-vector formulation

We deal with a long-standing problem about how to design an energy-stable numerical scheme for solving the motion of a closed curve under {\sl anisotropic surface diffusion} with a general anisotropic surface energy $\gamma(\boldsymbol{n})$ in two dimensions, where $\boldsymbol{n}$ is the outward unit normal vector. By introducing a novel symmetric positive definite surface energy matrix $Z_k(\boldsymbol{n})$ depending on the Cahn-Hoffman $\boldsymbol{\xi}$-vector and a stabilizing function $k(\boldsymbol{n})$, we first reformulate the anisotropic surface diffusion into a conservative form and then derive a new symmetrized variational formulation for the anisotropic surface diffusion with weakly or strongly anisotropic surface energies. A semi-discretization in space for the symmetrized variational formulation is proposed and its area (or mass) conservation and energy dissipation are proved. The semi-discretization is then discretized in time by either an implicit structural-preserving scheme (SP-PFEM) which preserves the area in the discretized level or a semi-implicit energy-stable method (ES-PFEM) which needs only solve a linear system at each time step. Under a relatively simple and mild condition on $\gamma(\boldsymbol{n})$, we show that both SP-PFEM and ES-PFEM are unconditionally energy-stable for almost all anisotropic surface energies $\gamma(\boldsymbol{n})$ arising in practical applications. Specifically, for several commonly-used anisotropic surface energies, we construct $Z_k(\boldsymbol{n})$ explicitly. Finally, extensive numerical results are reported to demonstrate the high performance of the proposed numerical schemes.
Frequent-Pattern Based Broadcast Scheduling for Conflict Avoidance in Multi-Channel Data Dissemination Systems

With the popularity of mobile devices, using the traditional client-server model to handle a large number of requests is very challenging. Wireless data broadcasting can be used to provide services to many users at the same time, so reducing the average access time has become a popular research topic. For example, some location-based services (LBS) consider using multiple channels to disseminate information to reduce access time. However, data conflicts may occur when multiple channels are used, where multiple data items associated with the request are broadcasted at about the same time. In this article, we consider the channel switching time and identify the data conflict issue in an on-demand multichannel dissemination system. We model the considered problem as a data broadcast with conflict avoidance (DBCA) problem and prove it is NP-complete. We hence propose the frequent-pattern-based broadcast scheduling (FPBS), which provides a new variant of the frequent pattern tree, FP ∗ -tree, to schedule the requested data. Using FPBS, the system can avoid data conflicts when assigning data items to time slots in the channels. In the simulation, we discussed two modes of FPBS: online and offline. The results show that, compared with the existing heuristic methods, FPBS can shorten the average access time by 30%.
Learning Oriented Remote Sensing Object Detection via Naive Geometric Computing

Detecting oriented objects along with estimating their rotation information is one crucial step for analyzing remote sensing images. Despite that many methods proposed recently have achieved remarkable performance, most of them directly learn to predict object directions under the supervision of only one (e.g. the rotation angle) or a few (e.g. several coordinates) groundtruth values individually. Oriented object detection would be more accurate and robust if extra constraints, with respect to proposal and rotation information regression, are adopted for joint supervision during training. To this end, we innovatively propose a mechanism that simultaneously learns the regression of horizontal proposals, oriented proposals, and rotation angles of objects in a consistent manner, via naive geometric computing, as one additional steady constraint (see Figure 1). An oriented center prior guided label assignment strategy is proposed for further enhancing the quality of proposals, yielding better performance. Extensive experiments demonstrate the model equipped with our idea significantly outperforms the baseline by a large margin to achieve a new state-of-the-art result without any extra computational burden during inference. Our proposed idea is simple and intuitive that can be readily implemented. Source codes and trained models are involved in supplementary files.
Dielectrophoretic Trapping and Electrical Characterization of DNA Origami Structures

Electrical conductivity of DNA has been a controversial topic since it was first proposed in 1962. Disparities in the experimental results can often be explained by differences of the ambient or experimental conditions. We report the dielectrophoretic trapping and electrical characterization of two rod-like DNA origami structures with different modifications. These were 12helix bundles, with either thiol-ends or polyA single-stranded overhangs, and 30-helix bundles, with thiol-ends and either with or without a PEGPLL coating. The observed impedance spectra showed ohmic resistances ranging from 500 kΩ to 3.4 MΩ for the 30-helix bundle structures with a PEG-PLL coating. DNA origami structures without the coating were always nonconductive. Depending on the structure, destruction of the gold nanoelectrodes occurred frequently during the trapping of DNA origami nanostructures. This indicates high currents, resulting from the trapped conductive nanostructures.
What to Learn, and How: Toward Effective Learning from Rationales

Learning from rationales seeks to augment model prediction accuracy using human-annotated rationales (i.e. subsets of input tokens) that justify their chosen labels, often in the form of intermediate or multitask supervision. While intuitive, this idea has proven elusive in practice. We make two observations about human rationales via empirical analyses:1) maximizing rationale supervision accuracy is not necessarily the optimal objective for improving model accuracy; 2) human rationales vary in whether they provide sufficient information for the model to exploit for prediction.Building on these insights, we propose several novel loss functions and learning strategies, and evaluate their effectiveness on three datasets with human rationales. Our results demonstrate consistent improvements over baselines in both label and rationale accuracy, including a 3% accuracy improvement on MultiRC. Our work highlights the importance of understanding properties of human explanations and exploiting them accordingly in model training.