Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multimodal Characterization of Low-Dimensional Thalamocortical Structural Connectivity Patterns

The human thalamus is a bilateral and heterogeneous grey matter structure that plays a crucial role in coordinating whole-brain activity. Investigations of its complex structural and functional internal organization revealed to a certain degree overlapping parcellations, however, a consensus on thalamic subnuclei boundaries remains absent. Recent work suggests that thalamic organization might additionally reflect continuous axes transcending nuclear boundaries. In this study, we used a multimodal approach to uncover how low-dimensional axes that describe thalamic connectivity patterns to the cortex are related to internal thalamic microstructural features, functional connectivity, and structural covariance. We computed a thalamocortical structural connectome via probabilistic tractography on diffusion MRI and derived two main axes of thalamic organization. The principal thalamic gradient, extending from medial to lateral and differentiating between transmodal and unimodal nuclei, was related to intrathalamic myelin profiles, and patterns of functional connectivity, while the secondary axis showed correspondence to core-matrix cell type distributions. Lastly, exploring multimodal thalamocortical associations on a global scale, we observed that the medial-to- lateral gradient consistently differentiated limbic, frontoparietal, and default mode network nodes from dorsal and ventral attention networks across modalities. However, the link with sensory modalities varied. In sum, we show the coherence between lower dimensional patterns of thalamocortical structural connectivity and various modalities, shedding light on multiscale thalamic organization.
Scalable single-cell pooled CRISPR screens with conventional knockout vector libraries

Current methods for single-cell RNA profiling of pooled CRISPR screens are limited, either by indirect capture of single guide RNAs (sgRNAs) or by custom modification of plasmid libraries. Here, we present a direct sgRNA capture platform called Native sgRNA Capture and sequencing (NSC-seq) that enables single-cell CRISPR screens using common knockout plasmid libraries, facilitating genotype-phenotype mapping at multiple scales in vitro and in vivo. Additionally, we characterize sgRNA expression in three whole-genome knockout libraries, revealing a substantial subset of truncated (isoform) spacer reads. We provide this dataset as a reference of expressed sgRNA isoforms that may potentially have compromised CRISPR gene editing efficacy and precision.
Manipulation of the nuclear envelope-associated protein SLAP during mammalian brain development affects cortical lamination and exploratory behavior

Here we report the first characterization of the effects resulting from the manipulation of Soluble-Lamin Associated Protein (SLAP) expression during mammalian brain development. We found that SLAP localizes to the nuclear envelope and when overexpressed causes changes in nuclear morphology and lengthening of mitosis. SLAP overexpression in apical progenitors of the developing mouse brain altered asymmetric cell division, neurogenic commitment and neuronal migration ultimately resulting in unbalance in the proportion of upper, relative to deeper, neuronal layers. Several of these effects were also recapitulated upon Cas9-mediated knock-down. Ultimately, SLAP overexpression during development resulted in a reduction in subcortical projections of young mice and, notably, reduced their exploratory behavior. Our study shows the potential relevance of the previously uncharacterized nuclear envelope protein SLAP in neurodevelopmental disorders.
Somite-independent regeneration of the axolotl primary body axis

Primary body axis development is a highly conserved process that proceeds through somitogenesis and further subdivision into dermatome, myotome, and sclerotome. Defects in somitic-clock genes such as Hes7 lead to vertebral segmentation defects in mice and fish. Here we show that in the axolotl, Hes7 is also necessary for proper embryonic vertebral segmentation but is, surprisingly, dispensable during axolotl tail regeneration. Using genetic barcoding and fate mapping we found that during regeneration, the somitic derivatives (muscle, cartilage, tendon, fibroblasts) arose from tendon-like, Lfng+multi-potent stem cells residing at the myotendonal junction that we term “asomitic stem cells”. Throughout homeostasis and regeneration these stem cells display a distinct gene regulatory state compared to developmental progenitors with comparable lineage potential. These observations contrast to axolotl limb regeneration that proceeds via fibroblast dedifferentiation and the subsequent recapitulation of a limb developmental program. Taken together our research shows that divergent strategies are deployed between limb and tail regeneration, and that regeneration of complex body parts does not necessarily involve the complete redeployment of developmental programs.
Thyroid hormone receptor beta (THRB) dependent regulation of diurnal hepatic lipid metabolism in adult male mice

Thyroid hormones (THs) are critical regulators of systemic energy metabolism and homeostasis. In the liver, high TH action protects against steatosis by enhancing cholesterol and triglyceride turnover, with thyroid hormone receptor beta (THRB) signaling playing a pivotal role. This study probed the potential interaction between THRB action and another critical regulator of liver energy metabolism, the circadian clock. Liver transcriptome analysis of THRB deficient (THRBKO) mice under normal chow conditions revealed a markedly modest impact of THRB deletion. Temporal transcriptome and lipidome profiling uncovered significant alterations in diurnal metabolic rhythms attributable to THRB deficiency pointing to a pro-steatotic state with elevated levels of cholesterol, tri- and diacylglycerides, and fatty acids. These findings were confirmed by THRB agonization in hepatocytes under steatosis-promoting conditions in vitro. Integration of transcriptome profiles from THRBKO mice and mice with induced high or low TH action identified a subset of TH responsive but THRB insensitive genes implicated in immune processes. In summary, our study reveals a complex time-of-day dependent interaction of different TH-related signals in the regulation of liver physiology indicating an opportunity for chronopharmacological approaches to TH/THR(B) manipulation in fatty liver diseases.
SpotTarget: Rethinking the Effect of Target Edges for Link Prediction in Graph Neural Networks

Graph Neural Networks (GNNs) have demonstrated promising outcomes across various tasks, including node classification and link prediction. Despite their remarkable success in various high-impact applications, we have identified three common pitfalls in message passing for link prediction, especially within industrial settings. Particularly, in prevalent GNN frameworks (e.g., DGL and PyTorch-Geometric), the target edges (i.e., the edges being predicted) consistently exist as message passing edges in the graph during training. Consequently, this results in overfitting and distribution shift , both of which adversely impact the generalizability to test the target edges. Additionally, during test time, the failure to exclude the test target edges leads to implicit test leakage caused by neighborhood aggregation. In this paper, we analyze these three pitfalls and investigate the impact of including or excluding target edges on the performance of nodes with varying degrees during training and test phases. Our theoretical and empirical analysis demonstrates that low-degree nodes are more susceptible to these pitfalls. These pitfalls can have detrimental consequences when GNNs are implemented in production systems. To systematically address these pitfalls, we propose SpotTarget, an effective and efficient GNN training framework. During training, SpotTarget leverages our insight regarding low-degree nodes and excludes train target edges connected to at least one low-degree node. During test time, it emulates real-world scenarios of GNN usage in production and excludes all test target edges. Our experiments conducted on diverse real-world datasets, demonstrate that SpotTarget significantly enhances GNNs, achieving up to a 15 × increase in accuracy in sparse graphs. Furthermore, SpotTarget consistently and dramatically improves the performance for low-degree nodes in dense graphs.
Petrogenesis of Strongly Peraluminous Granites in Markan Area,Songpan Fold Belt and Its Tectonic Implication

In the Songpan fold belt,Indosinian post-collision granitoids,including adakitic,A-type and I-type granitoids,are widespread.However,studies on Indosinian strongly peraluminous granite in this area are rare.The Markan granites,including medium-grained two-mica granite and medium-fine grained two-mica granite,are strongly peraluminous granites with Al index(A/CNK)=1.10-1.20. By using LA-ICP-MS zircon dating method,magma crystallization ages 208±2 Ma for the medium-grained two-mica granite and 200±2 Ma for the medium-fine grained two-mica granite are obtained.Both the two-mica granites show K2O/Na2O=1.13-1.75,and are enriched in Rb,Th and U etc while depleted in Sr,Ba,Co and Ni etc.REE compositions display strongly fractionated patterns,with(La/Yb)N =6.08-51.84 and Eu/Eu=0.15-0.65. In Sr-Nd-Hf isotopic compositions,they have initial 87Sr/86Sr ratios(ISr)of 0.707 12-0.711 37,eNd(t)values of-10.36 to-8.43 and zircon eHf(t)values of-11.8 to-1.1. Geochemical and Sr-Nd-Hf isotopic compositions suggest that the magmas for the medium-grained and medium-fine grained two-mica granites were derived from patial melting of argillaceous and greywacke sediments,respectively.According to the combined analysis of geological background,regional tectono-magmatic events and magmatism composition,Indosinian lithospheric delamination can account for the magma generation of the Markan strongly peraluminous granites.The Indosinian lithospheric delamination resulted in mantle asthenospheric upwelling,which promoted not only partial melting of the thickened lower crust(e.g.the adakitic and I-type granitoid magma gerneration),but also partial melting of the middle-lower crust(e.g.the Markan granite gerneration). This indicates that the Indosinian lithospheric delamination in the Songpan fold belt had resulted in partial melting at different levels of crust.
Faster Sampling from Log-Concave Densities over Polytopes via Efficient Linear Solvers

We consider the problem of sampling from a log-concave distribution π ( θ ) ∝ e − f ( θ ) constrained to a polytope K := { θ ∈ R d : Aθ ≤ b } , where A ∈ R m × d and b ∈ R m . The fastest-known algorithm Mangoubi & Vish-noi (2022) for the setting when f is O (1)-Lipschitz or O (1)-smooth runs in roughly O ( md × md ω − 1 ) arithmetic operations, where the md ω − 1 term arises because each Markov chain step requires computing a matrix inversion and determinant (here ω ≈ 2 . 37 is the matrix multiplication constant). We present a nearly-optimal implementation of this Markov chain with per-step complexity which is roughly the number of non-zero entries of A while the number of Markov chain steps remains the same. The key technical ingredients are 1) to show that the matrices that arise in this Dikin walk change slowly, 2) to deploy efficient linear solvers that can leverage this slow change to speed up matrix inversion by using information computed in previous steps, and 3) to speed up the computation of the determinan-tal term in the Metropolis filter step via a randomized Taylor series-based estimator. This result directly improves the runtime for applications that involve sampling from Gibbs distributions constrained to polytopes that arise in Bayesian statistics and private optimization.
Analysis and Approximate Inference of Large Random Kronecker Graphs

Random graph models are playing an increasingly important role in various fields ranging from social networks, telecommunication systems, to physiologic and biological networks. Within this landscape, the random Kronecker graph model, emerges as a prominent framework for scrutinizing intricate real-world networks. In this paper, we investigate large random Kronecker graphs, i.e., the number of graph vertices $N$ is large. Built upon recent advances in random matrix theory (RMT) and high-dimensional statistics, we prove that the adjacency of a large random Kronecker graph can be decomposed, in a spectral norm sense, into two parts: a small-rank (of rank $O(\log N)$) signal matrix that is linear in the graph parameters and a zero-mean random noise matrix. Based on this result, we propose a ``denoise-and-solve'' approach to infer the key graph parameters, with significantly reduced computational complexity. Experiments on both graph inference and classification are presented to evaluate the our proposed method. In both tasks, the proposed approach yields comparable or advantageous performance, than widely-used graph inference (e.g., KronFit) and graph neural net baselines, at a time cost that scales linearly as the graph size $N$.
On the Complexity of Finite-Sum Smooth Optimization under the Polyak-Łojasiewicz Condition

This paper considers the optimization problem of the form $\min_{{\bf x}\in{\mathbb R}^d} f({\bf x})\triangleq \frac{1}{n}\sum_{i=1}^n f_i({\bf x})$, where $f(\cdot)$ satisfies the Polyak--{\L}ojasiewicz (PL) condition with parameter $\mu$ and $\{f_i(\cdot)\}_{i=1}^n$ is $L$-mean-squared smooth. We show that any gradient method requires at least $\Omega(n+\kappa\sqrt{n}\log(1/\epsilon))$ incremental first-order oracle (IFO) calls to find an $\epsilon$-suboptimal solution, where $\kappa\triangleq L/\mu$ is the condition number of the problem. This result nearly matches upper bounds of IFO complexity for best-known first-order methods. We also study the problem of minimizing the PL function in the distributed setting such that the individuals $f_1(\cdot),\dots,f_n(\cdot)$ are located on a connected network of $n$ agents. We provide lower bounds of $\Omega(\kappa/\sqrt{\gamma}\,\log(1/\epsilon))$, $\Omega((\kappa+\tau\kappa/\sqrt{\gamma}\,)\log(1/\epsilon))$ and $\Omega\big(n+\kappa\sqrt{n}\log(1/\epsilon)\big)$ for communication rounds, time cost and local first-order oracle calls respectively, where $\gamma\in(0,1]$ is the spectral gap of the mixing matrix associated with the network and~$\tau>0$ is the time cost of per communication round. Furthermore, we propose a decentralized first-order method that nearly matches above lower bounds in expectation.
Homograph Attacks on Maghreb Sentiment Analyzers

We examine the impact of homograph attacks on the Sentiment Analysis (SA) task of different Arabic dialects from the Maghreb North-African countries. Homograph attacks result in a 65.3% decrease in transformer classification from an F1-score of 0.95 to 0.33 when data is written in"Arabizi". The goal of this study is to highlight LLMs weaknesses' and to prioritize ethical and responsible Machine Learning.
Inverse Reinforcement Learning by Estimating Expertise of Demonstrators

In Imitation Learning (IL), utilizing suboptimal and heterogeneous demonstrations presents a substantial challenge due to the varied nature of real-world data. However, standard IL algorithms consider these datasets as homogeneous, thereby inheriting the deficiencies of suboptimal demonstrators. Previous approaches to this issue typically rely on impractical assumptions like high-quality data subsets, confidence rankings, or explicit environmental knowledge. This paper introduces IRLEED, Inverse Reinforcement Learning by Estimating Expertise of Demonstrators, a novel framework that overcomes these hurdles without prior knowledge of demonstrator expertise. IRLEED enhances existing Inverse Reinforcement Learning (IRL) algorithms by combining a general model for demonstrator suboptimality to address reward bias and action variance, with a Maximum Entropy IRL framework to efficiently derive the optimal policy from diverse, suboptimal demonstrations. Experiments in both online and offline IL settings, with simulated and human-generated data, demonstrate IRLEED's adaptability and effectiveness, making it a versatile solution for learning from suboptimal demonstrations.
Amorphous quantum magnets in a two-dimensional Rydberg atom array

Amorphous solids, i.e., systems which feature well-defined short-range properties but lack long-range order, constitute an important research topic in condensed matter. While their microscopic structure is known to differ from their crystalline counterpart, there are still many open questions concerning the emergent collective behavior in amorphous materials. This is particularly the case in the quantum regime, where the numerical simulations are extremely challenging. In this article, we instead propose to explore amorphous quantum magnets with an analog quantum simulator. To this end, we first present an algorithm to generate amorphous quantum magnets, suitable for Rydberg simulators of the Ising model. Subsequently, we use semiclassical approaches to get a preliminary insight of the physics of the model. In particular, we calculate mean-field phase diagrams, and use the linear-spin-wave theory to study localization properties and dynamical structure factors of the excitations. Finally, we outline an experimental proposal based on Rydberg atoms in programmable tweezer arrays, thus opening the road towards the study of amorphous quantum magnets in regimes difficult to simulate classically.
CompeteSMoE - Effective Training of Sparse Mixture of Experts via Competition

Sparse mixture of experts (SMoE) offers an appealing solution to scale up the model complexity beyond the mean of increasing the network's depth or width. However, effective training of SMoE has proven to be challenging due to the representation collapse issue, which causes parameter redundancy and limited representation potentials. In this work, we propose a competition mechanism to address this fundamental challenge of representation collapse. By routing inputs only to experts with the highest neural response, we show that, under mild assumptions, competition enjoys the same convergence rate as the optimal estimator. We further propose CompeteSMoE, an effective and efficient algorithm to train large language models by deploying a simple router that predicts the competition outcomes. Consequently, CompeteSMoE enjoys strong performance gains from the competition routing policy while having low computation overheads. Our extensive empirical evaluations on two transformer architectures and a wide range of tasks demonstrate the efficacy, robustness, and scalability of CompeteSMoE compared to state-of-the-art SMoE strategies.
Aalap: AI Assistant for Legal & Paralegal Functions in India

Using proprietary Large Language Models on legal tasks poses challenges due to data privacy issues, domain data heterogeneity, domain knowledge sophistication, and domain objectives uniqueness. We created Aalalp, a fine-tuned Mistral 7B model on instructions data related to specific Indian legal tasks. The performance of Aalap is better than gpt-3.5-turbo in 31\% of our test data and obtains an equivalent score in 34\% of the test data as evaluated by GPT4. Training Aalap mainly focuses on teaching legal reasoning rather than legal recall. Aalap is definitely helpful for the day-to-day activities of lawyers, judges, or anyone working in legal systems.
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations

Learning neural subset selection tasks, such as compound selection in AI-aided drug discovery, have become increasingly pivotal across diverse applications. The existing methodologies in the field primarily concentrate on constructing models that capture the relationship between utility function values and subsets within their respective supersets. However, these approaches tend to overlook the valuable information contained within the superset when utilizing neural networks to model set functions. In this work, we address this oversight by adopting a probabilistic perspective. Our theoretical findings demonstrate that when the target value is conditioned on both the input set and subset, it is essential to incorporate an \textit{invariant sufficient statistic} of the superset into the subset of interest for effective learning. This ensures that the output value remains invariant to permutations of the subset and its corresponding superset, enabling identification of the specific superset from which the subset originated. Motivated by these insights, we propose a simple yet effective information aggregation module designed to merge the representations of subsets and supersets from a permutation invariance perspective. Comprehensive empirical evaluations across diverse tasks and datasets validate the enhanced efficacy of our approach over conventional methods, underscoring the practicality and potency of our proposed strategies in real-world contexts.
Hybrid-Prediction Integrated Planning for Autonomous Driving

Autonomous driving systems require the ability to fully understand and predict the surrounding environment to make informed decisions in complex scenarios. Recent advancements in learning-based systems have highlighted the importance of integrating prediction and planning modules. However, this integration has brought forth three major challenges: inherent trade-offs by sole prediction, consistency between prediction patterns, and social coherence in prediction and planning. To address these challenges, we introduce a hybrid-prediction integrated planning (HPP) system, which possesses three novelly designed modules. First, we introduce marginal-conditioned occupancy prediction to align joint occupancy with agent-wise perceptions. Our proposed MS-OccFormer module achieves multi-stage alignment per occupancy forecasting with consistent awareness from agent-wise motion predictions. Second, we propose a game-theoretic motion predictor, GTFormer, to model the interactive future among individual agents with their joint predictive awareness. Third, hybrid prediction patterns are concurrently integrated with Ego Planner and optimized by prediction guidance. HPP achieves state-of-the-art performance on the nuScenes dataset, demonstrating superior accuracy and consistency for end-to-end paradigms in prediction and planning. Moreover, we test the long-term open-loop and closed-loop performance of HPP on the Waymo Open Motion Dataset and CARLA benchmark, surpassing other integrated prediction and planning pipelines with enhanced accuracy and compatibility.
Test-Time Adaptation for Depth Completion

It is common to observe performance degradation when transferring models trained on some (source) datasets to target testing data due to a domain gap between them. Existing methods for bridging this gap, such as domain adaptation (DA), may require the source data on which the model was trained (often not available), while others, i.e., source-free DA, require many passes through the testing data. We propose an online test-time adaptation method for depth completion, the task of inferring a dense depth map from a single image and associated sparse depth map, that closes the performance gap in a single pass. We first present a study on how the domain shift in each data modality affects model performance. Based on our observations that the sparse depth modality exhibits a much smaller covariate shift than the image, we design an embedding module trained in the source domain that preserves a mapping from features encoding only sparse depth to those encoding image and sparse depth. During test time, sparse depth features are projected using this map as a proxy for source domain features and are used as guidance to train a set of auxiliary parameters (i.e., adaptation layer) to align image and sparse depth features from the target test domain to that of the source domain. We evaluate our method on indoor and outdoor scenarios and show that it improves over baselines by an average of 21.1%.