While advancements in the reasoning abilities of LLMs have significantly enhanced their performance in solving mathematical problems, coding tasks, and general puzzles, their effectiveness in accurately adhering to instructions remains inconsistent, particularly with more complex directives. Our investigation identifies lazy reasoning during the thinking stage as the primary factor contributing to poor instruction adherence. To mitigate this issue, we propose a comprehensive framework designed to enable rigorous reasoning processes involving preview and self-checking, essential for satisfying strict instruction constraints. Specifically, we first generate instructions with complex constraints and apply a filtering process to obtain valid prompts, resulting in three distinct prompt datasets categorized as hard, easy, and pass. Then, we employ rejection sampling on the pass prompts to curate a small yet high-quality dataset, enabling a cold-start initialization of the model and facilitating its adaptation to effective reasoning patterns. Subsequently, we employ an entropy-preserving supervised fine-tuning (Entropy-SFT) strategy coupled with token-wise entropy-adaptive (TEA-RL) reinforcement learning guided by rule-based dense rewards. This approach encourages the model to transform its reasoning mechanism, ultimately fostering generalizable reasoning abilities that encompass preview and self-checking. Extensive experiments conducted on instruction-following benchmarks demonstrate remarkable performance improvements across various model scales.

This brief proposes an average-6.5T twin cell for a deep sub-micrometer 64kb SRAM, which utilizes two identical asymmetric single-ended (SE) 6T cells in a column with a shared read assist device to improve read margin and write ability. It enables the SRAM to achieve read-disturb-free, near/sub-threshold operation and compact array layout, resulting in area and energy efficiencies. The average-6.5T SRAM test chip is fabricated using a 65 nm CMOS logic process. Its cell area shows only 5.6% overhead compared to the standard 6T cell, and is smaller than that of other low-voltage SRAMs. Measured full read and write functionality is performed with VDD down to 0.39 V, which is lower than that of standard 6T and 8T SRAMs. In addition, its minimum energy point of 6.3 pJ is obtained at 0.48 V.

Lossy speech codecs often introduce coding distortions such as coding noise and constrained bandwidth, which can affect the quality of the decoded speech. This paper proposes a method called FT-CSR, which is used for coded speech restoration. FT-CSR reduces coding noise and recovers missing frequencies sequentially using a cascaded frequency-time domain model. In experiments using the Opus codec, FT-CSR was found to be effective across bitrates ranging from 8 to 16 kbps and outperformed the baseline on both objective and subjective measurements. FT-CSR achieves a MOS-POLQA score of 3.6 or higher and improves MOS-POLQA by more than 0.23 when compared to decoded speech. The results of the subjective test show that FT-CSR can improve MOS by over 0.85 for decoded speech.

Motion compensation is a key technology in video coding for removing the temporal redundancy between video frames. Considering the incompatibility between traditional interpolation filters and diversified video content, the inter prediction method still has considerable room for improvement. This paper proposed a distortion-aware convolutional neural network-based interpolation filter (DA-NNIF) to further improve the interpolation prediction accuracy of sub-pixels with one model. Distortion parameters are introduced into the proposed network to reflect the quantization noise of reference frames. The experimental result shows that the proposed method achieves on average 1.47 % BD-rate reduction on Y component for ClassB, ClassC and ClassD sequences under the random access configuration of AVS3.

Answer selection task requires finding appropriate answers to questions from informative but crowdsourced candidates. A key factor impeding its solution by current answer selection approaches is the redundancy and lengthiness issues of crowdsourced answers. Recently, Deng et al. (2020) constructed a new dataset, WikiHowQA, which contains a corresponding reference summary for each original lengthy answer. And their experiments show that leveraging the answer summaries helps to attend the essential information in original lengthy answers and improve the answer selection performance under certain circumstances. However, when given a question and a set of long candidate answers, human beings could effortlessly identify the correct answer without the aid of additional answer summaries since the original answers contain all the information volume that answer summaries contain. In addition, pretrained language models have been shown superior or comparable to human beings on many natural language processing tasks. Motivated by those, we design a series of neural models, either pretraining-based or non-pretraining-based, to check wether the additional answer summaries are helpful for ranking the relevancy degrees of question-answer pairs on WikiHowQA dataset. Extensive automated experiments and hand analysis show that the additional answer summaries are not useful for achieving the best performance.

Multi-document reading comprehension task requires collecting evidences from different documents for answering questions.Previous research works either use the extractive modeling method to naively integrate the scores from different documents on the encoder side or use the generative modeling method to collect the clues from different documents on the decoder side individually.However, any single modeling method cannot make full of the advantages of both.In this work, we propose a novel method that tries to employ a multi-view fusion and multi-decoding mechanism to achieve it.For one thing, our approach leverages question-centered fusion mechanism and cross-attention mechanism to gather finegrained fusion of evidence clues from different documents in the encoder and decoder concurrently.For another, our method simultaneously employs both the extractive decoding approach and the generative decoding method to effectively guide the training process.Compared with existing methods, our method can perform both extractive decoding and generative decoding independently and optionally.Our experiments on two mainstream multi-document reading comprehension datasets (Natural Questions and Triv-iaQA) demonstrate that our method can provide consistent improvements over previous state-of-the-art methods.

Machine reading comprehension of news articles remains to be a challenging task since the lengths of its context documents are long. Such reading comprehension task usually requires document-level language understanding while state-of-the-art, pretrained question answering models can only encode sequences with a predefined length limit. In this paper, we propose a novel Question-Oriented Propagation Network (QOPN) model for such task. Specifically, our proposed QOPN first uses a context encoding module to find local question-related clues. Then, it employs a multi-step reasoning module to aggregate question-focused information for iterative reasoning. The novel design put emphasis on capturing question-related information and allow long-range information integration, which is especially beneficial for long-context reading comprehension task. Experiments on two challenging machine comprehension datasets show that the proposed QOPN significantly outperforms previous state-of-the-art models.

Audio bandwidth extension can enhance subjective sound quality by increasing bandwidth of audio signal. This paper presents a novel multi-stage progressive method for time domain causal bandwidth extension. Each stage of the progressive model contains a light weight scale-up module to generate high frequency signal and a supervised attention module to guide features propagating between stages. Time-frequency two-step training method with weighted loss for progressive output is adopted to supervise bandwidth extension performance improves along stages. Test results show that multi-stage model can improve both objective results and perceptual quality progressively. The multi-stage progressive model makes bandwidth extension performance adjustable according to energy consumption, computing capacity and user preferences.

The automatic control of blast furnace (BF) ironmaking process has always been an important yet arduous task in metallurgic engineering and automation. In this article, a novel Kalman filter-based robust model-free adaptive predictive control (MFAPC) method is proposed for the direct data-driven control of molten iron quality in BF ironmaking. First, a compact-form dynamic linearization-based extended MFAPC method for multivariable molten iron quality control is proposed by generalizing the existing single-variable MFAPC method to multivariable systems. Based on it, a Kalman filter-based robust MFAPC is further proposed considering the problems of data loss and measurement noise in quality detection. Specifically, the robust mechanism in the robust MFAPC combines a novel dynamic linearization method with a concept termed Pseudo-Jacobian matrix to predict the missing data during data loss. After that, a Kalman filter is constructed based on a prediction model to filter the measurement noise. The stability of the proposed control method is analyzed, and various data experiments using actual industrial data are performed to verify the effectiveness of the proposed methods.Note to Practitioners—The extremely complicated dynamics of blast furnace ironmaking process make the model-based controllers difficult to realize in practice. In this article, a novel robust model-free adaptive predictive control method is proposed for direct data-driven control of multivariate molten iron quality in the ironmaking process. This method directly uses the process input and output data to design the multivariable quality controller online by the compact-form dynamic linearization technology and the internal multilayer prediction mechanism, thus avoids the drawback of model-based controllers in troublesome process modeling. Moreover, the proposed method can effectively avoid the influence of data loss and measurement noise on the controller performance with the designed Kalman filter-based robust mechanism. The superiority and practicability of the proposed method are verified using various experiments against actual industrial data.

Soft error protection is a paramount requirement for memories exposed to radiation environment. To satisfy the demand, a radiation-hardened new-quatro 10T memory cell is proposed in this brief, which is immune to single-node upset and also has high resilience to multinode upset while features read-disturbance-free benefiting from its internal quad-node interlocked feedback mechanism. Simulation results show that it provides ample radiation robustness to single event and gives 1.75× improvements in multinode upset tolerance when compared with the previous 12T dual-interlocked storage cell (DICE-12T) bitcell, signifying the higher fault tolerance capability. In addition, the proposed design also achieves 6.48× enhancement in read noise margin when compared with the DICE-12T bitcell while compromising only 2.1× larger area than a reference 6T cell based on a 65-nm logic design rule, exhibiting the superiority in read stability.

Measles is a highly contagious and severe disease.Despite mass vaccination, it remains a leading cause of death in children in developing regions, killing 114,900 globally in 2014.In 2006, China committed to eliminating measles by 2012; to this end, the country enhanced its mandatory vaccination programs and achieved vaccination rates reported above 95% by 2008.However, in spite of these efforts, during the last 3 years (2013-2015) China documented 27,695, 52,656, and 42,874 confirmed measles cases.How measles manages to spread in China-the world's largest population-in the mass vaccination era remains poorly understood.To address this conundrum and provide insights for future public health efforts, we analyze the geospatial pattern of measles transmission across China during 2005-2014.We map measles incidence and incidence rates for each of the 344 cities in mainland China, identify the key socioeconomic and demographic features associated with high disease burden, and identify transmission clusters based on the synchrony of outbreak cycles.Using hierarchical cluster analysis, we identify 21 epidemic clusters, of which 12 were cross-regional.The cross-regional clusters included more underdeveloped cities with large numbers of emigrants than would be expected by chance (p = 0.011; bootstrap sampling), indicating that cities in these clusters were likely linked by internal worker migration in response to uneven economic development.In contrast, cities in regional clusters were more likely to have high rates of minorities and high natural growth rates than would be expected by chance (p = 0.074; bootstrap sampling).Our findings suggest that multiple highly connected foci of measles transmission coexist in China and that migrant workers likely facilitate the transmission of measles across regions.This complex connection renders eradication of measles challenging in China despite its high overall vaccination coverage.Future immunization programs should therefore target these transmission foci simultaneously.

Energy efficiency, reliability, and real-time are three key requirements of mission-critical embedded systems. Existing approaches over emphasize the worst case design of real-time embedded systems, which will lead to serious waste of resources. In this paper, we aim at the energy-efficient design of soft real-time and reliable applications on uniprocessor embedded systems. We consider soft real-time tasks with stochastic execution durations regarding certain distributions. Thereby, we provide real-time guarantee with probability consideration. We utilize dynamic voltage and frequency scaling (DVFS) for saving energy, and also take into account the impact of DVFS on reliability. Our objective is to minimize the expected energy consumption of the system subject to statistical reliability and deadline constraints. The design optimization problem is a typical multidimensional multiple-choice knapsack problem, which is NP-hard. We first propose a dynamic programming-based optimal algorithm to solve the problem. To reduce the time complexity, we then develop a (1+β)-approximation algorithm based on a binary search approach, where β is the approximating factor. The approximation algorithm can obtain the near-optimal solution with at most (1+β) times of optimal energy cost under given real-time and reliability constraints and has fully polynomial time complexity. Extensive experiments and a real-life synthetic application are conducted to evaluate the performance of the proposed techniques. Compared with existing approaches, the approximation approach can save much energy with low time overhead while guaranteeing the statistical deadline and reliability constraints.

A nondestructive column-selection-enabled 10T SRAM for aggressive power reduction is presented in this brief. It frees a half-selected behavior by exploiting the bitline-shared data-aware write scheme. The differential-VDD (Diff-VDD) technique is adopted to improve the write ability of the design. In addition, its decoupled read bitlines are given permission to be charged and discharged depending on the stored data bits. In combination with the proposed dropped-VDD biasing, it achieves the significant power reduction. The experimental results show that the proposed design provides the 3.3× improvement in the write margin compared with the standard Diff-10T SRAM. A 5.5-kb 10T SRAM in a 65-nm CMOS process has a total power of 51.25 μW and a leakage power of 41.8 μW when operating at 6.25 MHz at 0.5 V, achieving 56.3% reduction in dynamic power and 32.1% reduction in leakage power compared with the previous single-ended 10T SRAM.

Measles is a highly contagious and severe disease. Despite mass vaccination, it remains a leading cause of death in children in developing regions, killing 114,900 globally in 2014. In 2006, China committed to eliminating measles by 2012; to this end, the country enhanced its mandatory vaccination programs and achieved vaccination rates reported above 95% by 2008. However, in spite of these efforts, during the last 3 years (2013-2015) China documented 27,695, 52,656, and 42,874 confirmed measles cases. How measles manages to spread in China-the world's largest population-in the mass vaccination era remains poorly understood. To address this conundrum and provide insights for future public health efforts, we analyze the geospatial pattern of measles transmission across China during 2005-2014. We map measles incidence and incidence rates for each of the 344 cities in mainland China, identify the key socioeconomic and demographic features associated with high disease burden, and identify transmission clusters based on the synchrony of outbreak cycles. Using hierarchical cluster analysis, we identify 21 epidemic clusters, of which 12 were cross-regional. The cross-regional clusters included more underdeveloped cities with large numbers of emigrants than would be expected by chance (p = 0.011; bootstrap sampling), indicating that cities in these clusters were likely linked by internal worker migration in response to uneven economic development. In contrast, cities in regional clusters were more likely to have high rates of minorities and high natural growth rates than would be expected by chance (p = 0.074; bootstrap sampling). Our findings suggest that multiple highly connected foci of measles transmission coexist in China and that migrant workers likely facilitate the transmission of measles across regions. This complex connection renders eradication of measles challenging in China despite its high overall vaccination coverage. Future immunization programs should therefore target these transmission foci simultaneously.

In this paper, we target on energy-efficient design of soft real-time and reliable applications on uniprocessor embedded systems. We consider soft real-time tasks with stochastic execution times with given distribution. Instead of guaranteeing hard real-time constraint, the application may be finished after their deadlines with a certain probability. We utilize Dynamic Voltage and Frequency Scaling (DVFS) to save energy, and also take into account of the impact of DVFS on reliability. Our objective is to minimize the expected energy consumption of the system subject to statistical reliability and deadline constraints. Due to the huge complexity of solving the problem exactly, we develop a fast bi-search approach based on dynamic programming, which can find the near-optimal solution with energy cost at most (1+β) times of the optimal energy and has polynomial time complexity. Extensive experiments and a real-life application were conducted to evaluate the efficiency of the proposed techniques.

Fault injection attack has been a serious threat to security-critical embedded systems for a long time, yet existing research ignores addressing of the problem from a system-level perspective. This article presents an approach to the synthesis of secure real-time applications mapped on distributed embedded systems, which focuses on preventing fault injection attacks of the security protection on processing units. We utilize symmetric cryptographic service to protect confidentiality and deploy fault detection within a confidential algorithm to resist fault injection attacks. Several fault detection schemes are identified, and their fault coverage rates and time overheads are derived and measured. Our synthesis approach makes efforts to determine the best fault detection schemes for the encryption/decryption of messages such that the overall security strength of detecting a fault injection attack is maximized and the deadline constraint of the real-time applications is guaranteed. Due to the complexity of the problem, we propose an efficient algorithm based on the fruit fly optimization algorithm, and we compare it to the simulated annealing approach. Extensive experiments and a real-life application evaluation demonstrate the superiority of our approach.