Miracle-dz/UniFAR-FTUs huggingface dataset:
NeRFmentation: NeRF-based Augmentation for Monocular Depth Estimation

The capabilities of monocular depth estimation (MDE) models are limited by the availability of sufficient and diverse datasets. In the case of MDE models for autonomous driving, this issue is exacerbated by the linearity of the captured data trajectories. We propose a NeRF-based data augmentation pipeline to introduce synthetic data with more diverse viewing directions into training datasets and demonstrate the benefits of our approach to model performance and robustness. Our data augmentation pipeline, which we call"NeRFmentation", trains NeRFs on each scene in the dataset, filters out subpar NeRFs based on relevant metrics, and uses them to generate synthetic RGB-D images captured from new viewing directions. In this work, we apply our technique in conjunction with three state-of-the-art MDE architectures on the popular autonomous driving dataset KITTI, augmenting its training set of the Eigen split. We evaluate the resulting performance gain on the original test set, a separate popular driving set, and our own synthetic test set.
When To Grow? A Fitting Risk-Aware Policy for Layer Growing in Deep Neural Networks

Neural growth is the process of growing a small neural network to a large network and has been utilized to accelerate the training of deep neural networks. One crucial aspect of neural growth is determining the optimal growth timing. However, few studies investigate this systematically. Our study reveals that neural growth inherently exhibits a regularization effect, whose intensity is influenced by the chosen policy for growth timing. While this regularization effect may mitigate the overfitting risk of the model, it may lead to a notable accuracy drop when the model underfits. Yet, current approaches have not addressed this issue due to their lack of consideration of the regularization effect from neural growth. Motivated by these findings, we propose an under/over fitting risk-aware growth timing policy, which automatically adjusts the growth timing informed by the level of potential under/overfitting risks to address both risks. Comprehensive experiments conducted using CIFAR-10/100 and ImageNet datasets show that the proposed policy achieves accuracy improvements of up to 1.3% in models prone to underfitting while achieving similar accuracies in models suffering from overfitting compared to the existing methods.
Pharmacogenomic discovery of genetically targeted cancer therapies optimized against clinical outcomes

Despite the clinical success of dozens of genetically targeted cancer therapies, the vast majority of patients with tumors caused by loss-of-function (LoF) mutations do not have access to these treatments. This is primarily due to the challenge of developing a drug that treats a disease caused by the absence of a protein target. The success of PARP inhibitors has solidified synthetic lethality (SL) as a means to overcome this obstacle. Recent mapping of SL networks using pooled CRISPR-Cas9 screens is a promising approach for expanding this concept to treating cancers driven by additional LoF drivers. In practice, however, translating signals from cell lines, where these screens are typically conducted, to patient outcomes remains a challenge. We developed a pharmacogemic (PGx) approach called “Clinically Optimized Driver Associated PGx” (CODA-PGx) that accurately predicts genetically targeted therapies with clinical-stage efficacy in specific LoF driver contexts. Using approved targeted therapies and cancer drugs with available real-world evidence and molecular data from hundreds of patients, we discovered and optimized the key screening principles predictive of efficacy and overall patient survival. In addition to establishing basic technical conventions, such as drug concentration and screening kinetics, we found that replicating the driver perturbation in the right context, as well as selecting patients where those drivers are genuine founder mutations, were key to accurate translation. We used CODA-PGX to screen a diverse collection of clinical stage drugs and report dozens of novel LoF genetically targeted opportunities; many validated in xenografts and by real-world evidence. Notable examples include treating STAG2-mutant tumors with Carboplatin, SMARCB1-mutant tumors with Oxaliplatin, and TP53BP1-mutant tumors with Etoposide or Bleomycin. One Sentence Summary We identified principles of pharmacogenomic screening that predict clinical efficacy in cancer patients with specific driver mutations.
Detecting m6A RNA modification from nanopore sequencing using a semi-supervised learning framework

Direct nanopore-based RNA sequencing can be used to detect post-transcriptional base modifications, such as m6A methylation, based on the electric current signals produced by the distinct chemical structures of modified bases. A key challenge is the scarcity of adequate training data with known methylation modifications. We present Xron, a hybrid encoder-decoder framework that delivers a direct methylation-distinguishing basecaller by training on synthetic RNA data and immunoprecipitation-based experimental data in two steps. First, we generate data with more diverse modification combinations through in silico cross-linking. Second, we use this dataset to train an end-to-end neural network basecaller followed by fine-tuning on immunoprecipitation-based experimental data with label-smoothing. The trained neural network basecaller outperforms existing methylation detection methods on both read-level and site-level prediction scores. Xron is a standalone, end-to-end m6A-distinguishing basecaller capable of detecting methylated bases directly from raw sequencing signals, enabling de novo methylome assembly.
Visual familiarity learning at multiple timescales in the primate inferotemporal cortex

Humans and other primates can rapidly detect familiar objects and distinguish them from never-before-seen novel objects. We have an astonishing capacity to remember the details of visual scenes even after a single, fleeting experience. This ability is thought to rely in part on experience-dependent changes in the primate inferotemporal cortex (IT). Single neurons in IT encode visual familiarity by discriminating between novel and familiar stimuli, with stronger neural activity on average for novel images. However, key open questions are to understand how neural encoding in IT changes as images progress from novel to highly familiar, and what learning rules and computations can account for learning-dependent changes in IT activity. Here, we investigate the timescales over which novel stimuli become familiar by recording in IT as initially novel images become increasingly familiar both within and across days. We identified salient and persistent memory-related signals in IT that spanned multiple timescales of minutes, hours, and days. Average neural activity progressively decreased with familiarity as firing rates were strongest for novel, weaker for intermediately familiar, and weakest for highly familiar images. Neural signatures of familiarity learning were slow to develop as response reductions to initially-novel images emerged gradually over multiple days (or hundreds of views) of visual experience. In addition to slow changes that emerged across sessions, neural responses to novel images showed rapid decreases with familiarity within single sessions. To gain insight into the mechanisms underlying changes of visual responses with familiarity, we use computational modeling to investigate which plasticity rules are consistent with these changes. Learning rules inferred from the neural data revealed a strong diversity with many neurons following a ‘negative’ plasticity rule as they exhibited synaptic depression over the course of learning across multiple days. A recurrent network model with two plasticity time constants – a slow time constant for long timescales and a fast time constant for short timescales – captured key dynamic features accompanying the transition from novel to familiar, including a gradual decrease in firing rates over multiple sessions, and a rapid decrease in firing rates within single sessions. Our findings suggest that distinct and complementary plasticity rules operating at different timescales may underlie the inferotemporal code for visual familiarity.
Quantum Oblivious LWE Sampling and Insecurity of Standard Model Lattice-Based SNARKs

The Learning With Errors ($\mathsf{LWE}$) problem asks to find $\mathbf{s}$ from an input of the form $(\mathbf{A}, \mathbf{b} = \mathbf{A}\mathbf{s}+\mathbf{e}) \in (\mathbb{Z}/q\mathbb{Z})^{m \times n} \times (\mathbb{Z}/q\mathbb{Z})^{m}$, for a vector $\mathbf{e}$ that has small-magnitude entries. In this work, we do not focus on solving $\mathsf{LWE}$ but on the task of sampling instances. As these are extremely sparse in their range, it may seem plausible that the only way to proceed is to first create $\mathbf{s}$ and $\mathbf{e}$ and then set $\mathbf{b} = \mathbf{A}\mathbf{s}+\mathbf{e}$. In particular, such an instance sampler knows the solution. This raises the question whether it is possible to obliviously sample $(\mathbf{A}, \mathbf{A}\mathbf{s}+\mathbf{e})$, namely, without knowing the underlying $\mathbf{s}$. A variant of the assumption that oblivious $\mathsf{LWE}$ sampling is hard has been used in a series of works constructing Succinct Non-interactive Arguments of Knowledge (SNARKs) in the standard model. As the assumption is related to $\mathsf{LWE}$, these SNARKs have been conjectured to be secure in the presence of quantum adversaries. Our main result is a quantum polynomial-time algorithm that samples well-distributed $\mathsf{LWE}$ instances while provably not knowing the solution, under the assumption that $\mathsf{LWE}$ is hard. Moreover, the approach works for a vast range of $\mathsf{LWE}$ parametrizations, including those used in the above-mentioned SNARKs.
Invisible Reflections: Leveraging Infrared Laser Reflections to Target Traffic Sign Perception

All vehicles must follow the rules that govern traffic behavior, regardless of whether the vehicles are human-driven or Connected Autonomous Vehicles (CAVs). Road signs indicate locally active rules, such as speed limits and requirements to yield or stop. Recent research has demonstrated attacks, such as adding stickers or projected colored patches to signs, that cause CAV misinterpretation, resulting in potential safety issues. Humans can see and potentially defend against these attacks. But humans can not detect what they can not observe. We have developed an effective physical-world attack that leverages the sensitivity of filterless image sensors and the properties of Infrared Laser Reflections (ILRs), which are invisible to humans. The attack is designed to affect CAV cameras and perception, undermining traffic sign recognition by inducing misclassification. In this work, we formulate the threat model and requirements for an ILR-based traffic sign perception attack to succeed. We evaluate the effectiveness of the ILR attack with real-world experiments against two major traffic sign recognition architectures on four IR-sensitive cameras. Our black-box optimization methodology allows the attack to achieve up to a 100% attack success rate in indoor, static scenarios and a>80.5% attack success rate in our outdoor, moving vehicle scenarios. We find the latest state-of-the-art certifiable defense is ineffective against ILR attacks as it mis-certifies>33.5% of cases. To address this, we propose a detection strategy based on the physical properties of IR laser reflections which can detect 96% of ILR attacks.
Active Galactic Nuclei in a Mid-infrared Selected Galaxy Sample at z > 0.13: [Ne v]λ3426 Line Emission as a Benchmark

We present a 24 μm-selected spectroscopic sample z > 0.13 (median 〈z〉 = 0.41) in the Lockman Hole field, consisting of 4035 spectra. Our aim is to identify active galactic nuclei (AGNs) and determine their fraction in this mid-infrared-selected sample. In this work, we use the [Ne v]λ3426 emission line to spectroscopically identify AGNs. Combined with broad-line Type I AGNs selected in our previous study, our sample consists of 887 (∼22%) spectroscopically confirmed AGNs. We perform a stacking analysis on the remaining spectra and find that in various mid-infrared (MIR) wedge-selected AGN candidates, the stacked spectra still show significant [Ne v]λ3426 emission. In contrast, no clear [Ne v]λ3426 signal is detected in non-AGN candidates falling outside the wedges. Assuming a range of AGN MIR spectral energy distribution (SED) slope of −0.3 <α < 0.7, and an average star-forming relation derived from 65 star-forming templates, we develop a robust method to separate the AGN and star-forming contributions to the MIR SEDs using the rest-frame L 12/L 1.6 versus L 4.5/L 1.6 diagram. We separate the objects into bins of L 12 and find that the AGN fraction increases with increasing L 12. We also find that the stacked [Ne v]λ3426 strength scales with L 12. The pure AGN luminosity at 12 μm exhibits a positive correlation with the star formation rates, indicating possible coevolution and common gas supply between the AGNs and their host galaxies. Varying population properties across the redshift range explored contribute to the observed correlation.
Organic Chemistry in the H2-bearing, CO-rich Interstellar Ice Layer at Temperatures Relevant to Dense Cloud Interiors

Ice chemistry in the dense, cold interstellar medium (ISM) is probably responsible for the formation of interstellar complex organic molecules (COMs). Recent laboratory experiments performed at T ∼ 4 K have shown that irradiation of CO:N2 ice samples analog to the CO-rich interstellar ice layer can contribute to the formation of COMs when H2 molecules are present. We have tested this organic chemistry under a broader range of conditions relevant to the interior of dense clouds by irradiating CO:15N2:H2 ice samples with 2 keV electrons in the 4–15 K temperature range. The H2 ice abundance depended on both, the ice formation temperature and the thermal evolution of the samples. Formation of H-bearing organics such as formaldehyde (H2CO), ketene (C2H2O), and isocyanic acid (H15NCO) was observed upon irradiation of ice samples formed at temperatures up to 10 K, and also in ices formed at 6 K and subsequently warmed up and irradiated at temperatures up to 15 K. These results suggest that a fraction of the H2 molecules in dense cloud interiors might be entrapped in the CO-rich layer of interstellar ice mantles, and that energetic processing of this layer could entail an additional contribution to the formation of COMs in the coldest regions of the ISM.
Vegetation Canopy Micro-Climate: A Field-Project in Davis, California

Dry-bulb temperature, dew-point, wind speed, and wind direction were measured in and around an isolated vegetative canopy (orchard) in Davis CA. These meteorological elements were measured 1.5 m above ground along a transect of seven weather stations set up across the canopy and upwind/downwind open fields. The meteorological variables were recorded for two weeks in October 1986 so we could analyze their spatial and temporal fluctuations as well as their diumat cycles. Our objective was to quantify the impact of the canopy on microclimate. The results indicate significant nocturnal heat islands and daytime . oases, especially in clear weather. Inside the canopy 'within five meters of its upwind edge, daytime temperature dropped by as much as 4.5 °C, and the nighttime temperature rose by 1 °C. Deeper into the canopy and downwind, the daytime drop in temperature reached 6 °C, and the nighttime increase reached 2 °C. Data from this project were used to in conjunction with our models of evapotranspiration, shading, and wind-shielding effects of vegetative canopies.
Creating Personalized Synthetic Voices from Articulation Impaired Speech Using Augmented Reconstruction Loss

This research is about the creation of personalized synthetic voices for head and neck cancer survivors. It is focused particularly on tongue cancer patients whose speech might exhibit severe articulation impairment. Our goal is to restore normal articulation in the synthesized speech, while maximally preserving the target speaker's individuality in terms of both the voice timbre and speaking style. This is formulated as a task of learning from noisy labels. We propose to augment the commonly used speech reconstruction loss with two additional terms. The first term constitutes a regularization loss that mitigates the impact of distorted articulation in the training speech. The second term is a consistency loss that encourages correct articulation in the generated speech. These additional loss terms are obtained from frame-level articulation scores of original and generated speech, which are derived using a separately trained phone classifier. Experimental results on a real case of tongue cancer patient confirm that the synthetic voice achieves comparable articulation quality to unimpaired natural speech, while effectively maintaining the target speaker's individuality. Audio samples are available at https://myspeechproject.github. io/ArticulationRepair/.
Simultaneous Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications

Past research into robotic planning with temporal logic specifications, notably Linear Temporal Logic (LTL), was largely based on singular formulas for individual or groups of robots. But with increasing task complexity, LTL formulas unavoidably grow lengthy, complicating interpretation and specification generation, and straining the computational capacities of the planners. By leveraging the intrinsic structure of tasks, we introduced a hierarchical structure to LTL specifications with requirements on syntax and semantics, and proved that they are more expressive than their flat counterparts. Second, we employ a search-based approach to synthesize plans for a multi-robot system, accomplishing simultaneous task allocation and planning. The search space is approximated by loosely interconnected sub-spaces, with each sub-space corresponding to one LTL specification. The search is predominantly confined to a single sub-space, transitioning to another sub-space under certain conditions, determined by the decomposition of automatons. Moreover, multiple heuristics are formulated to expedite the search significantly. A theoretical analysis concerning completeness and optimality is conducted under mild assumptions. When compared with existing methods on service tasks, our method outperforms in terms of execution times with comparable solution quality. Finally, scalability is evaluated by testing a group of 30 robots and achieving reasonable runtimes.
Characterization of organic matter in the Torridonian using Raman spectroscopy

Abstract The degree of structural order in the organic carbon preserved in grey–black shales of the Neoproterozoic Torridon Group was characterized using Raman spectroscopy. All the samples contained kerogen rather than graphite, which is consistent with a low level of metamorphism. Samples of shale from the Diabaig Formation from six widely separated localities exhibited a consistently greater structural order than shales from five localities in formations higher in the Torridon Group. This supports previous models invoking an unconformity between the Diabaig Formation and the rest of the Torridon Group. The data further suggested that the Diabaig Formation experienced an additional episode of heating in the late stages of Grampian deformation.
Anatomy of Neural Language Models

The fields of generative AI and transfer learning have experienced remarkable advancements in recent years especially in the domain of Natural Language Processing (NLP). Transformers have been at the heart of these advancements where the cutting-edge transformer-based Language Models (LMs) have led to new state-of-the-art results in a wide spectrum of applications. While the number of research works involving neural LMs is exponentially increasing, their vast majority are high-level and far from self-contained. Consequently, a deep understanding of the literature in this area is a tough task especially in the absence of a unified mathematical framework explaining the main types of neural LMs. We address the aforementioned problem in this tutorial where the objective is to explain neural LMs in a detailed, simplified and unambiguous mathematical framework accompanied by clear graphical illustrations. Concrete examples on widely used models like BERT and GPT2 are explored. Finally, since transformers pretrained on language-modeling-like tasks have been widely adopted in computer vision and time series applications, we briefly explore some examples of such solutions in order to enable readers to understand how transformers work in the aforementioned domains and compare this use with the original one in NLP.
Connexin43 in mesenchymal lineage cells regulates body adiposity and energy metabolism in mice

Connexin43 (Cx43) is the most abundant gap junction protein present in the mesenchymal lineage. In mature adipocytes, Cx43 mediates white adipose tissue (WAT) “beiging” in response to cold exposure and maintains the mitochondrial integrity of brown adipose tissue (BAT). We found that genetic deletion of Gja1 (Cx43 gene) in cells that give rise to chondro-osteogenic and adipogenic precursors driven by the Dermo1/Twist2 promoter leads to lower body adiposity and partial protection against the weight gain and metabolic syndrome induced by a high fat diet (HFD) in both sexes. These protective effects from obesogenic diet are related to increased locomotion, fuel utilization, energy expenditure, non-shivering thermogenesis, and better glucose tolerance in conditionally Gja1 ablated mice. Accordingly, Gja1 mutant mice exhibit reduced adipocyte hypertrophy, partially preserved insulin sensitivity, increased BAT lipolysis and decreased whitening under HFD. This metabolic phenotype is not reproduced with more restricted Gja1 ablation in differentiated adipocytes, suggesting that Cx43 has a hitherto unknown function in adipocyte progenitors or other targeted cells, resulting in restrained energy expenditures and fat accumulation. These results disclose an hitherto unknown action of Cx43 in adiposity, and offer a promising new pharmacologic target for improving metabolic balance in diabetes and obesity. Graphical Abstract
Sur2f: A Hybrid Representation for High-Quality and Efficient Surface Reconstruction from Multi-view Images

Multi-view surface reconstruction is an ill-posed, inverse problem in 3D vision research. It involves modeling the geometry and appearance with appropriate surface representations. Most of the existing methods rely either on explicit meshes, using surface rendering of meshes for reconstruction, or on implicit field functions, using volume rendering of the fields for reconstruction. The two types of representations in fact have their respective merits. In this work, we propose a new hybrid representation, termed Sur2f, aiming to better benefit from both representations in a complementary manner. Technically, we learn two parallel streams of an implicit signed distance field and an explicit surrogate surface Sur2f mesh, and unify volume rendering of the implicit signed distance function (SDF) and surface rendering of the surrogate mesh with a shared, neural shader; the unified shading promotes their convergence to the same, underlying surface. We synchronize learning of the surrogate mesh by driving its deformation with functions induced from the implicit SDF. In addition, the synchronized surrogate mesh enables surface-guided volume sampling, which greatly improves the sampling efficiency per ray in volume rendering. We conduct thorough experiments showing that Sur$^2$f outperforms existing reconstruction methods and surface representations, including hybrid ones, in terms of both recovery quality and recovery efficiency.