Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Method to Detect Instrumental Music in a Large Scale Music Catalog

A large scale music catalog contains diverse types of sound recordings. In this paper, we present a methodology to identify instrumental music with high precision and high recall. Our method starts by separating a recording into a vocals track and a background track. Then, we process the vocals track with a singing voice detection model, to estimate the amount of singing voice in a song. Finally, we analyze the background track using a neural network trained to classify be-tween instrumental and non-instrumental sounds. We demonstrate the effectiveness of our approach on instrumental music detection through a comparative evaluation against several state-of-the-art models.
The M2 Pipeline: A Novel Tool for Rapidly Analyzing Host Metabolic Profiles from RNA-seq Data

A novel bioinformatics and data analytics pipeline that utilizes global gene expression datasets in combination with advanced computational modeling of metabolic pathways has been applied to predicting the metabolic profiles during viral infections. The Modeling Metabolism ($\mathrm{M}^{2}$) pipeline was used to analyze and compare complex metabolic profiles of four viral respiratory infections (rhinovirus infection, flu, COVID-19, and MERS-CoV infection) based on host RNA-seq datasets. During infection, multiple factors can affect the metabolic state of the host. Metabolic changes are biomarkers of host antiviral responses and viral pathogenesis. Therefore, analyzing the metabolic response to infectious diseases might provide valuable mechanistic insights into host-pathogen interactions and highlight metabolic changes that play important roles in disease pathogenesis. By using the $\mathrm{M}^{2}$ pipeline, we predicted that influenza virus, SARS-CoV-2, and MERS-CoV infections resulted in increased glycolytic activities and a reduced capacity for oxidative phosphorylation. Furthermore, SARS-CoV-2 and MERS-CoV infections both presented dysregulated pentose phosphate pathways (PPP), while influenza infection elicited an increase in PPP activity in correlation with disease severity and mortality. Notably, rhinovirus infection, the mildest respiratory infection studied, had little effect on the overall perturbation of host cellular metabolism. The $\mathrm{M}^{2}$ pipeline provides a rapid, comprehensive, and systems-wide analysis of metabolic profiles from host response RNA-seq datasets during emerging and re-emerging infections.
Integrated sensing and communication enabled multiple beamwidth and power allocation for connected automated vehicles

Connected autonomous vehicles (CAVs) are a promising paradigm for implementing intelligent transportation systems. However, in CAVs scenarios, the sensing blind areas cause serious safety hazards. Existing vehicle-to-vehicle (V2V) technology is difficult to break through the sensing blind area and ensure reliable sensing information. To overcome these problems, considering infrastructures as a means to extend the sensing range is feasible based on the integrated sensing and communication (ISAC) technology. The mmWave base station (mmBS) transmits multiple beams consisting of communication beams and sensing beams. The sensing beams are responsible for sensing objects within the CAVs blind area, while the communication beams are responsible for transmitting the sensed information to the CAVs. To reduce the impact of inter-beam interference, a joint multiple beamwidth and power allocation (JMBPA) algorithm is proposed. By maximizing the communication transmission rate under the sensing constraints. The proposed non-convex optimization problem is transformed into a standard difference of two convex functions (D.C.) problem. Finally, the superiority of the proposed JMBPA algorithm is verified by iterative solutions. The average transmission rate of communication beams remains over 3.4 Gbps, showcasing a significant improvement compared to other algorithms. Moreover, the satisfaction of sensing services remains steady.
vPIM: Efficient Virtual Address Translation for Scalable Processing-in-Memory Architectures

3D-stacked memory technologies make it possible to integrate computation logic into the memory stack to reduce data movement between CPU and memory, enabling processing-in-memory (PIM). PIM systems scale in capacity and bandwidth by connecting multiple PIM stacks through a memory network. They also need to be programmable, where having virtual memory support is critical. Existing address translation schemes, however, are not optimized for a scalable PIM system. In this work, we propose VPIM, a virtual address translation scheme for scalable, multi-stack PIM systems. VPIM optimizes contention of the memory network in a PIM system and reduces translation time with pre-translation. Our evaluation shows a speedup of 4.4× and 1.7× compared to conventional radix and cuckoo hash page tables in eight memory-intensive workloads.
Design of a Flexible Textile Antenna for Early Breast Tumor Detections

The breast tumor is the form of tumor that is diagnosed more frequently than any other type of tumor and it is the leading cause of death from breast cancer among females globally. Breast cancer has the greatest incidence and fatality rate among women worldwide. Early on, it was largely elderly women who were affected, but that has since shifted. Younger women have been more affected by breast tumor in recent years. The most effective methods for dealing with this condition right now include screening for it in early stages and diagnosing it. Rather than relying solely on traditional approaches, it encourages the researcher to instead focus on cutting-edge technologies. Objectives: Breast tumor detection using a textile-based (jeans) flexible antenna. Methods: As a substrate, jeans serve as a model for the proposed textile antenna, while copper serves as a patch (tuning fork shape) and ground plane in a simulated version of the antenna. Multiple simulations were run on CST MWS-2021 to determine the return loss ($S_{11}$), VSWR, 3D & 2-D radiation pattern, surface current radiation efficiency (%) with a focus on the specific absorption rate (SAR) all of which are important for optimizing antenna performance and ensuring human safety in the presence of electromagnetic waves. In order to guarantee the identification process for tumor of varying sizes (R=10, 20 & 30 mm), the return loss results are compared across several cases. The tuning fork shape textile antenna operates on ISM band (5.79 GHz). Findings: With the primary goal of identifying breast tumor, the proposed structure was designed with a flexible textile substrate (jeans), low cost and an excellent radiation efficiency %. Application: The proposed structure is a significant improvement over prior studies in terms of low fabrication cost, adaptability and radiation efficiency %, directivity, SAR value. The SAR value was simulated and found 1.37 W/Kg, 0.837 W/Kg for 1 gm and 10 gm tissue. The suggested antenna meets the SAR standards given by the FCC (1 gm) and the ICNIRP (10 gm). The most significant advantage, however, is that the proposed antenna can be used in conjunction with microwave scattering technology to aid in the identification and detection of breast tumor with just a marginal difference between healthy and unhealthy breast.
Covariance Blocking and Whitening Method for Successive Relative Transfer Function Vector Estimation in Multi-Speaker Scenarios

This paper addresses the challenge of estimating the relative transfer function (RTF) vectors of multiple speakers in a noisy and reverberant environment. More specifically, we consider a scenario where two speakers activate successively. In this scenario, the RTF vector of the first speaker can be estimated in a straightforward way and the main challenge lies in estimating the RTF vector of the second speaker during segments where both speakers are simultaneously active. To estimate the RTF vector of the second speaker the so-called blind oblique projection (BOP) method determines the oblique projection operator that optimally blocks the second speaker. Instead of blocking the second speaker, in this paper we propose a covariance blocking and whitening (CBW) method, which first blocks the first speaker and applies whitening using the estimated noise covariance matrix and then estimates the RTF vector of the second speaker based on a singular value decomposition. When using the estimated RTF vectors of both speakers in a linearly constrained minimum variance beamformer, simulation results using real-world recordings for multiple speaker positions demonstrate that the proposed CBW method outperforms the conventional BOP and covariance whitening methods in terms of signal-to-interferer-and-noise ratio improvement.
Tea Leaf Disease Identification using Improved Convolution Neural Network

Plant diseases are a significant concern for the agricultural industry, as they can reduce crop yields and cause economic losses. Tea is a popular and widely consumed beverage in India, and the tea crop can be affected by different leaf diseases. Early detection of the diseases is essential to prevent the spread of other crops and minimize production losses. Traditional methods of detecting leaf diseases involve manually interpreting the images, which can be time-consuming and laborious. To address this, artificial intelligence techniques, specifically deep learning models are used for more accurate and efficient detection of tea leaf disease. This study compares the performance of several deep learning models including ResNet50, Resnet50-RS, ResNetlOl-V2, and Modified-ResNet50-V2 (M-RN50V2) and finds that the M-RN50V2 model has the highest accuracy at 90.S4%. Overall, this work aims to promote sustainable agriculture by leveraging cutting-edge technology for disease classification.
Comparative Study of Point Cloud Classification Using Deep Learning Neural Networks

Point cloud processing using neural network is challenging due to its irregular and unstructured nature. In recent development, two general types of deep learning neural networks can generate point cloud in canonical representation for good classification performance, i.e. point-based method and neighborhood-based method. Point-based methods process every single point with neural linking to find its representation, while neighborhood-based methods compute the local correlation between each point and its neighbors. To study the difference of both approaches, a comparative study on the benchmarking PointNet and Dynamic Graph Convolutional Neural Network (DGCNN) with several experiments are provided in this paper to highlight their advantages and limitation. The comparison between DGCNN and PointNet is made in terms of training time in an epoch, inference time, accuracy and the number of parameters on point cloud classification tasks using the ModelNet40 dataset. The confusion matrices are visualized to provide an in-depth comparison of inter-class and intra-class of objects classification. As a result, DGCNN achieves better overall accuracy and a lower number of parameters (92.87% and 3.47 million parameters) when compared to PointNet (90.05% and 1.81 million parameters). However, PointNet achieves a faster training time (32 seconds) and inference time (3 seconds) as compared to DGCNN training time (162 seconds) and inference time (11 seconds).
Fast FPGA Accelerator of Graph Cut Algorithm with Out-of-order Parallel Execution in Folding Grid Architecture

Graph cut is a popular approach to solving optimization tasks related to Min-cut/Max-flow problems. However, existing FPGA accelerators of graph cut have difficulty in handling large grid graphs and achieving real-time performance. To address the issue, we propose a novel folding grid architecture that maps an actual one-layered large 2-dimension grid graph into a virtual multi-layered small 2-dimension grid graph. The new architecture not only enables the virtual multi-layered grid graph to execute on a small-size processor array but also adds the potential to concurrently execute grid graph nodes in different layers. In addition, we also propose a novel out-of-order parallel execution technique to fully utilize the architecture parallelism potential. Compared to the state-of-the-art, experimental results show that our design can solve the graph cut problem for grid graphs of 1920 × 1080 nodes in real-time (above 60fps) and achieve a 5.4× improvement in execution time with similar FPGA resources.
PIE-DRAM: Postponing IECC to Enhance DRAM performance with access table

This paper proposes a novel memory architecture, PIE-DRAM, to mitigate the performance overhead caused by IECC. Unlike conventional IECC architectures, the proposed method separates IECC from data path to enable independent IECC operations from memory read or write access. Based on recent memory access histories, memory controller selectively determines the usage of IECC to alleviate IECC overhead, thereby the proposed architecture enhances the memory performance. Experimental results show that, from memory intensive workloads, 6% IPC (Instructions Per Cycle) improvement is achieved solely by applying the proposed DRAM architecture utilizing the locality and modified RMW.
A monoclonal antibody-based sandwich enzyme-linked immunosorbent assay for detection of secreted α-defensin Short title: Quantification of cryptdin-4 in the bowel lumen

Title A monoclonal antibody-based sandwich enzyme-linked immunosorbent assay for detection of secreted alpha-defensin Author(s) Nakamura, Kiminori; Sakuragi, Naoya; Ayabe, Tokiyoshi Citation Analytical biochemistry, 443(2): 124-131 Issue Date 2013-12-15 Doc URL http://hdl.handle. net/2115/57304 Type article (author version) Additional Information There are other files related to this item in HUSCAP. Check the above URL. File Information Nakamura K_Anal Biochem_2013-1.pdf
Long Memory and Asia Crisis: Implication for Capital Controls and Management

: “Financial markets are given to excesses and if a boom/bust sequence progresses beyond a certain point, it will never revert to where it came from. Instead of acting like a pendulum, financial markets have recently acted more like a wrecking ball, knocking over one economy after another. ” There are three parts to this paper. The first part examines the time series behaviour of the equity indices of ten countries. Using Robinson (1995a)’s modified GPH estimator and Robinson (1994a)’s average periodogram estimator for the differencing parameter, we have detected that not all the financial series exhibit long range dependence. Non-detection of long memory lends support to imposition of policies to block free capital flows. The results suggest that Indonesia has a stronger case to restrict capital flows than Malaysia. In the second part, we attempt to determine the degree of the contagion effects. This has important implications for diversification of portfolio risks as the increased co-dependence of asset prices increases risk in the short-term. Using correlations of equity indices across markets, we can get an indication of the severity of the contagion. The evidence suggests that these effects increased significantly and systematically as the Asian crisis worsen shortly after it started on 2 July 1997. The third part discusses the implications of the results for fund management. We argue that new regulations and hedge fund techniques have contributed to large capital flows that cause severe damage to economies. In the interim period of under-regulation of “leverage” and before the establishment of a new international financial architecture, adopting hedge fund’s market neutral or zero investment techniques may be a way to preserve Asian capital.
State-Dependent or Time-Dependent Pricing : Does It Matter for Recent U . S . Inflation ?

In the 1988-2004 micro data collected by the U.S. Bureau of Labor Statistics for the CPI, price changes are frequent (every 4-7 months, depending on the treatment of sale prices) and large in absolute value (on the order of 10%). The size and timing of price changes varies considerably for a given item, but the size and probability of a price change are unrelated to the time since the last price change. Movements in aggregate inflation reflect movements in the size of price changes rather than the fraction of items changing price, due to offsetting movements in the fraction of price increases and decreases. Neither leading time-dependent models (Taylor or Calvo) nor 1 generation state-dependent models match all of these facts. Some 2 generation state-dependent models, however, appear broadly consistent with the empirical patterns. ____________ This research was conducted with restricted access to Bureau of Labor Statistics (BLS) data. John Greenlees, David Johnson, Walter Lane, Rob McClelland, and Teague Ruder provided us invaluable assistance and guidance in using BLS data. The views expressed here are those of the authors and do not necessarily reflect the views of the Bank of Canada or the BLS. Rodrigo Barros, Adam Cagliarini, Ryoji Hiraguchi, Romans Pancs, and Benjamin Malin provided superlative research assistance. We thank numerous seminar participants, an editor, and three referees for helpful comments. Send comments and questions to Pete@Klenow.net and okryvtsov@bankofcanada.ca.
Safe DNN-type Controller Synthesis for Nonlinear Systems via Meta Reinforcement Learning

There is a pressing need to synthesize provable safety controllers for nonlinear systems as they are embedded in many safety-critical applications. In this paper, we propose a safe Meta Reinforcement Learning (Meta-RL) approach to synthesize deep neural network (DNN) controllers for nonlinear systems subject to safety constraints. Our approach incorporates two phases: Meta-RL for training the controller network, and formal safety verification based on polynomial optimization solving. In the training phase, we provide a training framework which pre-trains a unified meta-initial controller for control systems by meta-learning. An important benefit of the proposed Meta-RL approach lies in that it is much more effective and succeeds in more controller training tasks compared with existing typical RL methods, e.g., Deep Deterministic Policy Gradient (DDPG). To formally verify the safety properties of the closed-loop system with the learned controller, we develop a verification procedure by using polynomial inclusion computation in combination with barrier certificate generation. Experiments on a set of benchmarks, including systems with dimension up to 12, demonstrate the effectiveness and applicability of our method.
The Impact of Feature Selection on Malware Classification Using Chi-Square and Machine Learning

The Internet of Things (IoT) is a network of physical objects, automobiles, household appliances, and other items that are integrated with sensors, software, and connections to gather and share data via the Internet. The rapid proliferation of the Internet of Things devices has ushered in a wave of new security challenges, specifically in the realm of malware detection and these challenges necessitate innovative solutions. Consequently, the primary objective of this study is to develop an advanced malware detection system leveraging machine learning algorithms in tandem with natural language processing techniques like tokenization and vectorization, in addition to a feature selection method named Chi-Square. The proposed method is tested using the IoTPot dataset and compared with recent research in the field, where it outperformed the current work with respect to the accuracy, F1-score, recall, and precision. Furthermore, the proposed method was compared with time-based consulting and demonstrated superior performance with NLP and Chi-square than without, making it more suitable for resources constrained by such IoT systems. We also provide the code for the proposed method to foster transparency.1. 1https://github.com/AREEG94FAHAD/chisqaure
PD-L1 blockade restores CAR T cell activity through IFNγ-regulation of CD163+ macrophages

Background The immune suppressive tumor microenvironment (TME) that inhibits T cell infiltration, survival, and anti-tumor activity has posed a major challenge for developing effective immunotherapies for solid tumors. Chimeric antigen receptor (CAR)-engineered T cell therapy has shown unprecedented clinical response in treating patients with hematological malignancies, and intense investigation is underway to achieve similar responses with solid tumors. Immunologically cold tumors, including prostate cancers, are often infiltrated with abundant tumor-associated macrophages (TAMs), and infiltration of CD163+ M2 macrophages correlates with tumor progression and poor responses to immunotherapy. However, the impact of TAMs on CAR T cell activity alone and in combination with TME immunomodulators is unclear. Methods To model this in vitro, we utilized a novel co-culture system with tumor cells, CAR T cells, and polarized M1 or M2 macrophages from CD14+ PBMCs collected from healthy human donors. Tumor cell killing, T cell activation and proliferation, and macrophage phenotypes were evaluated by flow cytometry, cytokine production, RNA sequencing, and functional blockade of signaling pathways using antibodies and small molecule inhibitors. We also evaluated the TME in humanized mice following CAR T cell therapy for validation of our in vitro findings. Results We observed inhibition of CAR T cell activity with the presence of M2 macrophages, but not M1 macrophages, coinciding with a robust induction of PD-L1 in M2 macrophages. We observed similar PD-L1 expression in TAMs following CAR T cell therapy in the TME of humanized mice. PD-L1, but not PD-1, blockade in combination with CAR T cell therapy altered phenotypes to more M1-like subsets and led to loss of CD163+ M2 macrophages via IFNγ signaling, resulting in improved anti-tumor activity of CAR T cells. Conclusion This study reveals an alternative mechanism by which the combination of CAR T cells and immune checkpoint blockade modulates the immune landscape of solid tumors to enhance therapeutic efficacy of CAR T cells.
Construction of the Campus Body Temperature Screening System Based on Artificial Intelligence Technology

With the rapid development of artificial intelligence technology, it is increasingly important to detect human body temperature in public places on campus. Based on this demand, the dissertation establishes an artificial intelligence campus body temperature screening system. The paper first builds the overall architecture of the system. Using infrared thermal imaging equipment to measure the temperature and real-time monitoring of the campus. Then, the infrared thermal imaging is composed of the infrared thermal imaging to introduce the hardware composition of the system. Putting a method of temperature correction. Finally, the face detection algorithm is designed to improve the MTCNN network model. The algorithm is improved by detecting facial losses, and the losses of the frame return, face classification and key point positioning are conducted for network model training. At the end of the article, the accuracy of the improvement method was greatly improved through the experimental comparison.