MOTIVATION: Multiple sequence alignment (MSA) is a fundamental operation in bioinformatics, yet existing MSA tools are struggling to keep up with the speed and volume of incoming data. This is because the runtimes and memory requirements of current MSA tools become untenable when processing large numbers of long input sequences, and they also fail to fully harness the parallelism provided by modern CPUs and GPUs. RESULTS: We present Tall and Wide Alignments at High Throughput (TWILIGHT), a novel MSA tool optimized for speed, accuracy, scalability, and memory constraints, with both CPU and GPU support. TWILIGHT incorporates innovative parallelization and memory-efficiency strategies that enable it to build ultralarge alignments at high speed even on memory-constrained devices. On challenging datasets, TWILIGHT outperformed all other tools in speed and accuracy. It scaled beyond the limits of existing tools and performed an alignment of 1 million RNASim sequences within 30 min while utilizing <16 GB of memory. TWILIGHT is the first tool to align over 8 million publicly available SARS-CoV-2 sequences, setting a new standard for large-scale genomic alignment and data analysis. AVAILABILITY AND IMPLEMENTATION: TWILIGHT's code is freely available under the MIT license at https://github.com/TurakhiaLab/TWILIGHT. The test datasets and experimental results, including our alignment of 8 million SARS-CoV-2 sequences, are available at https://zenodo.org/records/14722035.

A Physically Unclonable Function (PUF) circuit is purposefully engineered to leverage the inherent variations in manufacturing processes to generate distinct identities. However, the efficacy of PUFs can be compromised due to the influence of external factors such as noise and environmental variations, which can lead to instability of identities. In this paper, a novel post-processing stabilization technique is introduced, utilizing a mismatch recombination algorithm and resulting in a 31× reduction of the Bit Error Rate (BER) to 0.19%. Through the application of this stabilization technique in conjunction with a modified self-compared Ring Oscillator (RO) architecture, the proposed PUF achieves a uniqueness of 49.96%. Moreover, the proposed PUF exhibits substantial resilience against temperature variations, maintaining a BER of under 2% across the temperature range from 0°C to 45°C. The proposed PUF was implemented and verified on a Xilinx Artix-7 FPGA.

Compounding, a prevalent word-formation process, presents an interesting challenge for computational models. Indeed, the relations between compounds and their constituents are often complicated. It is particularly so in Chinese morphology, where each character is almost simultaneously bound and free when treated as a morpheme. To model such word-formation process, we propose the Notch (NOnlinear Transformation of CHaracter embeddings) model and the character Jacobians. The Notch model first learns the non-linear relations between the constituents and words, and the character Jacobians further describes the character’s role in each word. In a series of experiments, we show that the Notch model predicts the embeddings of the real words from their constituents but helps account for the behavioral data of the pseudowords. Moreover, we also demonstrated that character Jacobians reflect the characters’ meanings. Taken together, the Notch model and character Jacobians may provide a new perspective on studying the word-formation process and morphology with modern deep learning.

Constructions are direct form-meaning pairs with possible schematic slots. These slots are simultaneously constrained by the embedded construction itself and the sentential context. We propose that the constraint could be described by a conditional probability distribution. However, as this conditional probability is inevitably complex, we utilize language models to capture this distribution. Therefore, we build CxLM, a deep learning-based masked language model explicitly tuned to constructions’ schematic slots. We first compile a construction dataset consisting of over ten thousand constructions in Taiwan Mandarin. Next, an experiment is conducted on the dataset to examine to what extent a pretrained masked language model is aware of the constructions. We then fine-tune the model specifically to perform a cloze task on the opening slots. We find that the fine-tuned model predicts masked slots more accurately than baselines and generates both structurally and semantically plausible word samples. Finally, we release CxLM and its dataset as publicly available resources and hope to serve as new quantitative tools in studying construction grammar.

The morphological status of affixes in Chinese has long been a matter of debate.How one might apply the conventional criteria of free/bound and content/function features to distinguish word-forming affixes from bound roots in Chinese is still far from clear.Issues involving polysemy and diachronic dynamics further blur the boundaries.In this paper, we propose three quantitative features in a computational model of affixoid behavior in Mandarin Chinese.The results show that, except for in a very few cases, there are no clear criteria that can be used to identify an affix's status in an isolating language like Chinese.A diachronic check using contextualized embeddings with the WordNet Sense Inventory also demonstrates the possible role of the polysemy of lexical roots across diachronic settings.

Constructing semantic relations in WordNet has been a labour-intensive task, especially in a dynamic and fastchanging language environment.Combined with recent advancements of contextualized embeddings, this paper proposes the concept of morphologyguided sense vectors, which can be used to semi-automatically augment semantic relations in Chinese Wordnet (CWN).This paper (1) built sense vectors with pre-trained contextualized embedding models; (2) demonstrated the sense vectors computed were consistent with the sense distinctions made in CWN; and (3) predicted the potential semantically-related sense pairs with high accuracy by sense vectors model.

There are four major components in application systems with internet-of-things (IoT): sensors, communications, computation and service, where large amount of data are acquired for ultra-big data analysis to discover the context information and knowledge behind signals. To support such large-scale data size and computation tasks, it is not feasible to employ centralized solutions on cloud servers. Thanks for the advances of silicon technology, the cost of computation become lower, and it is possible to distribute computation on every node in IoT. In this paper, we take video sensing network as an example to show the idea of distributed computing in IoT. Existing related works are reviewed and the architecture of a system-on-a-chip solution for distributed smart cameras is proposed with coarse-grained reconfigurable image stream processing architecture. It can accelerate various computer vision algorithms for distributed smart cameras in IoT.

Video object segmentation and tracking are two essential building blocks of smart surveillance systems. However, there are several issues that need to be resolved. Threshold decision is a difficult problem for video object segmentation with a multi-background model. In addition, some conditions make robust video object tracking difficult. These conditions include nonrigid object motion, target appearance variations due to changes in illumination, and background clutter. In this paper, a video object segmentation and tracking framework is proposed for smart cameras in visual surveillance networks with two major contributions. First, we propose a robust threshold decision algorithm for video object segmentation with a multi-background model. Second, we propose a video object tracking framework based on a particle filter with the likelihood function composed of diffusion distance for measuring color histogram similarity and motion clue from video object segmentation. The proposed framework can track nonrigid moving objects under drastic changes in illumination and background clutter. Experimental results show that the presented algorithms perform well for several challenging sequences, and our proposed methods are effective for the aforementioned issues.

In the next-generation visual surveillance systems, content analysis tools will be integrated. In this paper, to accelerate these tools, it is proposed to integrate a hardware content analysis engine into a smart camera system-on-a-chip (SoC). A smart camera SoC hardware architecture with the proposed visual content analysis engine is first presented. This engine consists of dedicated accelerators and a programmable morphology coprocessor. Stream processing design concept, frame-level pipelining, and subword level parallelism are employed together to efficiently utilize the bandwidth of the system bus and achieve high throughput. The implementation results show that, with 168 K logic gates and 40.63 Kb on-chip memory, a processing speed of 30 640 x 480 frames/s can be achieved, while the operations of video object segmentation, object description and tracking, and face detection and scoring are supported.