Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Acoustic Echo Cancellation and Speech Dereverberation Using Kalman filters

This paper proposes a joint acoustic echo cancellation (AEC) and speech dereverberation (DR) algorithm in the short-time Fourier transform domain. The reverberant microphone signals are described using an auto-regressive (AR) model. The AR coefficients and the loudspeaker-to-microphone acoustic transfer functions (ATFs) are considered time-varying and are modeled simultaneously using a first-order Markov process. This leads to a solution where these parameters can be optimally estimated using Kalman filters. It is shown that the proposed algorithm outperforms vanilla solutions that solve AEC and DR sequentially and one state-of-the-art joint DRAEC algorithm based on semi-blind source separation, in terms of both speech quality and echo reduction performance.
Local polarization dynamics in ferroelectric materials

Ferroelectrics and multiferroics have recently emerged as perspective materials for information technology and data storage applications. The combination of extremely narrow domain wall width and the capability to manipulate polarization by electric field opens the pathway toward ultrahigh (>10 TBit inch−2) storage densities and small (sub-10 nm) feature sizes. The coupling between polarization and chemical and transport properties enables applications in ferroelectric lithography and electroresistive devices. The progress in these applications, as well as fundamental studies of polarization dynamics and the role of defects and disorder on domain nucleation and wall motion, requires the capability to probe these effects on the nanometer scale. In this review, we summarize the recent progress in applications of piezoresponse force microscopy (PFM) for imaging, manipulation and spectroscopy of ferroelectric switching processes. We briefly introduce the principles and relevant instrumental aspects of PFM, with special emphasis on resolution and information limits. The local imaging studies of domain dynamics, including local switching and relaxation accessed through imaging experiments and spectroscopic studies of polarization switching, are discussed in detail. Finally, we review the recent progress on understanding and exploiting photochemical processes on ferroelectric surfaces, the role of surface adsorbates, and imaging and switching in liquids. Beyond classical applications, probing local bias-induced transition dynamics by PFM opens the pathway to studies of the influence of a single defect on electrochemical and solid state processes, thus providing model systems for batteries, fuel cells and supercapacitor applications.
Quantum Advantage from One-Way Functions

We demonstrate quantum advantage with several basic assumptions, specifically based on only the existence of OWFs. We introduce inefficient-verifier proofs of quantumness (IV-PoQ), and construct it from classical bit commitments. IV-PoQ is an interactive protocol between a verifier and a quantum prover consisting of two phases. In the first phase, the verifier is probabilistic polynomial-time, and it interacts with the prover. In the second phase, the verifier becomes inefficient, and makes its decision based on the transcript of the first phase. If the prover is honest, the inefficient verifier accepts with high probability, but any classical malicious prover only has a small probability of being accepted by the inefficient verifier. Our construction demonstrates the following results: (1)If one-way functions exist, then IV-PoQ exist. (2)If distributional collision-resistant hash functions exist (which exist if hard-on-average problems in $\mathbf{SZK}$ exist), then constant-round IV-PoQ exist. We also demonstrate quantum advantage based on worst-case-hard assumptions. We define auxiliary-input IV-PoQ (AI-IV-PoQ) that only require that for any malicious prover, there exist infinitely many auxiliary inputs under which the prover cannot cheat. We construct AI-IV-PoQ from an auxiliary-input version of commitments in a similar way, showing that (1)If auxiliary-input one-way functions exist (which exist if $\mathbf{CZK}\not\subseteq\mathbf{BPP}$), then AI-IV-PoQ exist. (2)If auxiliary-input collision-resistant hash functions exist (which is equivalent to $\mathbf{PWPP}\nsubseteq \mathbf{FBPP}$) or $\mathbf{SZK}\nsubseteq \mathbf{BPP}$, then constant-round AI-IV-PoQ exist.
REIN: A Comprehensive Benchmark Framework for Data Cleaning Methods in ML Pipelines

Nowadays, machine learning (ML) plays a vital role in many aspects of our daily life. In essence, building well-performing ML applications requires the provision of high-quality data throughout the entire life-cycle of such applications. Nevertheless, most of the real-world tabular data suffer from different types of discrepancies, such as missing values, outliers, duplicates, pattern violation, and inconsistencies. Such discrepancies typically emerge while collecting, transferring, storing, and/or integrating the data. To deal with these discrepancies, numerous data cleaning methods have been introduced. However, the majority of such methods broadly overlook the requirements imposed by downstream ML models. As a result, the potential of utilizing these data cleaning methods in ML pipelines is predominantly unrevealed. In this work, we introduce a comprehensive benchmark, called REIN1, to thoroughly investigate the impact of data cleaning methods on various ML models. Through the benchmark, we provide answers to important research questions, e.g., where and whether data cleaning is a necessary step in ML pipelines. To this end, the benchmark examines 38 simple and advanced error detection and repair methods. To evaluate these methods, we utilized a wide collection of ML models trained on 14 publicly-available datasets covering different domains and encompassing realistic as well as synthetic error profiles.
Random survival forests with multivariate longitudinal endogenous covariates

Predicting the individual risk of clinical events using the complete patient history is a major challenge in personalized medicine. Analytical methods have to account for a possibly large number of time-dependent predictors, which are often characterized by irregular and error-prone measurements, and are truncated early by the event. In this work, we extended the competing-risk random survival forests to handle such endogenous longitudinal predictors when predicting event probabilities. The method, implemented in the R package DynForest, internally transforms the time-dependent predictors at each node of each tree into time-fixed features (using mixed models) that can then be used as splitting candidates. The final individual event probability is computed as the average of leaf-specific Aalen–Johansen estimators over the trees. Using simulations, we compared the performances of DynForest to accurately predict an event with (i) a joint modeling alternative when considering two longitudinal predictors only, and with (ii) a regression calibration method that ignores the informative truncation by the event when dealing with a large number of longitudinal predictors. Through an application in dementia research, we also illustrated how DynForest can be used to develop a dynamic prediction tool for dementia from multimodal repeated markers, and quantify the importance of each marker.
Accuracy, precision, and agreement statistical tests: transforming the Bland-Altman plot method into inferential decision

Bland and Altman plot method is a widely cited and applied graphical approach to assess equivalence of quantitative measurement techniques, usually aiming to replace a traditional technique by a new, less invasive or less expensive one. Although easy to communicate, Bland and Altman plot is often misinterpreted by lacking of suitable inferential statistical support. Usual alternatives, such as Pearson’s correlation or ordinal least-square linear regression, also fail to locate the weakness of each measurement technique. Here, inferential statistics support for equivalence between measurement techniques is proposed in three nested tests based on structural regressions to assess the equivalence of structural means (accuracy), equivalence of structural variances (precision), and concordance with the structural bisector line (agreement in measurements obtained from the same subject), by analytical methods and robust approach by bootstrapping. Graphical outputs are also implemented to follow Bland and Altman’s principles for easy communication. The performance of this method is shown and confronted with ﬁve data sets from previously published articles that applied Bland and Altman’s method. In one case it was demonstrated strict equivalence, three cases showed partial equivalence, and one case showed poor equivalence. The developed R package containing open codes and data is available with installation instructions for free distribution at Harvard
Early Results from GLASS-JWST. XV. Properties of the Faintest Red Sources in the NIRCAM Deep Fields

We present a first look at the reddest 2–5 μm sources found in deep images from the GLASS Early Release Science program. We undertake a general search, i.e., not looking for any particular spectral signatures, for sources detected only in bands redder than is reachable with the Hubble Space Telescope, and which would likely not have been identified in pre-JWST surveys. We search for sources down to AB ∼27 (corresponding to >10σ detection threshold) in any of the F200W to F444W filters, with a >1 magnitude excess relative to F090W to F150W bands. Fainter than F444W >25 we find 56 such sources of which 37 have reasonably constrained spectral energy distributions to which we can fit photometric redshifts. We find the majority of this population (∼65%) as 2 < z < 6 star-forming low-attenuation galaxies that are faint at rest-frame ultraviolet-optical wavelengths, have stellar masses 108.5–109.5 M ⊙, and have observed fluxes at >2 μm boosted by a combination of the Balmer break and emission lines. The typical implied rest equivalent widths are ∼200 Å with some extreme objects up to ∼1000 Å. This is in contrast with brighter magnitudes where the red sources tend to be z < 3 quiescent galaxies and dusty star-forming objects. Our general selection criteria for red sources allow us to independently identify other phenomena as diverse as extremely low-mass (∼108 M ⊙) quiescent galaxies at z < 1, recovering recently identified z > 11 galaxies and a very cool brown dwarf.
Spatial distribution of pre-main sequence stars in Taurus.

We derive characteristic properties of the nonrandom spatial distribution of pre-main-sequence stars in the Taurus-Auriga molecular cloud by applying several different statistical techniques. We find that a power-law form for the two-point angular correlation function (with index −1.2) reproduces the overall shape of the actual pre-main-sequence distribution in Taurus at small angular scales (∼0.3°). This result is consistent with the existence of real clustering in the T Tauri distribution. With the aid of the nearest-neighbor distribution technique, we determine a median projected separation of −0.3 pc for young stars in this cloud, even after eliminating close pairs with separations less than 20″ (−3000 AU at the distance of Taurus) from our sample
Bayesian hierarchical models in ecological studies of health–environment effects

We describe Bayesian hierarchical models and illustrate their use in epidemiological studies of the effects of environment on health. The framework of Bayesian hierarchical models refers to a generic model building strategy in which unobserved quantities (e.g. statistical parameters, missing or mismeasured data, random effects, etc.) are organized into a small number of discrete levels with logically distinct and scientifically interpretable functions, and probabilistic relationships between them that capture inherent features of the data. It has proved to be successful for analysing many types of complex epidemiological and biomedical data. The general applicability of Bayesian hierarchical models has been enhanced by advances in computational algorithms, notably those belonging to the family of stochastic algorithms based on Markov chain Monte Carlo techniques.
Comparing Artificial, Animal and Scientific Intelligence: A Dialogue with Giuseppe Longo

The most recent tool for acting on the world, the exosomatization of cognitive activities, is often considered an autonomous and objective replacement of knowledge construction. We show the intrinsic limits of the mechanistic myths in AI, from classical to Deep Learning techniques, and its relation to the human construction of sense. Human activities in a changing ecosystem – in their somatic and sensible dimensionalities proper to any living experiences – are at the core of our analysis. By this, we stress the key role of the knowing subject, far away from any allegedly objective big collections of data. The production of organized structures of physics, biology and in societal analysis will be compared and distinguished by trying to set on more robust grounds the constructive as well as the disruptive roles of entropic, negentropic, anti-entropic dynamics that are different concepts in different domains, to be handled with care: the use of machine learning and optimization methods as tools and models to analyse and manage human activities in view of their scientific and political ideology of technoscientific governance. They suppose that which they try to produce is objective, that is, standardized and controllable behaviours. In this dialogue we stress a mirror symmetry between the lack of theoretical interpretation of scientific data and the lack of democracy in this fiction of neutrality. Moreover, bad analogies constitute an obstacle to grasp the anteriority of biological and ecological constraints which enable and limit all artificial products of human intelligence. We will thus stress biological specificity, the role of normativity and constraints in evolution, of labour in structuring the human historical construction of sense by common activities.
STRING FIELD THEORY AND PHYSICAL INTERPRETATION OF D=1 STRINGS

We describe a field theoretic formulation for one-dimensional string theory. It is given by the collective field representation of the matrix model and leads to a physical interpretation of the theory as that of a massless scalar field in two dimensions. The additional dimension, coming from the large-N color of the matrix model, has an extent which goes to infinity in the continuum limit. The interactions of the field theory are non-zero only at the boundaries of this additional dimension.
Logic Shrinkage: Learned Connectivity Sparsification for LUT-Based Neural Networks

Field-programmable gate array (FPGA)–specific deep neural network (DNN) architectures using native lookup tables (LUTs) as independently trainable inference operators have been shown to achieve favorable area-accuracy and energy-accuracy trade-offs. The first work in this area, LUTNet, exhibited state-of-the-art performance for standard DNN benchmarks. In this article, we propose the learned optimization of such LUT-based topologies, resulting in higher-efficiency designs than via the direct use of off-the-shelf, hand-designed networks. Existing implementations of this class of architecture require the manual specification of the number of inputs per LUT, K. Choosing appropriate K a priori is challenging. Doing so at even high granularity, for example, per layer, is a time-consuming and error-prone process that leaves FPGAs’ spatial flexibility underexploited. Furthermore, prior works see LUT inputs connected randomly, which does not guarantee a good choice of network topology. To address these issues, we propose logic shrinkage, a fine-grained netlist pruning methodology enabling K to be automatically learned for every LUT in a neural network targeted for FPGA inference. By removing LUT inputs determined to be of low importance, our method increases the efficiency of the resultant accelerators. Our GPU-friendly solution to LUT input removal is capable of processing large topologies during their training with negligible slowdown. With logic shrinkage, we improve the area and energy efficiency of the best-performing LUTNet implementation of the CNV network classifying CIFAR-10 by 1.54× and 1.31×, respectively, while matching its accuracy. This implementation also reaches 2.71× the area efficiency of an equally accurate, heavily pruned binary neural network (BNN). On ImageNet, with the Bi-Real Net architecture, employment of logic shrinkage results in a post-synthesis area reduction of 2.67× vs. LUTNet, allowing for implementation that was previously impossible on today’s largest FPGAs. We validate the benefits of logic shrinkage in the context of real application deployment by implementing a face mask detection DNN using a BNN, LUTNet, and logic-shrunk layers. Our results show that logic shrinkage results in area gains versus LUTNet (up to 1.20×) and equally pruned BNNs (up to 1.08×), along with accuracy improvements.
A Review on Tools, Mechanics, Benefits, and Challenges of Gamified Software Testing

Gamification is an established practice in Software Engineering to increase effectiveness and engagement in many practices. This manuscript provides a characterization of the application of gamification to the Software Testing area. Such practice in fact reportedly suffers from low engagement by both personnel in industrial contexts and learners in educational contexts. Our goal is to identify the application areas and utilized gamified techniques and mechanics, the provided benefits and drawbacks, as well as the open challenges in the field. To this purpose, we conducted a Multivocal Literature Review to identify white and grey literature sources addressing gamified software testing. We analyzed 73 contributions and summarized the most common gamified mechanics, concepts, tools, and domains where they are mostly applied. We conclude that gamification in software testing is mostly applied to the test creation phase with simple white-box unit or mutation testing tools and is mostly used to foster good behaviors by promoting the testers’ accomplishment. Key research areas and main challenges in the field are: careful design of tailored gamified mechanics for specific testing techniques; the need for technological improvements to enable crowdsourcing, cooperation, and concurrency; the necessity for empirical and large-scale evaluation of the benefits delivered by gamification mechanics.
Pseudorandom Generators Based on One-Way Functions

Pseudorandom Generators is an important notion of cryptography. A new randomized iterating method of one-way function is proposed, after the analyzing of current research on pseudorandom generators based on one-way function. On the basis of this randomized iteration, a pseudorandom generator with linear seeds length is constructed using general regular one-way function and universal hash function. The output sequence of the proposed PRNG is unpredictable and the length of the seeds is linear to the input length of the one-way function.
Investigation of MHD slow shocks propagating along the Io flux tube

One aspect of the Io-Jupiter interaction studied by many authors is the generation of Alfven waves by Io moving in the magnetized plasma. In our study, we focus on an additional mechanism of the interaction between Io and Jupiter based on MHD slow shocks propagating from Io toward Jupiter along a magnetic flux tube. These MHD slow shocks are produced by plasma flow injected by Io, which is considered as a source of ionized particles. The propagation of the slow shocks is calculated along a given magnetic flux tube from Io to Jupiter. The total pressure is assumed to be a known function of the distance measured along the tube. It is determined as the magnetic pressure corresponding to the undisturbed Jovian magnetic field calculated in a dipole approximation. The material coordinates are used to describe the plasma flow along the magnetic tube. The peculiarity of this problem stems from the fact that the total pressure increases by a factor of 10 5 , whereas the cross section of the magnetic flux tube decreases by a factor of 300. The result is that the plasma velocity after the shock front substantially increases toward Jupiter with increasing magnetic pressure. The electric potential dierence along the magnetic field is estimated, which is produced by the accelerated plasma flow propagating with the MHD slow shocks.
Sensitivity Measures, ANOVA-like Techniques and the Use of Bootstrap

Sobol' sensitivity indices,used in variance based global sensitivity analysis of model output,are compared with the Analysis of Variance in classical factorial design. Monte Carlo computation of Sobol' indices is described briefly, and a bootstrap approach is presented,which can be used to produce a confidence interval for the true,unknown indices.
Characterization of human XPD helicase activity with Single Molecule Magnetic Tweezers

XPD helicase is a DNA unwinding enzyme involved in multiple cellular processes. As part of TFIIH, XPD opens a repair bubble in DNA for access by proteins in the nucleotide excision repair pathway. XPD uses the energy from ATP hydrolysis to translocate in the 5’ to 3’ direction on one strand of duplex DNA, displacing the opposite strand in the process. We used magnetic tweezers assays to measure the double-stranded DNA (dsDNA) unwinding and single-stranded DNA (ssDNA) translocation activities of human XPD by itself. In our experimental setup, hXPD exhibits low unwinding processivity of ~14 bp and slow overall unwinding rate of ~0.3 bp/s. Individual unwinding and translocation events were composed of fast and slow runs and pauses. Analysis of these events gave similar mean run sizes and rates for unwinding and translocation, suggesting that unwinding is a reflection of translocation. The analysis also revealed that hXPD spent similar time stalling and unwinding. hXPD translocated on ssDNA at a similar overall rate as that of unwinding, pointing to an active helicase. However, we observed modest effects of DNA sequence on stalling and unwinding initiation position. Considering the slow unwinding rate, high probability of base pair separation at the ssDNA/dsDNA fork, and the observed DNA sequence dependences, we propose that hXPD is most likely a partially active helicase. Our results provide detailed information on the basal activity of hXPD which enhances our mechanistic understanding of hXPD activity. SIGNIFICANCE Human XPD helicase is a major component of the general transcription factor TFIIH. TFIIH is essential in both transcription and nucleotide excision repair. Mutations in hXPD are associated with cancers and autosomal recessive disorders. Here we directly measured the dsDNA unwinding and ssDNA translocation of human XPD helicase by itself. Our measurements provide detailed information on the basal activity of human XPD, which enhance our mechanistic understanding of the activity of XPD in the cell, provide a basis for better understanding of the clinical phenotypes, and aid in drug design targeting hXPD related diseases.