LSTD: A Low-Shot Transfer Detector for Object Detection

Recent advances in object detection are mainly driven by deep learning with large-scale detection benchmarks. However, the fully-annotated training set is often limited for a target detection task, which may deteriorate the performance of deep detectors. To address this challenge, we propose a novel low-shot transfer detector (LSTD) in this paper, where we leverage rich source-domain knowledge to construct an effective target-domain detector with very few training examples. The main contributions are described as follows. First, we design a flexible deep architecture of LSTD to alleviate transfer difficulties in low-shot detection. This architecture can integrate the advantages of both SSD and Faster RCNN in a unified deep framework. Second, we introduce a novel regularized transfer learning framework for low-shot detection, where the transfer knowledge (TK) and background depression (BD) regularizations are proposed to leverage object knowledge respectively from source and target domains, in order to further enhance fine-tuning with a few target images. Finally, we examine our LSTD on a number of challenging low-shot detection experiments, where LSTD outperforms other state-of-the-art approaches. The results demonstrate that LSTD is a preferable deep detector for low-shot scenarios.Comment: Accepted by AAAI201

Uniform lower bound for the least common multiple of a polynomial sequence

Let $n$ be a positive integer and $f(x)$ be a polynomial with nonnegative integer coefficients. We prove that ${\rm lcm}_{\lceil n/2\rceil \le i\le n} \{f(i)\}\ge 2^n$ except that $f(x)=x$ and $n=1, 2, 3, 4, 6$ and that $f(x)=x^s$ with $s\ge 2$ being an integer and $n=1$, where $\lceil n/2\rceil$ denotes the smallest integer which is not less than $n/2$. This improves and extends the lower bounds obtained by Nair in 1982, Farhi in 2007 and Oon in 2013.Comment: 6 pages. To appear in Comptes Rendus Mathematiqu

The elementary symmetric functions of a reciprocal polynomial sequence

Erd\"{o}s and Niven proved in 1946 that for any positive integers $m$ and $d$, there are at most finitely many integers $n$ for which at least one of the elementary symmetric functions of $1/m, 1/(m+d), ..., 1/(m+(n-1)d)$ are integers. Recently, Wang and Hong refined this result by showing that if $n\geq 4$, then none of the elementary symmetric functions of $1/m, 1/(m+d), ..., 1/(m+(n-1)d)$ is an integer for any positive integers $m$ and $d$. Let $f$ be a polynomial of degree at least $2$ and of nonnegative integer coefficients. In this paper, we show that none of the elementary symmetric functions of $1/f(1), 1/f(2), ..., 1/f(n)$ is an integer except for $f(x)=x^{m}$ with $m\geq2$ being an integer and $n=1$.Comment: 4 pages. To appear in Comptes Rendus Mathematiqu

Witnessing criticality in non-Hermitian systems via entropic uncertainty relation

Non-Hermitian systems with exceptional points lead to many intriguing phenomena due to the coalescence of both eigenvalues and corresponding eigenvectors, in comparison to Hermitian systems where only eigenvalues degenerate. In this paper, we have investigated entropic uncertainty relation (EUR) in a non-Hermitian system and revealed a general connection between the EUR and the exceptional points of non-Hermitian system. Compared to the unitarity dynamics determined by a Hermitian Hamiltonian, the behaviors of EUR can be well defined in two different ways depending on whether the system is located in unbroken phase or broken phase regimes. In unbroken phase regime, EUR undergoes an oscillatory behavior while in broken phase regime where the oscillation of EUR breaks down. The exceptional points mark the oscillatory and non-oscillatory behaviors of the EUR. In the dynamical limit, we have identified the witness of critical behavior of non-Hermitian systems in terms of the EUR. Our results reveal that the witness can detect exactly the critical points of non-Hermitian systems beyond (anti-) PT-symmetric systems. Our results may have potential applications to witness and detect phase transition in non-Hermitian systems.Comment: 8 pages,7fugure

Research on Multifunctional Integrated Internet Platforms

Throughout the development of China’s Internet industry for more than 20 years, we can tease out three stages that can represent the trend of the times: the first stage can be called the Portal Era with the entire Internet industry dominated by portal websites. However, with several major portals ’transitions after being unable to maintain effective operation of their profit model, the Portal Era came to an end. The second stage we call Social Network Era , represented by Sina Mircroblog and WeChat. The former had successful transitions after several ups and downs and the latter is trending and promising. Also vigorously developing are various social websites and mobile apps. In the midst of the laughter of those entrepreneurs, the industry came to a third stage of development: The Era of E-business. These three stages did not appear one after another in chronological order. Social networks and e-business platforms in fact exist simultaneously with two groups of people hitting on different roads that lead to the same destination. Social networks and business platforms are now already inescapably intertwined with each other: social networks attract traffic for e-business platforms while e-business platforms provide profit in return. What is more, big Internet giants aim to create a theme park magnificently situated in the internet world to provide one-stop services like portal browsing, e-business, third party payment, social networking and entertainment. At the same time, social network leaders also gradually provided e-commerce .and the third party payment service without willing to pave way for others. Longer ago, with the smart phones sweeping the world, the mobile internet era approached quietly while online and offline businesses increasingly converged. Therefore, we have reasons to believe that the next important stage of Internet development will be named as the era of multi-functional integrated Internet platforms

Status and Trend of Power Semiconductor Module Packaging for Electric Vehicles

Power semiconductor modules are the core components in power-train system of hybrid and electric vehicles (HEV/EV). With the global interests and efforts to popularize HEV/EV, automotive module has become one of the fast growing sectors of power semiconductor industry. However, the comprehensive requirements in power, frequency, efficiency, robustness, reliability, weight, volume, and cost of automotive module are stringent than industrial products due to extremely high standards of vehicle safety and harsh environment. The development of automotive power module is facing comprehensive challenges in designing of structure, material, and assembly technology. In this chapter, the status and trend of power semiconductor module packaging for HEV/EV are investigated. Firstly, the functionality of power electronics and module in HEV/EV power-train system, as well as the performance requirements by automotive industry, is addressed. A general overview of HEV/EV module design and manufacturing is discussed. Then, the typical state-of-the-art commercial and custom HEV/EV power modules are reviewed and evaluated. Lastly, the packaging trends of automotive module are investigated. The advanced assembly concept and technology are beneficial to thermal management, minimized parasitic parameters, enhancement of thermal and mechanical reliability, and the reduction of weight, volume, and cost

Development of a micro-indentation device for measuring the mechanical properties of soft materials

AbstractIndentation is a simple and nondestructive method to measure the mechanical properties of soft materials, such as hydrogels, elastomers and soft tissues. In this work, we have developed a micro-indentation system with high-precision to measure the mechanical properties of soft materials, where the shear modulus and Poisson's ratio of the materials can be obtained by analyzing the load–relaxation curve. We have validated the accuracy and stability of the system by comparing the measured mechanical properties of a polyethylene glycol sample with that obtained from a commercial instrument. The mechanical properties of another typical polydimethylsiloxane sample submerged in heptane are measured by using conical and spherical indenters, respectively. The measured values of shear modulus and Poisson's ratio are within a reasonable range

Progressive Object Transfer Detection

Recent development of object detection mainly depends on deep learning with large-scale benchmarks. However, collecting such fully-annotated data is often difficult or expensive for real-world applications, which restricts the power of deep neural networks in practice. Alternatively, humans can detect new objects with little annotation burden, since humans often use the prior knowledge to identify new objects with few elaborately-annotated examples, and subsequently generalize this capacity by exploiting objects from wild images. Inspired by this procedure of learning to detect, we propose a novel Progressive Object Transfer Detection (POTD) framework. Specifically, we make three main contributions in this paper. First, POTD can leverage various object supervision of different domains effectively into a progressive detection procedure. Via such human-like learning, one can boost a target detection task with few annotations. Second, POTD consists of two delicate transfer stages, i.e., Low-Shot Transfer Detection (LSTD), and Weakly-Supervised Transfer Detection (WSTD). In LSTD, we distill the implicit object knowledge of source detector to enhance target detector with few annotations. It can effectively warm up WSTD later on. In WSTD, we design a recurrent object labelling mechanism for learning to annotate weakly-labeled images. More importantly, we exploit the reliable object supervision from LSTD, which can further enhance the robustness of target detector in the WSTD stage. Finally, we perform extensive experiments on a number of challenging detection benchmarks with different settings. The results demonstrate that, our POTD outperforms the recent state-of-the-art approaches.Comment: TIP 201