Towards the Inferrence of Structural Similarity of Combinatorial Landscapes

One of the most common problem-solving heuristics is by analogy. For a given problem, a solver can be viewed as a strategic walk on its fitness landscape. Thus if a solver works for one problem instance, we expect it will also be effective for other instances whose fitness landscapes essentially share structural similarities with each other. However, due to the black-box nature of combinatorial optimization, it is far from trivial to infer such similarity in real-world scenarios. To bridge this gap, by using local optima network as a proxy of fitness landscapes, this paper proposed to leverage graph data mining techniques to conduct qualitative and quantitative analyses to explore the latent topological structural information embedded in those landscapes. By conducting large-scale empirical experiments on three classic combinatorial optimization problems, we gain concrete evidence to support the existence of structural similarity between landscapes of the same classes within neighboring dimensions. We also interrogated the relationship between landscapes of different problem classes

A Case Study of Choices of the Host University and Decisions to Stay or Leave the U.S. upon Graduation of Chinese Adult and Traditional Students

Chinese students are the largest group among all the international students. Many factors motivate them to study in the U.S. and their decision to stay or leave the U.S. after graduation. However, limited research investigates these aspects by differentiating students into non-traditional students and traditional student groups. As a result, this study conducted individual interviews to examine: 1) factors that influence Chinese students’ (non-traditional students vs. traditional students) choices of the host college or university in the U.S.; and 2) their decisions to stay or leave the U.S. after graduation. Eleven Chinese students participated in this study, including seven female students and four male students. Their ages range from 23 to 30 years old with the mean of 25.8. They are from majors across STEM and non-STEM fields. Among them, four had working experience who are considered as non-traditional students, while the rest are traditional students. Findings show that Chinese non-traditional students consider financial aid availability most when choosing the college abroad, while traditional students focused on the ranks of academic programs or colleges. In addition, most Chinese non-traditional students preferred to return to their home country. However, most traditional students would choose to remain in the U.S. after graduation. Meanwhile, most students desired to have some working experience in the U.S. It is hoped that this study would lead to a greater awareness of Chinese international students and enlighten higher education professionals and administrators with practical ideas to build a better campus environment and climate to serve this growing population

Detecting Violations of Differential Privacy for Quantum Algorithms

Quantum algorithms for solving a wide range of practical problems have been proposed in the last ten years, such as data search and analysis, product recommendation, and credit scoring. The concern about privacy and other ethical issues in quantum computing naturally rises up. In this paper, we define a formal framework for detecting violations of differential privacy for quantum algorithms. A detection algorithm is developed to verify whether a (noisy) quantum algorithm is differentially private and automatically generate bugging information when the violation of differential privacy is reported. The information consists of a pair of quantum states that violate the privacy, to illustrate the cause of the violation. Our algorithm is equipped with Tensor Networks, a highly efficient data structure, and executed both on TensorFlow Quantum and TorchQuantum which are the quantum extensions of famous machine learning platforms -- TensorFlow and PyTorch, respectively. The effectiveness and efficiency of our algorithm are confirmed by the experimental results of almost all types of quantum algorithms already implemented on realistic quantum computers, including quantum supremacy algorithms (beyond the capability of classical algorithms), quantum machine learning models, quantum approximate optimization algorithms, and variational quantum eigensolvers with up to 21 quantum bits

Spatial Evolution of the Effects of Urban Heat Island on Residents\u27 Health

Rising summer temperatures caused by the urban heat island (UHI) has considerable effects on the physical and mental health of urban residents globally. To categorize residents’ health risk areas and evaluate the characteristics of urban spatial evolution, based on data analysis methods, such as ArcGIS, ENVI software, and geostatistical analysis, data from meteorological stations, satellite images, and electronic maps were used to investigate spatial evolution and the process by which UHI affects the respiratory, circulatory, and cardiovascular systems and emotional health of the residents of Tianjin. Results show the UHI significantly increases respiratory, circulatory, and cardiovascular diseases. The emotional health of residents is also significantly affected with the affected level moving from level 1 to level 2-4. Highly concentrated areas in the urban center and patches with high health risks are found to be scattered and fragmented, as indicated by the phased pattern of spatially deteriorating hotspots. Hotspots expansion occurs unidirectionally to the south, surrounding the city center, while shrinking from the inside to the outside. The study identifies urban health space risks and provides theoretical guidance for urban space optimization and healthy urban planning

Image Blending Algorithm with Automatic Mask Generation

In recent years, image blending has gained popularity for its ability to create visually stunning content. However, the current image blending algorithms mainly have the following problems: manually creating image blending masks requires a lot of manpower and material resources; image blending algorithms cannot effectively solve the problems of brightness distortion and low resolution. To this end, we propose a new image blending method with automatic mask generation: it combines semantic object detection and segmentation with mask generation to achieve deep blended images based on our proposed new saturation loss and two-stage iteration of the PAN algorithm to fix brightness distortion and low-resolution issues. Results on publicly available datasets show that our method outperforms other classical image blending algorithms on various performance metrics, including PSNR and SSIM.Comment: 14 pages, 8 figure

Cortical Network Response to Acupuncture and the Effect of the Hegu Point:An FNIRS study

Acupuncture is a practice of treatment based on influencing specific points on the body by inserting needles. According to traditional Chinese medicine, the aim of acupuncture treatment for pain management is to use specific acupoints to relieve excess, activate qi (or vital energy), and improve blood circulation. In this context, the Hegu point is one of the most widely-used acupoints for this purpose, and it has been linked to having an analgesic effect. However, there exists considerable debate as to its scientific validity. In this pilot study, we aim to identify the functional connectivity related to the three main types of acupuncture manipulations and also identify an analgesic effect based on the hemodynamic response as measured by functional near-infrared spectroscopy (fNIRS). The cortical response of eleven healthy subjects was obtained using fNIRS during an acupuncture procedure. A multiscale analysis based on wavelet transform coherence was employed to assess the functional connectivity of corresponding channel pairs within the left and right somatosensory region. The wavelet analysis was focused on the very-low frequency oscillations (VLFO, 0.01–0.08 Hz) and the low frequency oscillations (LFO, 0.08–0.15 Hz). A mixed model analysis of variance was used to appraise statistical differences in the wavelet domain for the different acupuncture stimuli. The hemodynamic response after the acupuncture manipulations exhibited strong activations and distinctive cortical networks in each stimulus. The results of the statistical analysis showed significant differences ( p < 0.05 ) between the tasks in both frequency bands. These results suggest the existence of different stimuli-specific cortical networks in both frequency bands and the anaesthetic effect of the Hegu point as measured by fNIRS