ABSTRACT Edge computing systems with multiple tiers need smart ways to share their resources. They must handle different needs in different areas while keeping good service quality and making the most of what they have. Old methods that use average numbers do not work well when needs change quickly. Other smart methods only work with fixed system designs, not flexible ones. We made a new, better way to solve this problem. Our solution has two important parts: First, it works with any system design, no matter how many tiers it has. We do this by breaking down the big problem into smaller, similar problems that connect together. Second, instead of just dealing with random changes in needs, our method actually explores the optimality of nonlinear scheduling problems to work better. We turned the hard problem into easier steps in a dynamic programming way that can be solved one after another. Tests show that the results can be very close to the best possible solution (close to 98% to the best) and works fast. It can fit any system design and handles changing needs well. This makes it a useful tool for managing resources in modern edge computing systems.

Approximate computing is an energy-efficient computing paradigm for error-tolerant applications. To automatically synthesize approximate circuits, many iterative approximate logic synthesis (ALS) methods have been proposed. However, most of them do not consider applying multiple local approximate changes (LACs) in a single round, which can lead to a much shorter runtime. In this paper, we propose AccALS, a novel framework for Accelerating iterative ALS flows, based on simultaneous selection of multiple LACs in a single round. When selecting multiple LACs, there may exist conflicts among them. One important component of AccALS is a novel method to solve the conflicts. Another is an efficient measure for the mutual influence between two LACs. With its help, the problem of selecting multiple LACs is transformed into a maximum independent set problem to solve. The experimental results showed that compared to a state-of-the-art method, AccALS accelerates by up to 24.6× with a negligible circuit quality loss.

This letter focuses on discriminating true target from mainlobe jammers using a frequency diverse array-multiple-input multiple-output (FDA-MIMO) radar. To this end, a three-stage detection and discrimination architecture is proposed. Specifically, mainlobe jammers caused by large time delays can be discriminated from the true target based on the range degrees-of-freedom (DOFs) of FDA-MIMO radar, while the true target and jammers generated with small time delays are distinguishable in the fast time domain. In particular, a binary hypothesis test is formulated for discrimination, where the$H_{1}$hypothesis contains the true target, while the$H_{0}$hypothesis contains the unknown mainlobe jammer. The mainlobe jammer can be formulated as either a known subspace model or an unknown rank-one model. The former leads to a subspace-based selective detector (SSD) and the latter generates a rank-one-based SD (ROSD). Then, the formulated tests are handled using the generalized likelihood ratio test (GLRT) criterion. At the analysis stage, the selectivity and detection performance are compared with the conventional GLRT and selective receivers. Simulation results validate that the proposed detectors achieve satisfactory detection performance while providing the selectivity to mainlobe jammers.

In this letter, the problem of adaptive target detection for a frequency diverse array-multiple-input multiple-output (FDA-MIMO) radar in the presence of unknown mutual coupling (MC) is investigated. Unlike the conventional phased array (PA) radar, the signal model of FDA-MIMO radar in unknown MC is constructed using two symmetric Toeplitz matrices accounting for the effects of unknown MC at the transmitter and receiver. At the formulation stage, the detection problem is formulated as either a binary hypothesis test or a multi-hypothesis test. The former is tackled based on the generalized likelihood ratio test (GLRT) leading to a robust detector (RD). The latter resorts to the penalized test and multifamily LRT (MFLRT), resulting to Modified-Multiple Hypothesis Penalized Likelihood Ratio Test (M-MHPLRT) and MFLRT detectors, respectively. At the analysis stage, the effectiveness of the developed detectors is verified with comparisons among the benchmarks and existing detectors via Monte Carlo simulation results

Human papillomavirus (HPV) integrating into human genome is the main cause of cervical carcinogenesis. HPV integration selection preference shows strong dependence on local genomic environment. Due to this theory, it is possible to predict HPV integration sites. However, a published bioinformatic tool is not available to date. Thus, we developed an attention-based deep learning model DeepHPV to predict HPV integration sites by learning environment features automatically. In total, 3608 known HPV integration sites were applied to train the model, and 584 reviewed HPV integration sites were used as the testing dataset. DeepHPV showed an area under the receiver-operating characteristic (AUROC) of 0.6336 and an area under the precision recall (AUPR) of 0.5670. Adding RepeatMasker and TCGA Pan Cancer peaks improved the model performance to 0.8464 and 0.8501 in AUROC and 0.7985 and 0.8106 in AUPR, respectively. Next, we tested these trained models on independent database VISDB and found the model adding TCGA Pan Cancer performed better (AUROC: 0.7175, AUPR: 0.6284) than the model adding RepeatMasker peaks (AUROC: 0.6102, AUPR: 0.5577). Moreover, we introduced attention mechanism in DeepHPV and enriched the transcription factor binding sites including BHLHA15, CHR, COUP-TFII, DMRTA2, E2A, HIC1, INR, NPAS, Nr5a2, RARa, SCL, Snail1, Sox10, Sox3, Sox4, Sox6, STAT6, Tbet, Tbx5, TEAD, Tgif2, ZNF189, ZNF416 near attention intensive sites. Together, DeepHPV is a robust and explainable deep learning model, providing new insights into HPV integration preference and mechanism. Availability: DeepHPV is available as an open-source software and can be downloaded from https://github.com/JiuxingLiang/DeepHPV.git, Contact: [email protected], [email protected], [email protected].

MOTIVATION: Epstein-Barr virus (EBV) is one of the most prevalent DNA oncogenic viruses. The integration of EBV into the host genome has been reported to play an important role in cancer development. The preference of EBV integration showed strong dependence on the local genomic environment, which enables the prediction of EBV integration sites. RESULTS: An attention-based deep learning model, DeepEBV, was developed to predict EBV integration sites by learning local genomic features automatically. First, DeepEBV was trained and tested using the data from the dsVIS database. The results showed that DeepEBV with EBV integration sequences plus Repeat peaks and 2-fold data augmentation performed the best on the training dataset. Furthermore, the performance of the model was validated in an independent dataset. In addition, the motifs of DNA-binding proteins could influence the selection preference of viral insertional mutagenesis. Furthermore, the results showed that DeepEBV can predict EBV integration hotspot genes accurately. In summary, DeepEBV is a robust, accurate and explainable deep learning model, providing novel insights into EBV integration preferences and mechanisms. AVAILABILITYAND IMPLEMENTATION: DeepEBV is available as open-source software and can be downloaded from https://github.com/JiuxingLiang/DeepEBV.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This paper proposed a new strategy to assess cumulative actual dose by nonrigidly mapping treatment day dose distributions to the planning day space. The mappings were achieved using a novel integrated segmentation and nonrigid registration algorithm used in external beam prostate radiotherapy. By combining segmentation and registration, we can recover the treatment fraction image regions that correspond to the organs of interest by incorporating transformed planning day organs to guide and constrain the segmentation process; and conversely, accurate knowledge of important soft tissue structures will enable us achieve more precise nonrigid registration. The novel algorithm allows the clinician to set tighter planning margins around the target volume in the treatment plan. Clinical application and evaluation of dose delivery show the superiority of proposed method to the procedure currently used in clinical practice, i.e. manual segmentation followed by rigid registration.

One of the new features adopted in the latest H.264/AVC video coding standard is the mode decision (MD), which greatly improves the rate-distortion performance but it also takes up a significant encoding time to determine the best mode. This paper presents a predictive block-size selection algorithm to extraordinarily improve the encoding efficiency in H.264. The proposed algorithm mainly takes advantage of two efficient predictive methods: one is predictive skipping checking the sub-macroblock-level modes according to the sum of absolute difference (SAD) of each macroblock and the other is filtering some sub-macroblock-level modes with an adaptive threshold obtained from the mode information in the previous determined macroblock. Experimental results and comparative analysis are given to verify that our proposed algorithm can achieve a speed-up factor with 30% compared with the current Fast Full Search algorithm, with negligible average PSNR loss of 0.071dB and bit rate increase of 2.78%.