Miracle-dz/UniFAR-FTUs huggingface dataset:
On holomorphic functions attaining their weighted norms

We study holomorphic functions attaining weighted norms and its connections with the classical theory of norm attaining holomorphic functions. We prove that there are polynomials on (cid:96) p which attain their weighted but not their supremum norm and viceversa. Nevertheless, we also prove that in the context of polynomials of ﬁxed degree both norms are in fact equivalent. This leads us to the main problem of the paper, namely, whether the holomorphic functions attaining their weighted norm are dense. Although we exhibit an example where this does not hold, as the main theorem of our paper, we prove the denseness provided the domain space is uniformly convex. In fact, we provide a Bollob´as type theorem in this setting. For the proof of such a result we develop a new geometric technique.
Marginal Interventional Effects

Conventional causal estimands, such as the average treatment eﬀect (ATE), reﬂect how the mean outcome in a population or subpopulation would change if all units received treatment versus control. Real-world policy changes, however, are often incremental, changing the treatment status for only a small segment of the population who are at or near “the margin of participation. ” To capture this notion, two parallel lines of inquiry have developed in economics and in statistics and epidemiology that deﬁne, identify, and estimate what we call interventional eﬀects. In this article, we bridge these two strands of literature by deﬁning interventional eﬀect (IE) as the per capita eﬀect of a treatment intervention on an outcome of interest, and marginal interventional eﬀect (MIE) as its limit when the size of the intervention approaches zero. The IE and MIE can be viewed as the unconditional counterparts of the policy-relevant treatment eﬀect (PRTE) and marginal PRTE (MPRTE) proposed in the economics literature. However, diﬀerent from PRTE and MPRTE, IE and MIE are deﬁned without reference to a latent index model, and, as we show, can be identiﬁed either under unconfoundedness or through the use of instrumental variables. For both scenarios, we show that MIEs are typically identiﬁed without the strong positivity assumption required of the ATE, highlight several “stylized interventions” that may be of particular interest in policy analysis, discuss several parametric and semiparametric estimation strategies, and illustrate the proposed methods with an empirical example.
Determination of uncertainties in parton densities

We review various methods used to estimate uncertainties in quantum correlation functions, such as parton distribution functions (PDFs). Using a toy model of a PDF, we compare the uncertainty estimates yielded by the traditional Hessian and data resampling methods, as well as from explicitly Bayesian analyses using nested sampling or hybrid Markov chain Monte Carlo techniques. We investigate how uncertainty bands derived from neural network approaches depend on details of the network training, and how they compare to the uncertainties obtained from more traditional methods with a speciﬁc underlying parametrization. Our results show that utilizing a neural network on a simpliﬁed example of PDF data has the potential to inﬂate uncertainties, in part due to the cross validation procedure that is generally used to avoid overﬁtting data.
An Optical Parametric Amplifier via $ \chi ^{(2)}$ in AlGaAs Waveguides

We report parametric gain by utilizing <inline-formula><tex-math notation="LaTeX">$ \chi ^{(2)}$</tex-math></inline-formula> non-linearities in a semiconductor Bragg Reflection Waveguide (BRW) waveguide chip. Under the two-mode degenerate type II phase matching, it can be shown that more than 18 dBs of parametric gain for both TE and TM modes is tenable in 100 s of micrometers of device length. Polarization insensitive parametric gain can be attained within the 1550 nm region of the spectrum. These AlGaAs BRW waveguides exhibit sub-photon per pulse sensitivity. This is in sharp contrast to other types of parametric gain devices which utilize <inline-formula><tex-math notation="LaTeX">$ \chi ^{(3)}$</tex-math></inline-formula>, where the pump wavelength is in the vicinity of the signal wavelength. This sensitivity, which reached 0.1 photon/pulse, can usher a new era for on-chip quantum information processing using compact, micrometer-scale devices.
Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space

Sepsis is a leading cause of death in the ICU. It is a disease requiring complex interventions in a short period of time, but its optimal treatment strategy remains uncertain. Evidence suggests that the practices of currently used treatment strategies are problematic and may cause harm to patients. To address this decision problem, we propose a new medical decision model based on historical data to help clinicians recommend the best reference option for real-time treatment. Our model combines oﬄine reinforcement learning and deep reinforcement learning to solve the problem of traditional reinforcement learning in the medical ﬁeld due to the inability to interact with the environment, while enabling our model to make decisions in a continuous state-action space. We demonstrate that, on average, the treatments recommended by the model are more valuable and reliable than those recommended by clinicians. In a large validation dataset, we ﬁnd out that the patients whose actual doses from clinicians matched the decisions made by AI has the lowest mortality rates. Our model provides personalized and clinically interpretable treatment decisions for sepsis to improve patient care.
Blockchain Function Virtualization: A New Approach for Mobile Networks Beyond 5G

Many of the key enabling technologies of the fifth-generation (5G), such as network slicing, spectrum sharing, and federated learning, rely on a centralized authority. This may lead to pitfalls in terms of security or single point of failure. Distributed ledger technology, specifically blockchain, is currently employed by different applications related to the Internet of Things (IoT) and 5G to address the drawbacks of centralized systems. For this reason, mobile blockchain networks (MBNs) have recently attracted a great deal of attention. To add a transaction to the blockchain in MBNs, mobile or IoT users must perform various tasks like encryption, decryption, and mining. These tasks require energy and processing power, which impose limitations on mobile and IoT users' performance because they are usually battery powered and have a low processing power. One possible solution is to perform the tasks virtually on commodity servers provided by mobile edge computing (MEC) or cloud computing. To do so, all tasks needed to add a transaction to the blockchain can be treated as virtual blockchain functions that can be executed on commodity servers. We introduce a blockchain virtualization framework called blockchain function virtualization (BFV), through which all blockchain functions can be performed virtually by MEC or cloud computing. Furthermore, we describe applications of the BFV framework and resource allocation challenges brought by the BFV framework in mobile networks. In addition, to illustrate the advantages of BFV, we define an optimization problem to simultaneously minimize the energy consumption cost and maximize miners' rewards. Finally, simulation results show the performance of the proposed framework in terms of total energy consumption, transaction confirmation rate, and miners' average profit.
Automated Compliance Blueprint Optimization with Artificial Intelligence

For highly regulated industries such as banking and healthcare, one of the major hindrances to the adoption of cloud computing is compliance with regulatory standards. This is a complex problem due to many regulatory and technical specification (techspec) documents that the companies need to comply with. The critical problem is to establish the mapping between techspecs and regulation controls so that from day one, companies can comply with regulations with minimal effort. We demonstrate the practicality of an approach to automatically analyze regulatory standards using Artificial Intelligence (AI) techniques. We present early results to identify the mapping between techspecs and regulation controls, and discuss challenges that must be overcome for this solution to be fully practical.
Direct optical probing of ultrafast spin dynamics in a magnetic semiconductor

We uncovered for the first time the spin dynamics involved in the birth and growth of giant spin polarons in a magnetic semiconductor. For this purpose, we developed a new measurement technique, which for the first time provides direct access to the spin dynamics, irrespective of phonons and carriers involved in the process. Moreover, we solved for the first time the Landau-Liftshitz equation in the specific scenario of spin polarons, which fits our data excellently, and demonstrates for the first time that the spin polaron growth slows down dramatically when the sample is cooled in the paramagnetic phase. Finally, temperature dependent Monte Carlo simulations were performed, which are in excellent agreement with the observed slowdown, which demonstrates for the first time that fluctuations in the Weiss field play a decisive role in spin coherence generation induced by light in magnetic materials. These results offer a new tool and new insight for spin dynamics investigations.
Neural Inverse Transform Sampler

Any explicit functional representation $f$ of a density is hampered by two main obstacles when we wish to use it as a generative model: designing $f$ so that sampling is fast, and estimating $Z = \int f$ so that $Z^{-1}f$ integrates to 1. This becomes increasingly complicated as $f$ itself becomes complicated. In this paper, we show that when modeling one-dimensional conditional densities with a neural network, $Z$ can be exactly and efficiently computed by letting the network represent the cumulative distribution function of a target density, and applying a generalized fundamental theorem of calculus. We also derive a fast algorithm for sampling from the resulting representation by the inverse transform method. By extending these principles to higher dimensions, we introduce the \textbf{Neural Inverse Transform Sampler (NITS)}, a novel deep learning framework for modeling and sampling from general, multidimensional, compactly-supported probability densities. NITS is a highly expressive density estimator that boasts end-to-end differentiability, fast sampling, and exact and cheap likelihood evaluation. We demonstrate the applicability of NITS by applying it to realistic, high-dimensional density estimation tasks: likelihood-based generative modeling on the CIFAR-10 dataset, and density estimation on the UCI suite of benchmark datasets, where NITS produces compelling results rivaling or surpassing the state of the art.
Structural and Dynamical Analysis of the Quiescent Molecular Ridge in the Large Magellanic Cloud

We present a comparison of low-J 13CO and CS observations of four different regions in the LMC—the quiescent Molecular Ridge, 30 Doradus, N159, and N113, all at a resolution of ∼3 pc. The regions 30 Dor, N159, and N113 are actively forming massive stars, while the Molecular Ridge is forming almost no massive stars, despite its large reservoir of molecular gas and proximity to N159 and 30 Dor. We segment the emission from each region into hierarchical structures using dendrograms and analyze the sizes, masses, and line widths of these structures. We find that the Ridge has significantly lower kinetic energy at a given size scale and also lower surface densities than the other regions, resulting in higher virial parameters. This suggests that the Ridge is not forming massive stars as actively as the other regions because it has less dense gas and not because collapse is suppressed by excess kinetic energy. We also find that these physical conditions and energy balance vary significantly within the Ridge and that this variation appears only weakly correlated with distance from sites of massive-star formation such as R136 in 30 Dor, which is ∼1 kpc away. These variations also show only a weak correlation with local star formation activity within the clouds.
Chirality and Rashba-related effects in the spin texture of a 2D centro-symmetric ferromagnet: the case of CrI3 bilayer

The newly discovered two-dimensional magnetic semiconductors such as CrI$_3$ have triggered a surge of interest stemming from their exotic spin-dependent properties and potential applications in spintronics and magneto-optoelectronics. Using first-principle density-functional-theory calculations, we investigate the properties of the spin-polarization texture in momentum space in the prototype 2D centrosymmetric ferromagnetic (FM) bilayer of CrI$_3$, with perpendicular magnetization. The FM bilayer displays a rich in-plane spin texture in its highest valence bands. We show the existence of two distinct spin canting effects, which result from the coupling of FM order with structural chirality and electric polarization, in establishing the in-plane spin texture. The first effect is generated by the chirality of the layer stacking and the spin-orbit-polarized nature of the valence states, and yields the same canting on both layers. The second effect is a Rashba-related effect, which in a centrosymmetric ferromagnet induces in a single electronic state two opposite spin-canted components on the two layers. Furthermore, using the FM bilayer as an example, we provide general rules imposed by magnetic-space-group symmetries on spin-polarization vectors, serving as guidelines for the shape of the spin texture in the Brillouin zone of magnetic crystals. Finally, we show that the above effects can be effectively used to manipulate the spin texture via compressive strain, which induces in the FM bilayer valence-band-edge states with canted spins.
COVYT: Introducing the Coronavirus YouTube and TikTok speech dataset featuring the same speakers with and without infection

More than two years after its outbreak, the COVID-19 pandemic continues to plague medical systems around the world, putting a strain on scarce resources, and claiming human lives. From the very beginning, various AI-based COVID-19 detection and monitoring tools have been pursued in an attempt to stem the tide of infections through timely diagnosis. In particular, computer audition has been suggested as a non-invasive, cost-efficient, and eco-friendly alternative for detecting COVID-19 infections through vocal sounds. However, like all AI methods, also computer audition is heavily dependent on the quantity and quality of available data, and large-scale COVID-19 sound datasets are difficult to acquire -- amongst other reasons -- due to the sensitive nature of such data. To that end, we introduce the COVYT dataset -- a novel COVID-19 dataset collected from public sources containing more than 8 hours of speech from 65 speakers. As compared to other existing COVID-19 sound datasets, the unique feature of the COVYT dataset is that it comprises both COVID-19 positive and negative samples from all 65 speakers. We analyse the acoustic manifestation of COVID-19 on the basis of these perfectly speaker characteristic balanced `in-the-wild' data using interpretable audio descriptors, and investigate several classification scenarios that shed light into proper partitioning strategies for a fair speech-based COVID-19 detection.
Jointist: Joint Learning for Multi-instrument Transcription and Its Applications

In this paper, we introduce Jointist, an instrument-aware multi-instrument framework that is capable of transcribing, recognizing, and separating multiple musical instruments from an audio clip. Jointist consists of the instrument recognition module that conditions the other modules: the transcription module that outputs instrument-specific piano rolls, and the source separation module that utilizes instrument information and transcription results. The instrument conditioning is designed for an explicit multi-instrument functionality while the connection between the transcription and source separation modules is for better transcription performance. Our challenging problem formulation makes the model highly useful in the real world given that modern popular music typically consists of multiple instruments. However, its novelty necessitates a new perspective on how to evaluate such a model. During the experiment, we assess the model from various aspects, providing a new evaluation perspective for multi-instrument transcription. We also argue that transcription models can be utilized as a preprocessing module for other music analysis tasks. In the experiment on several downstream tasks, the symbolic representation provided by our transcription model turned out to be helpful to spectrograms in solving downbeat detection, chord recognition, and key estimation.
Changes in the Working Lives of VET Practitioners in Recent Years: A Case Study

The purpose of my case study, undertaken by means of interviews in the hospitality centres of two TAFE institutes in Melbourne, Victoria, Australia, was to gain an in-depth understanding of the changes which have occurred in recent years and their effects on the working lives of the VET practitioners. To achieve this I used a qualitative research method to seek answers to the research questions, by using a set of case study procedures which allowed me to systematically collect evidence, and produce findings based on the data collected. Recent changes in the workplace for Vocational Education and Training (VET) practitioners have been occasioned by four predominant factors: changes in state and federal government policies; changes in training packages in order to make them more relevant; advancement in technology and the way it has been integrated into a blended learning approach; and adoption of a business model approach in vocational education to ensure that centres within institutions remain profitable and viable. My findings indicate there has been a dramatic increase in the administrative workload placed on VET practitioners, caused by these changes. In particular, the pressures on VET practitioners to be more productive, and the pressure on administration staff (centre managers and above) to perform financially for their centres and their institutions and, in many cases, the lack of necessary communication and leadership skills required to build productive and enthused teams able to focus more on viability and profitability, is leaving trainers feeling confused and inadequate as they grapple with students who now must be treated as both learners and clients. As an outcome of my study, I have proposed a transformational model – applicable at both an individual and organisational level – that will alleviate the stress and the pressure in times of change, and transform the VET practitioner's workplace into one that will be more productive.
Meesmann Corneal Epithelial Dystrophy

Keratin 12 (K12) is an intermediate-filament protein expressed specifically in corneal epithelium. Recently, we isolated K12 cDNA from a human corneal epithelial cDNA library and determined its full sequence. Herein, we present the exon-intron boundary structure and chromosomal localization of human K12. In addition, we report four K12 mutations in Meesmann corneal epithelial dystrophy (MCD), an autosomal dominant disorder characterized by intraepithelial microcysts and corneal epithelial fragility in which mutations in keratin 3 (K3) and K12 have recently been implicated. In the human K12 gene, we identified seven introns, defining eight individual exons that cover the coding sequence. Together the exons and introns span -6 kb of genomic DNA. Using FISH, we found that the K12 gene mapped to 17q12, where a type I keratin cluster exists. In this study, four new K12 mutations (Argl35Gly, Argl351le, Tyr429Asp, and Leul4OArg) were identified in three unrelated MCD pedigrees and in one individual with MCD. All mutations were either in the highly conserved a-helix-initiation motif of rod domain 1A or in the ahelix-termination motif of rod domain 2B. These sites are essential for keratin filament assembly, suggesting that the mutations described above may be causative for MCD. Of particular interest, one of these mutations (Tyr429Asp), detected in both affected individuals in one of our pedigrees, is the first mutation to be identified within the a-helix-termination motif in type I keratin.
Conserved uORF Nascent Peptides that Control Translation

Cells utilize transcriptional and post-transcriptional mechanisms to alter gene expression in response to environmental cues. Gene-specific controls, including changing the translation of specific mRNAs, provides a rapid means to respond precisely to different conditions. Upstream open reading frames (uORFs) are known to control the translation of mRNAs. Recent studies in bacteria and eukaryotes have revealed the functions of evolutionarily conserved uORF-encoded peptides. Some of these uORF-encoded nascent peptides enable responses to specific metabolites to modulate the translation of their mRNAs by stalling ribosomes, and through ribosome stalling may also modulate the level of their mRNAs. In this review, we highlight several examples of conserved uORF nascent peptides that stall ribosomes to regulate gene expression in response to specific metabolites in bacteria, fungi, mammals, and plants.
Stein’s Method Meets Computational Statistics: A Review of Some Recent Developments

Stein's method compares probability distributions through the study of a class of linear operators called Stein operators. While mainly studied in probability and used to underpin theoretical statistics, Stein's method has led to significant advances in computational statistics in recent years. The goal of this survey is to bring together some of these recent developments and, in doing so, to stimulate further research into the successful field of Stein's method and statistics. The topics we discuss include tools to benchmark and compare sampling methods such as approximate Markov chain Monte Carlo, deterministic alternatives to sampling methods, control variate techniques, parameter estimation and goodness-of-fit testing.
Understanding the Properties of Generated Corpora

Models for text generation have become focal for many research tasks and especially for the generation of sentence corpora. However, understanding the properties of an automatically generated text corpus remains challenging. We propose a set of tools that examine the properties of generated text corpora. Applying these tools on various generated corpora allowed us to gain new insights into the properties of the generative models. As part of our characterization process, we found remarkable differences in the corpora generated by two leading generative technologies.