Binary (0-1) integer programming (BIP) is pivotal in scientific domains requiring discrete decision-making. As the advance of AI computing, recent works explore neural network-based solver for integer linear programming (ILP) problems. Yet, they lack scalability for tackling nonlinear challenges. To handle nonlinearities, state-of-the-art Branch-and-Cut solvers employ linear relaxations, leading to exponential growth in auxiliary variables and severe computation limitations. To overcome these limitations, we propose BIPNN, an unsupervised learning framework to solve BIP problems via hypergraph neural networks (HyperGNN). Specifically, (i) BIPNN reformulates BIPs-constrained, discrete, and nonlinear (sin, log, exp) optimization problems-into unconstrained, differentiable, and polynomial loss functions. The reformulation stems from the observation of a precise one-to-one mapping between polynomial BIP objectives and hypergraph structures, enabling the unsupervised training of HyperGNN to optimize BIP problems in an end-to-end manner. On this basis, (ii) we propose a GPU-accelerated and continuous-annealing-enhanced training pipeline for BIPNN. The pipeline enables BIPNN to optimize large-scale nonlinear terms in BIPs fully in parallel via straightforward gradient descent, thus significantly reducing the training cost while ensuring the generation of discrete, high-quality solutions. Extensive experiments on synthetic and real-world datasets highlight the superiority of our approach.

Radiation therapy is regarded as the mainstay treatment for cancer in clinic. Kilovoltage cone-beam CT (CBCT) images have been acquired for most treatment sites as the clinical routine for image-guided radiation therapy (IGRT). However, repeated CBCT scanning brings extra irradiation dose to the patients and decreases clinical efficiency. Sparse CBCT scanning is a possible solution to the problems mentioned above but at the cost of inferior image quality. To decrease the extra dose while maintaining the CBCT quality, deep learning (DL) methods are widely adopted. In this study, planning CT was used as prior information, and the corresponding strictly structure-preserved CBCT was simulated based on the attenuation information from the planning CT. We developed a hyper-resolution ultra-sparse-view CBCT reconstruction model, known as the planning CT-based strictly-structure-preserved neural network (PSSP-NET), using a generative adversarial network (GAN). This model utilized clinical CBCT projections with extremely low sampling rates for the rapid reconstruction of high-quality CBCT images, and its clinical performance was evaluated in head-and-neck cancer patients. Our experiments demonstrated enhanced performance and improved reconstruction speed.

The booming crowdsourced delivery leverages networks of local, part time couriers to meet growing customer demands for increasingly speedy shipping. By conducting city-wide low-cost delivery networks, crowd-sourced enabled instant delivery platforms have fought to figure out a way to offer instant shipping at low price. Nevertheless, limited ubiquitous computing infrastructures impede the discovery of equilibrium between cost reduction and sufficient shipping capacity. In this article, we present our endeavor towards understanding and designing a dynamic pricing framework based on a large-scale food-delivery platform. To reduce delivery fees in capacity overload regions and resolve the decline of service quality caused by insufficient shipping capacity, we address the challenge of quantifying the inter-regional shipping capacity disparities by accordingly applying a variety of techniques, including cooperative game theory, probability analysis and address entity recognition. In particular, we provide an$O(n^{2})$algorithm for the cooperative game based cost allocation, of which the exponential computational complexity$O(n^{2}2^{n})$is generally considered a major challenge in real-world applications. City-wide experiments in Nanjing and Shenzhen illustrate that we achieve a 10% decrease in delivery fees under overload shipping capacity and a 3% increase in completion rates in customer locations with poor service quality.

3-D radiotherapy is an effective treatment modality for breast cancer. In 3-D radiotherapy, delineation of the clinical target volume (CTV) is an essential step in the establishment of treatment plans. However, manual delineation is subjective and time consuming. In this study, we propose an automated segmentation model based on deep neural networks for the breast cancer CTV in planning computed tomography (CT). Our model is composed of three stages that work in a cascade manner, making it applicable to real-world scenarios. The first stage determines which slices contain CTVs, as not all CT slices include breast lesions. The second stage detects the region of the human body in an entire CT slice, eliminating boundary areas, which may have side effects for the segmentation of the CTV. The third stage delineates the CTV. To permit the network to focus on the breast mass in the slice, a novel dynamically strided convolution operation, which shows better performance than standard convolution, is proposed. To train and evaluate the model, a large dataset containing 455 cases and 50 425 CT slices is constructed. The proposed model achieves an average dice similarity coefficient (DSC) of 0.802 and 0.801 for right-0 and left-sided breast, respectively. Our method shows superior performance to that of previous state-of-the-art approaches.

Radiotherapy is an indispensable part of adjuvant therapy for cancer that improves local control, overall survival, and the opportunity for good quality of life. Organ delineation and dose plan design are the key steps in the treatment. Organ delineation controls the area of radiotherapy and dose planning controls its intensity. However, both tasks are time-consuming, exhausting, and subjective, and automated methods are desirable. Although automated methods have been studied, the previous studies either focus on organ segmentation or dose prediction, without considering them from a holistic perspective. In this paper, we treat organ segmentation and dose prediction as similar tasks, and propose an error correction framework to improve their performance based on the same mechanism. The proposed error correction framework consists of a prediction network and a calibration network. The biggest difference between our framework and previous studies is that the state-of-the-art networks can be used as a prediction network or calibration network, and then the performance can be improved by the error correction mechanism. To evaluate the framework, we conducted a series of experiments on dose prediction and organ segmentation. These experimental results show that the framework is superior to other state-of-the-art methods in both tasks.

Recent years have witnessed the rapid growth of mobile virtual network operators (MVNOs), which operate on top of existing cellular infrastructures of base carriers, while offering cheaper or more flexible data plans compared to those of the base carriers. In this paper, we present a two-year measurement study towards understanding various fundamental aspects of today's MVNO ecosystem, including its architecture, customers, performance, economics, and the complex interplay with the base carrier. Our study focuses on a large commercial MVNO with one million customers, operating atop a nation-wide base carrier. Our measurements clarify several key concerns raised by MVNO customers, such as inaccurate billing and potential performance discrimination with the base carrier. We also leverage big data analytics, statistical modeling, and machine learning to address the MVNO's key concerns with regard to data usage prediction, data plan reselling, customer churn mitigation, and billing delay reduction. Our proposed techniques can help achieve higher revenues and improved services for commercial MVNOs.

Recent years have witnessed the rapid growth of mobile virtual network operators (MVNOs), which operate on top of the existing cellular infrastructures of base carriers while offering cheaper or more flexible data plans compared to those of the base carriers. In this paper, we present a nearly two-year measurement study towards understanding various key aspects of today's MVNO ecosystem, including its architecture, performance, economics, customers, and the complex interplay with the base carrier. Our study focuses on a large commercial MVNO with \reviseabout 1 million customers, operating atop a nation-wide base carrier. Our measurements clarify several key concerns raised by MVNO customers, such as inaccurate billing and potential performance discrimination with the base carrier. We also leverage big data analytics and machine learning to optimize an MVNO's key businesses such as data plan reselling and customer churn mitigation. Our proposed techniques can help achieve %will lead to higher revenues and improved services for commercial MVNOs.

We present a new high-payload joint reversible data-hiding scheme for encrypted images. Instead of embedding data in the encrypted image directly, the content owner first uses an interpolation technique to estimate whether the location can be used for embedding and generates a location map before encryption. Next, the data hider embeds the additional data through flipping the most significant bits (MSBs) of the encrypted image according to the location map. At the receiver side, before extracting the additional data and reconstructing the image, the receiver decrypts the image first. Experimental results demonstrate that the proposed method can achieve real reversibility, which means data extraction and image recovery are free of error. Moreover, our scheme can embed more payloads than most existing reversible data hiding schemes in encrypted images.

Summary Replacement algorithms have been widely used as key technologies for cache management in areas such as file systems or database management. A replacement algorithm determines which page to be evicted when the cache is full and a new page is referenced. Because replacement policies considering only recency or frequency such as LRU (Least Recently Used) and LFU (Least Frequently Used) do not perform well, replacement polices that take both recency and frequency into account have been intensively studied. As a classical replacement policy, LRFU (Least Recently/Frequently Used) policy subsumes the LRU and LFU policy. However, because LFU is not able to adapt to the change of page accessing pattern and it is hard to select a suitable λ for each certain trace, LRFU cannot always guarantee a good performance. In this paper, we propose a Window‐LRFU policy, to subsume the LRU and Window‐LFU policies. Experimental results show that the Window‐LRFU policy outperforms LRFU and has at least competitive performance than other classical algorithms. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract The preservation of high peak signal‐to‐noise ratio of the watermarked image is required in some applications. The chaos‐based fragile watermarking proposed by Rawat et al. in 2011 can meet this requirement, for only one LSB plane of the original image is modified by the watermark data. However, there are three drawbacks in the original scheme: (i) insecurity, the host image can be tampered without being detected; (ii) the detected tampered area is incomplete; and (iii) the most important thing is that the tampered area is unrecoverable. Enlightened by the work of Rawat et al., we propose a new recoverable chaos‐based fragile watermarking scheme, in which all of the drawbacks in the original scheme have been overcome, while the size of the watermark is unchanged, therefore the high peak signal‐to‐noise ratio characteristic can be preserved. The chaotic map is used to ensure the security; the idea of block dividing is adopted to improve the tamper detection and to make storage space for the recovery data embedding; and the border matching recovery based on vector quantization is proposed to improve the recovery result of the target image. Experiments show that our method is secure and recoverable, and the recovery ability is extremely satisfactory. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Key‐based interval splitting arithmetic coding (KAC) possesses both encryption and compression capabilities. However, it possesses vulnerability to chosen‐plaintext attack because the attacker can explore the relationship between the key and the codeword to deduce the secret key. In order to resist this attack, we propose to introduce perturbation into KAC. The perturbation‐based KAC not only avoids the flaw of KAC that the splitting keys are usually located at the endpoint of certain codeword or at the border of two codewords but also removes the restriction that the keys are only allowed in certain sub‐intervals, which result in great convenience to the key scheduler. In addition, based on generalized arithmetic coding using Generalized Luröth Series, we study the phase‐space splitting of a chaotic map for generalized KAC and suggest the generalized perturbation‐based KAC. This leads to the design of a joint compression and encryption scheme with more powerful cryptographic features. Copyright © 2015 John Wiley & Sons, Ltd.

In ad hoc wireless networks, a Connected Dominating Set (CDS) has been extensively used as a Virtual Backbone (VB) for routing. The majority of approximation algorithms for constructing a small CDS in wireless ad hoc networks follow a general two-phased approach. The first phase is to construct a Dominating Set (DS) and the second phase is to connect the nodes in it. Generally, in the first phase, a Maximum Independent Set (MIS) is used as the DS. The relation between the size of a Maximum Independent Set and a Minimum Connected Dominating Set (MCDS) plays the key role in the performance analyses of these two-phased algorithms. In homogeneous wireless ad hoc networks modeled as Unit Disk Graphs (UDG) and Unit Ball Graphs (UBG), the relation between them has been well studied. However, in heterogeneous wireless networks which generally modeled as Disk Graphs with Bidirectional links (DGB) and Ball Graphs with Bidirectional links (BGB), upper bounds for the size of MISs have seldom been studied. In this paper, we give tighter upper bounds for the size of MISs in heterogeneous wireless ad hoc networks. When the maximum and minimum transmission range are relatively close, our result is much better. In DGB, when the transmission range ratio is (1,1.152], (1.152,1.307], (1.307,1.407], (1.407,1.462], (1.462,1.515], (1.515,1.618], (1.618,1.932], we prove that the size of any Maximal Independent Set (MIS) is upper bounded by 6opt + 1, 7opt + 1, 8opt + 1, 9opt + 1, 10opt + 1, 11opt + 1, 16.7778opt + 1.2222, where opt denotes the size of an optimal solution of the CDS problem.

Low bit-rate speech codecs have been widely used in audio communications like VoIP and mobile communications, so that steganography in low bit-rate audio streams would have broad applications in practice. In this paper, the authors propose a new algorithm for steganography in low bit-rate VoIP audio streams by integrating information hiding into the process of speech encoding. The proposed algorithm performs data embedding while pitch period prediction is conducted during low bit-rate speech encoding, thus maintaining synchronization between information hiding and speech encoding. The steganography algorithm can achieve high quality of speech and prevent detection of steganalysis, but also has great compatibility with a standard low bit-rate speech codec without causing further delay by data embedding and extraction. Testing shows, with the proposed algorithm, the data embedding rate of the secret message can attain 4 bits/frame (133.3 bits/second).