Miracle-dz/UniFAR-FTUs huggingface dataset:
QuTIE: quantum optimization for target identification by enzymes.

Summary Target identification by enzymes (TIE) problem aims to identify the set of enzymes in a given metabolic network, such that their inhibition eliminates a given set of target compounds associated with a disease while incurring minimum damage to the rest of the compounds. This is a NP-hard problem, and thus optimal solutions using classical computers fail to scale to large metabolic networks. In this article, we develop the first quantum optimization solution, called QuTIE (quantum optimization for target identification by enzymes), to this NP-hard problem. We do that by developing an equivalent formulation of the TIE problem in quadratic unconstrained binary optimization form. We then map it to a logical graph, and embed the logical graph on a quantum hardware graph. Our experimental results on 27 metabolic networks from Escherichia coli, Homo sapiens, and Mus musculus show that QuTIE yields solutions that are optimal or almost optimal. Our experiments also demonstrate that QuTIE can successfully identify enzyme targets already verified in wet-lab experiments for 14 major disease classes. Availability and implementation Code and sample data are available at: https://github.com/ngominhhoang/Quantum-Target-Identification-by-Enzymes.
Empirical Bayes inference in sparse high-dimensional generalized linear models

High-dimensional linear models have been extensively studied in the recent literature, but the developments in high-dimensional generalized linear models, or GLMs, have been much slower. In this paper, we propose the use an empirical or data-driven prior specification leading to an empirical Bayes posterior distribution which can be used for estimation of and inference on the coefficient vector in a high-dimensional GLM, as well as for variable selection. For our proposed method, we prove that the posterior distribution concentrates around the true/sparse coefficient vector at the optimal rate and, furthermore, provide conditions under which the posterior can achieve variable selection consistency. Computation of the proposed empirical Bayes posterior is simple and efficient, and, in terms of variable selection in logistic and Poisson regression, is shown to perform well in simulations compared to existing Bayesian and non-Bayesian methods.
The Extended [C ii] under Construction? Observation of the Brightest High-z Lensed Star-forming Galaxy at z = 6.2

We present results of [C ii] 158 μm emission line observations, and report the spectroscopic redshift confirmation of a strongly lensed (μ ∼ 20) star-forming galaxy, MACS0308-zD1 at z = 6.2078 ± 0.0002. The [C ii] emission line is detected with a signal-to-noise ratio >6 within the rest-frame UV-bright clump of the lensed galaxy (zD1.1) and exhibits multiple velocity components; the narrow [C ii] has a velocity full width half maximum (FWHM) of 110 ± 20 km s−1, while broader [C ii] is seen with an FWHM of 230 ± 50 km s−1. The broader [C ii] component is blueshifted (−80 ± 20 km s−1) with respect to the narrow [C ii] component, and has a morphology that extends beyond the UV-bright clump. We find that, while the narrow [C ii] emission is most likely associated with zD1.1, the broader component is possibly associated with a physically distinct gas component from zD1.1 (e.g., outflowing or inflowing gas). Based on the nondetection of λ 158μm dust continuum, we find that MACS0308-zD1's star formation activity occurs in a dust-free environment indicated by a strong upper limit of infrared luminosity ≲9 × 108 L ⊙. Targeting this strongly lensed faint galaxy for follow-up Atacama Large Millimeter/submillimeter Array and JWST observations will be crucial to characterize the details of typical galaxy growth in the early Universe.
Identifying Promising Candidate Radiotherapy Protocols via GPU-GA in-silico

Around half of all cancer patients, world-wide, will receive some form of radiotherapy (RT) as part of their treatment. And yet, despite the rapid advance of high-throughput screening to identify successful chemotherapy drug candidates, there is no current analogue for RT protocol screening or discovery at any scale. Here we introduce and demonstrate the application of a high-throughput/high-fidelity coupled tumour-irradiation simulation approach, we call"GPU-GA", and apply it to human breast cancer analogue - EMT6/Ro spheroids. By analysing over 9.5 million candidate protocols, GPU-GA yields significant gains in tumour suppression versus prior state-of-the-art high-fidelity/-low-throughput computational search under two clinically relevant benchmarks. By extending the search space to hypofractionated areas (>2 Gy/day) yet within total dose limits, further tumour suppression of up to 33.7% compared to state-of-the-art is obtained. GPU-GA could be applied to any cell line with sufficient empirical data, and to many clinically relevant RT considerations.
A note on $\theta_2$

It is a classic result of Selberg in the 1950's that $\theta_2 = 2/3$, where $\theta_2$ is the level of distribution of the divisor function in arithmetic progressions (defined more precisely below). Selberg applies this estimate, together with his $\Lambda^2$ sieve, to prove weak forms of the binary Goldbach and twin prime conjectures. In this note, we give an unconditional proof of Selberg's first result with smooth weights for prime moduli, and put forth a hypothesis via subconvexity to bootstrap to level $4/5$. Contingent outcomes of our proposal is an improvement of Selberg's second result on approximations to twin primes and in lowering bounds on gaps between primes.
Using channel correlation to improve decoding - ORBGRAND-AI

To meet the Ultra Reliable Low Latency Communication (URLLC) needs of modern applications, there have been significant advances in the development of short error correction codes and corresponding soft detection decoders. A substantial hindrance to delivering low-latency is, however, the reliance on interleaving to break up omnipresent channel correlations to ensure that decoder input matches decoder assumptions. Consequently, even when using short codes, the need to wait to interleave data at the sender and de-interleave at the receiver results in significant latency that acts contrary to the goals of URLLC. Moreover, interleaving provably reduces capacity in channels with correlation, so that potential decoding performance is degraded. Here we introduce a variant of Ordered Reliability Bits Guessing Random Additive Noise Decoding (ORBGRAND), which we call ORBGRAND-Approximate Independence (ORBGRAND-AI), a soft-detection decoder that can decode any moderate redundancy code and overcomes the limitation of existing decoding paradigms by leveraging channel correlations and circumventing the need for interleaving. By leveraging correlation, not only is latency reduced, but error correction performance can be enhanced by multiple dB, while decoding complexity is also reduced, offering one potential solution for the provision of URLLC.
Signal Subspace Methods Which Are Robust to Impulsive Noise

We consider the problem of estimating a signal subspace in the presence of interference that contaminates some proportion of the received observations. Our emphasis is on detecting the contaminated observations so that the signal subspace can be estimated with the contaminated observations discarded. To this end, we employ a signal model which explicitly includes an interference term that is distinct from environmental noise. To detect when the interference term is nonzero, we estimate the interference term using an optimization problem with a sparsity-inducing group SLOPE penalty which accounts for simultaneous sparsity across all channels of the multichannel signal. We propose an iterative algorithm which efficiently computes the observations estimated to contain interference. Theoretical support for the accuracy of our interference estimator is provided by bounding its false discovery rate, the expected proportion of uncontaminated observations among those estimated to be contaminated. Finally, we demonstrate the empirical performance of our contributions in a number of simulated experiments.
LayoutDM: Discrete Diffusion Model for Controllable Layout Generation

Controllable layout generation aims at synthesizing plausible arrangement of element bounding boxes with optional constraints, such as type or position of a specific element. In this work, we try to solve a broad range of layout generation tasks in a single model that is based on discrete state-space diffusion models. Our model, named Lay-outDM, naturally handles the structured layout data in the discrete representation and learns to progressively infer a noiseless layout from the initial input, where we model the layout corruption process by modality-wise discrete diffusion. For conditional generation, we propose to inject layout constraints in the form of masking or logit adjustment during inference. We show in the experiments that our Lay-outDM successfully generates high-quality layouts and outperforms both task-specific and task-agnostic baselines on several layout tasks. 11Please find the code and models at: https://cyberagentailab.github. io/layout-drn.
Blind Acoustic Room Parameter Estimation Using Phase Features

Modeling room acoustics in a real-world settings involves some degree of blind parameter estimation from noisy and reverberant audio. Modern approaches leverage convolutional neural networks (CNNs) in tandem with time-frequency representations. Using short-time Fourier transforms to develop these spectrogram-like features has shown promising results, but this method implicitly discards a significant amount of audio information in the phase domain. Inspired by recent works in speech enhancement, we propose utilizing phase-related features to extend recent approaches to blindly estimate the so-called "reverberation fingerprint" parameters, namely, volume and RT60. The addition of these features is shown to outperform existing methods that rely solely on magnitude-based spectral features across a wide range of acoustics spaces. We evaluate the effectiveness of the deployment of these phase features in both single-parameter and multi-parameter estimation strategies, using a task-specific dataset that consists of publicly available room impulse responses (RIRs), synthesized RIRs, and in-house measurements of real acoustic spaces.
On Vector Spaces with Formal Infinite Sums

We discuss possible definitions of categories of vector spaces over a fixed field $\mathbf{k}$ enriched with a notion of formal infinite linear combination in the likes of the formal infinite linear combinations one has in the context of generalized power series. We call these \emph{categories of reasonable strong vector spaces} (c.r.s.v.s.). We show that, in a precise sense, the more general possible definition for a reasonable strong vector space is that of a small $\mathbf{k}$-additive endofunctor of $\mathrm{Vect}_\mathbf{k}$ such that for every cardinal $\lambda$ there are no non-zero natural transformations from $\mathrm{Vect}(\mathbf{k},-)^\lambda /\mathrm{Vect}(\mathbf{k}, -)^{\oplus \lambda}$ to $X$. We denote the category such functors $\Sigma\mathrm{Vect}$. We compare $\Sigma\mathrm{Vect}$ to the category of ``naive"strong vector spaces $B\Sigma\mathrm{Vect}$ constructed in a specific way and to the c.r. s.v. s. $K\mathrm{TVect}_s$ of separated linearly topologized spaces that are generated by linearly compact spaces. We study the number of iterations of a natural approximate reflector on $\mathrm{Ind}\text{-}(\mathrm{Vect}^{\text{op}})$ needed to construct the orthogonal reflector onto $\Sigma\mathrm{Vect}$ because it relates to the problem of constructing the smallest subspace of an $X \in \Sigma\mathrm{Vect}$ closed under taking infinite linear combinations containing a given linear subspace of $H$ of $X$. We give a complete answer in the case $X$ is $\aleph_1$-ary. Finally we show how the natural monoidal closed structure on $\mathrm{Ind}\text{-}(\mathrm{Vect}^{\text{op}})$ induces one on $\Sigma\mathrm{Vect}$ and apply this to define an infinite-sum-sensitive notion of K\"ahler differentials for generalized power series. Most of the technical results apply to a more general class of reflective subcategories of $\mathrm{Ind}\text{-}(\mathrm{Vect}^{\text{op}})$.
Architext: Language-Driven Generative Architecture Design

Architectural design is a highly complex practice that involves a wide diversity of disciplines, technologies, proprietary design software, expertise, and an almost infinite number of constraints, across a vast array of design tasks. Enabling intuitive, accessible, and scalable design processes is an important step towards performance-driven and sustainable design for all. To that end, we introduce Architext, a novel semantic generation assistive tool. Architext enables design generation with only natural language prompts, given to large-scale Language Models, as input. We conduct a thorough quantitative evaluation of Architext's downstream task performance, focusing on semantic accuracy and diversity for a number of pre-trained language models ranging from 120 million to 6 billion parameters. Architext models are able to learn the specific design task, generating valid residential layouts at a near 100% rate. Accuracy shows great improvement when scaling the models, with the largest model (GPT-J) yielding impressive accuracy ranging between 25% to over 80% for different prompt categories. We open source the finetuned Architext models and our synthetic dataset, hoping to inspire experimentation in this exciting area of design research.
Kinematics, Structure, and Mass Outflow Rates of Extreme Starburst Galactic Outflows

We present results on the properties of extreme gas outflows in massive (M * ∼ 1011 M ⊙), compact, starburst (star formation rate, SFR∼ 200 M ⊙ yr−1) galaxies at z = 0.4–0.7 with very high star formation surface densities (ΣSFR ∼ 2000 M ⊙ yr−1 kpc−2). Using optical Keck/HIRES spectroscopy of 14 HizEA starburst galaxies, we identify outflows with maximum velocities of 820–2860 km s−1. High-resolution spectroscopy allows us to measure precise column densities and covering fractions as a function of outflow velocity and characterize the kinematics and structure of the cool gas outflow phase (T ∼ 104 K). We find substantial variation in the absorption profiles, which likely reflects the complex morphology of inhomogeneously distributed, clumpy gas and the intricacy of the turbulent mixing layers between the cold and hot outflow phases. There is not a straightforward correlation between the bursts in the galaxies’ star formation histories and their wind absorption line profiles, as might naively be expected for starburst-driven winds. The lack of strong Mg ii absorption at the systemic velocity is likely an orientation effect, where the observations are down the axis of a blowout. We infer high mass outflow rates of ∼50–2200 M ⊙ yr−1, assuming a fiducial outflow size of 5 kpc, and mass loading factors of η ∼ 5 for most of the sample. While these values have high uncertainties, they suggest that starburst galaxies are capable of ejecting very large amounts of cool gas that will substantially impact their future evolution.
Orbit determination using analytic partial derivatives of perturbed motion

State transition matrices for the motion of space vehicles have been calculated both numerically and analytically. The former method, for example, integrates the "variational equations. " It yields accurate results when the same force model is used in the variational equations as in the equations of motion. The main advantage of the latter method, i.e., using analytic partial derivatives, is faster computation, but, if the transition is made by Keplerian formulas to a time many revolutions from epoch, the accuracy of the matrix is severely degraded. It is shown in this paper that the state transition matrix of a satellite orbit may be calculated analytically including the effects of perturbations. This is accomplished by generating each perturbation in the Gaussian form (using three mutually perpendicular components of the perturbing acceleration) to find the transition matrix from epoch state to the state at any other time. This analytic method is considerably faster than the numerical integration of the variational equations and it is shown that the methods are in good agreement. The state vector considered includes orbit parameters, which are not singular for any elliptic orbits except retrograde equatorial, and the parameters associated with the perturbations. These perturbations include atmospheric drag, thrust periods, impulses, direct solar radiation pressure, oxidizer outgassing, and the geopotential. The method, however, can be extended to any perturbation, nor is it restricted to geocentric orbit calculations. Results are presented in tabular form. Partial derivatives of the satellite's position with respect to the orbital elements and other vehicular parameters, such as the ballistic parameter Cz>/4/m, are listed in the tables as obtained by each of three methods: by the analytical procedure, by integrating the variational equations, and by differencing neighboring trajectories. The comparisons are made on a satellite with a period of 90 min, over complete revolutions one day after epoch and one week after epoch.
Hotter than Expected: Hubble Space Telescope (HST)/WFC3 Phase-resolved Spectroscopy of a Rare Irradiated Brown Dwarf with Strong Internal Heat Flux

With infrared flux contrasts larger than typically seen in hot Jupiter, tidally locked white dwarf–brown dwarf binaries offer a superior opportunity to investigate atmospheric processes in irradiated atmospheres. NLTT5306 is such a system, with a M BD = 52 ± 3 M Jup brown dwarf, orbiting a T eff = 7756 ± 35 K white dwarf with an ultra-short period of ∼102 minutes. We present Hubble Space Telescope/Wide Field Camera 3 spectroscopic phase curves of NLTT5306, consisting of 47 spectra from 1.1 to 1.7 μm with an average signal-to-noise ratio ∼ 65 per wavelength. We extracted the phase-resolved spectra of the brown dwarf NLTT5306B, finding a small <100 K day–night temperature difference (∼5% of the average day-side temperature). Our best-fit model phase curves revealed a complex wavelength-dependence on amplitudes and relative phase offsets, suggesting longitudinal–vertical atmospheric structure. The night-side spectrum was well fit by a cloudy, nonirradiated atmospheric model while the day side was best matched by a cloudy, weakly irradiated model. Additionally, we created a simple radiative energy redistribution model of the atmosphere and found evidence for efficient day-to-night heat redistribution and a moderately high Bond albedo. We also discovered an internal heat flux much higher than expected given the published system age, leading to an age reassessment that resulted in NLTT5306B most likely being much younger. We find that NLTT5306B is the only known significantly irradiated brown dwarf where the global temperature structure is not dominated by external irradiation, but rather its own internal heat. Our study provides an essential insight into the drivers of global circulation and day-to-night heat transport as a function of irradiation, rotation rate, and internal heat.
Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations

Although CNNs are believed to be invariant to translations, recent works have shown this is not the case due to aliasing effects that stem from down-sampling layers. The existing architectural solutions to prevent the aliasing effects are partial since they do not solve those effects that originate in non-linearities. We propose an extended antialiasing method that tackles both down-sampling and nonlinear layers, thus creating truly alias-free, shift-invariant CNNs11Our code is available at github.com/hmichaeli/alias_free_convnets/. We show that the presented model is invariant to integer as well as fractional (i.e., sub-pixel) translations, thus outperforming other shift-invariant methods in terms of robustness to adversarial translations.
Towards Real-Time Single-Channel Speech Separation in Noisy and Reverberant Environments

Real-time single-channel speech separation aims to unmix an audio stream captured from a single microphone that contains multiple people talking at once, environmental noise, and reverberation into multiple de-reverberated and noise-free speech tracks, each track containing only one talker. While large state-of-the-art DNNs can achieve excellent separation from anechoic mixtures of speech, the main challenge is to create compact and causal models that can separate reverberant mixtures at inference time. In this paper, we explore low-complexity, resource-efficient, causal DNN architectures for real-time separation of two or more simultaneous speakers. A cascade of three neural network modules are trained to sequentially perform noise-suppression, separation, and de-reverberation. For comparison, a larger end-to-end model is trained to output two anechoic speech signals directly from noisy reverberant speech mixtures. We propose an efficient single-decoder architecture with "subtractive" separation for real-time recursive speech separation for two or more speakers. Evaluation on real monophonic recordings of speech mixtures, according to speech separation measures like SI-SDR, perceptual measures like DNS-MOS, and a novel proposed channel separation metric, show that these compact causal models can separate speech mixtures with low latency, and perform on par with large offline state-of-the-art models like SepFormer.
Feature representations useful for predicting image memorability

Prediction of image memorability has attracted interest in various fields. Consequently, the prediction accuracy of convolutional neural network (CNN) models has been approaching the empirical upper bound estimated based on human consistency. However, identifying which feature representations embedded in CNN models are responsible for the high memorability prediction accuracy remains an open question. To tackle this problem, we sought to identify memorability-related feature representations in CNN models using brain similarity. Specifically, memorability prediction accuracy and brain similarity were examined across 16,860 layers in 64 CNN models pretrained for object recognition. A clear tendency was observed in this comprehensive analysis that layers with high memorability prediction accuracy had higher brain similarity with the inferior temporal (IT) cortex, which is the highest stage in the ventral visual pathway. Furthermore, fine-tuning of the 64 CNN models for memorability prediction revealed that brain similarity with the IT cortex at the penultimate layer positively correlated with the memorability prediction accuracy of the models. This analysis also showed that the best fine-tuned model provided accuracy comparable to state-of-the-art CNN models developed for memorability prediction. Overall, the results of this study indicated that the CNN models' great success in predicting memorability relies on feature representation acquisition, similar to the IT cortex. This study advances our understanding of feature representations and their use in predicting image memorability.
Multiway clustering of 3-order tensor via affinity matrix

We propose a new method of multiway clustering for 3-order tensors via affinity matrix (MCAM). Based on a notion of similarity between the tensor slices and the spread of information of each slice, our model builds an affinity/similarity matrix on which we apply advanced clustering methods. The combination of all clusters of the three modes delivers the desired multiway clustering. Finally, MCAM achieves competitive results compared with other known algorithms on synthetics and real datasets.