The Impact of Terrorism on Foreign Direct Investment: Which Sectors are More Vulnerable?

The impact of conflict and violence on foreign direct investment (FDI) is not a topic that has been done justice by the literature, and what few studies exist have contradictory results. This paper studies the impact that transnational terrorism has on FDI inflows by economic sector, in developed countries. Results indicate a statistically significant negative correlation between terrorist events and total FDI inflows. Amongst a list of 12 broad industrial sectors, FDI inflows for manufacturing, trade and repair, and construction were found to have a statistically significant negative correlation with terrorist events

Training Recipe for N:M Structured Sparsity with Decaying Pruning Mask

Sparsity has become one of the promising methods to compress and accelerate Deep Neural Networks (DNNs). Among different categories of sparsity, structured sparsity has gained more attention due to its efficient execution on modern accelerators. Particularly, N:M sparsity is attractive because there are already hardware accelerator architectures that can leverage certain forms of N:M structured sparsity to yield higher compute-efficiency. In this work, we focus on N:M sparsity and extensively study and evaluate various training recipes for N:M sparsity in terms of the trade-off between model accuracy and compute cost (FLOPs). Building upon this study, we propose two new decay-based pruning methods, namely "pruning mask decay" and "sparse structure decay". Our evaluations indicate that these proposed methods consistently deliver state-of-the-art (SOTA) model accuracy, comparable to unstructured sparsity, on a Transformer-based model for a translation task. The increase in the accuracy of the sparse model using the new training recipes comes at the cost of marginal increase in the total training compute (FLOPs).Comment: 11 pages, 2 figures, and 9 tables. Published at the ICML Workshop on Sparsity in Neural Networks Advancing Understanding and Practice, 2022. First two authors contributed equall

Exploring the Potential of Chemical Constituents of Datura metel in Breast Cancer from Molecular Docking Studies

Breast cancer remains a pervasive health challenge worldwide, prompting the exploration of novel therapeutic prospects. Datura metel has long been recognized for its pharmacological properties, particularly in containing various bioactive compounds like alkaloids, flavonoids, and terpenoids. This review focuses on the potential of chemical constituents sourced from Datura metel, a traditional medicinal plant, in combating breast cancer, primarily through molecular docking studies. The review meticulously scrutinizes the chemical composition of Datura metel, emphasizing the identified compounds known for their therapeutic attributes. Through an extensive analysis of molecular docking studies, the interactions between these Datura metel constituents and crucial molecular targets associated with breast cancer are elucidated. The phytoconstituents (compound 1-13) were found to be more potent as compare to Tomoxifen citrate as standard anticancer drug. The findings presented herein beckon for further exploration, highlighting a promising avenue in the pursuit of effective and targeted treatments for breast cancer. In conclusion, this review emphasizes the synergistic integration of computational approaches with traditional knowledge, accelerating the discovery and development of innovative breast cancer therapies

JaxPruner: A concise library for sparsity research

This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research. JaxPruner aims to accelerate research on sparse neural networks by providing concise implementations of popular pruning and sparse training algorithms with minimal memory and latency overhead. Algorithms implemented in JaxPruner use a common API and work seamlessly with the popular optimization library Optax, which, in turn, enables easy integration with existing JAX based libraries. We demonstrate this ease of integration by providing examples in four different codebases: Scenic, t5x, Dopamine and FedJAX and provide baseline experiments on popular benchmarks.Comment: Jaxpruner is hosted at http://github.com/google-research/jaxprune

PaLM: Scaling Language Modeling with Pathways

Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies

Improvement of Voltage output for Distribution System under Transient Condition with Dynamic Voltage Restorer

Abstract-Voltage sags and swells in the medium and low voltage distribution grid are considered to be the most frequent type of power quality problems based on recent power quality studies. Their impact on sensitive loads is severe. In this paper, the performance of voltage-source converter-based series compensators used for load voltage control in electrical power distribution network has been analyzed and compared, when a nonlinear load is connected across the load bus. Possible control schemes and their effects on the oscillation attenuation are also studied. Such studied control schemes include the commonly used single voltage loop control, voltage feedback plus reference feed forward control, and double-loop control with an outer voltage loop and an inner current loop. This research paper described DVR principles and voltage restoration methods for balanced and/or unbalanced voltage sags and swells in a distribution system. Simulation results were presented to illustrate and understand the performances of DVR under voltage sags/swells conditions. The MATLAB simulation verification of the results derived has been obtained using a model of the three-phase DVR

Isolation, characterization and UPLC-DAD based quantification of antiplasmodial isoquinoline alkaloids from Cissampelos pareira L.

C. pareira L. is a centuries-old traditional medicinal plant utilized to treat various diseases like asthma, diarrhea, fever, heart disorders, snakebite, vomiting, malaria, pneumonia, dog bite, inflammation and abdominal pain. Globally, based on traditional knowledge, different parts of this plant are being used individually or in combination in various forms to manage malaria. However, the scientific investigation for validating the most effective part of this plant against malaria parasite has not been done. Therefore, current study aimed to evaluate in vitro antiplasmodial activity of extracts/fractions (whole plant) and decoctions from different parts (roots, stem, leaves and whole plant) of C. pareira against different strains of Plasmodium falciparum followed by antiplasmodial activity guided isolation and quantification of isoquinoline alkaloids in extracts/fractions and decoctions. All extracts/fractions/decoctions and molecules isolated from active fractions were investigated for antiplasmodial activity. Results showed that the chloroform fraction of whole plant was the most promising with IC50 (µg/mL) of 0.79 (Pf3D7) and 2.26 (PfINDO) followed by root decoction having IC50 (µg/mL) 10.22 (Pf3D7) and 7.7 (PfINDO). Among three isolated molecules, two bisbenzylisoquinoline alkaloids namely curine (2) [IC50 (µM) 1.46 (Pf3D7) and 0.51 (PfINDO)], and O,O-dimethylcurine (1) [IC50 (µM) 0.92 (Pf3D7) and 2.6 (PfINDO)], were found to be the most potent against P. falciparum strains. The antiplasmodial activity of chloroform fraction was further validated by the developed UPLC-DAD method, which showed the highest quantities of curine (2) (~107 mg/g) and O,O-dimethylcurine (1) (~15 mg/g) in this fraction. This study showed that the root decoction was more effective than decoctions of each of the other parts of the plant and whole plant hydroalcoholic extract. Further, for the first time, this study validates the traditional use of C. pareira whole plant to manage malaria, providing further opportunity to explore the tremendous structural and chemical diversity of isoquinoline alkaloids for antimalarial drug development