An Uncertainty-based Neural Network for Explainable Trajectory Segmentation

As a variant task of time-series segmentation, trajectory segmentation is a key task in the applications of transportation pattern recognition and traffic analysis. However, segmenting trajectory is faced with challenges of implicit patterns and sparse results. Although deep neural networks have tremendous advantages in terms of high-level feature learning performance, deploying as a blackbox seriously limits the real-world applications. Providing explainable segmentations has significance for result evaluation and decision making. Thus, in this article, we address trajectory segmentation by proposing a Bayesian Encoder-Decoder Network (BED-Net) to provide accurate detection with explainability and references for the following active-learning procedures. BED-Net consists of a segmentation module based on Monte Carlo dropout and an explanation module based on uncertainty learning that provides results evaluation and visualization. Experimental results on both benchmark and real-world datasets indicate that BED-Net outperforms the rival methods and offers excellent explainability in the applications of trajectory segmentation.

Molecular Phylogenetics of Physa acuta (Pulmonata: Basommatophora): an Invasive species in Central Punjab Pakistan

Physids belong to Class Gastropoda; belong to Phylum Mollusca and being bioindicators, intermediate hosts of parasites and pests hold a key position in the ecosystem. There are three species of Genus Physa i.e. P. fontinalis, Physa acuta and P. gyrina water bodies of Central Punjab and were characterized on the basis of molecular markers High level of genetic diversity was revealed by polymorphic RAPD, however SSR markers were not amplified. The multivariate analysis revealed polymorphism ranging from 9.09 percent to 50 percent among the three Physid species. Total number of 79 loci were observed for the three species under study and 24 loci were observed to be polymorphic. These RAPD fragment(s) can be developed into co dominant markers (SCAR) by cloning and can be further sequenced for the development of the Physa species specific markers to identify the introduced and native species in Pakistan.

Instant Basketball Defensive Trajectory Generation

Tactic learning in virtual reality (VR) has been proven to be effective for basketball training. Endowed with the ability of generating virtual defenders in real time according to the movement of virtual offenders controlled by the user, a VR basketball training system can bring more immersive and realistic experiences for the trainee. In this article, an autoregressive generative model for instantly producing basketball defensive trajectory is introduced. We further focus on the issue of preserving the diversity of the generated trajectories. A differentiable sampling mechanism is adopted to learn the continuous Gaussian distribution of player position. Moreover, several heuristic loss functions based on the domain knowledge of basketball are designed to make the generated trajectories assemble real situations in basketball games. We compare the proposed method with the state-of-the-art works in terms of both objective and subjective manners. The objective manner compares the average position, velocity, and acceleration of the generated defensive trajectories with the real ones to evaluate the fidelity of the results. In addition, more high-level aspects such as the empty space for offender and the defensive pressure of the generated trajectory are also considered in the objective evaluation. As for the subjective manner, visual comparison questionnaires on the proposed and other methods are thoroughly conducted. The experimental results show that the proposed method can achieve better performance than previous basketball defensive trajectory generation works in terms of different evaluation metrics.

Social Context-aware Person Search in Videos via Multi-modal Cues

Person search has long been treated as a crucial and challenging task to support deeper insight in personalized summarization and personality discovery. Traditional methods, e.g., person re-identification and face recognition techniques, which profile video characters based on visual information, are often limited by relatively fixed poses or small variation of viewpoints and suffer from more realistic scenes with high motion complexity (e.g., movies). At the same time, long videos such as movies often have logical story lines and are composed of continuously developmental plots. In this situation, different persons usually meet on a specific occasion, in which informative social cues are performed. We notice that these social cues could semantically profile their personality and benefit person search task in two aspects. First, persons with certain relationships usually co-occur in short intervals; in case one of them is easier to be identified, the social relation cues extracted from their co-occurrences could further benefit the identification for the harder ones. Second, social relations could reveal the association between certain scenes and characters (e.g., classmate relationship may only exist among students), which could narrow down candidates into certain persons with a specific relationship. In this way, high-level social relation cues could improve the effectiveness of person search. Along this line, in this article, we propose a social context-aware framework, which fuses visual and social contexts to profile persons in more semantic perspectives and better deal with person search task in complex scenarios. Specifically, we first segment videos into several independent scene units and abstract out social contexts within these scene units. Then, we construct inner-personal links through a graph formulation operation for each scene unit, in which both visual cues and relation cues are considered. Finally, we perform a relation-aware label propagation to identify characters’ occurrences, combining low-level semantic cues (i.e., visual cues) and high-level semantic cues (i.e., relation cues) to further enhance the accuracy. Experiments on real-world datasets validate that our solution outperforms several competitive baselines.

Buffer Placement and Sizing for High-Performance Dataflow Circuits

Commercial high-level synthesis tools typically produce statically scheduled circuits. Yet, effective C-to-circuit conversion of arbitrary software applications calls for dataflow circuits, as they can handle efficiently variable latencies (e.g., caches), unpredictable memory dependencies, and irregular control flow. Dataflow circuits exhibit an unconventional property: registers (usually referred to as “buffers”) can be placed anywhere in the circuit without changing its semantics, in strong contrast to what happens in traditional datapaths. Yet, although functionally irrelevant, this placement has a significant impact on the circuit’s timing and throughput. In this work, we show how to strategically place buffers into a dataflow circuit to optimize its performance. Our approach extracts a set of choice-free critical loops from arbitrary dataflow circuits and relies on the theory of marked graphs to optimize the buffer placement and sizing. Our performance optimization model supports important high-level synthesis features such as pipelined computational units, units with variable latency and throughput, and if-conversion. We demonstrate the performance benefits of our approach on a set of dataflow circuits obtained from imperative code.

AIgean : An Open Framework for Deploying Machine Learning on Heterogeneous Clusters

AIgean , pronounced like the sea, is an open framework to build and deploy machine learning (ML) algorithms on a heterogeneous cluster of devices (CPUs and FPGAs). We leverage two open source projects: Galapagos , for multi-FPGA deployment, and hls4ml , for generating ML kernels synthesizable using Vivado HLS. AIgean provides a full end-to-end multi-FPGA/CPU implementation of a neural network. The user supplies a high-level neural network description, and our tool flow is responsible for the synthesizing of the individual layers, partitioning layers across different nodes, as well as the bridging and routing required for these layers to communicate. If the user is an expert in a particular domain and would like to tinker with the implementation details of the neural network, we define a flexible implementation stack for ML that includes the layers of Algorithms, Cluster Deployment & Communication, and Hardware. This allows the user to modify specific layers of abstraction without having to worry about components outside of their area of expertise, highlighting the modularity of AIgean . We demonstrate the effectiveness of AIgean with two use cases: an autoencoder, and ResNet-50 running across 10 and 12 FPGAs. AIgean leverages the FPGA’s strength in low-latency computing, as our implementations target batch-1 implementations.

Leveraging Automatic High-Level Synthesis Resource Sharing to Maximize Dynamical Voltage Overscaling with Error Control

Approximate Computing has emerged as an alternative way to further reduce the power consumption of integrated circuits (ICs) by trading off errors at the output with simpler, more efficient logic. So far the main approaches in approximate computing have been to simplify the hardware circuit by pruning the circuit until the maximum error threshold is met. One of the critical issues, though, is the training data used to prune the circuit. The output error can significantly exceed the maximum error if the final workload does not match the training data. Thus, most previous work typically assumes that training data matches with the workload data distribution. In this work, we present a method that dynamically overscales the supply voltage based on different workload distribution at runtime. This allows to adaptively select the supply voltage that leads to the largest power savings while ensuring that the error will never exceed the maximum error threshold. This approach also allows restoring of the original error-free circuit if no matching workload distribution is found. The proposed method also leverages the ability of High-Level Synthesis (HLS) to automatically generate circuits with different properties by setting different synthesis constraints to maximize the available timing slack and, hence, maximize the power savings. Experimental results show that our proposed method works very well, saving on average 47.08% of power as compared to the exact output circuit and 20.25% more than a traditional approximation method.

Early Design Space Exploration Framework for Memristive Crossbar Arrays

For memristive crossbar arrays, currently, no high-level design validation and early space exploration tools exist in the literature. Such tools are essential to quickly verify the design functionality as well as compare design alternatives in terms of power and performance. In this work, we propose a VHDL-based framework that enables us to quickly perform behavioral simulation as well as estimate dynamic energy consumption and speed of any large memristive crossbar array. We propose a high-level (VHDL) model of a memristor based on which crossbar architectures can be modeled. The individual memristor model is embedded with power and delay numbers obtained from a detailed memristor model. We demonstrate the framework for MAGIC-style memristive crossbars. We validate the framework against detailed Verilog-A based model on fifteen combinational benchmarks. For the single row model, we obtained 153x simulation speedup over HSPICE, average estimation errors of 6.64% and 0% for dynamic energy consumption and cycle-time, respectively. For the transpose model, we obtained average estimation errors of 5.51% and 10.90% for dynamic energy consumption and cycle-time, respectively. We also extend our framework to support another prominent logic style and validate through a case study. The proposed framework can be easily extended to other emerging technologies.

Monitoring of Insecticides resistance against cotton Jassid (Amrasca biguttutla biguttutla) under laboratory conditions

Development of insecticides resistance mainly hinge with managements techniques for the control of Jassid, Amrasca biguttutla biguttutla. Five insecticides were applied against field collected and laboratory rared jassid populations during the years of 2017 to 2019 to profile their resistance level against field population of jassid through leaf dip method. Very low resistance level was found in jassid against confidor whereas high level of resistance was observed by pyriproxyfen against other test insecticides. Gradual resistance was observed against diafenthiuron. It is concluded that for the management of Jassid repetition of same insecticide should be avoided. The use of confidor may be reduced to overcome resistance against Jassid.

Chemical elements in sediments and in bivalve mollusks from estuarine regions in the south of Bahia State, northeast Brazil

Estuaries receive daily inputs of chemical elements which can impact the quality of water and sediment, as well as the health of biota. In addition to the sediment, bivalve mollusks have been used in the chemical monitoring of these systems. This study investigated the presence and contents of As, Ba, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn in superficial sediment and in bivalves (Crassostrea gasar, C. rhizophorae and Mytella guyanensis) from estuaries in the south / extreme south of Bahia State, northeast Brazil. The samples were evaluated with inductively coupled plasma optical emission spectrometry (ICP-OES, Varian 710). Except for Cd, all other elements were found in the samples, being that Co was exclusive in the sediment. The estuaries equivalent to sampling stations #1 - Valença, #2 - Taperoá, #3 - Ilhéus and #4 - Belmonte showed levels of metals compatibles with those established by the Brazilian legislation, however, the #5 - Santa Cruz Cabrália, in addition to the presence of As, presented a high level of Pb and Cu in C. gasar, which was attributed to the impacts of nautical activities in that locality.