Miracle-dz/UniFAR-FTUs huggingface dataset:
Parameter Constrained Transfer Learning for Low Dose PET Image Denoising

Positron emission tomography (PET) is widely used in clinical practice. However, the potential risk of PET-associated radiation dose to patients needs to be minimized. With reduction of the radiation dose, the resultant images may suffer from noise and artifacts which compromises the diagnostic performance. In this paper, we propose a parameter-constrained generative adversarial network with Wasserstein distance and perceptual loss (PC-WGAN) for low-dose PET image denoising. This method makes two main contributions: 1) a PC-WGAN framework is designed to denoise low-dose PET images without compromising structural details; and 2) a transfer learning strategy is developed to train PC-WGAN with parameters being constrained, which has major merits; namely, making the training process of PC-WGAN efficient and improving the quality of denoised images. The experimental results on clinical data show that the proposed network can suppress image noise more effectively while preserving better image fidelity than three selected state-of-the-art methods.
A greedy anytime algorithm for sparse PCA

The taxing computational effort that is involved in solving some high-dimensional statistical problems, in particular problems involving non-convex optimization, has popularized the development and analysis of algorithms that run efficiently (polynomial-time) but with no general guarantee on statistical consistency. In light of the ever-increasing compute power and decreasing costs, a more useful characterization of algorithms is by their ability to calibrate the invested computational effort with various characteristics of the input at hand and with the available computational resources. For example, design an algorithm that always guarantees statistical consistency of its output by increasing the running time as the SNR weakens. We propose a new greedy algorithm for the $\ell_0$-sparse PCA problem which supports the calibration principle. We provide both a rigorous analysis of our algorithm in the spiked covariance model, as well as simulation results and comparison with other existing methods. Our findings show that our algorithm recovers the spike in SNR regimes where all polynomial-time algorithms fail while running in a reasonable parallel-time on a cluster.
Energy-Efficient Task Offloading for Vehicular Edge Computing: Joint Optimization of Offloading and Bit Allocation

With the rapid development of vehicular networks, various applications that require high computation resources have emerged. To efficiently execute these applications, vehicular edge computing (VEC) can be employed. VEC offloads the computation tasks to the VEC node, i.e., the road side unit (RSU), which improves vehicular service and reduces energy consumption of the vehicle. However, communication environment is time-varying due to the movement of the vehicle, so that finding the optimal offloading parameters is still an open problem. Therefore, it is necessary to investigate an optimal offloading strategy for effective energy savings in energy-limited vehicles. In this paper, we consider the changes of communication environment due to various speeds of vehicles, which are not considered in previous studies. Then, we jointly optimize the offloading proportion and uplink/computation/downlink bit allocation of multiple vehicles, for the purpose of minimizing the total energy consumption of the vehicles under the delay constraint. Numerical results demonstrate that the proposed energy-efficient offloading strategy significantly reduces the total energy consumption.
Target-Oriented Deformation of Visual-Semantic Embedding Space

Multimodal embedding is a crucial research topic for cross-modal understanding, data mining, and translation. Many studies have attempted to extract representations from given entities and align them in a shared embedding space. However, because entities in different modalities exhibit different abstraction levels and modality-specific information, it is insufficient to embed related entities close to each other. In this study, we propose the Target-Oriented Deformation Network (TOD-Net), a novel module that continuously deforms the embedding space into a new space under a given condition, thereby adjusting similarities between entities. Unlike methods based on cross-modal attention, TOD-Net is a post-process applied to the embedding space learned by existing embedding systems and improves their performances of retrieval. In particular, when combined with cutting-edge models, TOD-Net gains the state-of-the-art cross-modal retrieval model associated with the MSCOCO dataset. Qualitative analysis reveals that TOD-Net successfully emphasizes entity-specific concepts and retrieves diverse targets via handling higher levels of diversity than existing models.
Lagrangian Description of Accreting Black Hole Systems in the Context of Emergent Spacetime

We make use of the Lagrangian description of fluid motion to highlight certain features in the context of spacetime geometry as emergent phenomena in fluid systems. By using Lagrangian Perturbation Theory (LPT), we find that if the flow is not barotropic it will not be locally irrotational, and as a result, emergent gravity can not be realised for such flow. Our work gives a new perspective of examining the perturbations in a fluid from a different approach (other than Eulerian approach) which is the way of using Lagrangian Perturbation Theory. We make use of Lagrangian description of motion to examine the propagation of Eikonal wave (wave having very short wavelength) from the reference frame of the observer moving with the background flow. We find that waves of ultra short wavelength propagate similar to waves in a static uniform medium in the near vicinity of the observer. We restrict ourselves to nonrelativistic flows in astrophysical black hole accretion.
FireNet: Real-time Segmentation of Fire Perimeter from Aerial Video

In this paper, we share our approach to real-time segmentation of fire perimeter from aerial full-motion infrared video. We start by describing the problem from a humanitarian aid and disaster response perspective. Specifically, we explain the importance of the problem, how it is currently resolved, and how our machine learning approach improves it. To test our models we annotate a large-scale dataset of 400,000 frames with guidance from domain experts. Finally, we share our approach currently deployed in production with inference speed of 20 frames per second and an accuracy of 92 (F1 Score).
Doctor-patient communication and the consulting room use of computers in general practice.

This thesis examines the effects of general practitioners' use of a computer during consultations, on doctor-patlent communication and the delivery of care. The IBM Sheffield Primary Care System was used by GPs during routine consultations for the review and update of patients' records. The system also provided an interactive protocol for the management of patients with chronic hypertension. Video recordings were made of over 800 consultations with and without use of the computer. These were used to identify a comprehensive and reliable set of measures of doctor-patient communication. With medical collaboration measures of the standard of delivery of care were also developed. The measures were then used to investigate the effects of computer use. When used for the review and update of records the computer had little effect on doctor-patient communication. Doctors showed more solidarity with patients (e. g. by offering supportive comments), but there was no effect on the amount of information and advice offered or questions asked by either doctor or patient. However, overall, there was a slight impairment of the doctor's interpersonal manner and delivery of care. When the computer was used in direct support of clinical decision-making (i. e. through the hypertension management protocol) there was a marked improvement in the doctors’ clinical performance in terms of the number of relevant verbal and physical examinations conducted and recorded. The findings suggest ways in which future systems should be designed and used to avoid possible adverse consequences for doctor-patient communications. A research framework, including new methodologies, is also offered for the evaluation of future consulting room systems. Similar evaluative studies need to be performed on the more sophisticated systems now available, especially the clinically oriented ones which offer most potential.
Multifrequency JVLA observations of the X-shaped radio galaxy in Abell 3670

Context. X-shaped radio galaxies (XRGs) exhibit a pair of bright primary lobes and a pair of weak secondary lobes (“wings”), which are oriented with an angle that gives the structure a cross-like shape. Though several theoretical models have been proposed to explain their origin, there is currently not a general consensus on a formation scenario. Aims. We analysed new multifrequency Karl G. Jansky Very Large Array (JVLA) radio data at 1.5, 5.5, 6, and 9 GHz of the candidate XRG in Abell 3670 (A3670) in order to characterise and classify it for the first time and to investigate its origin. Methods. We produced flux, spectral index, and radiative age maps of A3670 by means of the new radio data. We investigated the connection between the radio galaxy and its host, a brightest cluster galaxy (BCG) with two optical nuclei classified as a dumbbell galaxy. Finally we discussed the literature models and compared them to the observed properties of A3670. Results. We classify A3670 as a Fanaroff–Riley I-type XRG and measured a 1.4 GHz radio power of P1.4 = 1.7 × 1025 W Hz−1. By estimating the radiative age of the various source components, we find that the wings are Δt ≃ 20 Myr older than the lobes. We verified that the lobes and wings are aligned with the major and minor axes of the optical galaxy, respectively, and we estimated a black hole mass of MBH ∼ 109 M⊙, which is in agreement with the typical properties of the XRGs. Conclusions. Among the discussed scenarios, the jet-shell interaction model may best reproduce the observed properties of A3670. The gas of a stellar shell is responsible for the deflection of the jets, thus forming the wings. The presence of stellar shells in A3670 is plausible, but it needs further optical observations to be confirmed.
All of Linear Regression

Least squares linear regression is one of the oldest and widely used data analysis tools. Although the theoretical analysis of the ordinary least squares (OLS) estimator is as old, several fundamental questions are yet to be answered. Suppose regression observations $(X_1,Y_1),\ldots,(X_n,Y_n)\in\mathbb{R}^d\times\mathbb{R}$ (not necessarily independent) are available. Some of the questions we deal with are as follows: under what conditions, does the OLS estimator converge and what is the limit? What happens if the dimension is allowed to grow with $n$? What happens if the observations are dependent with dependence possibly strengthening with $n$? How to do statistical inference under these kinds of misspecification? What happens to the OLS estimator under variable selection? How to do inference under misspecification and variable selection? We answer all the questions raised above with one simple deterministic inequality which holds for any set of observations and any sample size. This implies that all our results are a finite sample (non-asymptotic) in nature. In the end, one only needs to bound certain random quantities under specific settings of interest to get concrete rates and we derive these bounds for the case of independent observations. In particular, the problem of inference after variable selection is studied, for the first time, when $d$, the number of covariates increases (almost exponentially) with sample size $n$. We provide comments on the ``right'' statistic to consider for inference under variable selection and efficient computation of quantiles.
Elementos da teoria de aprendizagem de máquina supervisionada

This is a set of lecture notes for an introductory course (advanced undergaduates or the 1st graduate course) on foundations of supervised machine learning (in Portuguese). The topics include: the geometry of the Hamming cube, concentration of measure, shattering and VC dimension, Glivenko-Cantelli classes, PAC learnability, universal consistency and the k-NN classifier in metric spaces, dimensionality reduction, universal approximation, sample compression. There are appendices on metric and normed spaces, measure theory, etc., making the notes self-contained. Este e um conjunto de notas de aula para um curso introdutorio (curso de graduacao avancado ou o 1o curso de pos) sobre fundamentos da aprendizagem de maquina supervisionada (em Portugues). Os topicos incluem: a geometria do cubo de Hamming, concentracao de medida, fragmentacao e dimensao de Vapnik-Chervonenkis, classes de Glivenko-Cantelli, aprendizabilidade PAC, consistencia universal e o classificador k-NN em espacos metricos, reducao de dimensionalidade, aproximacao universal, compressao amostral. Ha apendices sobre espacos metricos e normados, teoria de medida, etc., tornando as notas autosuficientes.
A Fireball and Potentially Hazardous Binary Near-Earth Asteroid (164121) 2003 YT1

We present a fireball detected in the night sky over Kyoto, Japan on UT 2017 April 28 at by the SonotaCo Network. The absolute visual magnitude is = −4.10 ± 0.42 mag. Luminous light curves obtain a meteoroid mass of m = 29 ± 1 g, corresponding to the size of as = 2.7 ± 0.1 cm. Orbital similarity assessed by D-criterions (see DSH = 0.0079) has identified a likely parent, the binary near-Earth asteroid (164121) 2003 YT1. The suggested binary formation process is a Yarkovsky–O’Keefe–Radzievskii–Paddack-driven rotational disintegration. The asynchronous state indicates the age of <104 yr, near or shorter than the upper limit to meteoroid stream lifetime. We examine potential dust production mechanisms for the asteroid, including rotational instability, resurfacing, impact, photoionization, radiation pressure sweeping, thermal fracture, and sublimation of ice. We find some of them capable of producing the meteoroid-scale particles. Rotational instability is presumed to cause mass shedding, in consideration of the recent precedents (e.g., asteroid (6478) Gault), possibly releasing millimeter–centimeter scale dust particles. Impacts by micrometeorites with a size ≃1 mm could be a trigger for ejecting the centimeter-sized particles. Radiation pressure can sweep out the millimeter-sized dust particles, while not sufficient for the centimeter-sized. The other mechanisms are unprovable or unidentified. The feasibility in the parental aspect of 2003 YT1 is somewhat reconciled with the fireball observation, yielding an insight into how we approach potentially hazardous objects.
Joint Sparse Beamforming and Power Control for a Large-Scale DAS With Network-Assisted Full Duplex

Network-assisted full duplex (NAFD) is a unified duplex technique for large-scale distributed antenna systems (DAS) with remote antenna units operating in either half-duplex mode or full-duplex mode and all connecting to central processing unit via backhaul links. Downlink sparse beamforming and uplink power control are effective methods to reduce cross-link interference involved in NAFD with finite-capacity backhaul. We study joint downlink beamforming and power control for NAFD by maximizing the aggregated uplink and downlink spectral efficiency subject to quality-of-service constraints and backhaul constraints. Two approaches have been proposed to solve the optimization problem, where the first approach converts the objective function to the difference of two convex functions with semi-definite relaxation, and then, an iterative SDR-block coordinate descent method is applied to solve the problem. The second method is based on sequential parametric convex approximation. Simulation results have shown that the proposed algorithms yield a higher spectral efficiency gain compared to the traditional time-division duplex scheme.
Counterfactual diagnosis

Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient's symptoms by determining the diseases \emph{causing} them. However, existing diagnostic algorithms are purely associative, identifying diseases that are strongly correlated with a patients symptoms and medical history. We show that this inability to disentangle correlation from causation can result in sub-optimal or dangerous diagnoses. To overcome this, we reformulate diagnosis as a counterfactual inference task and derive new counterfactual diagnostic algorithms. We show that this approach is closer to the diagnostic reasoning of clinicians and significantly improves the accuracy and safety of the resulting diagnoses. We compare our counterfactual algorithm to the standard Bayesian diagnostic algorithm and a cohort of 44 doctors using a test set of clinical vignettes. While the Bayesian algorithm achieves an accuracy comparable to the average doctor, placing in the top 48% of doctors in our cohort, our counterfactual algorithm places in the top 25% of doctors, achieving expert clinical accuracy. This improvement is achieved simply by changing how we query our model, without requiring any additional model improvements. Our results show that counterfactual reasoning is a vital missing ingredient for applying machine learning to medical diagnosis.
Excitation spectrum and supersolidity of a two-leg bosonic ring ladder

We consider a system of weakly interacting bosons confined on a planar double lattice ring subjected to two artificial gauge fields. This system is known to display three phases, the Meissner phase where the flow of particles is carried at the edges of the system without transverse current, a vortex phase characterized by non-zero transverse current, and a biased-ladder phase, characterized by an imbalance of the population of the two rings. We use the Bogoliubov approximation to determine the excitation spectrum in the three phases, the dynamic structure factor and the quantum fluctuation corrections to the first-order correlation function. Our analysis reveals supersolid features as well as Josephson modes, corresponding to out-of-phase modes of the finite ring.
The Memory Controller Wall: Benchmarking the Intel FPGA SDK for OpenCL Memory Interface

Supported by their high power efficiency and recent advancements in High Level Synthesis (HLS), FPGAs are quickly finding their way into HPC and cloud systems. Large amounts of work have been done so far on loop and area optimizations for different applications on FPGAs using HLS. However, a comprehensive analysis of the behavior and efficiency of the memory controller of FPGAs is missing in literature, which becomes even more crucial when the limited memory bandwidth of modern FPGAs compared to their GPU counterparts is taken into account. In this work, we will analyze the memory interface generated by Intel FPGA SDK for OpenCL with different configurations for input/output arrays, vector size, interleaving, kernel programming model, on-chip channels, operating frequency, padding, and multiple types of overlapped blocking. Our results point to multiple shortcomings in the memory controller of Intel FPGAs, especially with respect to memory access alignment, that can hinder the programmer’s ability in maximizing memory performance in their design. For some of these cases, we will provide work-arounds to improve memory bandwidth efficiency; however, a general solution will require major changes in the memory controller itself.
A unified view of likelihood ratio and reparameterization gradients

Reparameterization (RP) and likelihood ratio (LR) gradient estimators are used to estimate gradients of expectations throughout machine learning and reinforcement learning; however, they are usually explained as simple mathematical tricks, with no insight into their nature. We use a first principles approach to explain that LR and RP are alternative methods of keeping track of the movement of probability mass, and the two are connected via the divergence theorem. Moreover, we show that the space of all possible estimators combining LR and RP can be completely parameterized by a flow field $u(x)$ and an importance sampling distribution $q(x)$. We prove that there cannot exist a single-sample estimator of this type outside our characterized space, thus, clarifying where we should be searching for better Monte Carlo gradient estimators.
Efficacy and Safety of Intranasally Applied Dimetindene Maleate Solution

Summary The aim of this study was to investigate the efficacy and tolerability of intranas ally applied dimetindene (CAS 3614-69-5) 0.1 % spray in children suffering from seasonal allergic rhinitis. A total of 100 children under 14 years with acute seasonal allergic rhinitis parti cipated in this randomised, single blind, reference controlled, multi center, paral lel group study with two treatment groups. The study took place between 2nd April and 16th September 1996 dur ing the pollen season. Patients were ex amined at enrolment (day 1), day 8 and day 15. Patients kept diary throughout the 2 week treatment phase. Patients were randomised to receive either dimet indene 0.1 % (Fenistil ® Nasal Spray) or a levocabastine (CAS 79516-68-0) 0.05 % so lution as reference medication. Both medications were supplied in similar outer packages. A single blind approach was chosen, because the reference med ication levocabastine requires two spray puffs per nostril as a single dose, whereas for dimetindene maleate solu tion a single spray puff per nostril is suffi cient. Dimetindene 0.1 % was applied with 1 spray puff (= 0.14 mg dimetin dene) in each nostril and levocabastine with 2 spray puff (= 0.10 mg levocabas tine) in each nostril every day in the morning before leaving the house and in the evening before going to bed. Addi tional administration of the spray was al lowed up to 4 times a day if needed. Effi cacy was assessed as change in severity of characteristic symptoms associated with pollen rhinitis: nasal rhinorrhea, nasal itching, nasal sneezing and nasal congestion. In addition, changes in ocular symptoms, lacrimation, ocular itching and red eyes, global physician’s assessment of efficacy at the end of treatment were assessed. The primary criterion change of total nasal symptom severity score between day 1 and day 3 resulted in a statistically equivalent and therapeutically relevant symptom reduction for the two treatments. All secondary criteria showed a similar reduction in symptoms, thus underlining the consistency of the findings. Both nasal sprays were well tolerated. It is concluded from these results that dimetin-dene 0.1 % nasal spray solution is a safe and efficient treatment option for children under 14 years suffering from seasonal allergic rhinitis.