Accurate segmentation of lesions is crucial for diagnosis and treatment of early esophageal cancer (EEC). However, neither traditional nor deep learning-based methods up to today can meet the clinical requirements, with the mean Dice score - the most important metric in medical image analysis - hardly exceeding 0.75. In this paper, we present a novel deep learning approach for segmenting EEC lesions. Our method stands out for its uniqueness, as it relies solely on a single input image from a patient, forming the so-called "You-Only-Have-One" (YOHO) framework. On one hand, this "one-image-one-network" learning ensures complete patient privacy as it does not use any images from other patients as the training data. On the other hand, it avoids nearly all generalization-related problems since each trained network is applied only to the same input image itself. In particular, we can push the training to "over-fitting" as much as possible to increase the segmentation accuracy. Our technical details include an interaction with clinical doctors to utilize their expertise, a geometry-based data augmentation over a single lesion image to generate the training dataset (the biggest novelty), and an edge-enhanced UNet. We have evaluated YOHO over an EEC dataset collected by ourselves and achieved a mean Dice score of 0.888, which is much higher as compared to the existing deep-learning methods, thus representing a significant advance toward clinical applications. The code and dataset are available at: https://github.com/lhaippp/YOHO.

Abstract We propose a novel method to generate a high‐order triangular mesh for an input 2D domain with two key characteristics: (1) the mesh precisely conforms to a set of input piecewise rational domain curves, and (2) the geometric map on each curved triangle is injective. Central to the algorithm is a new sufficient condition for placing control points of a rational Bézier triangle to guarantee that the conformance and injectivity constraints are theoretically satisfied. Taking advantage of this condition, we provide an explicit construct that robustly creates higher‐order 2D meshes satisfying the two characteristics. We demonstrate the robustness and effectiveness of our algorithm over a data set containing 2200 examples.

At this stage, with the rapid development of society, FM TV and radio have become an indispensable information transmission medium. The transmitting antenna is the main technical system for FM broadcasting and TV stations transmitting and receiving signals. At present, FM dipole antennas are mostly used in television, broadcasting and other media. This paper introduces two types of FM dipole antennas commonly used in television and broadcast transmission systems: single dipole and double dipole. The performance of antenna bandwidth, gain, and standing wave ratio are described in detail. FM dual dipole antennas are used for array simulation.

We study the adjacent channel interference (ACI) between 5G base stations and the C-band satellite receiver. Since the experimental frequency band of the 5G network is adjacent to the downlink frequency of the C-band satellite, interference signals may enter the low noise block (LNB) in the receiver through receiving antennas, causing barrage jamming. A compact fourth-order cavity filter based on the generalized Chebyshev function is presented in this paper. By adding an electrical coupling probe between two cavities, up to four transmission zeros can be achieved in the stopband at the same time to greatly improve the selectivity performance of the filter.

Recent paradigm shifts in distributed computing such as the advent of cloud computing pose new challenges to the analysis of distributed executions. One important new characteristic is that the management staff of computing platforms and the developers of applications are separated by corporate boundaries. The net result is that once applications go wrong, the most readily available debugging aids for developers are the visible output of the application and any log files collected during their execution. In this paper, we propose the concept of task graphs as a foundation to represent distributed executions, and present a low overhead algorithm to infer task graphs from event log files. Intuitively, a task represents an autonomous segment of computation inside a thread. Edges between tasks represent their interactions and preserve programmers’ notion of data and control flows. Our technique leverages existing logging support where available or otherwise augments it with aspect-based instrumentation to collect events of a set of predefined types. We show how task graphs can improve the precision of anomaly detection in a request-oriented analysis of field software and help programmers understand the running of the Hadoop Distributed File System (HDFS).

Dynamic inference techniques have been demonstrated to provide useful support for various software engineering tasks including bug finding, test suite evaluation and improvement, and specification generation. To date, however, dynamic inference has only been used effectively on small programs under controlled conditions. In this paper, we identify reasons why scaling dynamic inference techniques has proven difficult, and introduce solutions that enable a dynamic inference technique to scale to large programs and work effectively with the imperfect traces typically available in industrial scenarios. We describe our approximate inference algorithm, present and evaluate heuristics for winnowing the large number of inferred properties to a manageable set of interesting properties, and report on experiments using inferred properties. We evaluate our techniques on JBoss and the Windows kernel. Our tool is able to infer many of the properties checked by the Static Driver Verifier and leads us to discover a previously unknown bug in Windows.

Bounded exhaustive testing (BET) is a verification technique in which software is automatically tested for all valid inputs up to specified size bounds. A particularly interesting case of BET arises in the context of systems that take structurally complex inputs. Early research suggests that the BET approach can reveal faults in small systems with inputs of low structural complexity, but its potential utility for larger systems with more complex input structures remains unclear. We set out to test its utility on one such system. We used Alloy and TestEra to generate inputs to test the Galileo dynamic fault tree analysis tool, for which we already had both a formal specification of the input space and a test oracle. An initial attempt to generate inputs using a straightforward translation of our specification to Alloy did not work well. The generator failed to generate inputs to meaningful bounds. We developed an approach in which we factored the specification, used TestEra to generate abstract inputs based on one factor, and passed the results through a postprocessor that reincorporated information from the second factor. Using this technique, we were able to generate test inputs to meaningful bounds, and the inputs revealed nontrivial faults in the Galileo implementation, our specification, and our oracle. Our results suggest that BET, combined with specification abstraction and factoring techniques, could become a valuable addition to our verification toolkit and that further investigation is warranted.

It is important that program maintainers understand important properties of the programs they modify and ensure that the changes they make do not alter essential properties in unintended ways. Manually documenting those properties, especially temporal ones that constrain the ordering of events, is difficult and rarely done in practice. We propose an automatic approach to inferring a target system's temporal properties based on analyzing its event traces. The core of our technique is a set of pre-defined property patterns among a few events. These patterns form a partial order in terms of their strictness. Our approach finds the strictest properties satisfied by a set of events based on the traces. We report results from experiments on two sets of programs: student solutions for a class assignment, and several recent versions of OpenSSL Comparing properties inferred from different implementations led us to discover important behavioral differences which revealed flaws in the programs. Differences in automatically inferred temporal properties can provide useful information to programmers evolving complex, often unspecified, programs whose correctness depends on preservation of undocumented temporal properties.

The contribution of this paper is an experiment that shows the potential value of a combination of selective reverse engineering to formal specifications and bounded exhaustive testing to improve the assurance levels of complex software. A key problem is to scale up test input generation so that meaningful results can be obtained. We present an approach, using Alloy and TestEra for test input generation, which we evaluate by experimental application to the Galileo dynamic fault tree analysis tool.

Model checking requires a specification of the target system's desirable properties, some of which are temporal. Formulating a temporal property of the system based on either its abstract model or implementation requires a deep understanding of its behavior and sophisticated knowledge of the chosen formalism. This has been a major impediment to documenting and verifying temporal properties. We propose a dynamic approach to automatically infer a program's temporal properties based on a set of property pattern templates. We describe a preliminary implementation of this approach, and report on our experience using it to discover interesting temporal properties of a small program.