Challenges in video based object detection in maritime scenario using computer vision

This paper discusses the technical challenges in maritime image processing and machine vision problems for video streams generated by cameras. Even well documented problems of horizon detection and registration of frames in a video are very challenging in maritime scenarios. More advanced problems of background subtraction and object detection in video streams are very challenging. Challenges arising from the dynamic nature of the background, unavailability of static cues, presence of small objects at distant backgrounds, illumination effects, all contribute to the challenges as discussed here

Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling

Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) is a Takagi-Sugeno-type fuzzy inference system for online learning which can be applied for dynamic time series prediction. To the best of our knowledge, this is the first time that DENFIS has been used for rainfall-runoff (R-R) modeling. DENFIS model results were compared to the results obtained from the physically-based Storm Water Management Model (SWMM) and an Adaptive Network-based Fuzzy Inference System (ANFIS) which employs offline learning. Data from a small (5.6 km2) catchment in Singapore, comprising 11 separated storm events were analyzed. Rainfall was the only input used for the DENFIS and ANFIS models and the output was discharge at the present time. It is concluded that DENFIS results are better or at least comparable to SWMM, but similar to ANFIS. These results indicate a strong potential for DENFIS to be used in R-R modeling

Toward Better Understanding on How Group A <em>Streptococcus</em> Manipulates Human Fibrinolytic System

Group A Streptococcus pyogenes (GAS) is a human pathogen that commonly causes superficial infections such as pharyngitis, but can also lead to systemic and fatal diseases. GAS infection remains to be a major threat in regions with insufficient medical infrastructures, leading to half a million deaths annually worldwide. The pathogenesis of GAS is mediated by a number of virulence factors, which function to facilitate bacterial colonization, immune evasion, and deep tissue invasion. In this review, we will discuss the mechanism of molecular interaction between the host protein and virulence factors that target the fibrinolytic system, including streptokinase (SK), plasminogen-binding group A streptococcal M-like protein (PAM), and streptococcal inhibitor of complement (SIC). We will discuss our current understanding, through structural studies, on how these proteins manipulate the fibrinolytic system during infection

Deformation of the Fermi surface in the extended Hubbard model

The deformation of the Fermi surface induced by Coulomb interactions is investigated in the t-t'-Hubbard model. The interplay of the local U and extended V interactions is analyzed. It is found that exchange interactions V enhance small anisotropies producing deformations of the Fermi surface which break the point group symmetry of the square lattice at the Van Hove filling. This Pomeranchuck instability competes with ferromagnetism and is suppressed at a critical value of U(V). The interaction V renormalizes the t' parameter to smaller values what favours nesting. It also induces changes on the topology of the Fermi surface which can go from hole to electron-like what may explain recent ARPES experiments.Comment: 5 pages, 4 ps figure

The Characterization of TA-289, a Novel Antifungal from Fusarium sp.

Natural products offer vast structural and chemical diversity highly sought after in drug discovery research. Saccharomyces cerevisiae makes an ideal model eukaryotic organism for drug mode-of-action studies owing to ease of growth, sophistication of genetic tools and overall homology to higher eukaryotes. Equisetin and a closely related novel natural product, TA-289, are cytotoxic to fermenting yeast, but seemingly less so when yeast actively respire. Cell cycle analyses by flow cytometry revealed a cell cycle block at S-G2/M phase caused by TA-289; previously described oxidative stress-inducing compounds causing cell cycle delay led to further investigation in the involvement of equisetin and TA-289 in mitochondrial-mediated generation of reactive oxygen species. Chemical genomic profiling involving genome-wide scans of yeast deletion mutant strains for TA-289 sensitivity revealed sensitization of genes involved in the mitochondria, DNA damage repair and oxidative stress responses, consistent with a possible mechanism-of-action at the mitochondrion. Flow cytometric detection of reactive oxygen species (ROS) generation caused by TA-289 suggests that the compound may induce cell death via ROS production. The generation of a mutant strain resistant to TA-289 also displayed resistance to a known oxidant, H2O2, at concentrations that were cytotoxic to wild-type cells. The resistant mutant displayed a higher basal level of ROS production compared to the wild-type parent, indicating that the resistance mutation led to an up-regulation of antioxidant capacity which provides cell survival in the presence of TA-289. Yeast mitochondrial morphology was visualized by confocal light microscopy, where it was observed that cells treated with TA-289 displayed abnormal mitochondria phenotypes, further indicating that the compound is acting primarily at the mitochondrion. Similar effects observed with equisetin treatment suggest that both compounds share the same mechanism, eliciting cell death via ROS production in the mitochondrial respiratory chain

Learning quantum states and unitaries of bounded gate complexity

While quantum state tomography is notoriously hard, most states hold little interest to practically-minded tomographers. Given that states and unitaries appearing in Nature are of bounded gate complexity, it is natural to ask if efficient learning becomes possible. In this work, we prove that to learn a state generated by a quantum circuit with $G$ two-qubit gates to a small trace distance, a sample complexity scaling linearly in $G$ is necessary and sufficient. We also prove that the optimal query complexity to learn a unitary generated by $G$ gates to a small average-case error scales linearly in $G$. While sample-efficient learning can be achieved, we show that under reasonable cryptographic conjectures, the computational complexity for learning states and unitaries of gate complexity $G$ must scale exponentially in $G$. We illustrate how these results establish fundamental limitations on the expressivity of quantum machine learning models and provide new perspectives on no-free-lunch theorems in unitary learning. Together, our results answer how the complexity of learning quantum states and unitaries relate to the complexity of creating these states and unitaries.Comment: 8 pages, 1 figure, 1 table + 56-page appendi

The Role of Federated Learning in a Wireless World with Foundation Models

Foundation models (FMs) are general-purpose artificial intelligence (AI) models that have recently enabled multiple brand-new generative AI applications. The rapid advances in FMs serve as an important contextual backdrop for the vision of next-generation wireless networks, where federated learning (FL) is a key enabler of distributed network intelligence. Currently, the exploration of the interplay between FMs and FL is still in its nascent stage. Naturally, FMs are capable of boosting the performance of FL, and FL could also leverage decentralized data and computing resources to assist in the training of FMs. However, the exceptionally high requirements that FMs have for computing resources, storage, and communication overhead would pose critical challenges to FL-enabled wireless networks. In this article, we explore the extent to which FMs are suitable for FL over wireless networks, including a broad overview of research challenges and opportunities. In particular, we discuss multiple new paradigms for realizing future intelligent networks that integrate FMs and FL. We also consolidate several broad research directions associated with these paradigms.Comment: 8 pages, 5 figures, 1 tabl

Analog IC test and product engineering curriculum for Malaysia microelectronics industry

Production test is a significant driver of semiconductor manufacturing cost. Parallel with the advances of semiconductor fabrication, the need for a pool of talented product and test engineers is significantly increasing. This paper describes the academia-industries collaboration effort in developing an analogue electronic test and product engineering to boost-up technical competencies of electronic engineering graduates particularly in microelectronic major. The program has been successfully conducted at Universiti Putra Malaysia with strong support from Texas Instruments and Teradyne