Moving Peoples and Motion Pictures: Migration in Film and Other Media

Nearly from the start, cinema has registered, dramatized, and produced images of migration and its attendant anxieties. Indeed, movies have been fuelled by the movements of peoples thanks to the striking stories and images these always engender. After glancing at two distinct efforts in the 1960s in which cinema aimed to capture a mass phenomenon for a mass audience (one from Classic Hollywood, the other from the periphery of India), I will interrogate 21st-century strategies to come to terms with what the art form’s limitations may be. Can cinema get its arms around something so complex, multidimensional, and contested as migration? Jia Zhangke’s success in bringing internal Chinese migration to light may not be easily replicated by filmmakers in other nations faced with migration issues that cluster at their borders. Perhaps other art forms are naturally more capable in this regard. To isolate what cinema has done best, however, I will draw attention to films set on the edges of Europe.

The introduction of Physa acuta (Gastropoda: Physidae) on Ilha Grande, Southeast Brazil, from initial stages to an established population

This study presents a four-year follow-up of an introduced population of Physa acuta Draparnaud, 1805, from initial stages to an established population. This introduction occurred on a small impacted stream of Vila do Abraão, the main village of Ilha Grande (Angra dos Reis, Rio de Janeiro, Brazil). The population size increased during the study, and presented a relationship to environmental factors, especially with rainfall. On the initial stages of introduction prevailed the smaller specimens, but on the overall, predominated the intermediate size classes. After less than a year, P. acuta becomes established on this stream and was possibly affecting the other species found on the stream. The information presented here is useful to understand the invasion process of invasive snails, as well as directing conservation efforts.

Interpretation of 25-OH-D3, NEFA, and calcium correlations among cow and calf pairs

In the present study, the aim was to determine alteration of NEFA, calcium and vitamin D3 levels in cow and calf pairs at parturition as well as correlation between each parameter levels. For this purpose, a cow-side device employing the enzymatic colorimetric method was used for measurement of NEFA and calcium levels. On the other hand, serum 25(OH)D3 analysis was performed using the fluorescence immunochromatographic method at the laboratory in the Faculty. Blood samples were taken from Vena jugularis of 15 Simmental cow and calf pairs immediately after parturition and placed in serum and heparinized tubes. In cow and calf pairs, concentration of vitamin D3 (15.6-120 and 31.8-120 ng/mL, respectively), NEFA (0.12-1.2 and 0.09-0.8 mmol/L, respectively) and calcium (1.8 ± 0.9 and 2.2 ± 0.6 mmol/L, respectively) were determined. There was no significant correlation between NEFA, Ca, and vitamin D3 in cows and calf pairs. Taking into account several co-factors that influenced test results, which could not easily be excluded, further studies may be warranted with larger cow-calf pair populations. In conclusion, vitamin D3 concentration in calves is not affected by the negative energy balance of dams in the parturition period.

A Unified FPGA Virtualization Framework for General-Purpose Deep Neural Networks in the Cloud

INFerence-as-a-Service (INFaaS) has become a primary workload in the cloud. However, existing FPGA-based Deep Neural Network (DNN) accelerators are mainly optimized for the fastest speed of a single task, while the multi-tenancy of INFaaS has not been explored yet. As the demand for INFaaS keeps growing, simply increasing the number of FPGA-based DNN accelerators is not cost-effective, while merely sharing these single-task optimized DNN accelerators in a time-division multiplexing way could lead to poor isolation and high-performance loss for INFaaS. On the other hand, current cloud-based DNN accelerators have excessive compilation overhead, especially when scaling out to multi-FPGA systems for multi-tenant sharing, leading to unacceptable compilation costs for both offline deployment and online reconfiguration. Therefore, it is far from providing efficient and flexible FPGA virtualization for public and private cloud scenarios. Aiming to solve these problems, we propose a unified virtualization framework for general-purpose deep neural networks in the cloud, enabling multi-tenant sharing for both the Convolution Neural Network (CNN), and the Recurrent Neural Network (RNN) accelerators on a single FPGA. The isolation is enabled by introducing a two-level instruction dispatch module and a multi-core based hardware resources pool. Such designs provide isolated and runtime-programmable hardware resources, which further leads to performance isolation for multi-tenant sharing. On the other hand, to overcome the heavy re-compilation overheads, a tiling-based instruction frame package design and a two-stage static-dynamic compilation, are proposed. Only the lightweight runtime information is re-compiled with ∼1 ms overhead, thus guaranteeing the private cloud’s performance. Finally, the extensive experimental results show that the proposed virtualized solutions achieve up to 3.12× and 6.18× higher throughput in the private cloud compared with the static CNN and RNN baseline designs, respectively.

Towards Fine-Grained Online Adaptive Approximation Control for Dense SLAM on Embedded GPUs

Dense SLAM is an important application on an embedded environment. However, embedded platforms usually fail to provide enough computation resources for high-accuracy real-time dense SLAM, even with high-parallelism architecture such as GPUs. To tackle this problem, one solution is to design proper approximation techniques for dense SLAM on embedded GPUs. In this work, we propose two novel approximation techniques, critical data identification and redundant branch elimination. We also analyze the error characteristics of the other two techniques—loop skipping and thread approximation. Then, we propose SLaPP, an online adaptive approximation controller, which aims to control the error to be under an acceptable threshold. The evaluation shows SLaPP can achieve 2.0× performance speedup and 30% energy saving on average compared to the case without approximation.

Accelerating On-Chip Training with Ferroelectric-Based Hybrid Precision Synapse

In this article, we propose a hardware accelerator design using ferroelectric transistor (FeFET)-based hybrid precision synapse (HPS) for deep neural network (DNN) on-chip training. The drain erase scheme for FeFET programming is incorporated for both FeFET HPS design and FeFET buffer design. By using drain erase, high-density FeFET buffers can be integrated onchip to store the intermediate input-output activations and gradients, which reduces the energy consuming off-chip DRAM access. Architectural evaluation results show that the energy efficiency could be improved by 1.2× ∼ 2.1×, 3.9× ∼ 6.0× compared to the other HPS-based designs and emerging non-volatile memory baselines, respectively. The chip area is reduced by 19% ∼ 36% compared with designs using SRAM on-chip buffer even though the capacity of FeFET buffer is increased. Besides, by utilizing drain erase scheme for FeFET programming, the chip area is reduced by 11% ∼ 28.5% compared with the designs using body erase scheme.

The Aquaponic Ecosystem Using IoT and IA Solutions

For solving the negative impact of the human evolution in earth, water, pollution and quality of feed. A system of aquaponic is proposed to manage gardening and recover up to 90% of water used for plants. Aquaponic is a system that combines two names: aquaculture which is the farming of fish and hydroponic which is the cultivation of plants (off-soil). On the other hand, the possibility of using the phytotron system. The objective of this solution is to collect performance measures, to control the watering conditions of plants (water level, temperature, humidity, ...) With a cloud support and other possibilities offered by the internet of things (IoT). The paper at hand aim to provide a smart solution integrates the phytotron solution in order to control the first part wish is the hydroponic and the second part concerning the aquaculture in order to offer a smart environment for the cycle of fish’s life.

The Aquaponic Ecosystem Using IoT and IA Solutions

For solving the negative impact of the human evolution in earth, water, pollution and quality of feed. A system of aquaponic is proposed to manage gardening and recover up to 90% of water used for plants. Aquaponic is a system that combines two names: aquaculture which is the farming of fish and hydroponic which is the cultivation of plants (off-soil). On the other hand, the possibility of using the phytotron system. The objective of this solution is to collect performance measures, to control the watering conditions of plants (water level, temperature, humidity, ...) With a cloud support and other possibilities offered by the internet of things (IoT). The paper at hand aim to provide a smart solution integrates the phytotron solution in order to control the first part wish is the hydroponic and the second part concerning the aquaculture in order to offer a smart environment for the cycle of fish’s life.

Hyaluronic Acid Stabilized Doxorubicin Nano-Precipitations for Osteosarcoma Treatment

Doxorubicin (Dox) is a wide-spectrum drug to treat different kinds of cancers. However, in clinical practice, Dox usually showed untargeted distributions to the other organs, which can cause serious side effects, such as cardiotoxity. Herein, the formulation of Dox into nanoparticles is critical to enhance its distribution to tumors. Herein, we used polysaccharide, hyaluronic acid, to stabilize the Dox to form nano-precipitations (PD NPs) for the therapy of osteosarcoma. The PD NPs showed enhanced drug accumulation to tumor cells and realized better anticancer effects than free drugs.