Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing proteomic and transcriptomic features of missense variants in amyotrophic lateral sclerosis genes

Background: Within recent years, there has been a growing number of genes associated with amyotrophic lateral sclerosis (ALS), resulting in an increasing number of novel variants, particularly missense variants, many of which are of unknown clinical significance. Here, we leverage the sequencing efforts of the ALS Knowledge Portal (3,864 individuals with ALS and 7,839 controls) and Project MinE ALS Sequencing Consortium (4,366 individuals with ALS and 1,832 controls) to perform proteomic and transcriptomic characterization of missense variants in 24 ALS-associated genes. Results: Using predicted human protein structures from AlphaFold, we determined that missense variants carried by individuals with ALS were significantly enriched in {beta}-sheets and -helices, as well as in core, buried, or moderately buried regions. At the same time, we identified that hydrophobic amino acid residues, compositionally biased protein regions and protein-protein interaction regions are predominantly enriched in missense variants carried by individuals with ALS. Assessment of expression level based on transcriptomics also revealed enrichment of variants of high and medium expression across all tissues and within the brain. We further explored enriched features of interest using burden analyses to determine whether individual genes were driving the enrichment signal. A case study is presented for SOD1 to demonstrate proof of concept of how enriched features may aid in defining variant pathogenicity. Conclusions: Our results present proteomic and transcriptomic features that are important indicators of missense variant pathogenicity in ALS and are distinct from features associated with neurodevelopmental disorders.
Direct use of regression quantiles to construct confidence sets in linear models

Direct use of the empirical quantile function provides a standard distribution-free approach to constructing confidence intervals and confidence bands for population quantiles. We apply this method to construct confidence intervals and confidence bands for regression quantiles and to construct prediction intervals based on sample regression quantiles. Comparison of the direct method with the studentization and the bootstrap methods are discussed. Simulation results show that the direct method has the advantage of robustness against departure from the normality assumption of the error terms.
Optical frequency comb generation using a new compacted hybrid Raman Bi-based erbium doped fiber amplifier in a linear cavity

A new compacted hybrid Raman bismuth-based erbium doped fiber amplifier (Bi-EDFA) is demonstrated to generate an optical frequency comb source via Brillouin seed signals. A highly nonlinear Bi-EDF of length 2.15 m is incorporated as both the Raman and Bi-EDF gain medium. Using a Brillouin pump with a wavelength at the maximum Raman gain but 2 nm lower than the Bi-EDFA free-running wavelength region, an optical frequency comb having 55 Brillouin Stokes lines is generated. The peak value of the Raman gain is about 6 × 10−13 m W−1 at the wavelength downshifted in frequency by about 14.2 THz (473 cm−1). This experiment suggests that by using a Bi-EDF instead of an EDF in a conventional EDFA, higher amplification can be obtained when the resulted Raman gain overlaps with the intense erbium gain prepared in this fiber.
On a kinetic equation for the transport of particles in turbulent flows

The work described is part of a long‐term study in pursuit of a kinetic equation for particles suspended in a turbulent flow. This equation represents the transport of the average particle phase space density and can be used to derive the continuum equations and constitutive relations for a two‐fluid model of a dispersed particle flow and to establish the correct form of boundary conditions. A key role in this study is played by the equation of state of the dispersed particles, which is derived here for a Langevin equation of motion with a random driving force not limited to white noise. A suitable form of kinetic equation is then sought that reproduces this equation of state and predicts Gaussian spatial diffusion in the long‐term limit with the correct form for the diffusion coefficient in homogeneous stationary turbulence. It is shown that both these requirements are automatically satisfied by constructing forms for the phase space diffusion coefficient that preserve invariance to a random Galilean tra.
Carrier distribution in (0001)InGaN∕GaN multiple quantum well light-emitting diodes

We study the carrier distribution in multi quantum well (multi-QW) InGaN light-emitting diodes. Conventional wisdom would assume that a large number of QWs lead to a smaller carrier density per QW, enabling efficient carrier recombination at high currents. We use angle-resolved far-field measurements to determine the location of spontaneous emission in a series of multi-QW samples. They reveal that, no matter how many QWs are grown, only the QW nearest the p layer emits light under electrical pumping, which can limit the performances of high-power devices.
Antiferromagnetic order and consequences on the transport properties of Ba4Ru3O10

Barium ruthenate Ba$_{4}$Ru$_{3}$O$_{10}$, in which Ru$_{3}$O$_{12}$ trimers are connected together to form a chequered two-dimensional framework, has been synthesised and its structural, magnetic and transport properties studied between 300 K and 2 K. The paramagnetic to antiferromagnetic transition at $T_N \sim 105$ K evidenced on the susceptibility curve coincides with an increase of electron localization in transport measurements. Thermoelectric power and Hall coefficient measurements both exhibit dramatic changes at $T_N$, characteristic of a reconstruction of the bands structure near the Fermi level. No pronounced structural changes are observed at TN in this compound. The magnetic scattering signal on the neutron powder diffraction patterns below $T_N$ is weak, but can be tentatively modelled with an antiferromagnetic ordering of the spins at both ends of a trimer, the spin of the more symmetric Ru site remaining idle. Crystal field and strong spin-orbit coupling at the Ru$^{4+}$ site seem to be the key parameters to understand the magnetic state of Ba$_{4}$Ru$_{3}$O$_{10}$.
Modal analysis and gain calculations for a sheet electron beam in a ridged waveguide slow-wave structure

A mode analysis is presented for the case of a planar electron beam in a ridged waveguide slow-wave structure. By matching boundary conditions between a Fourier expansion of the mode between the ridges with a space-harmonic expansion of the mode in the region below the ridges, a dispersion relation for a traveling-wave interaction is found. The dispersion relation is numerically solved for both the case with and without beam. For the nominal geometry, gains as high as 30 dB/cm at 300 GHz are found for a 15 A, 155 keV beam.
Ultra-precise single-ion atomic mass measurements on deuterium and helium-3

The former University of Washington Penning Trap Mass Spectrometer (UW-PTMS), now located at the Max Planck Institute for Nuclear Physics in Heidelberg, Germany, was used in the decade before the move to determine new values for the deuteron atomic mass, M (2H+) = 2.013 553 212 745(40) u, and the deuterium atomic mass, M (2H) = 2.014 101 778 052(40) u, both of which are now more than an order-of-magnitude more accurate than the previous best 1994-MIT measurements of these quantities. The new value for the deuteron’s mass can then be used with the accepted 2010-CODATA proton mass and the most recent 1999-measurement of the 2.2 MeV gamma-ray binding energy of the deuteron to refine the neutron’s mass to mn = 1.008 664 916 018(435) u which has about half the uncertainty relative to the value computed using that previous 1994-MIT deuterium measurement. As a result, further improvements of mn must now come from a more accurate determination of the wavelength of this gamma ray. In this same period of time, this spectrometer has also been used to determine new values for the helion atomic mass, M (3He2+) = 3.014 932 246 668(43) u, and the neutral helium-3 atomic mass, M (3He) = 3.016 029 321 675(43) u, which are both about 60 times more accurate than the 2006-SMILETRAP measurements, but disagree with the 4.4-times less-accurate 2015-Florida-State measurements by 0.76 nu. It is expected that these helium-3 results will be used in the future 3H/3He mass ratio (to be determined by the Heidelberg, Germany version of the old UW-PTMS) in order to generate a more accurate value for the tritium atomic mass.
Edge convex smooth interpolation curve networks with minimum $L_{\infty}$-norm of the second derivative

We consider the extremal problem of interpolation of convex scattered data in $\mathbb{R}^3$ by smooth edge convex curve networks with minimal $L_p$-norm of the second derivative for $1<p\leq\infty$. The problem for $p=2$ was set and solved by Andersson et al. (1995). Vlachkova (2019) extended the results in (Andersson et al., 1995) and solved the problem for $1<p<\infty$. The minimum edge convex $L_p$-norm network for $1<p<\infty$ is obtained from the solution to a system of nonlinear equations with coefficients determined by the data. The solution in the case $1<p<\infty$ is unique for strictly convex data. The corresponding extremal problem for $p=\infty$ remained open. Here we show that the extremal interpolation problem for $p=\infty$ always has a solution. We give a characterization of this solution. We show that a solution to the problem for $p=\infty$ can be found by solving a system of nonlinear equations in the case where it exists.
Boosted top tagging and its interpretation using Shapley values

: Top tagging has emerged to be a fast-evolving subject due to the top quark’s signiﬁcant role in probing physics beyond the standard model. For the reconstruction of top jets, machine learning models have shown a signiﬁcant improvement in the tagging and classiﬁcation performance compared to the previous methods. In this work, we build top taggers using N -Subjettiness ratios and several observables of the Energy Correlation functions as input features to train the eXtreme Gradient BOOSTed decision tree ( XGBOOST ). It is observed that the performance of the taggers depends on how well the top jets are matched to their truth-level partons. Furthermore, we use SHapley Additive exPlanation (SHAP) framework to calculate the feature importance of the trained models. It helps us to estimate how much each feature of the data contributed to the model’s prediction and what regions are of more importance for each input variable. Finally, we combine all the tagger variables to form a hybrid tagger and interpret the results using the Shapley values.
Lightweight Monocular Depth Estimation

Monocular depth estimation can play an important role in addressing the issue of deriving scene geometry from 2D images. It has been used in a variety of industries, including robots, self-driving cars, scene comprehension, 3D reconstructions, and others. The goal of our method is to create a lightweight machine-learning model in order to predict the depth value of each pixel given only a single RGB image as input with the Unet structure of the image segmentation network. We use the NYU Depth V2 dataset to test the structure and compare the result with other methods. The proposed method achieves relatively high accuracy and low rootmean-square error.
Accelerating CNN inference on long vector architectures via co-design

CPU-based inference can be deployed as an alternative to off-chip accelerators. In this context, emerging vector architectures are a promising option, owing to their high efficiency. Yet the large design space of convolutional algorithms and hardware implementations makes the selection of design options challenging. In this paper, we present our ongoing research into co-designing future vector architectures for CPU-based Convolutional Neural Networks (CNN) inference focusing on the im2col+GEMM and Winograd kernels. Using the Gem5 simulator we explore the impact of several hardware microarchitectural features including (i) vector lanes, (ii) vector lengths, (iii) cache sizes, and (iv) options for integrating the vector unit into the CPU pipeline. In the context of im2col+GEMM, we study the impact of several BLIS-like algorithmic optimizations such as (1) utilization of vector registers, (2) loop unrolling, (3) loop reorder, (4) manual vectorization, (5) prefetching, and (6) packing of matrices, on the RISC-V Vector Extension and ARM-SVE ISAs. We use the YOLOv3 and VGG16 network models for our evaluation. Our co-design study shows that BLIS-like optimizations are not beneficial to all types of vector microarchitectures. We additionally demonstrate that longer vector lengths (of at least 8192 bits) and larger caches (of 256MB) can boost performance by 5×, with our optimized CNN kernels, compared to a vector length of 512-bit and 1MB of L2 cache. In the context of Winograd, we present our novel approach of inter-tile parallelization across the input/output channels by using 8×8 tiles per channel to vectorize the algorithm on vector length agnostic (VLA) architectures. Our method exploits longer vector lengths and offers high memory reuse, resulting in performance improvement of up to 2.4× for non-strided convolutional layers with 3×3 kernel size, compared to our optimized im2col+GEMM approach on the Fujitsu A64FX processor. Our co-design study furthermore reveals that Winograd requires smaller cache sizes (up to 64MB) compared to im2col+GEMM.
Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology

The rise of internet-based services and products in the late 1990's brought about an unprecedented opportunity for online businesses to engage in large scale data-driven decision making. Over the past two decades, organizations such as Airbnb, Alibaba, Amazon, Baidu, Booking, Alphabet's Google, LinkedIn, Lyft, Meta's Facebook, Microsoft, Netflix, Twitter, Uber, and Yandex have invested tremendous resources in online controlled experiments (OCEs) to assess the impact of innovation on their customers and businesses. Running OCEs at scale has presented a host of challenges requiring solutions from many domains. In this paper we review challenges that require new statistical methodologies to address them. In particular, we discuss the practice and culture of online experimentation, as well as its statistics literature, placing the current methodologies within their relevant statistical lineages and providing illustrative examples of OCE applications. Our goal is to raise academic statisticians' awareness of these new research opportunities to increase collaboration between academia and the online industry.
Metadata-guided Consistency Learning for High Content Images

High content imaging assays can capture rich phenotypic response data for large sets of compound treatments, aiding in the characterization and discovery of novel drugs. However, extracting representative features from high content images that can capture subtle nuances in phenotypes remains challenging. The lack of high-quality labels makes it difficult to achieve satisfactory results with supervised deep learning. Self-Supervised learning methods have shown great success on natural images, and offer an attractive alternative also to microscopy images. However, we find that self-supervised learning techniques underperform on high content imaging assays. One challenge is the undesirable domain shifts present in the data known as batch effects, which are caused by biological noise or uncontrolled experimental conditions. To this end, we introduce Cross-Domain Consistency Learning (CDCL), a self-supervised approach that is able to learn in the presence of batch effects. CDCL enforces the learning of biological similarities while disregarding undesirable batch-specific signals, leading to more useful and versatile representations. These features are organised according to their morphological changes and are more useful for downstream tasks -- such as distinguishing treatments and mechanism of action.
Beyond SOT: It's Time to Track Multiple Generic Objects at Once

Generic Object Tracking (GOT) is the problem of tracking target objects, speciﬁed by bounding boxes in the ﬁrst frame of a video. While the task has received much attention in the last decades, researchers have almost exclusively focused on the single object setting. Multi-object GOT ben-eﬁts from a wider applicability, rendering it more attractive in real-world applications. We attribute the lack of research interest into this problem to the absence of suitable benchmarks. In this work, we introduce a new large-scale GOT benchmark, LaGOT, containing multiple annotated target objects per sequence. Our benchmark allows researchers to tackle key remaining challenges in GOT, aiming to increase robustness and reduce computation through joint tracking of multiple objects simultaneously. Furthermore, we pro-pose a Transformer-based GOT tracker TaMOS capable of joint processing of multiple objects through shared computation. TaMOs achieves a 4 × faster run-time in case of 10 concurrent objects compared to tracking each object independently and outperforms existing single object trackers on our new benchmark. Finally, TaMOs achieves highly competitive results on single-object GOT datasets, setting a new state-of-the-art on TrackingNet with a success rate AUC of 84.4%. Our benchmark, code, and trained models will be made publicly available.
A Mutation-based Text Generation for Adversarial Machine Learning Applications

Many natural language related applications involve text generation, created by humans or machines. While in many of those applications machines support humans, yet in few others, (e.g. adversarial machine learning, social bots and trolls) machines try to impersonate humans. In this scope, we proposed and evaluated several mutation-based text generation approaches. Unlike machine-based generated text, mutation-based generated text needs human text samples as inputs. We showed examples of mutation operators but this work can be extended in many aspects such as proposing new text-based mutation operators based on the nature of the application.
Morpheus: An A-sized AUV with morphing fins and algorithms for agile maneuvering

—We designed and constructed an A-sized base autonomous underwater vehicle (AUV), augmented with a stack of modular and extendable hardware and software, including autonomy, navigation, control and high ﬁdelity simulation capabilities ( A-size stands for the standard sonobuoy form factor, with a maximum diameter of 124 mm). Subsequently, we extended this base vehicle with a novel tuna-inspired morphing ﬁn payload module (referred to as the Morpheus AUV), to achieve good directional stability and exceptional maneuverability; properties that are highly desirable for rigid hull AUVs, but are presently difﬁcult to achieve because they impose contradictory requirements. The morphing ﬁn payload allows the base AUV to dynamically change its stability- maneuverability qualities by using morphing ﬁns, which can be deployed, deﬂected and retracted, as needed. The base vehicle and Morpheus AUV were both extensively ﬁeld tested in-water in the Charles river, Massachusetts, USA; by conducting hundreds of hours of operations over a period of two years. The maneuvering capability of the Morpheus AUV was evaluated with and without the use of morphing ﬁns to quantify the performance improvement. The Morpheus AUV was able to showcase an exceptional turning rate of around 25-35 deg s − 1 . A maximum turn rate improvement of around 35% - 50% was gained through the use of morphing ﬁns.
Recurrent flow patterns as a basis for turbulence: predicting statistics from structures

A dynamical systems approach to turbulence envisions the flow as a trajectory through a high-dimensional state space transiently visiting the neighbourhoods of unstable simple invariant solutions (E. Hopf, Commun. Appl. Maths 1, 303, 1948). The hope has always been to turn this appealing picture into a predictive framework where the statistics of the flow follows from a weighted sum of the statistics of each simple invariant solution. Two outstanding obstacles have prevented this goal from being achieved: (1) paucity of known solutions and (2) the lack of a rational theory for predicting the required weights. Here we describe a method to substantially solve these problems, and thereby provide the first compelling evidence that the PDFs of a fully developed turbulent flow can be reconstructed with a set of unstable periodic orbits. Our new method for finding solutions uses automatic differentiation, with high-quality guesses constructed by minimising a trajectory-dependent loss function. We use this approach to find hundreds of new solutions in turbulent, two-dimensional Kolmogorov flow. Robust statistical predictions are then computed by learning weights after converting a turbulent trajectory into a Markov chain for which the states are individual solutions, and the nearest solution to a given snapshot is determined using a deep convolutional autoencoder. To our knowledge, this is the first time the PDFs of a spatio-temporally-chaotic system have been successfully reproduced with a set of simple invariant states, and provides a fascinating connection between self-sustaining dynamical processes and the more well-known statistical properties of turbulence.
CSI: Contrastive data Stratification for Interaction prediction and its application to compound–protein interaction prediction

Abstract Motivation Accurately predicting the likelihood of interaction between two objects (compound–protein sequence, user–item, author–paper, etc.) is a fundamental problem in Computer Science. Current deep-learning models rely on learning accurate representations of the interacting objects. Importantly, relationships between the interacting objects, or features of the interaction, offer an opportunity to partition the data to create multi-views of the interacting objects. The resulting congruent and non-congruent views can then be exploited via contrastive learning techniques to learn enhanced representations of the objects. Results We present a novel method, Contrastive Stratification for Interaction Prediction (CSI), to stratify (partition) a dataset in a manner that can be exploited via Contrastive Multiview Coding to learn embeddings that maximize the mutual information across congruent data views. CSI assigns a key and multiple views to each data point, where data partitions under a particular key form congruent views of the data. We showcase the effectiveness of CSI by applying it to the compound–protein sequence interaction prediction problem, a pressing problem whose solution promises to expedite drug delivery (drug–protein interaction prediction), metabolic engineering, and synthetic biology (compound–enzyme interaction prediction) applications. Comparing CSI with a baseline model that does not utilize data stratification and contrastive learning, and show gains in average precision ranging from 13.7% to 39% using compounds and sequences as keys across multiple drug–target and enzymatic datasets, and gains ranging from 16.9% to 63% using reaction features as keys across enzymatic datasets. Availability and implementation Code and dataset available at https://github.com/HassounLab/CSI.