Enhanced thermopower in an intergrowth cobalt oxide Li0.48_{0.48}Na0.35_{0.35}CoO2_{2}

We report the measurements of thermopower, electrical resistivity and thermal conductivity in a complex cobalt oxide Li$_{0.48}$Na$_{0.35}$CoO$_{2}$, whose crystal structure can be viewed as an intergrowth of the O3 phase of Li$_{x}$CoO$_{2}$ and the P2 phase of Na$_{y}$CoO$_{2}$ along the c axis. The compound shows large room-temperature thermopower of $\sim$180 $\mu$V/K, which is substantially higher than those of Li$_{x}$CoO$_{2}$ and Na$_{y}$CoO$_{2}$. The figure of merit for the polycrystalline sample increases rapidly with increasing temperature, and it achieves nearly 10$^{-4}$ K$^{-1}$ at 300 K, suggesting that Li$_{x}$Na$_{y}$CoO$_{2}$ system is a promising candidate for thermoelectric applications.Comment: Submitted to AP

A JNK-Dependent Pathway Is Required for TNFα-Induced Apoptosis

AbstractTumor necrosis factor (TNFα) receptor signaling can simultaneously activate caspase 8, the transcription factor, NF-κB and the kinase, JNK. While activation of caspase 8 is required for TNFα-induced apoptosis, and induction of NF-κB inhibits cell death, the precise function of JNK activation in TNFα signaling is not clearly understood. Here, we report that TNFα-mediated caspase 8 cleavage and apoptosis require a sequential pathway involving JNK, Bid, and Smac/DIABLO. Activation of JNK induces caspase 8-independent cleavage of Bid at a distinct site to generate the Bid cleavage product jBid. Translocation of jBid to mitochondria leads to preferential release of Smac/DIABLO, but not cytochrome c. The released Smac/DIABLO then disrupts the TRAF2-cIAP1 complex. We propose that the JNK pathway described here is required to relieve the inhibition imposed by TRAF2-cIAP1 on caspase 8 activation and induction of apoptosis. Further, our findings define a mechanism for crosstalk between intrinsic and extrinsic cell death pathways

FederBoost: Private Federated Learning for GBDT

An emerging trend in machine learning and artificial intelligence is federated learning (FL), which allows multiple participants to contribute various training data to train a better model. It promises to keep the training data local for each participant, leading to low communication complexity and high privacy. However, there are still two problems in FL remain unsolved: (1) unable to handle vertically partitioned data, and (2) unable to support decision trees. Existing FL solutions for vertically partitioned data or decision trees require heavy cryptographic operations. In this paper, we propose a framework named FederBoost for private federated learning of gradient boosting decision trees (GBDT). It supports running GBDT over both horizontally and vertically partitioned data. The key observation for designing FederBoost is that the whole training process of GBDT relies on the order of the data instead of the values. Consequently, vertical FederBoost does not require any cryptographic operation and horizontal FederBoost only requires lightweight secure aggregation. We fully implement FederBoost and evaluate its utility and efficiency through extensive experiments performed on three public datasets. Our experimental results show that both vertical and horizontal FederBoost achieve the same level of AUC with centralized training where all data are collected in a central server; and both of them can finish training within half an hour even in WAN.Comment: 15 pages, 8 figure

DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders

Image colorization is a challenging problem due to multi-modal uncertainty and high ill-posedness. Directly training a deep neural network usually leads to incorrect semantic colors and low color richness. While transformer-based methods can deliver better results, they often rely on manually designed priors, suffer from poor generalization ability, and introduce color bleeding effects. To address these issues, we propose DDColor, an end-to-end method with dual decoders for image colorization. Our approach includes a pixel decoder and a query-based color decoder. The former restores the spatial resolution of the image, while the latter utilizes rich visual features to refine color queries, thus avoiding hand-crafted priors. Our two decoders work together to establish correlations between color and multi-scale semantic representations via cross-attention, significantly alleviating the color bleeding effect. Additionally, a simple yet effective colorfulness loss is introduced to enhance the color richness. Extensive experiments demonstrate that DDColor achieves superior performance to existing state-of-the-art works both quantitatively and qualitatively. The codes and models are publicly available at https://github.com/piddnad/DDColor.Comment: ICCV 2023; Code: https://github.com/piddnad/DDColo

FishMOT: A Simple and Effective Method for Fish Tracking Based on IoU Matching

The tracking of various fish species plays a profoundly significant role in understanding the behavior of individual fish and their groups. Present tracking methods suffer from issues of low accuracy or poor robustness. In order to address these concerns, this paper proposes a novel tracking approach, named FishMOT (Fish Multiple Object Tracking). This method combines object detection techniques with the IoU matching algorithm, thereby achieving efficient, precise, and robust fish detection and tracking. Diverging from other approaches, this method eliminates the need for multiple feature extractions and identity assignments for each individual, instead directly utilizing the output results of the detector for tracking, thereby significantly reducing computational time and storage space. Furthermore, this method imposes minimal requirements on factors such as video quality and variations in individual appearance. As long as the detector can accurately locate and identify fish, effective tracking can be achieved. This approach enhances robustness and generalizability. Moreover, the algorithm employed in this method addresses the issue of missed detections without relying on complex feature matching or graph optimization algorithms. This contributes to improved accuracy and reliability. Experimental trials were conducted in the open-source video dataset provided by idtracker.ai, and comparisons were made with state-of-the-art detector-based multi-object tracking methods. Additionally, comparisons were made with idtracker.ai and TRex, two tools that demonstrate exceptional performance in the field of animal tracking. The experimental results demonstrate that the proposed method outperforms other approaches in various evaluation metrics, exhibiting faster speed and lower memory requirements. The source codes and pre-trained models are available at: https://github.com/gakkistar/FishMO

RSFNet: A White-Box Image Retouching Approach using Region-Specific Color Filters

Retouching images is an essential aspect of enhancing the visual appeal of photos. Although users often share common aesthetic preferences, their retouching methods may vary based on their individual preferences. Therefore, there is a need for white-box approaches that produce satisfying results and enable users to conveniently edit their images simultaneously. Recent white-box retouching methods rely on cascaded global filters that provide image-level filter arguments but cannot perform fine-grained retouching. In contrast, colorists typically employ a divide-and-conquer approach, performing a series of region-specific fine-grained enhancements when using traditional tools like Davinci Resolve. We draw on this insight to develop a white-box framework for photo retouching using parallel region-specific filters, called RSFNet. Our model generates filter arguments (e.g., saturation, contrast, hue) and attention maps of regions for each filter simultaneously. Instead of cascading filters, RSFNet employs linear summations of filters, allowing for a more diverse range of filter classes that can be trained more easily. Our experiments demonstrate that RSFNet achieves state-of-the-art results, offering satisfying aesthetic appeal and increased user convenience for editable white-box retouching.Comment: Accepted by ICCV 202

Recent Advances in Ambipolar Transistors for Functional Applications

Ambipolar transistors represent a class of transistors where positive (holes) and negative (electrons) charge carriers both can transport concurrently within the semiconducting channel. The basic switching states of ambipolar transistors are comprised of common offâ state and separated onâ state mainly impelled by holes or electrons. During the past years, diverse materials are synthesized and utilized for implementing ambipolar charge transport and their further emerging applications comprising ambipolar memory, synaptic, logic, and lightâ emitting transistors on account of their special bidirectional carrierâ transporting characteristic. Within this review, recent developments of ambipolar transistor field involving fundamental principles, interface modifications, selected semiconducting material systems, device structures, ambipolar characteristics, and promising applications are highlighted. The existed challenges and prospective for researching ambipolar transistors in electronics and optoelectronics are also discussed. It is expected that the review and outlook are well timed and instrumental for the rapid progress of academic sector of ambipolar transistors in lighting, display, memory, as well as neuromorphic computing for artificial intelligence.Ambipolar transistors represent transistors that allow synchronous transport of electrons and holes and their accumulation within semiconductors. This review provides a comprehensive summary of recent advances in various semiconducting materials realized in ambipolar transistors and their functional memory, synapse, logic, as well as lightâ emitting applications.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151885/1/adfm201902105_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151885/2/adfm201902105.pd