Miracle-dz/UniFAR-FTUs huggingface dataset:
Retrograde endocannabinoid signaling at inhibitory synapses in vivo

Endocannabinoid (eCB)–mediated suppression of inhibitory synapses has been hypothesized, but this has not yet been demonstrated to occur in vivo because of the difficulty in tracking eCB dynamics and synaptic plasticity during behavior. In mice navigating a linear track, we observed location-specific eCB signaling in hippocampal CA1 place cells, and this was detected both in the postsynaptic membrane and the presynaptic inhibitory axons. All-optical in vivo investigation of synaptic responses revealed that postsynaptic depolarization was followed by a suppression of inhibitory synaptic potentials. Furthermore, interneuron-specific cannabinoid receptor deletion altered place cell tuning. Therefore, rapid, postsynaptic, activity-dependent eCB signaling modulates inhibitory synapses on a timescale of seconds during behavior. Editor’s summary In vitro work has shown that endocannabinoids mediate a form of synaptic plasticity called depolarization-induced suppression of inhibition (DSI). However, whether DSI occurs in vivo and if it contributes to physiological function have needed more investigation. Dudok et al. now demonstrate that a subpopulation of cells in the hippocampus, which fire in specific locations called place cells, trigger endocannabinoid signaling in their place field that can be detected both in the postsynaptic membrane and the presynaptic inhibitory axons. The authors show that inhibiting the endocannabinoid signaling alters place cell firing. These results reveal that a form of DSI-like plasticity occurs in vivo and plays an important role in shaping hippocampal spatial representation. —Mattia Maroso Place cell activation in mice navigating a linear track triggers endocannabinoid signaling, suppressing inhibitory inputs.
Intrapersonal Variation in Destination Choice

The dynamics in travel behavior have been exploring widely in travel demand industry with a focus on interpersonal variability. And, numerous research has been conducted to analyze travel behavior. Nevertheless, intrapersonal variation of travel behavior in destination choice modeling is still underestimated. These models are capable to accommodate population heterogeneity; but still not often used in large scale. In this paper, destination choice models are developed to estimate the intrapersonal variation in travel behavior. The models incorporate the effects associated with trip characteristics and spatial information on travel behavior. Two years (2014 and 2015) data is used to develop the model from the Dutch Mobile Mobility Panel (DMMP) by using mixed logit model. Total 68626 valid trips are recorded for 442 respondents who participated in both years. Data were collected by a smartphone app that uses global positioning system (GPS), and automatically detects departure-arrival times, origin-destinations, modes. Based on the activity purposes, data are segmented into fixed (work, education, appointment, etc.) and flexible (shopping, leisure, sports, tour, etc.) destination. Discrete destination alternatives are defined based on individuals’ behavior of destination repetition and statistical distribution of the spatial repetition index to capture the intrapersonal variation in destination choice. The model results and probabilities show that the intrapersonal variation is high for the less repeated locations, which clearly represents the variation (or novelty) seeking behavior for choosing destinations. Nevertheless, this variation exists also in the departure time and mode repetition, which is particularly high for the less repeated locations. Elasticity revealed the connection between activity, departure time and destination. Travel time and departure time is found significant parameters. For example, people trust the bicycle and walking travel time, and while the morning is likely to travel towards fixed destinations, afternoon and evening is likely for travelling to the flexible destinations. Fixed destination trips are mostly in the commercial and industrial area and unlike to be performed during the weekend, while retail and recreation grounds are likely for flexible destinations. Accessibility of public transport is found more reliable than bicyle and car. Lastly, built environment variables are found strongly correlated with mode choice.
Proximal Dogleg Opportunistic Majorization for Nonconvex and Nonsmooth Optimization

We consider minimizing a function consisting of a quadratic term and a proximable term which is possibly nonconvex and nonsmooth. This problem is also known as scaled proximal operator. Despite its simple form, existing methods suffer from slow convergence or high implementation complexity or both. To overcome these limitations, we develop a fast and user-friendly second-order proximal algorithm. Key innovation involves building and solving a series of opportunistically majorized problems along a hybrid Newton direction. The approach directly uses the precise Hessian of the quadratic term, and calculates the inverse only once, eliminating the iterative numerical approximation of the Hessian, a common practice in quasi-Newton methods. The algorithm's convergence to a critical point is established, and local convergence rate is derived based on the Kurdyka-Lojasiewicz property of the objective function. Numerical comparisons are conducted on well-known optimization problems. The results demonstrate that the proposed algorithm not only achieves a faster convergence but also tends to converge to a better local optimum compare to benchmark algorithms.
Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection

The recently developed and publicly available synthetic image generation methods and services make it possible to create extremely realistic imagery on demand, raising great risks for the integrity and safety of online information. State-of-the-art Synthetic Image Detection (SID) research has led to strong evidence on the advantages of feature extraction from foundation models. However, such extracted features mostly encapsulate high-level visual semantics instead of fine-grained details, which are more important for the SID task. On the contrary, shallow layers encode low-level visual information. In this work, we leverage the image representations extracted by intermediate Transformer blocks of CLIP's image-encoder via a lightweight network that maps them to a learnable forgery-aware vector space capable of generalizing exceptionally well. We also employ a trainable module to incorporate the importance of each Transformer block to the final prediction. Our method is compared against the state-of-the-art by evaluating it on 20 test datasets and exhibits an average +10.6% absolute performance improvement. Notably, the best performing models require just a single epoch for training (~8 minutes). Code available at https://github.com/mever-team/rine.
False Discovery Rate Control for Confounder Selection Using Mirror Statistics

While data-driven confounder selection requires careful consideration, it is frequently employed in observational studies to adjust for confounding factors. Widely recognized criteria for confounder selection include the minimal set approach, which involves selecting variables relevant to both treatment and outcome, and the union set approach, which involves selecting variables for either treatment or outcome. These approaches are often implemented using heuristics and off-the-shelf statistical methods, where the degree of uncertainty may not be clear. In this paper, we focus on the false discovery rate (FDR) to measure uncertainty in confounder selection. We define the FDR specific to confounder selection and propose methods based on the mirror statistic, a recently developed approach for FDR control that does not rely on p-values. The proposed methods are free from p-values and require only the assumption of some symmetry in the distribution of the mirror statistic. It can be easily combined with sparse estimation and other methods that involve difficulties in deriving p-values. The properties of the proposed method are investigated by exhaustive numerical experiments. Particularly in high-dimensional data scenarios, our method outperforms conventional methods.
Prognostic Covariate Adjustment for Logistic Regression in Randomized Controlled Trials

Randomized controlled trials (RCTs) with binary primary endpoints introduce novel challenges for inferring the causal effects of treatments. The most significant challenge is non-collapsibility, in which the conditional odds ratio estimand under covariate adjustment differs from the unconditional estimand in the logistic regression analysis of RCT data. This issue gives rise to apparent paradoxes, such as the variance of the estimator for the conditional odds ratio from a covariate-adjusted model being greater than the variance of the estimator from the unadjusted model. We address this challenge in the context of adjustment based on predictions of control outcomes from generative artificial intelligence (AI) algorithms, which are referred to as prognostic scores. We demonstrate that prognostic score adjustment in logistic regression increases the power of the Wald test for the conditional odds ratio under a fixed sample size, or alternatively reduces the necessary sample size to achieve a desired power, compared to the unadjusted analysis. We derive formulae for prospective calculations of the power gain and sample size reduction that can result from adjustment for the prognostic score. Furthermore, we utilize g-computation to expand the scope of prognostic score adjustment to inferences on the marginal risk difference, relative risk, and odds ratio estimands. We demonstrate the validity of our formulae via extensive simulation studies that encompass different types of logistic regression model specifications. Our simulation studies also indicate how prognostic score adjustment can reduce the variance of g-computation estimators for the marginal estimands while maintaining frequentist properties such as asymptotic unbiasedness and Type I error rate control. Our methodology can ultimately enable more definitive and conclusive analyses for RCTs with binary primary endpoints.
Observational constrained Weyl type $f(Q,T)$ gravity cosmological model and the dynamical system analysis

Using the cosmological date sets, the cosmological parameters are constrained in this paper, with some well known form of Hubble parameter. To understand the dynamics of the Weyl type $f(Q,T)$, functional form $f(Q,T)$ has been introduced, where $Q$ and $T$ respectively represents the nonmetricity scalar and trace of energy-momentum tensor. Using the constrained values of the parameters, the other geometrical parameters are analysed and the accelerating behaviour has been shown. Further to get the complete evolutionary behaviour of the Universe, the dynamical system analysis has been performed.
Anatomy-guided fiber trajectory distribution estimation for cranial nerves tractography

Diffusion MRI tractography is an important tool for identifying and analyzing the intracranial course of cranial nerves (CNs). However, the complex environment of the skull base leads to ambiguous spatial correspondence between diffusion directions and fiber geometry, and existing diffusion tractography methods of CNs identification are prone to producing erroneous trajectories and missing true positive connections. To overcome the above challenge, we propose a novel CNs identification framework with anatomy-guided fiber trajectory distribution, which incorporates anatomical shape prior knowledge during the process of CNs tracing to build diffusion tensor vector fields. We introduce higher-order streamline differential equations for continuous flow field representations to directly characterize the fiber trajectory distribution of CNs from the tract-based level. The experimental results on the vivo HCP dataset and the clinical MDM dataset demonstrate that the proposed method reduces false-positive fiber production compared to competing methods and produces reconstructed CNs (i.e. CN II, CN III, CN V, and CN VII/VIII) that are judged to better correspond to the known anatomy.
Percolation threshold and critical point of a nuclear reactor

Neutrons in a nuclear reactor move along trajectories corresponding to Cayley trees associated with branching random processes. The probability of percolation, the appearance of such a state of the Bethe lattice in which there is at least one continuous path through neighboring conducting nodes through the entire lattice, corresponds to the probability of the occurrence of a self-sustaining fission chain reaction. At a critical value of the last probability, a (conditionally) infinite cluster of neutrons appears. The probability of percolation, depending on the operating time of the reactor and its size, is associated with the criticality of the reactor. The temporal behavior of the neutron multiplication factor is estimated. Particular attention is paid to the early stages of the development of a self-sustaining chain reaction of nuclear fission. The possibilities of determining the boundaries of the critical region are indicated.
DMSA - Dense Multi Scan Adjustment for LiDAR Inertial Odometry and Global Optimization

We propose a new method for fine registering multiple point clouds simultaneously. The approach is characterized by being dense, therefore point clouds are not reduced to pre-selected features in advance. Furthermore, the approach is robust against small overlaps and dynamic objects, since no direct correspondences are assumed between point clouds. Instead, all points are merged into a global point cloud, whose scattering is then iteratively reduced. This is achieved by dividing the global point cloud into uniform grid cells whose contents are subsequently modeled by normal distributions. We show that the proposed approach can be used in a sliding window continuous trajectory optimization combined with IMU measurements to obtain a highly accurate and robust LiDAR inertial odometry estimation. Furthermore, we show that the proposed approach is also suitable for large scale keyframe optimization to increase accuracy. We provide the source code and some experimental data on https://github.com/davidskdds/DMSA_LiDAR_SLAM. git.
Training-set-free two-stage deep learning for spectroscopic data de-noising

De-noising is a prominent step in the spectra post-processing procedure. Previous machine learning-based methods are fast but mostly based on supervised learning and require a training set that may be typically expensive in real experimental measurements. Unsupervised learning-based algorithms are slow and require many iterations to achieve convergence. Here, we bridge this gap by proposing a training-set-free two-stage deep learning method. We show that the fuzzy fixed input in previous methods can be improved by introducing an adaptive prior. Combined with more advanced optimization techniques, our approach can achieve five times acceleration compared to previous work. Theoretically, we study the landscape of a corresponding non-convex linear problem, and our results indicates that this problem has benign geometry for first-order algorithms to converge.
Enhancing the Power of Gaussian Graphical Model Inference by Modeling the Graph Structure

For the problem of inferring a Gaussian graphical model (GGM), this work explores the application of a recent approach from the multiple testing literature for graph inference. The main idea of the method by Rebafka et al. (2022) is to model the data by a latent variable model, the so-called noisy stochastic block model (NSBM), and then use the associated ${\ell}$-values to infer the graph. The inferred graph controls the false discovery rate, that means that the proportion of falsely declared edges does not exceed a user-defined nominal level. Here it is shown that any test statistic from the GGM literature can be used as input for the NSBM approach to perform GGM inference. To make the approach feasible in practice, a new, computationally efficient inference algorithm for the NSBM is developed relying on a greedy approach to maximize the integrated complete-data likelihood. Then an extensive numerical study illustrates that the NSBM approach outperforms the state of the art for any of the here considered GGM-test statistics. In particular in sparse settings and on real datasets a significant gain in power is observed.
Navigating Beyond Dropout: An Intriguing Solution Towards Generalizable Image Super Resolution

Deep learning has led to a dramatic leap on Single Image Super-Resolution (SISR) performances in recent years. %Despite the substantial advancement% While most existing work assumes a simple and fixed degradation model (e.g., bicubic downsampling), the research of Blind SR seeks to improve model generalization ability with unknown degradation. Recently, Kong et al pioneer the investigation of a more suitable training strategy for Blind SR using Dropout. Although such method indeed brings substantial generalization improvements via mitigating overfitting, we argue that Dropout simultaneously introduces undesirable side-effect that compromises model's capacity to faithfully reconstruct fine details. We show both the theoretical and experimental analyses in our paper, and furthermore, we present another easy yet effective training strategy that enhances the generalization ability of the model by simply modulating its first and second-order features statistics. Experimental results have shown that our method could serve as a model-agnostic regularization and outperforms Dropout on seven benchmark datasets including both synthetic and real-world scenarios.
CEBin: A Cost-Effective Framework for Large-Scale Binary Code Similarity Detection

Binary code similarity detection (BCSD) is a fundamental technique for various application. Many BCSD solutions have been proposed recently, which mostly are embedding-based, but have shown limited accuracy and efficiency especially when the volume of target binaries to search is large. To address this issue, we propose a cost-effective BCSD framework, CEBin, which fuses embedding-based and comparison-based approaches to significantly improve accuracy while minimizing overheads. Specifically, CEBin utilizes a refined embedding-based approach to extract features of target code, which efficiently narrows down the scope of candidate similar code and boosts performance. Then, it utilizes a comparison-based approach that performs a pairwise comparison on the candidates to capture more nuanced and complex relationships, which greatly improves the accuracy of similarity detection. By bridging the gap between embedding-based and comparison-based approaches, CEBin is able to provide an effective and efficient solution for detecting similar code (including vulnerable ones) in large-scale software ecosystems. Experimental results on three well-known datasets demonstrate the superiority of CEBin over existing state-of-the-art (SOTA) baselines. To further evaluate the usefulness of BCSD in real world, we construct a large-scale benchmark of vulnerability, offering the first precise evaluation scheme to assess BCSD methods for the 1-day vulnerability detection task. CEBin could identify the similar function from millions of candidate functions in just a few seconds and achieves an impressive recall rate of $85.46\%$ on this more practical but challenging task, which are several order of magnitudes faster and $4.07\times$ better than the best SOTA baseline. Our code is available at https://github.com/Hustcw/CEBin.
A simple model of global cascades on random hypergraphs

This study investigates the dynamics of social contagion within hypergraphs, reflecting the modern intricacies of group influence on individual decision-making as magnified by social media platforms. We introduce a novel social contagion model that captures the nuanced interplay between individual caution in decision-making (\(\phi_k\)) and the efficiency of information transmission within group interactions (\(\phi_m\)). By integrating dual-threshold mechanisms, our model delineates the conditions under which individuals assimilate group behaviors, opinions, or attitudes. Our findings reveal a critical balance in the transmission of information, with higher individual thresholds (\(\phi_k\)) necessitating greater group consensus (\(\phi_m\)) to precipitate a global cascade. The model underscores the pivotal role of group interactions in social contagion processes, challenging traditional contagion models that focus on pairwise interactions. We demonstrate the utility of our model through extensive simulations, revealing that the collective dynamics of social contagion are significantly influenced by the structural parameters of the underlying hypergraph. The implications of our work extend to a variety of domains, from marketing to public health, offering a robust framework to better understand and leverage the phenomena of social contagion in an increasingly interconnected world.
Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification

Hierarchical text classification (HTC) is a challenging subtask of multi-label classification due to its complex taxonomic structure. Nearly all recent HTC works focus on how the labels are structured but ignore the sub-structure of ground-truth labels according to each input text which contains fruitful label co-occurrence information. In this work, we introduce this local hierarchy with an adversarial framework. We propose a HiAdv framework that can fit in nearly all HTC models and optimize them with the local hierarchy as auxiliary information. We test on two typical HTC models and find that HiAdv is effective in all scenarios and is adept at dealing with complex taxonomic hierarchies. Further experiments demonstrate that the promotion of our framework indeed comes from the local hierarchy and the local hierarchy is beneficial for rare classes which have insufficient training data.
On the semiclassical bounce with strong minimal assumptions

We explore the possibility of avoiding cosmological singularity with a bounce solution in the early Universe. The main finding is that simple and well-known semiclassical correction, which describes the mixing of radiation and gravity in the effective action, may provide an analytic solution with a bounce. The solution requires a positive beta function for the total radiation term and the contraction of the Universe at the initial instant. The numerical estimate shows that the bounce may occur in an acceptable range of energies, but only under strong assumptions about the particle physics beyond the Standard Model.
Aligning Language Models for Versatile Text-based Item Retrieval

This paper addresses the gap between general-purpose text embeddings and the specific demands of item retrieval tasks. We demonstrate the shortcomings of existing models in capturing the nuances necessary for zero-shot performance on item retrieval tasks. To overcome these limitations, we propose generate in-domain dataset from ten tasks tailored to unlocking models' representation ability for item retrieval. Our empirical studies demonstrate that fine-tuning embedding models on the dataset leads to remarkable improvements in a variety of retrieval tasks. We also illustrate the practical application of our refined model in a conversational setting, where it enhances the capabilities of LLM-based Recommender Agents like Chat-Rec. Our code is available at https://github.com/microsoft/RecAI.