Miracle-dz/UniFAR-FTUs huggingface dataset:
Noise Flow: Noise Modeling with Conditional Normalizing Flows —Supplemental Material—

Figure 1 shows the training loss over 2000 epochs of the DnCNN [1] image denoiser using the three noise synthesis models: (1) DnCNN-Gauss: the Gaussian model; (2) DnCNN-CamNLF: the heteroscedastic Gaussian model represented by the camera-calibrated noise level functions (NLFs); and (3) DnCNN-NoiseFlow: our Noise Flow model. Despite that training loss of the DnCNN-CamNLF is the lowest, the training behaviour indicated that DnCNNNoiseFlow is the most stable. The DnCNN-Gauss model is also stable, however, it still yields lower testing performance, as shown in Figure 2.
COVID-19 booster vaccination during pregnancy enhances maternal binding and neutralizing antibody responses and transplacental antibody transfer to the newborn (DMID 21-0004)

Abstract: Importance: COVID-19 vaccination is recommended during pregnancy for the protection of the mother. Little is known about the immune response to booster vaccinations during pregnancy. Objective: To measure immune responses to COVID-19 primary and booster mRNA vaccination during pregnancy and transplacental antibody transfer to the newborn. Design: Prospective cohort study of pregnant participants enrolled from July 2021 to January 2022, with follow up through and up to 12 months after delivery. Setting: Multicenter study conducted at 9 academic sites. Participants: Pregnant participants who received COVID-19 vaccination during pregnancy and their newborns. Exposure(s): Primary or booster COVID-19 mRNA vaccination during pregnancy. Main Outcome(s) and Measure(s): SARS-CoV-2 binding and neutralizing antibody (nAb) titers after primary or booster COVID-19 mRNA vaccination during pregnancy and antibody transfer to the newborn. Immune responses were compared between primary and booster vaccine recipients in maternal sera at delivery and in cord blood, after adjusting for days since last vaccination. Results: In this interim analysis, 167 participants received a primary 2-dose series and 73 received a booster dose of mRNA vaccine during pregnancy. Booster vaccination resulted in significantly higher binding and nAb titers, including to the Omicron BA.1 variant, in maternal serum at delivery and cord blood compared to a primary 2-dose series (range 0.55 to 0.88 log10 higher, p<0.0001 for all comparisons). Although levels were significantly lower than to the prototypical D614G variant, nAb to Omicron were present at delivery in 9% (GMT ID50 12.7) of Pfizer and 22% (GMT ID50 14.7) of Moderna recipients, and in 73% (GMT ID50 60.2) of boosted participants (p<0.0001). Transplacental antibody transfer was efficient regardless of vaccination regimen (median transfer ratio range: 1.55-1.77 for binding IgG and 1.00-1.78 for nAb). Conclusions and Relevance: COVID-19 mRNA vaccination during pregnancy elicited robust immune responses in mothers and efficient transplacental antibody transfer to the newborn. A booster dose during pregnancy significantly increased maternal and cord blood antibody levels, including against Omicron. Findings support continued use of COVID-19 vaccines during pregnancy, including booster doses. Trial Registration: clinical trials.gov; Registration Number: NCT05031468; https://clinicaltrials.gov/ct2/show/NCT05031468
Degenerative Adversarial NeuroImage Nets for 4D Simulations: Application in Longitudinal MRI

Accurate and realistic simulation of medical images is a growing area of research relevant to many healthcare applications. However, current image simulators have been unsuccessful when deployed on longitudinal clinical data --- for example, disease progression modelling designed to generate 3D MRI sequences (4D). Failures are typically due to inability to produce subject-specific simulation, and inefficient implementations incapable of synthesizing spatiotemporal images in high resolution. Memory limitations preclude training of the full-4D model, necessitating techniques that discard spatiotemporal information, such as 2D slice-by-slice implementations or patch-based approaches. Here we introduce a novel technique to address this challenge, called Profile Weight Functions (PWF). We demonstrate the power of PWFs by extending a recent framework for neuroimage simulation from 2D (plus time) to 3D (plus time), which is not currently available. To our knowledge, we are the first to implement a disease progression simulator able to predict accurate sequences of realistic, high-resolution, 3D medical images. We demonstrate our framework by training a model using 9652 T1-weighted MRI from the Alzheimer's Disease Neuroimaging Initiative dataset. We validate our results on a separate test set of 1216 MRI, demonstrating the capability to synthesize a personalized time-series of images given a single-time point and other metadata.
A general strategy to construct small molecule biosensors

Biosensors for small molecules can be used in applications that range from metabolic engineering to orthogonal control of transcription. Despite their broad utility, it remains a longstanding challenge to rapidly create new small molecule biosensors that are robust and specific. Here, we produce biosensors based on a ligand-binding domain (LBD) by using a method that, in principle, can be applied to any target molecule. The LBD may be fused to any protein-based reporter such a fluorescent protein or a transcriptional activator and is destabilized by mutation such that the fusion accumulates only in cells containing the target ligand. We illustrate the power of this method by developing biosensors for digoxin and progesterone in Saccharomyces cerevisiae and demonstrating portability to both mammalian cell culture and multicellular plants. Addition of ligand to yeast, mammalian, or plant cells expressing a biosensor activates transcription with a dynamic range of up to ~100-fold. We use the biosensors to improve the biotransformation of pregnenolone to progesterone in yeast by creating a novel selection to enable directed evolution of a metabolic pathway and to regulate Cas9 activity in mammalian cells. We further propose to expand the biosensor development and deployment to new systems such as prokaryotes and cell-free lysates. Lastly, we propose an antibody
Models of thermal processes for design optimization of power plants based on renewable energy sources and fuel cells

The system of mathematical models of thermal processes in hybrid power plants based on solar and wind renewable energy sources and methanol fuel cell has been developed to be used for the power plant design parameters optimization. Main assumptions and main equations for the heat and mass exchange in power plant are presented, methods and verification results for the power plant operation are given. Power plant design optimization and operation modes are discussed as well as experimental data collected during power plant operation. Forecast example for temperature fields during low ambient temperature operation is shown.
Indoor Channel Characteristics for Visible Light Communications

In this letter, we present indoor multipath dispersion characteristics for visible light communications (VLC). Since the VLC uses a wide spectrum between 380 nm and 780 nm, the conventional narrowband model for infrared may not apply. We generalize the Barry's model by including wavelength-dependent white LED characteristics and spectral reflectance of indoor reflectors. We perform a computer simulation to compare the power delay profile of the VLC with that of infrared communications. From our studies, we show that the VLC provides a larger transmission bandwidth than infrared communications.
Analyzing Code Comments to Boost Program Comprehension

1st Yusuke Shinyama Department of Computer Science Tokyo Institute of Technology Tokyo, Japan euske@sde.cs.titech.ac.jp 2nd Yoshitaka Arahori Department of Computer Science Tokyo Institute of Technology Tokyo, Japan arahori@cs.titech.ac.jp 3rd Katsuhiko Gondow Department of Computer Science Tokyo Institute of Technology Tokyo, Japan gondow@cs.titech.ac.jp Abstract—We are trying to find source code comments that help programmers understand a nontrivial part of source code. One of such examples would be explaining to assign a zero as a way to “clear” a buffer. Such comments are invaluable to programmers and identifying them correctly would be of great help. Toward this goal, we developed a method to discover explanatory code comments in a source code. We first propose eleven distinct categories of code comments. We then developed a decision-tree based classifier that can identify explanatory comments with 60% precision and 80% recall. We analyzed 2,000 GitHub projects that are written in two languages: Java and Python. This task is novel in that it focuses on a microscopic comment (“local comment”) within a method or function, in contrast to the prior efforts that focused on APIor methodlevel comments. We also investigated how different category of comments is used in different projects. Our key finding is that there are two dominant types of comments: preconditional and postconditional. Our findings also suggest that many English code comments have a certain grammatical structure that are consistent across different projects.
Structural Analysis of Si(OEt)4 Deposits on Au(111)/SiO2 Substrates at Nanometer Scale using Focused Electron Beam Induced Deposition

The focused electron beam induced deposition (FEBID) process was used by employing a Gemini SEM with a beam characteristic of 1keV and 24pA for the deposition of pillars and line shaped deposits with heights between 9nm to 1um and widths from 5nm to 0.5um. All structures have been analyzed to their composition looking at a desired Si : O : C content of 1: 2 : 0. The C content of the structure was found to be ~over 60% for older deposits kept in air (~at room temperature) and less than 50% for younger deposits, only 12 hours old. Using a deposition of Si(OEt)4 at high rates and a deposition temperature of under 0 degC, an Si content of our structure between 10at% and 15at% (compositional percentage) was obtained. The FEBID structures have been deposited on Au(111) over an SiO2 wafer. The Au(111) was chosen as a substrate for the deposition of Si(OEt)4 due to its structural and morphological properties, with its surface granulation following a Chevron pattern, and the Au(111) defects having a higher contribution to the change in the composition of the final content of the structure with the increase in O ratio and a reduction in the shapes heights.
Modular compactifications of $\mathcal M_{2,n}$ with Gorenstein singularities

We study the geometry of Gorenstein curve singularities of genus two, and of their stable limits. These singularities come in two families, corresponding to either Weierstrass or conjugate points on a semistable tail. For every $1\leq m <n$, a stability condition - using one of the markings as a reference point, and therefore not $\mathfrak S_n$-symmetric - defines proper Deligne-Mumford stacks $\overline{\mathcal M}_{2,n}^{(m)}$ containing the locus of smooth curves as a dense open substack.
Functional Optimal Transport: Mapping Estimation and Domain Adaptation for Functional data

Optimal transport (OT) has generated much recent interest by its capability of ﬁnding mappings that transport mass from one distribution to another, and found useful roles in machine learning tasks such as unsupervised learning, domain adaptation and transfer learning. On the other hand, in many applications data are generated by complex mechanisms involving convoluted spaces of functions, curves and surfaces in high dimensions. Functional data analysis provides a useful framework of treatment for such domains. In this paper we introduce a novel formulation of optimal transport problem in functional spaces and develop an efﬁ-cient learning algorithm for ﬁnding the stochastic map between functional domains. We apply our method to synthetic datasets and study the geometric properties of the transport map. Experiments on real-world datasets of robot arm trajectories and digit numbers further demonstrate the effectiveness of our method on applications of domain adaptation and generative modeling.
Hybrid Rotation Averaging: A Globally Guaranteed Fast and Robust Rotation Averaging Approach

We address rotation averaging and its application to real-world 3D reconstruction. Local optimisation based approaches are the defacto choice, though they only guarantee a local optimum. Global optimizers ensure global optimality in low noise conditions, but they are inefﬁcient and may easily deviate under the inﬂuence of outliers or elevated noise levels. We push the envelope of global rotation averaging by formulating it as a semi-deﬁnite program that can be solved efﬁciently by applying the Burer-Monteiro method. Both memory and time requirements are thereby largely reduced through a low-rank factorisation. Combined with a fast view graph ﬁltering as preprocessing, and a local optimiser as post-processing, the proposed hybrid approach is robust to outliers. Compared against state-of-the-art globally optimal methods, our approach is 1 ∼ 2 orders of magnitude faster while maintaining the same or better accuracy. We apply the proposed hybrid rotation averaging approach to incremental Structure from Motion (SfM) by adding the resulting global rotations as regular-izers to bundle adjustment. Overall, we demonstrate high practicality of the proposed method as bad camera poses are effectively corrected and drift is reduced.
0 Algorithm XXX : Mongoose , A Graph Coarsening and Partitioning Library

Partitioning graphs is a common and useful operation in many areas, from parallel computing to VLSI design to sparse matrix algorithms. In this paper, we introduce Mongoose, a multilevel hybrid graph partitioning algorithm and library. Building on previous work in multilevel partitioning frameworks and combinatoric approaches, we introduce novel stall-reducing and stall-free coarsening strategies, as well as an efficient hybrid algorithm leveraging 1) traditional combinatoric methods and 2) continuous quadratic programming formulations. We demonstrate how this new hybrid algorithm outperforms either strategy in isolation, and we also compare Mongoose to METIS and demonstrate its effectiveness on large and social networking (power law) graphs. CCS Concepts: rMathematics of computing→ Graph algorithms; Quadratic programming; Nonconvex optimization; Mathematical software performance;
Contributions to the measurement of depth in consumer imaging

This thesis aims to examine and investigate methods that could potentially utilize images captured by consumer cameras such as smartphones to estimate depth and generate a 3D structure. After more than a century of research in depth sensing and 3D reconstruction, there are still open and unsolved challenges, and ultimately a practical solution for each problem will have to rely on combining a range of techniques as there is no single best solution which can satisfy all the requirements of a depth sensing application. Based on this, a number of methods and frameworks are presented to take advantage of the existing consumer cameras in depth sensing applications. A method is presented to postprocess the depth maps with respect to the geometrical structure of the scene. Later, this method is adopted to evaluate the effectiveness of the deep learning approaches in monocular depth estimation. To utilize the current mono cameras available on smartphones, a framework is presented to use the pre-capturing small motions for 3D reconstruction and depth sensing applications. Similarly, a mono camera can be used to capture a sequence of images in different focal planes known as focal stack. A framework is designed to estimate dense depth map from focal stack in a reasonably fast processing time for high resolution images. Lastly, to investigate the potentials of the current consumer multi-camera arrays, a framework is proposed to estimate dense depth map from these cameras. The advanced capabilities of today’s smartphones brings hope that we can arrive at a consensual depth sensing imaging system in the next decade or so, and hopefully some of the contributions of this research will contribute in part to this solution.
Antitumor activity of total flavonoids from Tetrastigma hemsleyanum Diels et Gilg is associated with the inhibition of regulatory T cells in mice

Objective To determine the antitumor activity of Radix tetrastigmae flavonoids and their inhibitory effect on regulatory T cells (Tregs) in mice. Materials and methods Total flavonoids were isolated from Radix tetrastigmae, the root of Tetrastigma hemsleyanum Diels et Gilg, and administered to C57BL/6 mice by oral gavage after inoculation with Lewis lung carcinoma (LLC) cells. The effects of total flavonoids on tumor growth in vivo were examined. Flow cytometry was used to study the effects on Tregs, and enzyme-linked immunosorbent assay was used to analyze the changes in the serum levels of transforming growth factor β, prostaglandin E2, and cyclooxygenase 2 after tumor inoculation and flavonoid administration. Results Total flavonoids from T. hemsleyanum Diels et Gilg significantly inhibited tumor growth in C57BL/6 mice inoculated with LLCs. These flavonoids dramatically suppressed regulatory T-cell development in tumor-bearing mice. Further studies revealed that total flavonoids significantly decreased the serum levels of transforming growth factor β, prostaglandin E2, and cyclooxygenase 2 in tumor-bearing mice, which may be responsible for the inhibition of Tregs. Conclusion The antitumor activity of total flavonoids from T. hemsleyanum Diels et Gilg is associated with the inhibition of Tregs in a mouse tumor model. Total flavonoids from T. hemsleyanum Diels et Gilg may be used as antitumor agents in cancer prevention and treatment.
The Prognostic Impact of K-RAS Mutations in Adult Acute Myeloid Leukemia Patients Treated with High Dose Cytarabine.

UNLABELLED Activating point mutation of the RAS gene has been generally accepted as an oncogenic event in a variety of malignancies. It represents one of the most common genetic alterations in acute myeloid leukemia (AML). However there is still controversy about its clinical relevance on the treatment outcome of this leukemia. OBJECTIVE This study aimed to clarify the biologic and prognostic impact of K-RAS mutations in relation to the dose of cytarabine (ara-C) used in post induction consolidation chemotherapy in adult AML patients. PATIENTS AND METHODS The study comprised 71de novo AML patients with a male: Female ratio of 1.4: 1; their ages ranged from 21-59 years with a median of 37 years. They were subjected to full clinical evaluation, routine laboratory investigations, cytogenetic studies by G banding and K-RAS mutation detection using realtime PCR. The patients were randomized into 2 groups (gps) according to the ara-C dose used in consolidation treatment, HDAC gp receiving 400 mg ara-C and LDAC gp receiving 100mg ara-C. They were followed over a period of 5 years. RESULTS Mutations in the K-RAS gene (mutRAS) were detected in 23 patients (32%) with the remaining 48 patients (68%) having wild type RAS (wtRAS). Blast cell percentage was significantly lower in mutRAS compared to wtRAS patients (p≤0.001). The M4 subtype of AML and cases with Inv 16 showed significantly higher frequencies in mutRAS compared to wtRAS patients, (p=0.015, 0.003, respectively). The patients were followed up for a median of 43 months (range 11-57 months). There was no significant difference in overall survival (OS) between mutRAS and wtRAS patients (p=0.326). Within the mutRAS patients treated with HDAC, cumulative OS was significantly higher than those treated with LDAC (p=0.001). This was not the case in the wtRAS group (p=0.285). There was no significant difference in disease free survival (DFS) between mutRAS and wtRAS groups (p=0.923). MutRAS patients treated with HDAC had a statistically higher cumulative DFS than mutRAS patients treated with LDAC (p=0.001). Patients with wtRAS also benefited from HDAC but to a lesser extent. Among patients with wtRAS, those treated with HDAC showed higher cumulative and median DFS than patients treated with LDAC (p=0.031). CONCLUSION Adult AML patients carrying mutations in the K-RAS gene benefit from higher cytarabine doses more than wtRAS patients, so pretreatment mutation detection could be an important predictor for treatment strategy and survival of adult AML patients. These findings counter the prevailing bias that oncogene mutations lead to more aggressive behavior in human malignancies. KEY WORDS K-RAS mutations - Acute myeloid leukemia - Cytarabine.
The impact of design choices of multiobjective antcolony optimization algorithms on performance: an experimental study on the biobjective TSP

Over the last few years, there have been a number of proposals of ant colony optimization (ACO) algorithms for tackling multiobjective combinatorial optimization problems. These proposals adapt ACO concepts in various ways, for example, some use multiple pheromone matrices and multiple heuristic matrices and others use multiple ant colonies. In this article, we carefully examine several of the most prominent of these proposals. In particular, we identify commonalities among the approaches by recasting the original formulation of the algorithms in different terms. For example, several proposals described in terms of multiple colonies can be cast equivalently using a single ant colony, where ants use different weights for aggregating the pheromone and/or the heuristic information. We study algorithmic choices for the various proposals and we identify previously undetected trade-offs in their performance
Random Attractor for Stochastic Hindmarsh-Rose Equations with Additive Noise

For stochastic Hindmarsh-Rose equations with addictive noises in the study of neurodynamics, the longtime and global pullback dynamics on a two-dimensional bounded domain is explored in this work. Using the additive transformation and by the sharp uniform estimates, we proved the pullback absorbing and the pullback asymptotically compact characteristics of the Hindmarsh-Rose random dynamical system in the $L^2$ Hilbert space. It shows the existence of a random attractor for this random dynamical system.
A one-dimensional analytical calculation method for obtaining normal shock losses in supersonic real gas flows

The calculation of isentropic flow and normal shock waves of real gases are important, especially in the preliminary design of turbo-machinery and test rigs. In an ideal gas, the relations for one-dimensional isentropic flow and normal shock waves are well known and can be found in standard textbooks. However, for fluids exhibiting strong deviations from the ideal gas assumption universal relations do not exist due to complex equations of state. This paper presents a analytical method for the prediction of isentropic real gas flows and normal shock waves, based on the Redlich-Kwong (RK) equation of state. Explicit expressions based on a series expansion for describing isentropic flow of Novec™ 649 are compared to Refprop data and ideal gas equations. For moderate pressures the RK method is in very good agreement with the Refprop data, while the ideal gas equations fail to predict the real gas behaviour. The same observations are made for normal shock calculations, where both real gas methods yield very close results. Especially the predicted stagnation pressure losses across a shock wave are in excellent agreement.