Mechanism of Transcriptional Silencing in Yeast

AbstractTranscriptional silencing is a phenomenon in which the transcription of a gene by RNA polymerase II or III is repressed or not, dependent only on the gene's chromosomal location. Two prevailing models exist for silencing: (1) steric hindrance in silenced chromatin inhibits the binding of upstream activator proteins or polymerase or (2) silencing primarily blocks steps downstream of transcription preinitiation complex formation. Here, we test these models quantitatively for the case of SIR2-dependent silencing in budding yeast, using foreign and endogenous reporter proteins, at transgenic and endogenous loci. Our results contradict both models and show instead that transcriptional silencing at several URA3 transgenes, and at the naturally silenced endogenous HMRa and HMLα mating type genes, acts downstream of gene activator protein binding to strongly reduce the occupancy of TFIIB, RNA polymerase II, and TFIIE at the silenced promoters

Investigation into the nature behind the interesting half levitation behavior of claimed superconductor LK-99

A recent article published by Lee et.al. claimed to have successfully achieved superconductivity at room temperature (RT) has become a topical issue. Besides the research paper, Lee and his team provided a demonstration video of LK-99 half levitating (HL) on a magnet. Such interesting HL appearance has drawn tremendous sensation both in academia and the network. However, the true identity of LK-99 still remains unclear, i.e., whether the HL behavior can necessarily indicate the diamagnetism behavior of the sample. Here, we fabricated our own LK-99 samples following the procedures reported by Lee et al. We found quite a few sample pieces showing the typical HL that is similar to those reported. Meanwhile, oxidation during the sample preparation was found to deleterious to acquiring HL in the sample, while furnace cooling or water quenching in the last step revealed little effect. However, our careful observations indicated that those HL pieces are more likely simple ferromagnetic. Then we conducted a comprehensive study on the behavior patterns of typical diamagnetism and ferromagnetic substances interacting with a Nd2Fe14B magnet, and provided instructions to distinguish the characteristics between ferromagnetic and diamagnetic to prevent misunderstanding of LK-99 like levitation behavior

LVOS: A Benchmark for Long-term Video Object Segmentation

Existing video object segmentation (VOS) benchmarks focus on short-term videos which just last about 3-5 seconds and where objects are visible most of the time. These videos are poorly representative of practical applications, and the absence of long-term datasets restricts further investigation of VOS on the application in realistic scenarios. So, in this paper, we present a new benchmark dataset named \textbf{LVOS}, which consists of 220 videos with a total duration of 421 minutes. To the best of our knowledge, LVOS is the first densely annotated long-term VOS dataset. The videos in our LVOS last 1.59 minutes on average, which is 20 times longer than videos in existing VOS datasets. Each video includes various attributes, especially challenges deriving from the wild, such as long-term reappearing and cross-temporal similar objeccts.Based on LVOS, we assess existing video object segmentation algorithms and propose a Diverse Dynamic Memory network (DDMemory) that consists of three complementary memory banks to exploit temporal information adequately. The experimental results demonstrate the strength and weaknesses of prior methods, pointing promising directions for further study. Data and code are available at https://lingyihongfd.github.io/lvos.github.io/.Comment: Accepted by ICCV 2023. Project page: https://lingyihongfd.github.io/lvos.github.io

PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video Segmentation

Panoramic videos contain richer spatial information and have attracted tremendous amounts of attention due to their exceptional experience in some fields such as autonomous driving and virtual reality. However, existing datasets for video segmentation only focus on conventional planar images. To address the challenge, in this paper, we present a panoramic video dataset, PanoVOS. The dataset provides 150 videos with high video resolutions and diverse motions. To quantify the domain gap between 2D planar videos and panoramic videos, we evaluate 15 off-the-shelf video object segmentation (VOS) models on PanoVOS. Through error analysis, we found that all of them fail to tackle pixel-level content discontinues of panoramic videos. Thus, we present a Panoramic Space Consistency Transformer (PSCFormer), which can effectively utilize the semantic boundary information of the previous frame for pixel-level matching with the current frame. Extensive experiments demonstrate that compared with the previous SOTA models, our PSCFormer network exhibits a great advantage in terms of segmentation results under the panoramic setting. Our dataset poses new challenges in panoramic VOS and we hope that our PanoVOS can advance the development of panoramic segmentation/tracking

A Constrained BA Algorithm for Rate-Distortion and Distortion-Rate Functions

The Blahut-Arimoto (BA) algorithm has played a fundamental role in the numerical computation of rate-distortion (RD) functions. This algorithm possesses a desirable monotonic convergence property by alternatively minimizing its Lagrangian with a fixed multiplier. In this paper, we propose a novel modification of the BA algorithm, wherein the multiplier is updated through a one-dimensional root-finding step using a monotonic univariate function, efficiently implemented by Newton's method in each iteration. Consequently, the modified algorithm directly computes the RD function for a given target distortion, without exploring the entire RD curve as in the original BA algorithm. Moreover, this modification presents a versatile framework, applicable to a wide range of problems, including the computation of distortion-rate (DR) functions. Theoretical analysis shows that the outputs of the modified algorithms still converge to the solutions of the RD and DR functions with rate $O(1/n)$, where $n$ is the number of iterations. Additionally, these algorithms provide $\varepsilon$-approximation solutions with $O\left(\frac{MN\log N}{\varepsilon}(1+\log |\log \varepsilon|)\right)$ arithmetic operations, where $M,N$ are the sizes of source and reproduced alphabets respectively. Numerical experiments demonstrate that the modified algorithms exhibit significant acceleration compared with the original BA algorithms and showcase commendable performance across classical source distributions such as discretized Gaussian, Laplacian and uniform sources.Comment: Version_

Boosting the Transferability of Adversarial Attacks with Global Momentum Initialization

Deep neural networks are vulnerable to adversarial examples, which attach human invisible perturbations to benign inputs. Simultaneously, adversarial examples exhibit transferability under different models, which makes practical black-box attacks feasible. However, existing methods are still incapable of achieving desired transfer attack performance. In this work, from the perspective of gradient optimization and consistency, we analyze and discover the gradient elimination phenomenon as well as the local momentum optimum dilemma. To tackle these issues, we propose Global Momentum Initialization (GI) to suppress gradient elimination and help search for the global optimum. Specifically, we perform gradient pre-convergence before the attack and carry out a global search during the pre-convergence stage. Our method can be easily combined with almost all existing transfer methods, and we improve the success rate of transfer attacks significantly by an average of 6.4% under various advanced defense mechanisms compared to state-of-the-art methods. Eventually, we achieve an attack success rate of 95.4%, fully illustrating the insecurity of existing defense mechanisms

Information Bottleneck Revisited: Posterior Probability Perspective with Optimal Transport

Information bottleneck (IB) is a paradigm to extract information in one target random variable from another relevant random variable, which has aroused great interest due to its potential to explain deep neural networks in terms of information compression and prediction. Despite its great importance, finding the optimal bottleneck variable involves a difficult nonconvex optimization problem due to the nonconvexity of mutual information constraint. The Blahut-Arimoto algorithm and its variants provide an approach by considering its Lagrangian with fixed Lagrange multiplier. However, only the strictly concave IB curve can be fully obtained by the BA algorithm, which strongly limits its application in machine learning and related fields, as strict concavity cannot be guaranteed in those problems. To overcome the above difficulty, we derive an entropy regularized optimal transport (OT) model for IB problem from a posterior probability perspective. Correspondingly, we use the alternating optimization procedure and generalize the Sinkhorn algorithm to solve the above OT model. The effectiveness and efficiency of our approach are demonstrated via numerical experiments.Comment: ISIT 202

Magnetic moment evolution and spin freezing in doped BaFe 2 As 2

Fe-K β X-ray emission spectroscopy measurements reveal an asymmetric doping dependence of the magnetic moments μbare in electron- and hole-doped BaFe2As2. At low temperature, μbare is nearly constant in hole-doped samples, whereas it decreases upon electron doping. Increasing temperature substantially enhances μbare in the hole-doped region, which is naturally explained by the theoretically predicted crossover into a spin-frozen state. Our measurements demonstrate the importance of Hund’s-coupling and electronic correlations, especially for hole-doped BaFe2As2, and the inadequacy of a fully localized or fully itinerant description of the 122 family of Fe pnictides