Miracle-dz/UniFAR-FTUs huggingface dataset:
Inducible Gene Manipulations in Serotonergic Neurons

An impairment of the serotonergic (5-HT) system has been implicated in the etiology of many neuropsychiatric disorders. Despite the considerable genetic evidence, the exact molecular and pathophysiological mechanisms underlying this dysfunction remain largely unknown. To address the lack of instruments for the molecular dissection of gene function in serotonergic neurons we have developed a new mouse transgenic tool that allows inducible Cre-mediated recombination of genes selectively in 5-HT neurons of all raphe nuclei. In this transgenic mouse line, the tamoxifen-inducible CreERT2 recombinase is expressed under the regulatory control of the mouse tryptophan hydroxylase 2 (Tph2) gene locus (177 kb). Tamoxifen treatment efficiently induced recombination selectively in serotonergic neurons with minimal background activity in vehicle-treated mice. These genetic manipulations can be initiated at any desired time during embryonic development, neonatal stage or adulthood. To illustrate the versatility of this new tool, we show that Brainbow-1.0LTPH2-CreERT2 mice display highly efficient recombination in serotonergic neurons with individual 5-HT neurons labeling with multiple distinct fluorescent colors. This labeling is well suited for visualization and tracing of serotonergic neurons and their network architecture. Finally, the applicability of TPH2-CreERT2 for loxP-flanked candidate gene manipulation is evidenced by our successful knockout induction of the ubiquitously expressed glucocorticoid-receptor exclusively in 5-HT neurons of adult mice. The TPH2-CreERT2 line will allow detailed analysis of gene function in both developing and adult serotonergic neurons.
The effect of self-differentiation training on the level of marital satisfaction among married female students

Introduction: The present study aimed to investigate the effect of self-differentiation training on the level of marital satisfaction among married female students in Izeh branch, Azad Islamic University. Materials and Methods: The sample size of this research consisted of 50 participants who were selected using simple random sampling method and were assigned into the experimental group (n=25) and control group (n=25). Measuring tool was Enrich Marital Satisfaction Scale (1989). The experimental group attended in self-differentiation training program for 8 sessions. The results analyzed by covariance test and SPSS software. Results: The results indicated that Self-differentiation training influences the level of marital satisfaction in married women (F=30.26, P ≤0.01). Conclusion: It seems that self-differentiation training can improve marital satisfaction among female students.
A mechanism for generating ENSO decadal variability

A coupled ocean‐atmosphere model of the Pacific basin is used to illustrate a mechanism by which El Niño and the Southern Oscillation (ENSO) may be modulated on decadal time scales. For reasonable choices of model parameters, solutions exhibit two types of oscillation, an ENSO‐like interannual mode and a decadal one. The decadal mode affects the equatorial zone by means of an oceanic teleconnection that involves transport variations of the North Pacific Subtropical Cell. Since almost half of the cool, thermocline water that upwells in the eastern equatorial Pacific participates in this cell, these variations significantly alter the extent of the cold tongue, and hence provide an efficient mechanism for modulating ENSO.
Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models

Multilingual large-scale Pretrained Language Models (PLMs) have been shown to store considerable amounts of factual knowledge, but large variations are observed across languages. With the ultimate goal of ensuring that users with different language backgrounds obtain consistent feedback from the same model, we study the cross-lingual consistency (CLC) of factual knowledge in various multilingual PLMs. To this end, we propose a Ranking-based Consistency (RankC) metric to evaluate knowledge consistency across languages independently from accuracy. Using this metric, we conduct an in-depth analysis of the determining factors for CLC, both at model level and at language-pair level. Among other results, we find that increasing model size leads to higher factual probing accuracy in most languages, but does not improve cross-lingual consistency. Finally, we conduct a case study on CLC when new factual associations are inserted in the PLMs via model editing. Results on a small sample of facts inserted in English reveal a clear pattern whereby the new piece of knowledge transfers only to languages with which English has a high RankC score.
Regret Analysis of the Posterior Sampling-based Learning Algorithm for Episodic POMDPs

Learning in POMDPs is known to be significantly harder than MDPs. In this paper, we consider online learning problem for episodic POMDPs with unknown transition and observation models. We propose a Posterior Sampling-based reinforcement learning algorithm for POMDPs (PS4POMDPs), which is much simpler and more implementable compared to state-of-the-art optimism-based online learning algorithms for POMDPs. We show that the Bayesian regret of the proposed algorithm scales as the square root of the number of episodes, matching the lower bound, and is polynomial in the other parameters. In a general setting, its regret scales exponentially in the horizon length $H$, and we show that this is inevitable by providing a lower bound. However, when the POMDP is undercomplete and weakly revealing (an assumption common in recent literature), we establish a polynomial Bayesian regret bound. We also propose a posterior sampling algorithm for multi-agent POMDPs, and show it too has sublinear regret.
The cokernel of a polynomial push-forward of a random integral matrix with concentrated residue

We prove new statistical results about the distribution of the cokernel of a random integral matrix with a concentrated residue. Given a prime $p$ and a positive integer $n$, consider a random $n \times n$ matrix $X_n$ over the ring $\mathbb{Z}_p$ of $p$-adic integers whose entries are independent. Previously, Wood showed that regardless of the distribution of $X_n$, as long as each entry of $X_n$ is not too concentrated on a single residue modulo $p$, the distribution of the cokernel $\mathrm{cok}(X_n)$ of $X_n$, up to isomorphism, weakly converges to the Cohen--Lenstra distribution, as $n \rightarrow \infty$. In this paper, we consider the case when $X_n$ has a concentrated residue $A_n$ so that $X_n = A_n + pB_n$, where $B_n$ is a random $n \times n$ matrix over $\mathbb{Z}_p$. We show that for every fixed $n$ and a non-constant monic polynomial $P(t) \in \mathbb{Z}_p[t]$, we can explicitly compute the distribution of $\mathrm{cok}(P(X_n))$ when $B_n$ is a Haar-random matrix. Using this, we also show that for specific choices of $A_n$ a much wider class of random matrices $B_n$ gives the same distribution of $\mathrm{cok}(P(X_n))$. For the Haar-random $B_n$, we deduce our result from an interesting equidistribution result for matrices over $\mathbb{Z}_p[t]/(P(t))$, which we prove by establishing a version of the Weierstrass preparation theorem for the noncommutative ring $\mathrm{M}_n(\mathbb{Z}_p)$ of $n \times n$ matrices over $\mathbb{Z}_p$.
Privacy in Large Language Models: Attacks, Defenses and Future Directions

The advancement of large language models (LLMs) has significantly enhanced the ability to effectively tackle various downstream NLP tasks and unify these tasks into generative pipelines. On the one hand, powerful language models, trained on massive textual data, have brought unparalleled accessibility and usability for both models and users. On the other hand, unrestricted access to these models can also introduce potential malicious and unintentional privacy risks. Despite ongoing efforts to address the safety and privacy concerns associated with LLMs, the problem remains unresolved. In this paper, we provide a comprehensive analysis of the current privacy attacks targeting LLMs and categorize them according to the adversary's assumed capabilities to shed light on the potential vulnerabilities present in LLMs. Then, we present a detailed overview of prominent defense strategies that have been developed to counter these privacy attacks. Beyond existing works, we identify upcoming privacy concerns as LLMs evolve. Lastly, we point out several potential avenues for future exploration.
A Non-monotonic Smooth Activation Function

Activation functions are crucial in deep learning models since they introduce non-linearity into the networks, allowing them to learn from errors and make adjustments, which is essential for learning complex patterns. The essential purpose of activation functions is to transform unprocessed input signals into significant output activations, promoting information transmission throughout the neural network. In this study, we propose a new activation function called Sqish, which is a non-monotonic and smooth function and an alternative to existing ones. We showed its superiority in classification, object detection, segmentation tasks, and adversarial robustness experiments. We got an 8.21% improvement over ReLU on the CIFAR100 dataset with the ShuffleNet V2 model in the FGSM adversarial attack. We also got a 5.87% improvement over ReLU on image classification on the CIFAR100 dataset with the ShuffleNet V2 model.
What Do Deep Saliency Models Learn about Visual Attention?

In recent years, deep saliency models have made significant progress in predicting human visual attention. However, the mechanisms behind their success remain largely unexplained due to the opaque nature of deep neural networks. In this paper, we present a novel analytic framework that sheds light on the implicit features learned by saliency models and provides principled interpretation and quantification of their contributions to saliency prediction. Our approach decomposes these implicit features into interpretable bases that are explicitly aligned with semantic attributes and reformulates saliency prediction as a weighted combination of probability maps connecting the bases and saliency. By applying our framework, we conduct extensive analyses from various perspectives, including the positive and negative weights of semantics, the impact of training data and architectural designs, the progressive influences of fine-tuning, and common failure patterns of state-of-the-art deep saliency models. Additionally, we demonstrate the effectiveness of our framework by exploring visual attention characteristics in various application scenarios, such as the atypical attention of people with autism spectrum disorder, attention to emotion-eliciting stimuli, and attention evolution over time. Our code is publicly available at \url{https://github.com/szzexpoi/saliency_analysis}.
A novel classification method of 2+1 spacetimes based on the Cotton scalars

A new effective approach to the algebraic classification of geometries in 2+1 gravity is presented. It uses five real Cotton scalars $\Psi_A$ of distinct boost weights, which are 3D analogues of the Newman-Penrose scalars representing the Weyl tensor in 4D. The classification into types I, II, D, III, N, O is directly related to the multiplicity of the four Cotton-aligned null directions (CANDs). We derive a synoptic algorithm based on the invariants constructed from $\Psi_A$, and we show its agreement with the Petrov scheme based on eigenvalues and canonical Jordan form of the Cotton-York tensor. Our method is simpler and also general because it can be used in any 2+1 theory, such as Einstein's gravity or topologically massive gravity. As an example we analyze the algebraic structure of Robinson-Trautman spacetimes which include charged black holes with a cosmological constant.
New Benchmarks for Asian Facial Recognition Tasks: Face Classification with Large Foundation Models

The face classification system is an important tool for recognizing personal identity properly. This paper introduces a new Large-Scale Korean Influencer Dataset named KoIn. Our presented dataset contains many real-world photos of Korean celebrities in various environments that might contain stage lighting, backup dancers, and background objects. These various images can be useful for training classification models classifying K-influencers. Most of the images in our proposed dataset have been collected from social network services (SNS) such as Instagram. Our dataset, KoIn, contains over 100,000 K-influencer photos from over 100 Korean celebrity classes. Moreover, our dataset provides additional hard case samples such as images including human faces with masks and hats. We note that the hard case samples are greatly useful in evaluating the robustness of the classification systems. We have extensively conducted several experiments utilizing various classification models to validate the effectiveness of our proposed dataset. Specifically, we demonstrate that recent state-of-the-art (SOTA) foundation architectures show decent classification performance when trained on our proposed dataset. In this paper, we also analyze the robustness performance against hard case samples of large-scale foundation models when we fine-tune the foundation models on the normal cases of the proposed dataset, KoIn. Our presented dataset and codes will be publicly available at https://github.com/dukong1/KoIn_Benchmark_Dataset.
JSMoCo: Joint Coil Sensitivity and Motion Correction in Parallel MRI with a Self-Calibrating Score-Based Diffusion Model

Magnetic Resonance Imaging (MRI) stands as a powerful modality in clinical diagnosis. However, it is known that MRI faces challenges such as long acquisition time and vulnerability to motion-induced artifacts. Despite the success of many existing motion correction algorithms, there has been limited research focused on correcting motion artifacts on the estimated coil sensitivity maps for fast MRI reconstruction. Existing methods might suffer from severe performance degradation due to error propagation resulting from the inaccurate coil sensitivity maps estimation. In this work, we propose to jointly estimate the motion parameters and coil sensitivity maps for under-sampled MRI reconstruction, referred to as JSMoCo. However, joint estimation of motion parameters and coil sensitivities results in a highly ill-posed inverse problem due to an increased number of unknowns. To address this, we introduce score-based diffusion models as powerful priors and leverage the MRI physical principles to efficiently constrain the solution space for this optimization problem. Specifically, we parameterize the rigid motion as three trainable variables and model coil sensitivity maps as polynomial functions. Leveraging the physical knowledge, we then employ Gibbs sampler for joint estimation, ensuring system consistency between sensitivity maps and desired images, avoiding error propagation from pre-estimated sensitivity maps to the reconstructed images. We conduct comprehensive experiments to evaluate the performance of JSMoCo on the fastMRI dataset. The results show that our method is capable of reconstructing high-quality MRI images from sparsely-sampled k-space data, even affected by motion. It achieves this by accurately estimating both motion parameters and coil sensitivities, effectively mitigating motion-related challenges during MRI reconstruction.
Sparse higher order partial least squares for simultaneous variable selection, dimension reduction, and tensor denoising

Partial Least Squares (PLS) regression emerged as an alternative to ordinary least squares for addressing multicollinearity in a wide range of scientific applications. As multidimensional tensor data is becoming more widespread, tensor adaptations of PLS have been developed. Our investigations reveal that the previously established asymptotic result of the PLS estimator for a tensor response breaks down as the tensor dimensions and the number of features increase relative to the sample size. To address this, we propose Sparse Higher Order Partial Least Squares (SHOPS) regression and an accompanying algorithm. SHOPS simultaneously accommodates variable selection, dimension reduction, and tensor association denoising. We establish the asymptotic accuracy of the SHOPS algorithm under a high-dimensional regime and verify these results through comprehensive simulation experiments, and applications to two contemporary high-dimensional biological data analysis.
Water Supply Maps for North-west Europe Developed by British Military Geologists during World War II: Innovative Mapping for Mobile Warfare

Abstract During World War II, between September 1943 and April 1944, temporary Royal Engineers officers W. B. R. King and F. W. Shotton remotely generated water supply maps at a scale of 1:50 000 for coastal areas of northern France eastward from the Cherbourg peninsula to Calais: the first series of British groundwater development potential maps. These maps guided drilling of at least 30 boreholes, needed to supply water to British installations in Normandy following the Allied landings that commenced on D-Day, 6 June 1944: part of the infrastructure necessary to facilitate the ensuing mobile campaign. Additionally, between April and September 1944, Shotton and King assisted by the Cambridge academic Maurice Black similarly compiled maps but 1:250 000 in scale for northern France eastwards from Brittany into Belgium. These guided emplacement of at least 20 Royal Engineer boreholes in Belgium during late 1944 and early 1945, part of the infrastructure for the final advance of Allied troops into Germany. Between November 1944 and May 1945, maps also at 1:250 000 and of similar but developing style were prepared for much of northern Germany by W. R. Williams and J. R. Foster-Smith, also temporary Royal Engineers officers but of the Inter-Service Topographical Department, easing the pressure of work on Shotton and his colleagues generated by campaign momentum. One of these maps guided emplacement of the few British boreholes sited in western Germany by May 1945, as the war in Europe came to an end. Overall, British military maps were innovations distinct in style from water supply maps compiled for contemporary German or US forces. They helped to pioneer British hydrogeological mapping, which has developed extensively post-war but under entirely civilian auspices.
Long-Term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker

Objective: Reconstructing freehand ultrasound in 3D without any external tracker has been a long-standing challenge in ultrasound-assisted procedures. We aim to define new ways of parameterising long-term dependencies, and evaluate the performance. Methods: First, long-term dependency is encoded by transformation positions within a frame sequence. This is achieved by combining a sequence model with a multi-transformation prediction. Second, two dependency factors are proposed, anatomical image content and scanning protocol, for contributing towards accurate reconstruction. Each factor is quantified experimentally by reducing respective training variances. Results: 1) The added long-term dependency up to 400 frames at 20 frames per second (fps) indeed improved reconstruction, with an up to 82.4% lowered accumulated error, compared with the baseline performance. The improvement was found to be dependent on sequence length, transformation interval and scanning protocol and, unexpectedly, not on the use of recurrent networks with long-short term modules; 2) Decreasing either anatomical or protocol variance in training led to poorer reconstruction accuracy. Interestingly, greater performance was gained from representative protocol patterns, than from representative anatomical features. Conclusion: The proposed algorithm uses hyperparameter tuning to effectively utilise long-term dependency. The proposed dependency factors are of practical significance in collecting diverse training data, regulating scanning protocols and developing efficient networks. Significance: The proposed new methodology with publicly available volunteer data and code11[Online]. Available: https://github.com/ucl-candi/freehand for parametersing the long-term dependency, experimentally shown to be valid sources of performance improvement, which could potentially lead to better model development and practical optimisation of the reconstruction application.
Random-order Contention Resolution via Continuous Induction: Tightness for Bipartite Matching under Vertex Arrivals

We introduce a new approach for designing Random-order Contention Resolution Schemes (RCRS) via exact solution in continuous time. Given a function $c(y):[0,1] \rightarrow [0,1]$, we show how to select each element which arrives at time $y \in [0,1]$ with probability exactly $c(y)$. We provide a rigorous algorithmic framework for achieving this, which discretizes the time interval and also needs to sample its past execution to ensure these exact selection probabilities. We showcase our framework in the context of online contention resolution schemes for matching with random-order vertex arrivals. For bipartite graphs with two-sided arrivals, we design a $(1+e^{-2})/2 \approx 0.567$-selectable RCRS, which we also show to be tight. Next, we show that the presence of short odd-length cycles is the only barrier to attaining a (tight) $(1+e^{-2})/2$-selectable RCRS on general graphs. By generalizing our bipartite RCRS, we design an RCRS for graphs with odd-length girth $g$ which is $(1+e^{-2})/2$-selectable as $g \rightarrow \infty$. This convergence happens very rapidly: for triangle-free graphs (i.e., $g \ge 5$), we attain a $121/240 + 7/16 e^2 \approx 0.563$-selectable RCRS. Finally, for general graphs we improve on the $8/15 \approx 0.533$-selectable RCRS of Fu et al. (ICALP, 2021) and design an RCRS which is at least $0.535$-selectable. Due to the reduction of Ezra et al. (EC, 2020), our bounds yield a $0.535$-competitive (respectively, $(1+e^{-2})/2$-competitive) algorithm for prophet secretary matching on general (respectively, bipartite) graphs under vertex arrivals.
Seasonal variation of barrier layer in the southeastern tropical Indian Ocean

[1] The isothermal layer, mixed layer, and barrier layer in the southeastern tropical Indian Ocean are investigated using historical hydrographic and recent Argo-float profile data. The annual average isothermal and mixed layers are deep (>50 m) near the equator, but they do not have the same topographic structure. The mixed layer is more orientated along the coastline owing to the effect of precipitation and runoff. This forms a thick barrier layer (>25 m) to the west of Sumatra. The thickness decreases to 10 m near eastern Java and less farther to the east and south. The seasonal variation of barrier layer along the equator and off Sumatra/Java is a consequence of interplay between annual and semiannual variations. Surface water convergence, wave propagation, and precipitation are essentially responsible for these variations. Annual (1 cpy) variation is dominant in most of the region studied except for two locations where semiannual variation is also large. The semiannual variation in isothermal depth (ID), mixed layer depth (MLD), and barrier layer thickness (BLT) is most evident along the equator, with a comparable strength to the annual variation. The other region of large semiannual variation in ID and MLD is off Northwest Australia, but no significant variation in BLT is observed there.