Manifold-based Shapley for SAR Recognization Network Explanation

Explainable artificial intelligence (XAI) holds immense significance in enhancing the deep neural network's transparency and credibility, particularly in some risky and high-cost scenarios, like synthetic aperture radar (SAR). Shapley is a game-based explanation technique with robust mathematical foundations. However, Shapley assumes that model's features are independent, rendering Shapley explanation invalid for high dimensional models. This study introduces a manifold-based Shapley method by projecting high-dimensional features into low-dimensional manifold features and subsequently obtaining Fusion-Shap, which aims at (1) addressing the issue of erroneous explanations encountered by traditional Shap; (2) resolving the challenge of interpretability that traditional Shap faces in complex scenarios.Comment: 5 pages, 4 figure

SAR Despeckling via Regional Denoising Diffusion Probabilistic Model

Speckle noise poses a significant challenge in maintaining the quality of synthetic aperture radar (SAR) images, so SAR despeckling techniques have drawn increasing attention. Despite the tremendous advancements of deep learning in fixed-scale SAR image despeckling, these methods still struggle to deal with large-scale SAR images. To address this problem, this paper introduces a novel despeckling approach termed Region Denoising Diffusion Probabilistic Model (R-DDPM) based on generative models. R-DDPM enables versatile despeckling of SAR images across various scales, accomplished within a single training session. Moreover, The artifacts in the fused SAR images can be avoided effectively with the utilization of region-guided inverse sampling. Experiments of our proposed R-DDPM on Sentinel-1 data demonstrates superior performance to existing methods.Comment: 5 pages, 5 figure

Dynamic Perceiver for Efficient Visual Recognition

Early exiting has become a promising approach to improving the inference efficiency of deep networks. By structuring models with multiple classifiers (exits), predictions for ``easy'' samples can be generated at earlier exits, negating the need for executing deeper layers. Current multi-exit networks typically implement linear classifiers at intermediate layers, compelling low-level features to encapsulate high-level semantics. This sub-optimal design invariably undermines the performance of later exits. In this paper, we propose Dynamic Perceiver (Dyn-Perceiver) to decouple the feature extraction procedure and the early classification task with a novel dual-branch architecture. A feature branch serves to extract image features, while a classification branch processes a latent code assigned for classification tasks. Bi-directional cross-attention layers are established to progressively fuse the information of both branches. Early exits are placed exclusively within the classification branch, thus eliminating the need for linear separability in low-level features. Dyn-Perceiver constitutes a versatile and adaptable framework that can be built upon various architectures. Experiments on image classification, action recognition, and object detection demonstrate that our method significantly improves the inference efficiency of different backbones, outperforming numerous competitive approaches across a broad range of computational budgets. Evaluation on both CPU and GPU platforms substantiate the superior practical efficiency of Dyn-Perceiver. Code is available at https://www.github.com/LeapLabTHU/Dynamic_Perceiver.Comment: Accepted at ICCV 202

A controllable IC-compatible thin-film fuse realized using electro-explosion

A controllable IC-compatible thin-film fuse was developed that had Al/SiO2 thin-film stacks on a silicon substrate. The micro fuse has both a traditional mode and a controllable mode when applied as a fuse. It blows at 800 mA and 913.8 mV in the traditional mode. In the controllable mode, it blows within 400 ns at 10 V. It can be used for small electronic elements as well as electropyrotechnic initiators to improve the no-firing current

Preparation and Evaluation of Novel Folate Isonitrile 99mTc Complexes as Potential Tumor Imaging Agents to Target Folate Receptors

In order to seek novel technetium-99m folate receptor-targeting agents, two folate derivatives (CN5FA and CNPFA) were synthesized and radiolabeled to obtain [99mTc]Tc-CN5FA and [99mTc]Tc-CNPFA complexes, which exhibited high radiochemical purity (>95%) without purification, hydrophilicity, and good stability in vitro. The KB cell competitive binding experiments indicated that [99mTc]Tc-CN5FA and [99mTc]Tc-CNPFA had specificity to folate receptor. Biodistribution studies in KB tumor-bearing mice illustrated that [99mTc]Tc-CN5FA and [99mTc]Tc-CNPFA had specific tumor uptake. Compared with [99mTc]Tc-CN5FA, the tumor/muscle ratios of [99mTc]Tc-CNPFA were higher, resulting in a better SPECT/CT imaging background. According to the results, the two 99mTc complexes have potential as tumor imaging agents to target folate receptors

A Hybrid Model for PM2.5 Concentration Forecasting Based on Neighbor Structural Information, a Case in North China

PM2.5 concentration prediction is an important task in atmospheric environment research, so many prediction models have been established, such as machine learning algorithm, which shows remarkable generalization ability. The time series data composed of PM2.5 concentration have the implied structural characteristics such as the sequence characteristic in time dimension and the high dimension characteristic in dynamic-mode space, which makes it different from other research data. However, when the machine learning algorithm is applied to the PM2.5 time series prediction, due to the principle of input data composition, the above structural characteristics can not be fully reflected. In our study, a neighbor structural information extraction algorithm based on dynamic decomposition is proposed to represent the structural characteristics of time series, and a new hybrid prediction system is established by using the extracted neighbor structural information to improve the accuracy of PM2.5 concentration prediction. During the process of extracting neighbor structural information, the original PM2.5 concentration series is decomposed into finite dynamic modes according to the neighborhood data, which reflects the time series structural characteristics. The hybrid model integrates the neighbor structural information in the form of input vector, which ensures the applicability of the neighbor structural information and retains the composition form the original prediction system. The experimental results of six cities show that the hybrid prediction systems integrating neighbor structural information are significantly superior to the traditional models, and also confirm that the neighbor structural information extraction algorithm can capture effective time series structural information

Ujian Nasional (UN) yang Akurat dan Adil untuk Sekolah-sekolah di Indonesia yang Sangat Bervariasi Kondisi dan Kualitasnya

Salah satu fungsi Ujian Nasional (UN) adalah sebagai alat ukur yang akurat untuk mengetahui kualitas atau mutu pendidikan di Indonesia secara menyeluruh di dalam wilayah NKRI. Bagaimana membuat Ujian Nasional (UN) tersebut hasil pengukurannya akurat dan adil untuk sekolah-sekolah di Indonesia yang sangat bervariasi kondisi dan kualitasnya?Pertama agar hasil pengukuran secara nasional akurat adalah data yang dikumpulkan diambil secara menyeluruh (sensus, bukan hanya sekedar survei). Selain itu, alat ukur atau ujian yang digunakan juga harus baik kualitasnya. Untuk mendapatkan alat ukur yang kualitasnya baik, soal-soalnya seharusnya diambil dari Bank Soal, karena semua soal-soal yang ada di dalam Bank Soal sudah jelas kualitas dan karakteristik. Kunci kedua, karena kondisi sekolah-sekolah dan kemampuan siswa di lndonesia sangat beragam, maka tingkat kesukaran soal-soal dalam UN seharusnya berbeda, sesuai dengan kemampuan peserta tesnya. Dengan menggunakan soal-soal yang ada di dalam Bank Soal yang sudah dikalibrasi dengan konsep Item Response Theory (Calibrated Item Bank), hal ini memungkinkan peserta UN mengerjakan perangkat tes yang berbeda sesuai dengan kemampuannya, tetapi tetap hasilnya dapat dibandingka

Thermal parameters and densities of adopted materials.

<p>Thermal parameters and densities of adopted materials.</p

Preparation and Bioevaluation of a Novel ^99mTc-Labeled Glucose Derivative Containing Cyclohexane as a Promising Tumor Imaging Agent

To develop novel tumor imaging agents with high tumor uptake and excellent tumor/non-target ratios, a glucose derivative containing cyclohexane (CNMCHDG) was synthesized and labeled with Tc-99m. [99mTc]Tc-CNMCHDG was prepared by a kit formulation that was straightforward to operate and fast. Without purification, [99mTc]Tc-CNMCHDG had a high radiochemical purity of over 95% and great in vitro stability and hydrophilicity (log P = −3.65 ± 0.10). In vitro cellular uptake studies showed that the uptake of [99mTc]Tc-CNMCHDG was significantly inhibited by pre-treatment with D-glucose and increased by pre-treatment with insulin. Preliminary cellular studies have demonstrated that the mechanism by which the complex enters into cells may be related to GLUTs. The results of biodistribution and SPECT imaging studies displayed high tumor uptake and good retention of [99mTc]Tc-CNMCHDG in A549 tumor-bearing mice (4.42 ± 0.36%ID/g at 120 min post-injection). Moreover, [99mTc]Tc-CNMCHDG exhibited excellent tumor-to-non-target ratios and a clean imaging background and is a potential candidate for clinical transformation