Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex and Estrous Cycle Are Not Mediators of S-Ketamine’s Rapid-Antidepressant Behavioral Effects in a Genetic Rat Model of Depression

Abstract Background Recent preclinical and clinical studies have shed light on the possible impact of sex and estrous/menstrual cycle on ketamine’s antidepressant action but with incongruous results. The preclinical studies that have shown the effects of ovarian sex hormones have not done so in animal models of depression. Thus, the aim of the present study is to scrutinize the acute behavioral responses to a subanesthetic dose of S-ketamine in males vs females and in different estrous phases in free-cycling females in a well-powered translational approach. Methods We evaluated the behavioral sensitivity to 20 mg/kg S-ketamine (i.p.) in male and female Flinders Sensitive Line rats (FSLs) and their counterpart Flinders Resistant Line rats (FRLs) subjected to the open field and forced swim tests. Female rats were disaggregated into different estrous phases, and the behavioral outcomes were compared. Results Acute administration of S-ketamine had robust antidepressant-like effects in FSLs. Within our study power, we could not detect sex– or estrous cycle–specific different antidepressant-like responses to S-ketamine in FSLs. Fluctuations in the levels of ovarian sex hormones across different estrous cycles did not behaviorally affect S-ketamine’s rapid-acting antidepressant mode of action. No sex-related or estrous cycle–related impact on behavioral despair was observed even among FRLs and saline-treated FSLs. Conclusions We conclude that physiological oscillations of estrogen and progesterone levels neither amplify nor diminish the behavioral antidepressant-like effect of S-ketamine. In addition, fluctuations of ovarian sex hormones do not predispose female animals to exhibit enhanced or reduced depressive-like and anxiety-like behaviors.
Effect of stimulation intensity of a differential target multiplexed SCS program in an animal model of neuropathic pain

Spinal cord stimulation (SCS) has been proven to be an effective treatment for patients suffering from intractable chronic neuropathic pain. Recent advances in the field include the utilization of programs that multiplex various signals to target different neural structures in the dorsal spinal cord associated with the painful area. Preclinical studies have been fundamental in understanding the mechanism by which this differential target multiplexed programming (DTMP) SCS approach works. Transcriptomic‐ and proteomic‐based studies demonstrated that DTMP can modulate expression levels of genes and proteins involved in pain‐related processes that have been affected by a neuropathic pain model. This work studied the effect of the intensity of DTMP signals on mechanical hypersensitivity and cell‐specific transcriptomes.
STRAP: Structured Object Affordance Segmentation with Point Supervision

With significant annotation savings, point supervision has been proven effective for numerous 2D and 3D scene understanding problems. This success is primarily attributed to the structured output space; i.e., samples with high spatial affinity tend to share the same labels. Sharing this spirit, we study affordance segmentation with point supervision, wherein the setting inherits an unexplored dual affinity-spatial affinity and label affinity. By label affinity, we refer to affordance segmentation as a multi-label prediction problem: A plate can be both holdable and containable. By spatial affinity, we refer to a universal prior that nearby pixels with similar visual features should share the same point annotation. To tackle label affinity, we devise a dense prediction network that enhances label relations by effectively densifying labels in a new domain (i.e., label co-occurrence). To address spatial affinity, we exploit a Transformer backbone for global patch interaction and a regularization loss. In experiments, we benchmark our method on the challenging CAD120 dataset, showing significant performance gains over prior methods.
Learning-Based One-Bit Maximum Likelihood Detection for Massive MIMO Systems: Dithering-Aided Adaptive Approach

In this paper, we propose a learning-based detection framework for uplink massive multiple-input and multiple-output (MIMO) systems with one-bit analog-to-digital converters. The learning-based detection only requires counting the occurrences of the quantized outputs of -1 and +1 for estimating a likelihood probability at each antenna. Accordingly, the key advantage of this approach is to perform maximum likelihood detection without explicit channel estimation which has been one of the primary challenges of one-bit quantized systems. However, due to the quasi-deterministic reception in the high signal-to-noise ratio (SNR) regime, one-bit observations in the high SNR regime are biased to either +1 or -1, and thus, the learning requires excessive training to estimate the small likelihood probabilities. To address this drawback, we propose a dither-and-learning technique to estimate likelihood functions from dithered signals. First, we add a dithering signal to artificially decrease the SNR and then infer the likelihood function from the quantized dithered signals by using an SNR estimate derived from a deep neural network-based estimator which is trained offline. We extend our technique by developing an adaptive dither-and-learning method that updates the dithering power according to the patterns observed in the quantized dithered signals. The proposed framework is also applied to channel-coded MIMO systems by computing a bit-wise and user-wise log-likelihood ratio from the refined likelihood probabilities. Simulation results validate the performance of the proposed methods in both uncoded and coded systems.
An Empirical Study of Multitask Learning to Improve Open Domain Dialogue Systems

Autoregressive models used to generate responses in open-domain dialogue systems often struggle to take long-term context into account and to maintain consistency over a dialogue. Previous research in open-domain dialogue generation has shown that the use of auxiliary tasks can introduce inductive biases that encourage the model to improve these qualities. However, most previous research has focused on encoder-only or encoder/decoder models, while the use of auxiliary tasks in encoder-only autoregressive models is under-explored. This paper describes an investigation where four different auxiliary tasks are added to small and medium-sized GPT-2 models fine-tuned on the PersonaChat and DailyDialog datasets. The results show that the introduction of the new auxiliary tasks leads to small but consistent improvement in evaluations of the investigated models.
Use of contraceptive implants at 12 months in women who intended to undergo immediate versus delayed postpartum insertion following high-risk pregnancy

Objective To assess the correlation between the intention to undergo immediate versus delayed postpartum contraceptive implant insertion following high-risk pregnancy, and the proportion of utilization and adverse effects. Methods We conducted a retrospective cohort study of women who gave birth after a high-risk pregnancy (according to the criteria defined by the Society for Maternal-Fetal Medicine) and intended to use contraceptive implants. The participants were classified into two groups based on whether they underwent immediate or delayed insertion. The primary outcome was the proportion of utilization of contraceptive implants at 12 months postpartum. We performed multivariate analyses to determine the relationships between the timing of insertion, characteristics, and methods used. Results Of the 482 women classified as having high-risk pregnancies, 103 intended to use contraceptive implants (54 immediate and 49 delayed insertions). Women in the immediate group were more likely to use contraceptive implants than those in the delayed group at 6 (95.2% vs. 26.2%, P<0.01) and 12 months (92.7% vs. 26.2, P<0.01). A higher proportion of participants in the immediate group reported spotting and prolonged bleeding at 12 months (51.1% vs. 23.8%, P=0.01 and 26.8% vs. 7.1%, P=0.01; respectively). However, satisfaction at 12 months was higher in the immediate group than in the delayed group. Conclusion Intention to undergo implant insertion during the immediate postpartum period appears to improve the utilization of highly effective contraception. Patients who underwent immediate implantation experienced more spotting, prolonged bleeding, and dysmenorrhea. This study supports the recommendation to provide immediate postpartum contraceptive implants to women following high-risk pregnancies.
Generalized Maxwell equations in exterior domains 4, Hodge-Helmholtz decompositions

We study in detail Hodge-Helmholtz decompositions in nonsmooth exterior domains Ω ⊂ RN filled with inhomogeneous and anisotropic media. We show decompositions of alternating differential forms belonging to the weighted L2space L s (Ω) , s ∈ R , into irrotational and solenoidal q-forms. These decompositions are essential tools in electro-magnetic theory for exterior domains. To the best of our knowledge these decompositions in exterior domains with nonsmooth boundaries and inhomogeneous and anisotropic media are fully new results. In the appendix we translate our results to the classical framework of vector analysis N = 3 and q = 1, 2 .
Géométrie nodale et valeurs propres de l’opérateur de Laplace et du p-laplacien

The main topic of the present thesis is spectral geometry. This area of mathematics is concerned with establishing links between the geometry of a Riemannian manifold and its spectrum. The spectrum of a closed Riemannian manifold M equipped with a Riemannian metric g associated with the Laplace-Beltrami operator ∆g is a sequence of non-negative numbers, 0 = λ0 < λ1(M, g) ≤ λ2(M, g) ≤ . ↗∞. The square root of any number of this sequence represents a frequency of vibration of the manifold. Every eigenvalue λ = λ(M, g) of the spectrum is associated to an eigenfunction uλ such that ∆guλ = λuλ. This thesis consists of four articles all related to various aspects of spectral geometry. The first paper, Superlevel sets and nodal extrema of Laplace eigenfunction [80], is presented in Chapter 1. Nodal geometry of various elliptic operators, such as the Laplace-Beltrami operator, is studied. The goal of this paper is to generalize a result due to L. Polterovich and M. Sodin in [83] that gives a bound on the distribution of nodal extrema on a Riemann surface for a large class of functions, including eigenfunctions of the Laplace-Beltrami operator. The proof given in [83] is only valid for Riemann surfaces. Therefore, in [80], I present a different approach to the problem that works for eigenfunctions of the Laplace-Beltrami operator on Riemannian manifolds of arbitrary dimension. The second and the third papers of this thesis are focused on a different elliptic operator, namely the p-Laplacian. This operator has the particularity of being non-linear. The article Principal frequency of the p-Laplacian and the inradius of Euclidean domains [79] is presented in Chapter 2. It discusses lower bounds on
Mean value formulas for classical solutions to subelliptic evolution equations in stratified Lie groups

We prove mean value formulas for classical solutions to second order linear differential equations in the form $$ \partial_t u = \sum_{i,j=1}^m X_i (a_{ij} X_j u) + X_0 u + f, $$ where $A = (a_{ij})_{i,j=1, \dots,m}$ is a bounded, symmetric and uniformly positive matrix with $C^1$ coefficients under the assumption that the operator $\sum_{j=1}^m X_j^2 + X_0 - \partial_t$ is hypoelliptic and the vector fields $X_1, \dots, X_m$ and $X_{m+1} :=X_0 - \partial_t$ are invariant with respect to a suitable homogeneous Lie group. Our results apply e.g. to degenerate Kolmogorov operators and parabolic equations on Carnot groups $\partial_t u = \sum_{i,j=1}^m X_i (a_{ij} X_j u) + f$.
Self-supervised Auxiliary Loss for Metric Learning in Music Similarity-based Retrieval and Auto-tagging

In the realm of music information retrieval, similarity-based retrieval and auto-tagging serve as essential components. Given the limitations and non-scalability of human supervision signals, it becomes crucial for models to learn from alternative sources to enhance their performance. Self-supervised learning, which exclusively relies on learning signals derived from music audio data, has demonstrated its efficacy in the context of auto-tagging. In this study, we propose a model that builds on the self-supervised learning approach to address the similarity-based retrieval challenge by introducing our method of metric learning with a self-supervised auxiliary loss. Furthermore, diverging from conventional self-supervised learning methodologies, we discovered the advantages of concurrently training the model with both self-supervision and supervision signals, without freezing pre-trained models. We also found that refraining from employing augmentation during the fine-tuning phase yields better results. Our experimental results confirm that the proposed methodology enhances retrieval and tagging performance metrics in two distinct scenarios: one where human-annotated tags are consistently available for all music tracks, and another where such tags are accessible only for a subset of tracks.
Complete reducibility in bad characteristic

Let $G$ be a simple algebraic group of exceptional type over an algebraically closed field of characteristic $p>0$. This paper continues a long-standing effort to classify the connected reductive subgroups of $G$. Having previously completed the classification when $p$ is sufficiently large, we focus here on the case that $p$ is bad for $G$. We classify the connected reductive subgroups of $G$ which are not $G$-completely reducible, whose simple components have rank at least $3$. For each such subgroup $X$, we determine the action of $X$ on the adjoint module $L(G)$ and on a minimal non-trivial $G$-module, and the connected centraliser of $X$ in $G$. As corollaries we obtain information on: subgroups which are maximal among connected reductive subgroups; products of commuting $G$-completely reducible subgroups; subgroups with trivial connected centraliser; and subgroups which act indecomposably on an adjoint or minimal module for $G$.
Deep learning universal crater detection using Segment Anything Model (SAM)

Craters are amongst the most important morphological features in planetary exploration. To that extent, detecting, mapping and counting craters is a mainstream process in planetary science, done primarily manually, which is a very laborious and time-consuming process. Recently, machine learning (ML) and computer vision have been successfully applied for both detecting craters and estimating their size. Existing ML approaches for automated crater detection have been trained in specific types of data e.g. digital elevation model (DEM), images and associated metadata for orbiters such as the Lunar Reconnaissance Orbiter Camera (LROC) etc. Due to that, each of the resulting ML schemes is applicable and reliable only to the type of data used during the training process. Data from different sources, angles and setups can compromise the reliability of these ML schemes. In this paper we present a universal crater detection scheme that is based on the recently proposed Segment Anything Model (SAM) from META AI. SAM is a prompt-able segmentation system with zero-shot generalization to unfamiliar objects and images without the need for additional training. Using SAM we can successfully identify crater-looking objects in any type of data (e,g, raw satellite images Level-1 and 2 products, DEMs etc.) for different setups (e.g. Lunar, Mars) and different capturing angles. Moreover, using shape indexes, we only keep the segmentation masks of crater-like features. These masks are subsequently fitted with an ellipse, recovering both the location and the size/geometry of the detected craters.
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis

As deep neural networks include a high number of parameters and operations, it can be a challenge to implement these models on devices with limited computational resources. Despite the development of novel pruning methods toward resource-efficient models, it has become evident that these models are not capable of handling"imbalanced"and"limited number of data points". We proposed a novel filter pruning method by considering the input and output of filters along with the values of the filters that deal with imbalanced datasets better than others. Our pruning method considers the fact that all information about the importance of a filter may not be reflected in the value of the filter. Instead, it is reflected in the changes made to the data after the filter is applied to it. In this work, three methods are compared with the same training conditions except for the ranking values of each method, and 14 methods are compared from other papers. We demonstrated that our model performed significantly better than other methods for imbalanced medical datasets. For example, when we removed up to 58% of FLOPs for the IDRID dataset and up to 45% for the ISIC dataset, our model was able to yield an equivalent (or even superior) result to the baseline model. To evaluate FLOP and parameter reduction using our model in real-world settings, we built a smartphone app, where we demonstrated a reduction of up to 79% in memory usage and 72% in prediction time. All codes and parameters for training different models are available at https://github.com/mohofar/Beta-Rank
A stationary axisymmetric vacuum solution for pure 2 gravity

The closed-form expression for pure 2 vacuum solution obtained in Phys. Rev. D 107, 104 008 (2023) lends itself to a generalization to axisymmetric setup via the modified Newman–Janis algorithm. We adopt the procedure put forth in Phys. Rev. D 90, 064 041 (2014) bypassing the complexification of the radial coordinate. The procedure presumes the existence of Boyer-Lindquist coordinates. Using the Event Horizon Telescope Collaboration results, we model the central black hole M87* by the thus obtained exact rotating metric, depending on the mass, rotation parameter and a third dimensionless parameter. The latter is constrained upon investigating the shadow angular size assuming mass and rotation parameters are those of M87*. Stability is investigated.
Outcomes of DCIS treated with breast conserving surgery without radiotherapy on recurrence, survival, and health‐related quality of life

Sector resection for Ductal Carcinoma in Situ (DCIS) allows wide excision without compromising breast shape. There are concerns that radiotherapy for some DCIS after sector resection is unnecessary and reduces patient satisfaction and quality of life without affecting survival. This study aimed to investigate whether women with DCIS managed with sector resection without radiotherapy had acceptable rates of recurrence and health‐related quality of life outcomes.
Gate electrostatic controllability enhancement in nanotube gate all around field effect transistor

Recently, short channel effects (SCE) and power consumption dissipation problems impose tremendous challenges that need imperative actions to be taken to deal with for field effect transistor to further scale down as semiconductor technology enters into sub-10 nm technology node. From 3 nm technology node and beyond, gate all around field effect transistor steps onto the history stage attributed to its improved SCE suppressing ability thanks to surrounding gate structure. Herein, we demonstrate the super electrostatic control ability of a double-gated nanotube gate all around field effect transistor (DG NT GAAFET) in comparison with nanotube (NT GAAFET) and nanowire gate all around field effect transistor (NW GAAFET) with the same device parameters designed. Ion boosts of 62% and 57% have been obtained in DG NT GAAFET in comparison with those of NT GAAFET and NW GAAFET. In addition, substantially suppressed SCEs have been obtained in DG NT GAAFET due to enhanced electrostatic control, which are certificated by improved Ioff, subthreshold swing (SS), and Ion/Ioff ratio obtained. On the other hand, the Ion of NT GAAFET is comparable with that of NW GAA-FET. Whereas its Ioff is 1 order smaller, SS is almost two times smaller compared with those of NW GAA-FET, manifesting the meliority of nanotube channel structure. In the end, the robustness of nanotube channel structure, especially double gated one, against channel length (Lg) scaling has been verified with Technology Computer Aided Design (TCAD) simulation study.
Hilbert Scheme of a Pair of Skew Lines on Cubic Threefolds

A pair of disjoint lines on a smooth cubic threefold determines an irreducible component of the Hilbert scheme. We prove that this component is smooth and isomorphic to the blow-up of the symmetric product of Fano varieties of lines on the diagonal. We also study its relation to the geometry of lines and singularities on the hyperplane sections and its relation to Bridgeland moduli spaces.
An In-depth Investigation of User Response Simulation for Conversational Search

Conversational search has seen increased recent attention in both the IR and NLP communities. It seeks to clarify and solve users' search needs through multi-turn natural language interactions. However, most existing systems are trained and demonstrated with recorded or artificial conversation logs. Eventually, conversational search systems should be trained, evaluated, and deployed in an open-ended setting with unseen conversation trajectories. A key challenge is that training and evaluating such systems both require a human-in-the-loop, which is expensive and does not scale. One strategy is to simulate users, thereby reducing the scaling costs. However, current user simulators are either limited to only responding to yes-no questions from the conversational search system or unable to produce high-quality responses in general. In this paper, we show that existing user simulation systems could be significantly improved by a smaller finetuned natural language generation model. However, rather than merely reporting it as the new state-of-the-art, we consider it a strong baseline and present an in-depth investigation of simulating user response for conversational search. Our goal is to supplement existing work with an insightful hand-analysis of unsolved challenges by the baseline and propose our solutions. The challenges we identified include (1) a blind spot that is difficult to learn, and (2) a specific type of misevaluation in the standard setup. We propose a new generation system to effectively cover the training blind spot and suggest a new evaluation setup to avoid misevaluation. Our proposed system leads to significant improvements over existing systems and large language models such as GPT-4. Additionally, our analysis provides insights into the nature of user simulation to facilitate future work.