我们介绍了用于有效索引字符串的RadixStringsPline（RSS）学习索引结构。RSS是一个基数树，每个索引固定数量的字节。RSS方法或超过传统字符串索引的性能，同时使用7-70 $ \ times $少的内存。RSS通过使用最小的字符串前缀来实现这一目标，以充分区分数据与索引整个字符串的大多数探测方法不同的数据。此外，RSS的界限错误性质加速了最后一英里的搜索，也可以启用内存有效的哈希表查找加速器。我们对艺术和热门的几个真实弦乐数据集进行基准RSS。我们的实验表明，这种研究线可能对未来的内存密集型数据库应用有望。

多语言语音识别已引起大幅关注，作为补偿低资源语言数据稀缺性的有效方法。端到端（E2E）建模比常规混合系统优选，这主要是由于没有词典要求。但是，在有限的数据方案中，混合DNN-HMM仍然优于E2E模型。此外，手动词典创建的问题已通过公开训练的素式训练型（G2P）（G2P）和多种语言的IPA音译来缓解。在本文中，在低资源语言的多语言设置中提出了一种混合DNN-HMM声学模型的新型方法。针对目标语言语言信号的不同单语言模型的后验分布融合在一起。为每个源目标语言对训练了一个单独的回归神经网络，以将后者从源声学模型转换为目标语言。与ASR培训相比，这些网络需要非常有限的数据。与多语言和单语基线相比，后融合的相对增益分别为14.65％和6.5％。跨语性模型融合表明，无需使用依赖语言的ASR的后代，就可以实现可比的结果。

多语言自动语音识别（ASR）系统大多受益于低资源语言，但相对于单语言对应物，多种语言的性能下降。有限的研究集中在理解多语言语音识别设置中的语言行为。在本文中，提出了一种新型的数据驱动方法来研究跨语性的声学表达相似性。该技术衡量了各种单语言模型与目标语音信号的后验分布之间的相似性。深度神经网络被训练为映射网络，以将分布从不同的声学模型转换为直接比较的形式。分析观察到，语言接近性无法通过集合音素的体积真正估计。对拟议的映射网络的熵分析表明，具有较小重叠的语言可以更适合跨语性转移，因此在多语言设置中更有益。最后，提出的后验变换方法被利用为目标语言的单语模型融合。比单语言对应物的相对提高约为8％。

我们日常生活中的深度学习是普遍存在的，包括自驾车，虚拟助理，社交网络服务，医疗服务，面部识别等，但是深度神经网络在训练和推理期间需要大量计算资源。该机器学习界主要集中在模型级优化（如深度学习模型的架构压缩），而系统社区则专注于实施级别优化。在其间，在算术界中提出了各种算术级优化技术。本文在模型，算术和实施级技术方面提供了关于资源有效的深度学习技术的调查，并确定了三种不同级别技术的资源有效的深度学习技术的研究差距。我们的调查基于我们的资源效率度量定义，阐明了较低级别技术的影响，并探讨了资源有效的深度学习研究的未来趋势。

未来几年物联网设备计数的预期增加促使有效算法的开发，可以帮助其有效管理，同时保持功耗低。在本文中，我们提出了一种智能多通道资源分配算法，用于Loradrl的密集Lora网络，并提供详细的性能评估。我们的结果表明，所提出的算法不仅显着提高了Lorawan的分组传递比（PDR），而且还能够支持移动终端设备（EDS），同时确保较低的功耗，因此增加了网络的寿命和容量。}大多数之前作品侧重于提出改进网络容量的不同MAC协议，即Lorawan，传输前的延迟等。我们展示通过使用Loradrl，我们可以通过Aloha \ TextColor {Black}与Lorasim相比，我们可以实现相同的效率LORA-MAB在将复杂性从EDS移动到网关的同时，因此使EDS更简单和更便宜。此外，我们在大规模的频率干扰攻击下测试Loradrl的性能，并显示其对环境变化的适应性。我们表明，与基于学习的技术相比，Loradrl的输出改善了最先进的技术的性能，从而提高了PR的500多种\％。

Automated synthesis of histology images has several potential applications in computational pathology. However, no existing method can generate realistic tissue images with a bespoke cellular layout or user-defined histology parameters. In this work, we propose a novel framework called SynCLay (Synthesis from Cellular Layouts) that can construct realistic and high-quality histology images from user-defined cellular layouts along with annotated cellular boundaries. Tissue image generation based on bespoke cellular layouts through the proposed framework allows users to generate different histological patterns from arbitrary topological arrangement of different types of cells. SynCLay generated synthetic images can be helpful in studying the role of different types of cells present in the tumor microenvironmet. Additionally, they can assist in balancing the distribution of cellular counts in tissue images for designing accurate cellular composition predictors by minimizing the effects of data imbalance. We train SynCLay in an adversarial manner and integrate a nuclear segmentation and classification model in its training to refine nuclear structures and generate nuclear masks in conjunction with synthetic images. During inference, we combine the model with another parametric model for generating colon images and associated cellular counts as annotations given the grade of differentiation and cell densities of different cells. We assess the generated images quantitatively and report on feedback from trained pathologists who assigned realism scores to a set of images generated by the framework. The average realism score across all pathologists for synthetic images was as high as that for the real images. We also show that augmenting limited real data with the synthetic data generated by our framework can significantly boost prediction performance of the cellular composition prediction task.

Effective management of public shared spaces such as car parking space, is one challenging transformational aspect for many cities, especially in the developing World. By leveraging sensing technologies, cloud computing, and Artificial Intelligence, Cities are increasingly being managed smartly. Smart Cities not only bring convenience to City dwellers, but also improve their quality of life as advocated for by United Nations in the 2030 Sustainable Development Goal on Sustainable Cities and Communities. Through integration of Internet of Things and Cloud Computing, this paper presents a successful proof-of-concept implementation of a framework for managing public car parking spaces. Reservation of parking slots is done through a cloud-hosted application, while access to and out of the parking slot is enabled through Radio Frequency Identification (RFID) technology which in real-time, accordingly triggers update of the parking slot availability in the cloud-hosted database. This framework could bring considerable convenience to City dwellers since motorists only have to drive to a parking space when sure of a vacant parking slot, an important stride towards realization of sustainable smart cities and communities.

Driving through pothole infested roads is a life hazard and economically costly. The experience is even worse for motorists using the pothole filled road for the first time. Pothole-filled road networks have been associated with severe traffic jam especially during peak times of the day. Besides not being fuel consumption friendly and being time wasting, traffic jams often lead to increased carbon emissions as well as noise pollution. Moreover, the risk of fatal accidents has also been strongly associated with potholes among other road network factors. Discovering potholes prior to using a particular road is therefore of significant importance. This work presents a successful demonstration of sensor-based pothole mapping agent that captures both the pothole's depth as well as its location coordinates, parameters that are then used to generate a pothole map for the agent's entire journey. The map can thus be shared with all motorists intending to use the same route.

Before the transition of AVs to urban roads and subsequently unprecedented changes in traffic conditions, evaluation of transportation policies and futuristic road design related to pedestrian crossing behavior is of vital importance. Recent studies analyzed the non-causal impact of various variables on pedestrian waiting time in the presence of AVs. However, we mainly investigate the causal effect of traffic density on pedestrian waiting time. We develop a Double/Debiased Machine Learning (DML) model in which the impact of confounders variable influencing both a policy and an outcome of interest is addressed, resulting in unbiased policy evaluation. Furthermore, we try to analyze the effect of traffic density by developing a copula-based joint model of two main components of pedestrian crossing behavior, pedestrian stress level and waiting time. The copula approach has been widely used in the literature, for addressing self-selection problems, which can be classified as a causality analysis in travel behavior modeling. The results obtained from copula approach and DML are compared based on the effect of traffic density. In DML model structure, the standard error term of density parameter is lower than copula approach and the confidence interval is considerably more reliable. In addition, despite the similar sign of effect, the copula approach estimates the effect of traffic density lower than DML, due to the spurious effect of confounders. In short, the DML model structure can flexibly adjust the impact of confounders by using machine learning algorithms and is more reliable for planning future policies.

Neuromorphic vision or event vision is an advanced vision technology, where in contrast to the visible camera that outputs pixels, the event vision generates neuromorphic events every time there is a brightness change which exceeds a specific threshold in the field of view (FOV). This study focuses on leveraging neuromorphic event data for roadside object detection. This is a proof of concept towards building artificial intelligence (AI) based pipelines which can be used for forward perception systems for advanced vehicular applications. The focus is on building efficient state-of-the-art object detection networks with better inference results for fast-moving forward perception using an event camera. In this article, the event-simulated A2D2 dataset is manually annotated and trained on two different YOLOv5 networks (small and large variants). To further assess its robustness, single model testing and ensemble model testing are carried out.