Miracle-dz/UniFAR-FTUs huggingface dataset:
BacHMMachine: An Interpretable and Scalable Model for Algorithmic Harmonization for Four-part Baroque Chorales

Algorithmic harmonization - the automated harmonization of a musical piece given its melodic line - is a challenging problem that has garnered much interest from both music theorists and computer scientists. One genre of particular interest is the four-part Baroque chorales of J.S. Bach. Methods for algorithmic chorale harmonization typically adopt a black-box,"data-driven"approach: they do not explicitly integrate principles from music theory but rely on a complex learning model trained with a large amount of chorale data. We propose instead a new harmonization model, called BacHMMachine, which employs a"theory-driven"framework guided by music composition principles, along with a"data-driven"model for learning compositional features within this framework. As its name suggests, BacHMMachine uses a novel Hidden Markov Model based on key and chord transitions, providing a probabilistic framework for learning key modulations and chordal progressions from a given melodic line. This allows for the generation of creative, yet musically coherent chorale harmonizations; integrating compositional principles allows for a much simpler model that results in vast decreases in computational burden and greater interpretability compared to state-of-the-art algorithmic harmonization methods, at no penalty to quality of harmonization or musicality. We demonstrate this improvement via comprehensive experiments and Turing tests comparing BacHMMachine to existing methods.
A Fast Method for Steady-State Memristor Crossbar Array Circuit Simulation

In this work we propose an effective preconditioning technique to accelerate the steady-state simulation of large-scale memristor crossbar arrays (MCAs). We exploit the structural regularity of MCAs to develop a specially-crafted preconditioner that can be efficiently evaluated utilizing tensor products and block matrix inversion. Numerical experiments demonstrate the efficacy of the proposed technique compared to mainstream preconditioners.
Analytic AC conductivities from holography

We ﬁnd exact, analytic solutions of the holographic AC conductivity at arbitrary frequency ω and temperature T , in contrast to previous works where the AC conductivity was analytically obtained usually at small ω and T . These solutions enable us to study the analyticity properties of the current-current correlator G ( ω ) in detail. The ﬁrst system we study is the AdS 5 planar black hole with momentum dissipation, whose extremal limit has an AdS 2 factor. Then we study AdS 4 and AdS 5 Einstein-dilaton systems whose special cases are maximal gauged supergravities. The solutions show how the poles move and how branch cuts emerge as the temperature varies. As a byproduct, we obtain an R-current correlator in N = 4 super-Yang-Mills theory on a sphere at ﬁnite temperature in the large N and strong coupling limit.
The eROSITA Final Equatorial-Depth Survey (eFEDS). A complete census of X-ray properties of Subaru Hyper Suprime-Cam weak lensing shear-selected clusters in the eFEDS footprint

The eFEDS survey is a proof-of-concept mini-survey designed to demonstrate the survey science capabilities of SRG/eROSITA. It covers an area of 140 square degrees where 542 galaxy clusters have been detected out to a redshift of 1.3. The eFEDS field is partly embedded in the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) S19A data release, which covers 510 square degrees, containing approximately 36 million galaxies. This galaxy catalogue is used to construct a sample of 180 shear-selected galaxy clusters. In the common area to both surveys, about 90 square degrees, we investigate the effects of selection methods in the galaxy cluster detection by comparing the X-ray selected, eFEDS, and the shear-selected, HSC-SSP S19A, galaxy cluster samples. There are 25 shear-selected clusters in the eFEDS footprint. The relation between X-ray bolometric luminosity and weak-lensing mass is investigated, and it is found that the normalization of the bolometric luminosity and mass relation of the X-ray selected and shear-selected samples is consistent within $1\sigma$. Moreover, we found that the dynamical state and merger fraction of the shear-selected clusters is not different from the X-ray selected ones. Four shear-selected clusters are undetected in X-rays. A close inspection reveals that one is the result of projection effects, while the other three have an X-ray flux below the ultimate eROSITA detection limit. Finally, 43% of the shear-selected clusters lie in superclusters. Our results indicate that the scaling relation between X-ray bolometric luminosity and true cluster mass of the shear-selected cluster sample is consistent with the eFEDS sample. There is no significant population of X-ray underluminous clusters, indicating that X-ray selected cluster samples are complete and can be used as an accurate cosmological probe.
Shallow Trotter circuits fulﬁl error-resilient quantum simulation of imaginary time

Computing the ground-state properties of quantum many-body systems is a promising application of near-term quantum hardware with a potential impact in many ﬁelds. Quantum phase estimation uses deep circuits and is infeasible without fault-tolerant technologies. Many quantum simulation algorithms developed recently work in an inexact and variational manner to exploit the power of shallow circuits. These algorithms rely on the assumption that variational circuits can produce the desired result. Here, we combine quantum Monte Carlo with quantum computing and propose a quasi-exact algorithm for imaginary-time simulation and ground-state computing. Unlike variational algorithms, our algorithm always approaches the exact solution when the Trotter circuit depth increases. Even when the circuit is shallow, our algorithm can yield an accurate ground-state energy. Compared with quantum phase estimation, the conventional quasi-exact algorithm, our algorithm can reduce the Trotter step number by thousands of times. We verify this resilience to Trotterisation errors in numerical simulation of up to 20 qubits and theoretical analysis. Our results demonstrate that non-variational and exact quantum simulation is promising even without a fully fault-tolerant quantum computer.
Efficacy of ozonated water, 2% chlorhexidine and 5.25% sodium hypochlorite on five microorganisms of endodontic infection: In vitro study

Introduction: The aim of this study was to assess the antimicrobial efficacy of ozonated water (4 mg/l), 2% chlorhexidine solution, 5.25% sodium hypochlorite solution on five common endodontic microorganisms. Methods: The organisms chosen in the study were Enterococcus faecalis, Streptococcus mutans, Staphylococcus aureus, Candida albicans, Kocuria rhizophila. Agar well diffusion test direct contact test were used as methods to assess antimicrobial effectiveness. In agar well diffusion test, the maximum zone of inhibition formed around the well in an agar plate after incubation of test materials against each microorganism for 24 h and 48 h were measured. In direct contact test, the colonies of E. faecalis formed on agar plates with each test material were calculated. Results: The results showed that 2% chlorhexidine showed highest zone size and minimum colony forming units indicating its highest potency and ozonated water was showed the least efficacy with a significant difference between both groups. The colony forming units showed an increase in number when ozonated water was used against E. faecalis
Disaggregating and Consolidating Network Functionalities with SuperNIC

Resource disaggregation has gained huge popularity in recent years. Existing works demonstrate how to disaggregate compute, memory, and storage resources. We, for the ﬁrst time, demonstrate how to disaggregate network resources by proposing a new distributed hardware framework called Su-perNIC . Each SuperNIC connects a small set of endpoints and consolidates network functionalities for these endpoints. We prototyped SuperNIC with FPGA and demonstrate its performance and cost beneﬁts with real network functions and customized disaggregated applications
Computationally-Efficient Climate Predictions using Multi-Fidelity Surrogate Modelling

Accurately modelling the Earth's climate has widespread applications ranging from forecasting local weather to understanding global climate change. Low-fidelity simulations of climate phenomena are readily available, but high-fidelity simulations are expensive to obtain. We therefore investigate the potential of Gaussian process-based multi-fidelity surrogate modelling as a way to produce high-fidelity climate predictions at low cost. Specifically, our model combines the predictions of a low-fidelity Global Climate Model (GCM) and those of a high-fidelity Regional Climate Model (RCM) to produce high-fidelity temperature predictions for a mountainous region on the coastline of Peru. We are able to produce high-fidelity temperature predictions at significantly lower computational cost compared to the high-fidelity model alone: our predictions have an average error of $15.62^\circ\text{C}^2$ yet our approach only evaluates the high-fidelity model on 6% of the region of interest.
MOVER: Mask, Over-generate and Rank for Hyperbole Generation

Despite being a common figure of speech, hyperbole is under-researched in Figurative Language Processing. In this paper, we tackle the challenging task of hyperbole generation to transfer a literal sentence into its hyperbolic paraphrase. To address the lack of available hyperbolic sentences, we construct HYPO-XL, the first large-scale English hyperbole corpus containing 17,862 hyperbolic sentences in a non-trivial way. Based on our corpus, we propose an unsupervised method for hyperbole generation that does not require parallel literal-hyperbole pairs. During training, we fine-tune BART to infill masked hyperbolic spans of sentences from HYPO-XL. During inference, we mask part of an input literal sentence and over-generate multiple possible hyperbolic versions. Then a BERT-based ranker selects the best candidate by hyperbolicity and paraphrase quality. Automatic and human evaluation results show that our model is effective at generating hyperbolic paraphrase sentences and outperforms several baseline systems.
Spacetime finite element methods for control problems subject to the wave equation

We consider the null controllability problem for the wave equation, and analyse a stabilized finite element method formulated on a global, unstructured spacetime mesh. We prove error estimates for the approximate control given by the computational method. The proofs are based on the regularity properties of the control given by the Hilbert Uniqueness Method, together with the stability properties of the numerical scheme. Numerical experiments illustrate the results.
Computing structure constants for rings of finite rank from minimal free resolutions

We show how the minimal free resolution of a set of n points in general position in projective space of dimension n − 2 explicitly determines structure constants for a ring of rank n. This generalises previously known constructions of Levi-Delone-Faddeev and Bhargava in the cases n = 3, 4, 5.
Frequent Itemset Mining with Multiple Minimum Supports: a Constraint-based Approach

The problem of discovering frequent itemsets including rare ones has received a great deal of attention. The mining process needs to be flexible enough to extract frequent and rare regularities at once. On the other hand, it has recently been shown that constraint programming is a flexible way to tackle data mining tasks. In this paper, we propose a constraint programming approach for mining itemsets with multiple minimum supports. Our approach provides the user with the possibility to express any kind of constraints on the minimum item supports. An experimental analysis shows the practical effectiveness of our approach compared to the state of the art.
Frame by frame completion probability of an NFL pass

American football is an increasingly popular sport, with a growing audience in many countries in the world. The most watched American football league in the world is the United States' National Football League (NFL), where every offensive play can be either a run or a pass, and in this work we focus on passes. Many factors can affect the probability of pass completion, such as receiver separation from the nearest defender, distance from receiver to passer, offense formation, among many others. When predicting the completion probability of a pass, it is essential to know who the target of the pass is. By using distance measures between players and the ball, it is possible to calculate empirical probabilities and predict very accurately who the target will be. The big question is: how likely is it for a pass to be completed in an NFL match while the ball is in the air? We developed a machine learning algorithm to answer this based on several predictors. Using data from the 2018 NFL season, we obtained conditional and marginal predictions for pass completion probability based on a random forest model. This is based on a two-stage procedure: first, we calculate the probability of each offensive player being the pass target, then, conditional on the target, we predict completion probability based on the random forest model. Finally, the general completion probability can be calculated using the law of total probability. We present animations for selected plays and show the pass completion probability evolution.
Inhibitors of cyclin dependent kinases and methods of use

A pharmaceutical composition comprising an inhibitor compound that selectively inhibits cyclin dependent kinase 4 (CDK4) and / or the cyclin-dependent kinase 6 (CDK6) for use in a method for increasing the effectiveness of growth factors for rescue and support populations of hematopoietic cells in a subject receiving a cancer treatment for cancer that does not depend on the activities of CDK4 / 6 for proliferation, wherein said method comprises (i) administering to a subject an amount pharmaceutically effective inhibitor compound, (ii) exposing said subject to a DNA damaging agent for cancer treatment and (iii) the administration of a growth factor for rescue and support of the population of hematopoietic cells.
High carrier mobility in single-crystal PtSe2 grown by molecular beam epitaxy on ZnO(0001)

PtSe2 is attracting considerable attention as a high mobility two-dimensional material with envisioned applications in microelectronics, photodetection and spintronics. The growth of high quality PtSe2 on insulating substrates with wafer-scale uniformity is a prerequisite for electronic transport investigations and practical use in devices. Here, we report the growth of highly oriented few-layers PtSe2 on ZnO(0001) by molecular beam epitaxy. The crystalline structure of the films is characterized with electron and x-ray diffraction, atomic force microscopy and transmission electron microscopy. The comparison with PtSe2 layers grown on graphene, sapphire, mica, SiO2 and Pt(111) shows that among insulating substrates, ZnO(0001) yields films of superior structural quality. Hall measurements performed on epitaxial ZnO/PtSe2 with five monolayers of PtSe2 show a clear semiconducting behaviour and a high mobility in excess of 200 cm2 V−1 s−1 at room temperature and up to 447 cm2 V−1 s−1 at low temperature.
Fast-Replanning Motion Control with Short-Term Aborting A

—Autonomously driving vehicles must be able to navigate in dynamic and unpredictable environments in a collision-free manner. So far, this has only been partially achieved in driverless cars and warehouse installations where marked structures such as roads, lanes, and trafﬁc signs simplify the motion planning and collision avoidance problem. We are presenting a new control framework for car-like vehicles that is suitable for virtually any environment. It is based on an unprecedentedly fast-paced A* implementation that allows the control cycle to run at a frequency of 33 Hz. Due to an efﬁcient heuristic consisting of rotate-translate-rotate motions laid out along the shortest path to the target, our Short Term Aborting A* (STAA*) can be aborted early in order to maintain a high and steady control rate. This enables us to place our STAA* algorithm as a low-level replanning controller that is well suited for navigation and collision avoidance in dynamic environments. While our STAA* expands states along the shortest path, it takes care of collision checking with the environment including predicted future states of moving obstacles, and returns the best solution found when the computation time runs out. Despite the bounded computation time, our STAA* does not get trapped in environmental minima due to the following of the shortest path. In simulated experiments, we demonstrate that our control approach is superior to an improved version of the Dynamic Window Approach with predictive collision avoidance capabilities [1].
Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning

In batch reinforcement learning, there can be poorly explored state-action pairs resulting in poorly learned, inaccurate models and poorly performing associated policies. Various regularization methods can mitigate the problem of learning overly-complex models in Markov decision processes (MDPs), however they operate in technically and intuitively distinct ways and lack a common form in which to compare them. This paper unifies three regularization methods in a common framework -- a weighted average transition matrix. Considering regularization methods in this common form illuminates how the MDP structure and the state-action pair distribution of the batch data set influence the relative performance of regularization methods. We confirm intuitions generated from the common framework by empirical evaluation across a range of MDPs and data collection policies.
Machine-Learned HASDM Model with Uncertainty Quantification

The first thermospheric neutral mass density model with robust and reliable uncertainty estimates is developed based on the SET HASDM density database. This database, created by Space Environment Technologies (SET), contains 20 years of outputs from the U.S. Space Force's High Accuracy Satellite Drag Model (HASDM), which represents the state-of-the-art for density and drag modeling. We utilize principal component analysis (PCA) for dimensionality reduction, creating the coefficients upon which nonlinear machine-learned (ML) regression models are trained. These models use three unique loss functions: mean square error (MSE), negative logarithm of predictive density (NLPD), and continuous ranked probability score (CRPS). Three input sets are also tested, showing improved performance when introducing time histories for geomagnetic indices. These models leverage Monte Carlo (MC) dropout to provide uncertainty estimates, and the use of the NLPD loss function results in well-calibrated uncertainty estimates without sacrificing model accuracy (<10% mean absolute error). By comparing the best HASDM-ML model to the HASDM database along satellite orbits, we found that the model provides robust and reliable uncertainties in the density space over all space weather conditions. A storm-time comparison shows that HASDM-ML also supplies meaningful uncertainty measurements during extreme events.
DNN-Based Decentralized Hybrid Beamforming for Cell-Free Massive MIMO

—Cell-free massive MIMO (CF-mMIMO) systems represent a promising approach to increase the spectral ef-ﬁciency of wireless communication systems. However, near-optimal hybrid beamforming solutions require a large amount of signaling exchange between access points (APs) and the network controller (NC). In this letter, we propose two unsupervised deep neural networks (DNN) architectures, fully and partially distributed, that can perform decentralized coordinated hybrid beamforming with zero or limited communication overhead between APs and NC, while achieving near-optimal sum-rate with a reduced computational complexity compared to conventional near-optimal solutions.
ST-PIL: Spatial-Temporal Periodic Interest Learning for Next Point-of-Interest Recommendation

Point-of-Interest (POI) recommendation is an important task in location-based social networks. It facilitates the relation modeling between users and locations. Recently, researchers recommend POIs by long- and short-term interests and achieve success. However, they fail to well capture the periodic interest. People tend to visit similar places at similar times or in similar areas. Existing models try to acquire such kind of periodicity by user's mobility status or time slot, which limits the performance of periodic interest. To this end, we propose to learn spatial-temporal periodic interest. Specifically, in the long-term module, we learn the temporal periodic interest of daily granularity, then utilize intra-level attention to form long-term interest. In the short-term module, we construct various short-term sequences to acquire the spatial-temporal periodic interest of hourly, areal, and hourly-areal granularities, respectively. Finally, we apply inter-level attention to automatically integrate multiple interests. Experiments on two real-world datasets demonstrate the state-of-the-art performance of our method.
Disaggregated Interventions to Reduce Inequality

A significant body of research in the data sciences considers unfair discrimination against social categories such as race or gender that could occur or be amplified as a result of algorithmic decisions. Simultaneously, real-world disparities continue to exist, even before algorithmic decisions are made. In this work, we draw on insights from the social sciences brought into the realm of causal modeling and constrained optimization, and develop a novel algorithmic framework for tackling pre-existing real-world disparities. The purpose of our framework, which we call the “impact remediation framework,” is to measure real-world disparities and discover the optimal intervention policies that could help improve equity or access to opportunity for those who are underserved with respect to an outcome of interest. We develop a disaggregated approach to tackling pre-existing disparities that relaxes the typical set of assumptions required for the use of social categories in structural causal models. Our approach flexibly incorporates counterfactuals and is compatible with various ontological assumptions about the nature of social categories. We demonstrate impact remediation with a hypothetical case study and compare our disaggregated approach to an existing state-of-the-art approach, comparing its structure and resulting policy recommendations. In contrast to most work on optimal policy learning, we explore disparity reduction itself as an objective, explicitly focusing the power of algorithms on reducing inequality.