Miracle-dz/UniFAR-FTUs huggingface dataset:
AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising

Spectral unmixing has been extensively studied with a variety of methods and is used in many applications. Recently, data-driven techniques with deep learning methods have obtained great attention to spectral unmixing for its superior learning ability to automatically learn the structure information. In particular, autoencoder (AE)-based architectures are elaborately designed to solve blind unmixing and model complex nonlinear mixtures. Nevertheless, these methods perform unmixing task as black boxes and lack interpretability. On the other hand, conventional unmixing methods carefully design the regularizer to add explicit information, in which algorithms such as plug-and-play (PnP) strategies utilize off-the-shelf denoisers to plug powerful priors. In this article, we propose a generic unmixing framework to integrate the AE network with regularization by denoising (RED), named AE-RED. More specifically, we decompose the unmixing optimized problem into two subproblems. The first one is solved using deep AEs to implicitly regularize the estimates and model the mixture mechanism. The second one leverages the denoiser to bring in explicit information. In this way, both the characteristics of the deep AE-based unmixing methods and priors provided by denoisers are merged into our well-designed framework to enhance the unmixing performance. The experiment results on both synthetic and real datasets show the superiority of our proposed framework compared with state-of-the-art unmixing approaches.
Lottery and Sprint: Generate a Board Game with Design Sprint Method on AutoGPT

In this paper, we introduce "Lottery and Sprint", a board game creation methodology that cooperates human design intuition with the structured Design Sprint framework executed by the AutoGPT system. By aligning AI-driven processes with human creativity, we aim to facilitate a collaborative game design experience. A user study is conducted to investigate the playability and enjoyment of the generated games, revealing both successes and challenges in employing systems like AutoGPT for board game design. Insights and future research directions are proposed to overcome identified limitations and enhance computational-driven game creation.
Energy Beamforming for RF Wireless Power Transfer With Dynamic Metasurface Antennas

Radio frequency (RF) wireless power transfer (WPT) is a promising technology for charging the Internet of Things. Practical RF-WPT systems usually require energy beamforming (EB), which can compensate for the severe propagation loss by directing beams toward the devices. The EB flexibility depends on the transmitter architecture, existing a trade-off between cost/complexity and degrees of freedom. Thus, simpler architectures such as dynamic metasurface antennas (DMAs) are gaining attention. Herein, we consider an RF-WPT system with a transmit DMA for meeting the energy harvesting requirements of multiple devices and formulate an optimization problem for the minimum-power design. First, we provide a mathematical model to capture the frequency-dependant signal propagation effect in the DMA architecture. Next, we propose a solution based on semi-definite programming and alternating optimization. Results show that a DMA-based structure can outperform a fully-digital implementation and that the required transmit power decreases with the antenna array size, while it increases and remains almost constant with frequency in DMA and FD, respectively.
An urn model for opinion propagation on networks

We consider a coupled Polya's urn scheme for social dynamics on networks. Agents hold continuum-valued opinions on a two-state issue and randomly converse with their neighbors on a graph, agreeing on one of the two states. The probability of agreeing on a given state is a simple function of both of agents' opinions, with higher importance given to agents who have participated in more conversations. Opinions are then updated based on the results of the conversation. We show that this system is governed by a discrete version of the stochastic heat equation, and prove that the system reaches a consensus of opinion.
Improving the Transferability of Time Series Forecasting with Decomposition Adaptation

Due to effective pattern mining and feature representation, neural forecasting models based on deep learning have achieved great progress. The premise of effective learning is to collect sufficient data. However, in time series forecasting, it is difficult to obtain enough data, which limits the performance of neural forecasting models. To alleviate the data scarcity limitation, we design Sequence Decomposition Adaptation Network (SeDAN) which is a novel transfer architecture to improve forecasting performance on the target domain by aligning transferable knowledge from cross-domain datasets. Rethinking the transferability of features in time series data, we propose Implicit Contrastive Decomposition to decompose the original features into components including seasonal and trend features, which are easier to transfer. Then we design the corresponding adaptation methods for decomposed features in different domains. Specifically, for seasonal features, we perform joint distribution adaptation and for trend features, we design an Optimal Local Adaptation. We conduct extensive experiments on five benchmark datasets for multivariate time series forecasting. The results demonstrate the effectiveness of our SeDAN. It can provide more efficient and stable knowledge transfer.
On Embedding B-Splines in Recursive State Estimation

We present a principled study on establishing a novel probabilistic framework for state estimation. B-splines are embedded in the state-space modeling as a continuous-time intermediate between the states of recurrent control points and asynchronous sensor measurements. Based thereon, the spline-embedded recursive estimation scheme is established w.r. t. common sensor fusion tasks, and the corresponding technique for modeling uncertain motion estimates is introduced. We evaluate the proposed estimation scheme using real-world-based synthesized data with a range-inertial setting. The numerical results demonstrate several advantages of spline embedding in recursive state estimation over the classic discrete-time filtering scheme.
Near-real-time stereo matching method using both cross-based support regions in stereo views

Abstract. This paper presents a near-real-time stereo matching method using both cross-based support regions in stereo views. By applying the logical AND operator to the cross-based support region in the reference image and target image, we can obtain an intersection support region, which is used as an adaptive matching window. The proposed method aggregates absolute difference estimates in the intersection support region, which are combined with the census transform results. The census transform with a fixed window size and shape is applied, and only the resultant binary code of the pixel in the intersection support region is used. From Middlebury images and their ground truth disparity maps, we compute the area similarity ratio of support regions in stereo views. Then, a conditional probability of observing a correct disparity estimate with respect to the area similarity ratio is examined. By taking a natural logarithm of the probability, a relative reliability weight about the area similarity of support regions is obtained. The initial matching cost is then combined with the reliability weight to obtain the final cost, and the disparity with the minimum cost is chosen as the final disparity estimate. Experimental results demonstrate that the proposed method can estimate accurate disparity maps.
Image Background Serves as Good Proxy for Out-of-distribution Data

Out-of-distribution (OOD) detection empowers the model trained on the closed image set to identify unknown data in the open world. Though many prior techniques have yielded considerable improvements in this research direction, two crucial obstacles still remain. Firstly, a unified perspective has yet to be presented to view the developed arts with individual designs, which is vital for providing insights into future work. Secondly, we expect sufficient natural OOD supervision to promote the generation of compact boundaries between the in-distribution (ID) and OOD data without collecting explicit OOD samples. To tackle these issues, we propose a general probabilistic framework to interpret many existing methods and an OOD-data-free model, namely \textbf{S}elf-supervised \textbf{S}ampling for \textbf{O}OD \textbf{D}etection (SSOD). SSOD efficiently exploits natural OOD signals from the ID data based on the local property of convolution. With these supervisions, it jointly optimizes the OOD detection and conventional ID classification in an end-to-end manner. Extensive experiments reveal that SSOD establishes competitive state-of-the-art performance on many large-scale benchmarks, outperforming the best previous method by a large margin, \eg, reporting \textbf{-6.28\%} FPR95 and \textbf{+0.77\%} AUROC on ImageNet, \textbf{-19.01\%} FPR95 and \textbf{+3.04\%} AUROC on CIFAR-10, and top-ranked performance on hard OOD datasets, \ie, ImageNet-O and OpenImage-O.
Safe Screening for Unbalanced Optimal Transport

This paper introduces a framework that utilizes the Safe Screening technique to accelerate the optimization process of the Unbalanced Optimal Transport (UOT) problem by proactively identifying and eliminating zero elements in the sparse solutions. We demonstrate the feasibility of applying Safe Screening to the UOT problem with $\ell_2$-penalty and KL-penalty by conducting an analysis of the solution's bounds and considering the local strong convexity of the dual problem. Considering the specific structural characteristics of the UOT in comparison to general Lasso problems on the index matrix, we specifically propose a novel approximate projection, an elliptical safe region construction, and a two-hyperplane relaxation method. These enhancements significantly improve the screening efficiency for the UOT's without altering the algorithm's complexity.
Neural Chronos ODE: Unveiling Temporal Patterns and Forecasting Future and Past Trends in Time Series Data

This work introduces Neural Chronos Ordinary Differential Equations (Neural CODE), a deep neural network architecture that fits a continuous-time ODE dynamics for predicting the chronology of a system both forward and backward in time. To train the model, we solve the ODE as an initial value problem and a final value problem, similar to Neural ODEs. We also explore two approaches to combining Neural CODE with Recurrent Neural Networks by replacing Neural ODE with Neural CODE (CODE-RNN), and incorporating a bidirectional RNN for full information flow in both time directions (CODE-BiRNN), and variants with other update cells namely GRU and LSTM: CODE-GRU, CODE-BiGRU, CODE-LSTM, CODE-BiLSTM. Experimental results demonstrate that Neural CODE outperforms Neural ODE in learning the dynamics of a spiral forward and backward in time, even with sparser data. We also compare the performance of CODE-RNN/-GRU/-LSTM and CODE-BiRNN/-BiGRU/-BiLSTM against ODE-RNN/-GRU/-LSTM on three real-life time series data tasks: imputation of missing data for lower and higher dimensional data, and forward and backward extrapolation with shorter and longer time horizons. Our findings show that the proposed architectures converge faster, with CODE-BiRNN/-BiGRU/-BiLSTM consistently outperforming the other architectures on all tasks.
Automatic Unit Test Generation for Deep Learning Frameworks based on API Knowledge

Many automatic unit test generation tools that can generate unit test cases with high coverage over a program have been proposed. However, most of these tools are ineffective on deep learning (DL) frameworks due to the fact that many of deep learning APIs expect inputs that follow specific API knowledge. To fill this gap, we propose MUTester to generate unit test cases for APIs of deep learning frameworks by leveraging the API constraints mined from the corresponding API documentation and the API usage patterns mined from code fragments in Stack Overflow (SO). Particularly, we first propose a set of 18 rules for mining API constraints from the API documents. We then use the frequent itemset mining technique to mine the API usage patterns from a large corpus of machine learning API related code fragments collected from SO. Finally, we use the above two types of API knowledge to guide the test generation of existing test generators for deep learning frameworks. To evaluate the performance of MUTester, we first collect 1,971 APIs from four widely-used deep learning frameworks (i.e., Scikit-learn, PyTorch, TensorFlow, and CNTK) and for each API, we further extract its API knowledge, i.e., API constraints and API usage. Given an API, MUTester combines its API knowledge with existing test generators (e.g., search-based test generator PyEvosuite and random test generator PyRandoop) to generate test cases to test the API. Results of our experiment show that MUTester can significantly improve the corresponding test generation methods and the improvement in code coverage is 15.7% to 27.0% on average. In addition, it can help reduce around 19.0% of invalid tests generated by the existing test generators. Our user study with 16 developers further demonstrates the practicality of MUTester in generating test cases for deep learning frameworks.
Signature of (anti)cooperativity in the stochastic fluctuations of small systems: application to the bacterial flagellar motor

The cooperative binding of molecular agents onto a substrate is pervasive in living systems. To study whether a system shows cooperativity, one can rely on a fluctuation analysis of quantities such as the number of substrate-bound units and the residence time in an occupancy state. Since the relative standard deviation from the statistical mean monotonically decreases with the number of binding sites, these techniques are only suitable for small enough systems, such as those implicated in stochastic processes inside cells. Here, we present a general-purpose grand canonical Hamiltonian description of a small one-dimensional (1D) lattice gas with either nearest-neighbor or long-range interactions as prototypical examples of cooperativity-influenced adsorption processes. First, we elucidate how the strength and sign of the interaction potential between neighboring bound particles on the lattice determine the intensity of the fluctuations of the mean occupancy. We then employ this relationship to compare the theoretical predictions of our model to data from single molecule experiments on bacterial flagellar motors (BFM) of E. coli. In this way, we find evidence that cooperativity controls the mechano-sensitive dynamical assembly of the torque-generating units, the so-called stator units, onto the BFM. Furthermore, in an attempt to quantify fluctuations and the adaptability of the BFM, we estimate the stator-stator interaction potential. Finally, we conclude that the system resides in a sweet spot of the parameter space (phase diagram) suitable for a smoothly adaptive system while minimizing fluctuations.
Predicting beauty, liking, and aesthetic quality: A comparative analysis of image databases for visual aesthetics research

In the fields of Experimental and Computational Aesthetics, numerous image datasets have been created over the last two decades. In the present work, we provide a comparative overview of twelve image datasets that include aesthetic ratings (beauty, liking or aesthetic quality) and investigate the reproducibility of results across different datasets. Specifically, we examine how consistently the ratings can be predicted by using either (A) a set of 20 previously studied statistical image properties, or (B) the layers of a convolutional neural network developed for object recognition. Our findings reveal substantial variation in the predictability of aesthetic ratings across the different datasets. However, consistent similarities were found for datasets containing either photographs or paintings, suggesting different relevant features in the aesthetic evaluation of these two image genres. To our surprise, statistical image properties and the convolutional neural network predict aesthetic ratings with similar accuracy, highlighting a significant overlap in the image information captured by the two methods. Nevertheless, the discrepancies between the datasets call into question the generalizability of previous research findings on single datasets. Our study underscores the importance of considering multiple datasets to improve the validity and generalizability of research results in the fields of experimental and computational aesthetics.
Analyzing Lack of Concordance Between the Proteome and Transcriptome in Paired scRNA-Seq and Multiplexed Spatial Proteomics

In this study, we analyze discordance between the transcriptome and proteome using paired scRNA-Seq and multiplexed spatial proteomics data from HuBMAP. Our findings highlight persistent transcripts in key immune markers, including CD45-RO, Ki67, CD45, CD20, and HLA-DR. CD45-RO is consistently expressed in memory T cells, while Ki67, associated with cell proliferation, also displays sustained expression. Furthermore, HLA-DR, part of the MHC class II molecules, demonstrates continuous expression, possibly crucial for APCs to trigger an effective immune response. This investigation provides novel insights into the complexity of gene expression regulation and protein function.
A Visual Attention Training Program for the Improvement of Auditory Comprehension in Aphasia

Attention as a non-linguistic cognitive component appears to play a role in language performance. Few research have identified evidence of contribution of attention training in improvement of auditory comprehension with Individuals with Aphasia (IWA). Based on the previous studies, it was hypothesized that visual attention training improves auditory comprehension in IWA with moderate severity. Three adults (two male and one female) with chronic aphasia participated in the study, all with attention and auditory comprehension deficits included in the study using the auditory comprehension subset of Persian version of the Western Aphasia Battery (P-WAB) and Farsi Aphasia Test (FAT). Adopted attention cognitive tests of Stroop and Continuous Performance Test (CPT) were used to measure cognitive capabilities pre-treatment. NEurocognitive Joyful Attentive Training Intervention (NEJATI) was used as an attention cognitive intervention in a 6-weeks training program through single subject non-concurrent multiple baseline design across participants to evaluate treatment effects. Cohen’s d-static was used to quantify treatment effect size. Two of the three participants showed significant improvements in their auditory comprehension skills with corresponding effect sizes that were large and medium in magnitude (d = 5.7 and d = 4.1 respectively). The third participant did not show any sign of improvement (d = 0.5). The hypothesis that attention training leads to improvement in auditory comprehension in moderate aphasia has been confirmed in this study. Cognitive factors can also play a role in the effectiveness of the treatment. This finding needs to be more investigated in larger population of aphasia patients with control for cognitive abilities variabilities.
Investigating Masking-based Data Generation in Language Models

The current era of natural language processing (NLP) has been defined by the prominence of pre-trained language models since the advent of BERT. A feature of BERT and models with similar architecture is the objective of masked language modeling, in which part of the input is intentionally masked and the model is trained to predict this piece of masked information. Data augmentation is a data-driven technique widely used in machine learning, including research areas like computer vision and natural language processing, to improve model performance by artificially augmenting the training data set by designated techniques. Masked language models (MLM), an essential training feature of BERT, have introduced a novel approach to perform effective pre-training on Transformer based models in natural language processing tasks. Recent studies have utilized masked language model to generate artificially augmented data for NLP downstream tasks. The experimental results show that Mask based data augmentation method provides a simple but efficient approach to improve the model performance. In this paper, we explore and discuss the broader utilization of these data augmentation methods based on MLM.
Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces

Impactful applications such as materials discovery, hardware design, neural architecture search, or portfolio optimization require optimizing high-dimensional black-box functions with mixed and combinatorial input spaces. While Bayesian optimization has recently made significant progress in solving such problems, an in-depth analysis reveals that the current state-of-the-art methods are not reliable. Their performances degrade substantially when the unknown optima of the function do not have a certain structure. To fill the need for a reliable algorithm for combinatorial and mixed spaces, this paper proposes Bounce that relies on a novel map of various variable types into nested embeddings of increasing dimensionality. Comprehensive experiments show that Bounce reliably achieves and often even improves upon state-of-the-art performance on a variety of high-dimensional problems.
A 3 TOPS/W RISC-V Parallel Cluster for Inference of Fine-Grain Mixed-Precision Quantized Neural Networks

The emerging trend of deploying complex algorithms, such as Deep Neural networks (DNNs), increasingly poses strict memory and energy efficiency requirements on Internet-of-Things (IoT) end-nodes. Mixed-precision quantization has been proposed as a technique to minimize a DNN’s memory footprint and maximize its execution efficiency, with negligible end-to-end precision degradation. In this work, we present a novel hardware and software stack for energy-efficient inference of mixed-precision Quantized Neural Networks (QNNs). We introduce Flex-V, a processor based on the RISC-V Instruction Set Architecture (ISA) that features fused Mac&Load mixed-precision dot product instructions; to avoid the exponential growth of the encoding space due to mixed-precision variants, we encode formats into the Control-Status Registers (CSRs). Flex-V core is integrated into a tightly-coupled cluster of eight processors; in addition, we provide a full framework for the end-to-end deployment of DNNs including a compiler, optimized libraries, and a memory-aware deployment flow. Our results show up to 91.5 MAC/cycle and 3.26 TOPS/W on the cluster, implemented in a commercial 22nm FDX technology, with up to $ 8.5 \times$ speed-up, and an area overhead of only 5.6% with respect to the baseline. To demonstrate the capabilities of the architecture, we benchmark it with end-to-end real-life QNNs, improving performance by $ 2 \times-2.5 \times$ with respect to existing solutions using fully flexible programmable processors.