Super enhancer acquisition drives expression of oncogenic PPP1R15B that regulates protein homeostasis in multiple myeloma by Xiong, Sinan, Zhou, Jianbiao, Tan, Tze King, Chung, Tae-Hoon, Tan, Tuan Zea, Toh, Sabrina Hui-Min, Tang, Nicole Xin Ning, Jia, Yunlu, See, Yi Xiang, Fullwood, Melissa Jane, Sanda, Takaomi, Chng, Wee-Joo

“…Here we generate a high-resolution contact map of myeloma-associated super-enhancers by integrating H3K27ac ChIP-seq and HiChIP from myeloma cell lines, patient-derived myeloma cells and normal plasma cells. …”
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Smart sleep monitoring: sparse sensor-based spatiotemporal CNN for sleep posture detection by Hu, Dikun, Gao, Weidong, Ang, Kai Keng, Hu, Mengjiao, Chuai, Gang, Huang, Rong

“…This S3CNN holistically incorporates a pair of spatial convolution neural networks to capture cardiorespiratory activity maps and a pair of temporal convolution neural networks to capture the heart rate and respiratory rate. …”
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Secure image encryption based on an ideal new nonlinear discrete dynamical system by Lin, M. Rongming, Ng, Teng Yong

“…It is well known that encryption algorithms developed based on Logistic map suffer from limited key space due to the narrow regions of system parameters which can be used, potential risk of security in the presence of numerous periodic windows within the key space, and weakness in known-plain-text attack due to the inherent correlation among the chaotic sequence used for encryption. …”
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Canonical Diffeomorphisms of Manifolds Near Spheres by Wang, Bing, Zhao, Xinrui

“…Let $$\{f_i\}_{i=1}^{n+1}$$ { f i } i = 1 n + 1 be the first $$(n+1)$$ ( n + 1 ) -eigenfunctions of (M, g) and $$f=(f_1, f_2, \ldots , f_{n+1})$$ f = ( f 1 , f 2 , … , f n + 1 ) . Then the map $${\tilde{f}}=\frac{f}{|f|}: M \rightarrow S^n$$ f ~ = f | f | : M → S n provides a diffeomorphism, and $${\tilde{f}}$$ f ~ satisfies a uniform bi-Hölder estimate. …”
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Deep Learning Based Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms by Ragnarsdottir, Hanna, Ozkan, Ece, Michel, Holger, Chin-Cheong, Kieran, Manduchi, Laura, Wellmann, Sven, Vogt, Julia E.

“…We complement our predictions with saliency maps and show that the learned model focuses on clinically relevant cardiac structures, motivating its usage in clinical practice. …”
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Petz recovery from subsystems in conformal field theory by Vardhan, Shreya, Wei, Annie Y., Zou, Yijian

“…We use an explicit recovery channel known as the twirled Petz map, and study distance measures such as the fidelity, relative entropy, and trace distance between the original state and the recovered state. …”
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Identification of a Gene Signature That Predicts Dependence upon YAP/TAZ-TEAD by Kanai, Ryan, Norton, Emily, Stern, Patrick, Hynes, Richard O., Lamar, John M.

“…We found that the genes in this signature are TEAD-dependent in several melanoma cell lines, and that their expression strongly correlates with YAP/TAZ activation in human melanomas. Using DepMap dependency data, we found that this YAP/TAZ signature was predictive of melanoma cell dependence upon YAP/TAZ or TEADs. …”
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Language, Camera, Autonomy! Prompt-engineered Robot Control for Rapidly Evolving Deployment by Macdonald, Jacob P., Mallick, Rohit, Wollaber, Allan B., Peña, Jaime D., McNeese, Nathan, Siu, Ho Chit

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Avoidance of Concave Obstacles Through Rotation of Nonlinear Dynamics by Huber, Lukas, Slotine, Jean-Jacques, Billard, Aude

“…We introduce a sequence of mappings to extend the approach for general nonlinear dynamics. …”
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Learning Bit Allocations for Z-Order Layouts in Analytic Data Systems by Gao, Jenny, Ding, Jialin, Sudhir, Sivaprasad, Madden, Samuel

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Semantic embedding for quantum algorithms by Rossi, Zane M, Chuang, Isaac L

“…This work constructs and characterizes the runtime and expressivity of QSP/QSVT protocols where circuit manipulation maps naturally to the algebraic manipulation of functional transforms (termed semantic embedding). …”
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Towards Efficient Planning for Navigation using Global Information in Large and Uncertain Environments by Kurtz, Martina Stadler

“…For example, many planners assume access to complete global information in the form of full environment maps, or they assume that locally good planning decisions under uncertainty will result in globally good planning outcomes. …”
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Theoretical Design of Molecular Nanostructures for Exciton Control by Castellanos, Maria A.

“…In Chapter 2, I develop a novel approach to map unitary quantum computing operations to Hamiltonians describing excitonic circuits in the presence of a model bath. …”
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Enabling Perspective-Aware Ai with Contextual Scene Graph Generation by Platnick, Daniel, Alirezaie, Marjan, Rahnama, Hossein

“…We evaluated PASGG-LM pipelines using state-of-the-art SGG models, including Motifs, Motifs-TDE, and RelTR, and showed that fine-tuning LLMs, particularly GPT-4o-mini and Llama-3.1-8B, improves performance in terms of R@K, mR@K, and mAP. Our method is capable of generating scene graphs that capture complex contextual aspects, advancing human–machine interaction by enhancing the representation of diverse perspectives. …”
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Decoding Surface Touch Typing from Hand-Tracking by Richardson, Mark, Durasoff, Matt, Wang, Robert

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Semantic sensitive satellite image retrieval by Li, Yikun

“…The main difficulty is that users think in terms of high-level semantics, which cannot be directly mapped onto extracted low-level features / signals. …”
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Tripartite partnership for the transformation of the Singapore maritime industry - Part B : Industry's perspective by Tan, Xian Jun

“…In responses to the threat of the changing economy, the Singapore government have launched several initiatives including the Industry Transport Maps (ITMs) to facilitate an effective tripartite partnership towards the transformation of Singapore’s Maritime Industry. …”
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Imaging and processing of tree trunk by Hong, Yuan Ming

“…Therefore, further investigations were conducted to better understand the peak time differences between 2D simulation and 3D simulation where the results can help to map the 2D simulations into 3D simulation using machine learning.…”
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Deep visual saliency on stereoscopic images by Nguyen, Anh-Duc, Kim, Jongyoo, Oh, Heeseok, Kim, Haksub, Lin, Weisi, Lee, Sanghoon

“…Our results from thorough experiments confirm that the predicted saliency maps are up to 70% correlated with human gaze patterns, which emphasize the need for the hand-crafted features as input to deep neural networks in S3D saliency detection.…”
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Ultra-low power real-ime object detection based on quantized CNNs by Chew, Jing Wei

“…Using this approach, the proposed model can run at 37.7 FPS on an NVIDIA Jetson Nano with a peak memory footprint of 17.1 MB, while attaining a reasonable mAP@0.50 Intersection over Union (IoU) of 0.37 on the Pascal Visual Object Classes (VOC) dataset. …”
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