Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Inverter-Based Resources Placement in Low-Inertia Power Systems

The placement of fast-acting Inverter-Based Resources (IBRs) in the grid can substantially impact frequency response, by rapidly injecting or removing power from the grid. In this work, we present an optimal placement algorithm that maximizes the benefits of utilizing IBRs in providing frequency response services. That is, we minimize the overall system frequency deviation while using a minimal amount of electric power injection from the IBRs. The proposed algorithm uses the resistance distance to place the IBRs at nodes that are central to the rest of the nodes in the network thus minimizing the distance of power flow. The proposed greedy algorithm achieves a near optimal performance and relies on the supermodularity of the resistance distances. We validate the performance of the placement algorithm on three IEEE test systems of varying sizes, by comparing its performance to an exhaustive search algorithm. We further evaluate the performance of the placed IBRs on an IEEE test system, to determine their impact on frequency (transient) stability.
Outlook for detecting the gravitational-wave displacement and spin memory effects with current and future gravitational-wave detectors

Gravitational wave memory effects arise from non-oscillatory components of gravitational wave signals, and they are predictions of general relativity in the nonlinear regime that have close connections to the asymptotic properties of isolated gravitating systems. There are many types of memory effects that have been studied in the literature. In this paper we focus on the"displacement"and"spin"memories, which are expected to be the largest of these effects from sources such as the binary black hole mergers which have already been detected by LIGO and Virgo. The displacement memory is a change in the relative separation of two initially comoving observers due to a burst of gravitational waves, whereas the spin memory is a portion of the change in relative separation of observers with initial relative velocity. As both of these effects are small, LIGO, Virgo, and KAGRA can only detect memory effects from individual events that are much louder (and thus rarer) than those that have been detected so far. By combining data from multiple events, however, these effects could be detected in a population of binary mergers. In this paper, we present new forecasts for how long current and future detectors will need to operate in order to measure these effects from populations of binary black hole systems that are consistent with the populations inferred from the detections from LIGO and Virgo's first three observing runs. We find that a second-generation detector network of LIGO, Virgo, and KAGRA operating at the O4 ("design") sensitivity for 1.5 years and then operating at the O5 ("plus") sensitivity for an additional year can detect the displacement memory. For Cosmic Explorer, we find that displacement memory could be detected for individual loud events, and that the spin memory could be detected in a population within 2 years of observation time.
Improving Zero-Shot Multilingual Translation with Universal Representations and Cross-Mappings

The many-to-many multilingual neural machine translation can translate between language pairs unseen during training, i.e., zero-shot translation. Improving zero-shot translation requires the model to learn universal representations and cross-mapping relationships to transfer the knowledge learned on the supervised directions to the zero-shot directions. In this work, we propose the state mover's distance based on the optimal theory to model the difference of the representations output by the encoder. Then, we bridge the gap between the semantic-equivalent representations of different languages at the token level by minimizing the proposed distance to learn universal representations. Besides, we propose an agreement-based training scheme, which can help the model make consistent predictions based on the semantic-equivalent sentences to learn universal cross-mapping relationships for all translation directions. The experimental results on diverse multilingual datasets show that our method can improve consistently compared with the baseline system and other contrast methods. The analysis proves that our method can better align the semantic space and improve the prediction consistency.
Estimation of Heterogeneous Treatment Effects Using a Conditional Moment Based Approach

We propose a new estimator for heterogeneous treatment effects in a partially linear model (PLM) with many exogenous covariates and a possibly endogenous treatment variable. The PLM has a parametric part that includes the treatment and the interactions between the treatment and exogenous characteristics, and a nonparametric part that contains those characteristics and many other covariates. The new estimator is a combination of a Robinson transformation to partial out the nonparametric part of the model, the Smooth Minimum Distance (SMD) approach to exploit all the information of the conditional mean independence restriction, and a Neyman-Orthogonalized first-order condition (FOC). With the SMD method, our estimator using only one valid binary instrument identifies both parameters. With the sparsity assumption, using regularized machine learning methods (i.e., the Lasso method) allows us to choose a relatively small number of polynomials of covariates. The Neyman-Orthogonalized FOC reduces the effect of the bias associated with the regularization method on estimates of the parameters of interest. Our new estimator allows for many covariates and is less biased, consistent, and √ n-asymptotically normal under standard regularity conditions. Our simulations show that our estimator behaves well with different sets of instruments, but the GMM type estimators do not. We estimate the heterogeneous treatment effects of Medicaid on individual outcome variables from the Oregon Health Insurance Experiment. We find using our new method with only one valid instrument produces more significant and more reliable results for heterogeneous treatment effects of health insurance programs on economic outcomes than using GMM type estimators. ∗I am very grateful for the guidance and helpful comments from Bertille Antoine at Simon Fraser University. I would also like to thank Chris Bidner, Irene Botosaru, Sam Hwang, Dongwoo Kim, Brian Krauth, Chris Muris, Krishna Pendakur, Kevin Schnepel, Hitoshi Shigeoka, Kevin Song, Purevdorj Tuvaandorj, and Simon Woodcock for constructive discussions and suggestions.
Joint Uplink-Downlink Capacity and Coverage Optimization via Site-Specific Learning of Antenna Settings

We propose a novel framework for optimizing antenna parameter settings in a heterogeneous cellular network. We formulate an optimization problem for both coverage and capacity - in both the downlink (DL) and uplink (UL) - which configures the tilt angle, vertical half-power beamwidth (HPBW), and horizontal HPBW of each cell's antenna array across the network. The novel data-driven framework proposed for this non-convex problem, inspired by Bayesian optimization (BO) and differential evolution algorithms, is sample-efficient and converges quickly, while being scalable to large networks. By jointly optimizing DL and UL performance, we take into account the different signal power and interference characteristics of these two links, allowing a graceful trade-off between coverage and capacity in each one. Our experiments on a state-of-the-art 5G NR cellular system-level simulator developed by AT&T Labs show that the proposed algorithm consistently and significantly outperforms the 3GPP default settings, random search, and conventional BO. In one realistic setting, and compared to conventional BO, our approach increases the average sum-log-rate by over 60% while decreasing the outage probability by over 80%. Compared to the 3GPP default settings, the gains from our approach are considerably larger. The results also indicate that the practically important combination of DL throughput and UL coverage can be greatly improved by joint UL-DL optimization.
Polariton lasing in AlGaN microring with GaN/AlGaN quantum wells

Microcavity polaritons are strongly interacting hybrid light-matter quasiparticles, which are promising for the development of novel light sources and active photonic devices. Here, we report polariton lasing in the UV spectral range in microring resonators based on GaN/AlGaN slab waveguides, with experiments carried out from 4 K up to room temperature. Stimulated polariton relaxation into multiple ring resonator modes is observed, which exhibit threshold-like dependence of the emission intensity with pulse energy. The strong exciton-photon coupling regime is confirmed by the significant reduction of the free spectral range with energy and the blueshift of the exciton-like modes with increasing pulse energy. Importantly, the exciton emission shows no broadening with power, further confirming that lasing is observed at electron-hole densities well below the Mott transition. Overall, our work paves the way towards developmentof novel UV devices based on the high-speed slab waveguide polariton geometry operating up to room temperature with potential to be integrated into complex photonic circuits.
Time-Domain Sensitivity of the Tracking Error

A strictly time-domain. formulation of the log-sensitivity of the error signal to structured plant uncertainty is presented and analyzed through simple but representative classical and quantum systems. Results demonstrate that across a wide range of physical systems, maximization of performance (minimization of the error signal) asymptotically or at a specific time comes at the cost of increased log-sensitivity, implying a time-domain constraint analogous to the frequency-domain identity $\mathbf {S(s) + T(s) = I}$. While of limited value in classical problems based on asymptotic stabilization or tracking, such a time-domain formulation is valuable in assessing the reduced robustness cost concomitant with high-fidelity quantum control schemes predicated on time-based performance measures.
Leveraging Label Correlations in a Multi-Label Setting: a Case Study in Emotion

Detecting emotions expressed in text has become critical to a range of fields. In this work, we investigate ways to exploit label correlations in multi-label emotion recognition models to improve emotion detection. First, we develop two modeling approaches to the problem in order to capture word associations of the emotion words themselves, by either including the emotions in the input, or by leveraging Masked Language Modeling (MLM). Second, we inte-grate pairwise constraints of emotion representations as regularization terms alongside the classification loss of the models. We split these terms into two categories, local and global. The former dynamically change based on the gold labels, while the latter remain static during training. We demonstrate state-of-the-art performance across Spanish, English, and Arabic in SemEval 2018 Task 1 E-c using monolingual BERT-based models. On top of better performance, we also demonstrate improved robustness. Code is available at https://github.com/gchochla/Demux-MEmo.1
NNSVS: A Neural Network-Based Singing Voice Synthesis Toolkit

This paper describes the design of NNSVS, an open-source soft-ware for neural network-based singing voice synthesis research. NNSVS is inspired by Sinsy, an open-source pioneer in singing voice synthesis research, and provides many additional features such as multi-stream models, autoregressive fundamental frequency models, and neural vocoders. Furthermore, NNSVS provides extensive documentation and numerous scripts to build complete singing voice synthesis systems. Experimental results demonstrate that our best system significantly outperforms our reproduction of Sinsy and other baseline systems. The toolkit is available at https://github.com/nnsvs/nnsvs.
An approach for projecting the timing of abrupt winter Arctic sea ice loss

Abstract. Abrupt and irreversible winter Arctic sea ice loss may occur under anthropogenic warming due to the disappearance of a sea ice equilibrium at a threshold value of CO2, commonly referred to as a tipping point. Previous work has been unable to conclusively identify whether a tipping point in winter Arctic sea ice exists because fully coupled climate models are too computationally expensive to run to equilibrium for many CO2 values. Here, we explore the deviation of sea ice from its equilibrium state under realistic rates of CO2 increase to demonstrate for the first time how a few time-dependent CO2 experiments can be used to predict the existence and timing of sea ice tipping points without running the model to steady state. This study highlights the inefficacy of using a single experiment with slow-changing CO2 to discover changes in the sea ice steady state and provides a novel alternate method that can be developed for the identification of tipping points in realistic climate models.
MSPred: Video Prediction at Multiple Spatio-Temporal Scales with Hierarchical Recurrent Networks

Autonomous systems not only need to understand their current environment, but should also be able to predict future actions conditioned on past states, for instance based on captured camera frames. However, existing models mainly focus on forecasting future video frames for short time-horizons, hence being of limited use for long-term action planning. We propose Multi-Scale Hierarchical Prediction (MSPred), a novel video prediction model able to simultaneously forecast future possible outcomes of different levels of granularity at different spatio-temporal scales. By combining spatial and temporal downsampling, MSPred efficiently predicts abstract representations such as human poses or locations over long time horizons, while still maintaining a competitive performance for video frame prediction. In our experiments, we demonstrate that MSPred accurately predicts future video frames as well as high-level representations (e.g. keypoints or semantics) on bin-picking and action recognition datasets, while consistently outperforming popular approaches for future frame prediction. Furthermore, we ablate different modules and design choices in MSPred, experimentally validating that combining features of different spatial and temporal granularity leads to a superior performance. Code and models to reproduce our experiments can be found in https://github.com/AIS-Bonn/MSPred.
ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search

Executing large test suites is time and resource consuming, sometimes impossible, and such test suites typically contain many redundant test cases. Hence, test case (suite) minimization is used to remove redundant test cases that are unlikely to detect new faults. However, most test case minimization techniques rely on code coverage (white-box), model-based features, or requirements specifications, which are not always (entirely) accessible by test engineers. Code coverage analysis also leads to scalability issues, especially when applied to large industrial systems. Recently, a set of novel techniques was proposed, called FAST-R, relying solely on test case code for test case minimization, which appeared to be much more efficient than white-box techniques. However, it achieved a comparable low fault detection capability for Java projects, thus making its application challenging in practice. In this paper, we propose ATM (AST-based Test case Minimizer), a similarity-based, search-based test case minimization technique, taking a specific budget as input, that also relies exclusively on the source code of test cases but attempts to achieve higher fault detection through finer-grained similarity analysis and a dedicated search algorithm. ATM transforms test case code into Abstract Syntax Trees (AST) and relies on four tree-based similarity measures to apply evolutionary search, specifically genetic algorithms, to minimize test cases. We evaluated the effectiveness and efficiency of ATM on a large dataset of 16 Java projects with 661 faulty versions using three budgets ranging from 25% to 75% of test suites. ATM achieved significantly higher fault detection rates (0.82 on average), compared to FAST-R (0.61 on average) and random minimization (0.52 on average), when running only 50% of the test cases, within practically acceptable time (1.1 - 4.3 hours, on average, per project version), given that minimization is only occasionally applied when many new test cases are created (major releases). Results achieved for other budgets were consistent.
Entropy of Quantum Measurements

If a is a quantum effect and ρ is a state, we define the ρ-entropy Sa(ρ) which gives the amount of uncertainty that a measurement of a provides about ρ. The smaller Sa(ρ) is, the more information a measurement of a gives about ρ. In Entropy for Effects, we provide bounds on Sa(ρ) and show that if a+b is an effect, then Sa+b(ρ)≥Sa(ρ)+Sb(ρ). We then prove a result concerning convex mixtures of effects. We also consider sequential products of effects and their ρ-entropies. In Entropy of Observables and Instruments, we employ Sa(ρ) to define the ρ-entropy SA(ρ) for an observable A. We show that SA(ρ) directly provides the ρ-entropy SI(ρ) for an instrument I. We establish bounds for SA(ρ) and prove characterizations for when these bounds are obtained. These give simplified proofs of results given in the literature. We also consider ρ-entropies for measurement models, sequential products of observables and coarse-graining of observables. Various examples that illustrate the theory are provided.
Feature Engineering vs BERT on Twitter Data

In this paper, we compare the performances of traditional machine learning models using feature engineering and word vectors and the state-of-the-art language model BERT using word embeddings on three datasets. We also consider the time and cost ef ﬁ ciency of feature engineering compared to BERT. From our results we conclude that the use of the BERT model was only worth the time and cost trade-off for one of the three datasets we used for comparison, where the BERT model signi ﬁ cantly outperformed any kind of traditional classi ﬁ er that uses feature vectors, instead of embeddings. Using the BERT model for the other datasets only achieved an increase of 0.03 and 0.05 of accuracy and F1 score respectively, which could be argued makes its use not worth the time and cost of GPU.
Treasure Maps for Detections of Extreme Energy Cosmic Rays

The origin of ultra-high-energy cosmic rays is a 60 yr old mystery. We show that with more events at the highest energies (above 150 EeV) it may be possible to limit the character of the sources and learn about the intervening magnetic fields. Individual sources become more prominent, relative to the background, as the horizon diminishes. An event-by-event, composition-dependent observatory would allow a “tomography” of the sources as different mass and energy groups probe different GZK horizons. A major goal here is to provide a methodology to distinguish between steady and transient or highly variable sources. Using recent Galactic magnetic field models, we calculate “treasure” sky maps to identify the most promising directions for detecting Extreme Energy Cosmic Rays doublets, events that are close in arrival time and direction. On this basis, we predict the incidence of doublets as a function of the nature of the source host galaxy. Based on the asymmetry in the distribution of time delays, we show that observation of doublets might distinguish source models. In particular, the Telescope Array hotspot could exhibit temporal variability as it is in a “magnetic window” of small time delays. These considerations could improve the use of data with existing facilities and the planning of future ones such as Global Cosmic Ray Observatory (GCOS).
Bebert: Efficient And Robust Binary Ensemble Bert

Pre-trained BERT models have achieved impressive accuracy on natural language processing (NLP) tasks. However, their excessive amount of parameters hinders them from efficient deployment on edge devices. Binarization of the BERT models can significantly alleviate this issue but comes with a severe accuracy drop compared with their full-precision counterparts. In this paper, we propose an efficient and robust binary ensemble BERT (BEBERT) to bridge the accuracy gap. To the best of our knowledge, this is the first work employing ensemble techniques on binary BERTs, yielding BEBERT, which achieves superior accuracy while retaining computational efficiency. Furthermore, we remove the knowledge distillation procedures during ensemble to speed up the training process without compromising accuracy. Experimental results on the GLUE benchmark show that the proposed BEBERT significantly outperforms the existing binary BERT models in accuracy and robustness with a 2× speedup on training time. Moreover, our BEBERT has only a negligible accuracy loss of 0.3% compared to the full-precision baseline while saving 15× and 13× in FLOPs and model size, respectively. In addition, BEBERT also outperforms other compressed BERTs in accuracy by up to 6.7%.
Assessing Phrase Break of ESL Speech with Pre-trained Language Models and Large Language Models

This work introduces approaches to assessing phrase breaks in ESL learners' speech using pre-trained language models (PLMs) and large language models (LLMs). There are two tasks: overall assessment of phrase break for a speech clip and fine-grained assessment of every possible phrase break position. To leverage NLP models, speech input is first force-aligned with texts, and then pre-processed into a token sequence, including words and phrase break information. To utilize PLMs, we propose a pre-training and fine-tuning pipeline with the processed tokens. This process includes pre-training with a replaced break token detection module and fine-tuning with text classification and sequence labeling. To employ LLMs, we design prompts for ChatGPT. The experiments show that with the PLMs, the dependence on labeled training data has been greatly reduced, and the performance has improved. Meanwhile, we verify that ChatGPT, a renowned LLM, has potential for further advancement in this area.
Foundations of mathematical economics

This book provides a comprehensive introduction to the mathematical foundations of economics, from basic set theory to fixed point theorems and constrained optimization. Rather than simply offer a collection of problem-solving techniques, the book emphasizes the unifying mathematical principles that underlie economics. Features include an extended presentation of separation theorems and their applications, an account of constraint qualification in constrained optimization, and an introduction to monotone comparative statics. These topics are developed by way of more than 800 exercises. The book is designed to be used as a graduate text, a resource for self-study, and a reference for the professional economist.