Current emotional text-to-speech tasks have achieved high-quality emotional speech by incorporating emotion modules into text-to-speech models. However, there has been limited in-depth research on embedding emotion modules within TTS models, and the expression of emotion in synthesized speech is constrained by both the TTS model and the emotion module, often preventing optimal results. This paper presents a novel TTS model, Grad-DiT, based on the DiT architecture of diffusion models, aimed at enhancing the naturalness and expressiveness of TTS. Unlike traditional U-net architectures, Grad-DiT leverages the Transformer architecture to better capture contextual information in text, resulting in more natural speech generation. Building on this model, we propose Emo-DiT, which incorporates an Emotion Feature Reconstruction (EFR) module to enable the synthesis of speech with specific emotional expressions. Experimental results show that Grad-DiT not only surpasses existing TTS models in speech quality but also significantly improves real-time performance and inference speed. Compared to traditional emotion generation methods, Emo-DiT offers more precise emotional expression, enabling the synthesis of speech with distinctive emotional characteristics and providing substantial support for future applications in emotional speech synthesis.

The management level of Smart Parking Systems (SPS) relies heavily on accurate parking occupancy information, making low-cost, high-precision wireless parking sensors (WPS), powered by batteries, widely used in urban parking lots. However, the performance of magnetometer-based WPS is often disrupted by electromagnetic interference (EMI) from underground high-voltage cables and subways, limiting their reliability in urban environments. This paper proposes an Anti-electromagnetic Interference Parking Detection (AeIPD) method to address this issue. AeIPD combines traditional Received Signal Strength (RSS) features with antenna impedance measurements, utilizing two Bluetooth Low Energy (BLE) transceivers to enhance detection robustness under EMI conditions. Compared to existing methods, AeIPD significantly improves resilience to EMI, providing a more reliable and robust solution for parking detection even in environments with severe interference. This approach offers a cost-effective, scalable solution for large-scale deployment in modern SPS, overcoming the limitations of traditional magnetometer-based systems. Experimental results demonstrate that AeIPD outperforms current parking detection methods, offering a more reliable and robust alternative for smart parking applications.

Multimodal Sentiment Analysis (MSA) is the task of understanding human emotions by analyzing a combination of different data sources, such as text, audio, and visual inputs.Although recent advances have improved emotion modeling across modalities, existing methods still struggle with two fundamental challenges: balancing global and fine-grained sentiment contributions, and over-reliance on the text modality.To address these issues, we propose DPDF-LQ (Dual-Path Dynamic Fusion with Learnable Query), an architecture that processes inputs through two complementary paths: global and local.The global path is responsible for establishing cross-modal dependencies, while the local path captures finegrained representations.Additionally, we introduce the key module Dynamic Global Learnable Query Attention (DGLQA) in the global path, which dynamically allocates weights to each modality to capture their relevant features and learn global representations.Extensive experiments on the CMU-MOSI and CMU-MOSEI benchmarks demonstrate that DPDF-LQ achieves state-of-the-art performance, particularly in fine-grained sentiment prediction by effectively combining global and local features.

Bronchial intubation, facilitated by fiberoptic bronchoscopy, is a crucial aspect of lung isolation technology. However, intelligent examination of bronchoscope still needs to be explored to improve uneven quality of examination and reduce high training cost. Consequently, the development of an artificial intelligence (AI)-powered computer-assisted intubation system could significantly enhance the anesthesiologists’ accuracy in identifying the left and right main bronchus, as well as the bronchial lumen (Left, Right). A total of 1800 bronchoscopy images were collected from Shanghai Changzheng Hospital to develop an AI-assisted system based on convolutional neural network (CNN) model. A clinical evaluation was designed to assess the accuracy of bronchoscopy doctors’ identification of lumen with and without AI-assisted system. For the independent test dataset, the optimal accuracy of models ranged between 81.5% and 96.1%. In the YOLO series models, the accuracy of YOLOv3, YOLOv5 and YOLOv7 were 93.4%, 95.1% and 96.1% respectively. In the Faster RCNN model, the accuracy of VGG-16 and ResNet-50 were 81.5% and 82.6% respectively, the Frames Per Second (FPS) were 55, 64, 96, 143, 67 respectively. Furthermore, the clinical evaluation was conducted by 9 anesthetists who were divided into three groups according to their work experience. The identification results indicated that the YOLOv5 model showed better performance (accuracy of 100%) than almost all human experts (average accuracy of 81.25%, 92.5%, and 90.0%). Consequently, the YOLOv5 model was selected as the final AI-assisted system. In conclusion, our AI-assisted system has significantly improved the identification accuracy of the anatomical structure of bronchial lumen, providing a valuable reference for bronchoscope-guided endobronchial intubation.

Numerous studies have emphasized the essential function of video laryngoscopes in tracheal intubation, where providing clear visualizations of throat anatomy significantly raises success rates. Clinically, less-experienced doctors may encounter difficulties due to limited familiarity with these structures, and in emergency situations, achieving intubation on the first attempt is crucial for patient safety. Although deep learning is widely applied in medical imaging for its strong recognition capabilities, there remains a gap in applying it to throat image recognition. In this study, we gathered and labeled datasets from video laryngoscopes, verified by clinical experts. We designed a deep learning segmentation model, MP-UNet, that incorporates a multi-scale feature extraction block and a Pyramid Fusion Attention block. With these modules, the model is particularly effective at managing the feature extraction and fusion requirements for throat structures across different scales. Our model showed a minimum 10% improvement in IoU, the most critical metric, over the original U-Net. Compared to other models, MP-UNet also demonstrated strong segmentation performance on the throat dataset.

The microphysical processes' spatial variety and temporal evolution tendency during super typhoon Leima is investigated through measurements from an S-band polarimetric radar and six disdrometers. The results show that collision-coalescence/the collision-breakup occurred near/around the center of the typhoon, deduced from radar-measured and DSD-derived ZDR. In the vertical time-series view of ZDR, the collision-breakup may transition to the collision-breakup near the surface around the altitude of 0.9 km, and the collision-coalescence, collision-breakup, and coalescence-breakup balance may dominate during different periods of typhoon precipitation.

The unique network consisting of three different wavelength (C-, S- and X-band) radar in Eastern China is utilized to demonstrate their coordination capability of better coverage of low atmospheric layers in convective situations. After correcting attenuation effects on horizontal reflectivity (ZH) of all three wavelength radars based on the self-consistency approach, X-band radar exhibits even larger ZHthan S- and C-band, which exactly captures the severe developing convective rainbands on the lower atmospheric layer. In this sense, the X-band radar effectively plays a gap-filling coordination role in this S- and C-band radar network in convective situations.

The Smart Parking System (SPS) has played an important role in solving the parking problem in urban areas recently, resulting in that the battery-powered Wireless Parking Detectors (WPDs) based on Internet of Things (IoT) technology have been widely used in SPS to collect basic data sets. However, the daily turnover rates of parking spaces are generally less than 10 vehicles in Shanghai, China, so that WPDs waste a lot of energy in collecting and processing of redundant sensor data when the parking space is occupied or free. Hence, an environmental Energy Harvesting-driven Parking Detection (EHPD) method, which includes a vehicle-induced vibration energy harvesting and conditioning circuit, as well as a parking detection algorithm based on the signatures of vehicle-induced seismic signals and wireless energy attenuated by vehicles, is proposed in the paper. In EHPD, the signatures of pulse signals converted from vehicle-induced seismic signals are used to drive the wireless energy features to participate in achieving parking detection. Finally, the proposed EHPD has been evaluated in a real testbed, and the experimental results show that it can achieve the same functions as the existing magnetometer-based WPD devices with lower cost and energy consumption.

The co-occurrence analysis of Medical Subject Heading (MeSH) terms extracted from the PubMed database is popularly used in bibliometrics. Practically for making the result interpretable, it is necessary to apply a certain filter procedure of co-occurrence matrix for removing the low-frequency items due to their low representativeness. Unfortunately, there is rare research referring to determine a critical threshold to remove the noise of co-occurrence matrix. Here, we proposed a probabilistic model for co-occurrence analysis that can provide statistical inferences about whether the paired items co-occur randomly. With help of this model, the dimensionality of co-occurrence matrix could be reduced according to the selected threshold. The conceptual model framework, simulation and practical applications are illustrated in the manuscript. Further details (including all reproducible codes) can be downloaded from the project website: https://github.com/xizhou/co-occurrence-analysis.git.

In a multicore system, many applications share the last-level cache (LLC) and memory bandwidth. These resources need to be carefully managed in a coordinated way to maximize performance. DRAM is still the technology of choice in most systems. However, as traditional DRAM technology faces energy, reliability, and scalability challenges, nonvolatile memory (NVM) technologies are gaining traction. While DRAM is read/write symmetric (a read operation has comparable latency and energy consumption as a write operation), many NVM technologies (such as Phase-Change Memory, PCM) experience read/write asymmetry: write operations are typically much slower and more power hungry than read operations. Whether the memory’s characteristics are symmetric or asymmetric influences the way shared resources are managed. We propose two symmetry-agnostic schemes to manage a shared LLC through way partitioning and memory through bandwidth allocation. The proposals work well for both symmetric and asymmetric memory. First, an exhaustive search is proposed to find the best combination of a cache way partition and bandwidth allocation. Second, an approximate scheme, derived from a theoretical model, is proposed without the overhead of exhaustive search. Simulation results show that the approximate scheme improves weighted speedup by at least 14% on average (regardless of the memory symmetry) over a state-of-the-art way partitioning and memory bandwidth allocation. Simulation results also show that the approximate scheme achieves comparable weighted speedup as a state-of-the-art multiple resource management scheme, XChange, for symmetric memory, and outperforms it by an average of 10% for asymmetric memory.

For memory-intensiv e workloads with large memory footprints, superpages are effective to avoid address translation overhead, which can be a critical performance bottleneck. A superpage is a large virtual memory page that is mapped to an equivalently-sized amount of contiguous physical memory pages. Superpage mapping assumes physical memory does not contain retired pages, which is an important technique to improve memory resilience: the OS avoids allocating physical pages that have detected errors. Retired pages create unusable "holes" in the physical memory. We show that even a small percentage of retired pages makes it very difficult to find enough contiguous memory to form superpages. To address this problem, we propose GTSM, or gap-tolerant sequential mapping, that allows superpages to be formed even in the presence of retired physical pages. A new page table format is also proposed to support GTSM. This format has similar storage efficiency as traditional superpaging to hold address translations in the last-level cache. To further compress the page table and improve cache hit rates for address translation in large memory footprint workloads, we also propose an extended format that reduces the page table size by 50%. In comparison to an ideal memory without any retired physical pages, we show that our technique, with retired pages, achieves nearly 96.8% of the performance of traditional 2MB superpaging.

Phase-Change Memory (PCM) has emerged as a promising low-power candidate to replace DRAM in main memory. Hybrid memory architecture comprised of a large PCM and a small DRAM is a popular solution to mitigate undesirable characteristics of PCM writes. Because PCM writes are much slower than reads, writebacks from the last-level cache consume a large portion of memory bandwidth, and thus, impact performance. Effectively utilizing shared resources, such as the last-level cache and the memory bandwidth, is crucial to achieving high performance for multi-core systems. Although existing memory bandwidth allocation schemes improve system performance, no current approach uses writeback information to partition bandwidth for hybrid memory. We use a writeback-aware analytic model to derive the allocation strategy for bandwidth partitioning of phase-change memory. From the derivation of the model, Writeback-aware Bandwidth Partitioning (WBP) is proposed as a new runtime mechanism to partition PCM service cycles among applications. WBP uses a partitioning weight to indicate the importance of writebacks (in addition to LLC misses) to bandwidth allocation. A companion Dynamic Weight Adjustment (DWA) scheme dynamically selects the partitioning weight to maximize system performance. Simulation results show that WBP and DWA improve performance by 24.9% (weighted speedup) over bandwidth partitioning schemes that do not take writebacks into consideration in a 8-core system.

Write bandwidth is an inherent performance bottleneck for Phase Change Memory (PCM) for two reasons. First, PCM cells have long programming time, and second, only a limited number of PCM cells can be programmed concurrently due to programming current and write circuit constraints,

Limited PCM write bandwidth is a critical obstacle to achieve good performance from hybrid DRAM/PCM memory systems. The write bandwidth is severely restricted in PCM devices, which harms application performance. Indeed, as we show, it is more important to reduce PCM write traffic than to reduce PCM read latency for application performance. To reduce the number of PCM writes, we propose a DRAM cache organization that employs compression. A new delta compression technique for modified data is used to achieve a large compression ratio. Our approach can selectively and predictively apply compression to improve its efficiency and performance. Our approach is designed to facilitate adoption in existing main memory compression frameworks. We describe an instance of how to incorporate delta compression in IBM's MXT memory compression architecture when used for DRAM cache in a hybrid main memory. For fourteen representative memory-intensive workloads, on average, our delta compression technique reduces the number of PCM writes by 54.3%, and improves IPC performance by 24.4%.

Phase-Change Memory (PCM) has emerged as a promising low-power main memory candidate to replace DRAM. The main problems of PCM are that writes are much slower and more power hungry than reads, write bandwidth is much lower than read bandwidth, and limited write endurance. Adding an extra layer of cache, which is logically the last-level cache (LLC), can mitigate the drawbacks of PCM. However, writebacks from the LLC might (a) overwhelm the limited PCM write bandwidth and stall the application, (b) shorten lifetime, and (c) increase energy consumption. Cache partitioning and replacement schemes are important to achieve high throughput for multi-core systems. However, we noted that no existing partitioning and replacement policy takes into account the writeback information. This paper proposes two writeback-aware schemes to manage the LLC for PCM main memory systems. Writeback-aware Cache Partitioning (WCP) is a runtime mechanism that partitions a shared LLC among multiple applications. Unlike past partitioning schemes, our scheme considers the reduction in cache misses as well as writebacks. Write Queue Balancing (WQB) replacement policy manages the cache partition of each application intelligently so that the writebacks are distributed evenly among PCM write queues. In this way, applications rarely stall due to unbalanced PCM write traffic among write queues. Our evaluation shows that WCP and WQB result in, on average, 21% improvement in throughput, 49% reduction in PCM writes, and 14% reduction in energy over a state-of-the-art cache partitioning scheme.

Multi-core processors are effective for reducing energy consumption in computer systems, since modern multi- core chips allow for power management of individual cores. However, multiple cores impose higher demand on the memory subsystem, which is extremely power hungry. In addition to the small steps towards managing power in DRAMs, Phase-Change Memory (PCM) has emerged as a low-power alternative that is especially helpful for energy-aware embedded real-time systems. However, there are three drawbacks to PCM: its high latency, high energy consumption when writing, and low endurance. In real-time systems, the impact of PCM's high access latency is of special interest, as it has a negative effect on the number of deadlines that are met by the system. In this paper, we examine the memory subsystem and add a real-time scheduler for prioritizing requests at the bottleneck resource, the PCM controller. Adding support for external priorities, we use rate monotonic (RM) and earliest deadline first (EDF) prioritization at the PCM and show that it does reduce the number of deadline misses, but not sufficiently. We examine two additional schemes for prioritizing PCM requests (critical read boosting and read over write). We show that the scheduler of the PCM controller has a significant influence on the percentage of missed deadlines: critical read boosting and read over write can reduce the percentage of missed deadlines by 80% in the best case with negligible energy overhead.

The problem of access control on outsourced data to "honest but curious" cloud servers has received considerable attention, especially in scenarios involving potentially huge sets of data files, where re-encryption and re-transmission by the data owner may not be acceptable. Considering the user privacy and data security in cloud environment, in this paper, we propose a solution to achieve flexible and fine-grained access control on outsourced data files. In particular, we look at the problem of defining and assigning keys to users based on different attribute sets, and hiding access policies as well as users information to the third-party cloud servers. Our proposed scheme is partially based on our observation that, in practical application scenarios each user can be associated with a set of attributes which are meaningful in the access policy and data file context. The access policy can thus be defined as a logical expression formula over different attribute sets to reflect the scope of data file that the kind of users is allowed to access. As any access policy can be represented as such a logical expression formula, fine-grained access control can be accomplished.

The introduction of Phase-Change Memory (PCM) as a main memory technology has great potential to achieve a large energy reduction. PCM has desirable energy and scalability properties, but its use for main memory also poses challenges such as limited write endurance with at most 107writes per bit cell before failure. This paper describes techniques to enhance the lifetime of PCM when used for main memory. Our techniques are (a) writeback minimization with new cache replacement policies, (b) avoidance of unnecessary writes, which write only the bit cells that are actually changed, and (c) endurance management with a novel PCM-aware swap algorithm for wear-leveling. A failure detection algorithm is also incorporated to improve the reliability of PCM. With these approaches, the lifetime of a PCM main memory is increased from just a few days to over 8 years.