Energy Minimization With Network Coding via Latin Hypercubes

© 2016 IEEE. Network coding is mostly used to achieve the capacity of communication networks. In this paper, motivated by the nanoscale communications where the energy cost for the channel symbols is asymmetric due to the widely employed on-off keying modulation, we design energy-minimizing network codes. We develop the best mapping between the input and output symbols at the network coding node that minimizes the average codeword energy using Latin squares, which we call the minimum energy network code (MENC). We define the class of networks composed of coding nodes with N incoming and 1 outgoing symbols as in-N networks. First, we derive the condition on the network code to minimize the average energy in in-two networks and propose two linear MENCs. Later, we investigate the MENCs for in-N networks using the Latin hypercubes and propose a low-energy network code (LENC) to reduce the average energy with network coding. We compare MENC with the classical XOR and random network codes for in-two networks. The performance comparison between LENC and random network codes for in-N networks shows that the proposed network codes provide significant energy gains

The Prevalence of Social Science in Gay Rights Cases: The Synergistic Influences of Historical Context, Justificatory Citation, and Dissemination Efforts

Disjunctive legal change is often accompanied by a period of frantic activity as the competing forces of stasis and evolution vie for domination. Nowhere is the battle for legal change likely to be more sharply joined than when the findings of modern science, in their varied and multifarious forms, are pitted directly against prevailing moral or societal precepts. One of the latest incarnations of this trend is the battle over the legal recognition of gay rights. In recent history, the courts have been inundated by gay litigants seeking the rights and protections already afforded other discrete groups within society. In the resulting legal skirmishes, gay individuals are resorting with increasing regularity to the sciences in an effort to overcome the moral opprobrium surrounding homosexuality. The judicial opinions which have resulted from the onslaught of gay litigants have not remained untouched by the scientific information adduced. Rather, as this Article will demonstrate, a disproportionally large number of gay rights opinions contain citations and references to social science information. These judicial opinions have become artifacts of the battle between modern science and existing moral conceptions of homosexuality and provide a discrete microcosm within which to examine science\u27s contribution to legal change. The lessons derived from gay rights cases may help to elucidate other contexts in which science and morality meet head-on

Chemical proteomics approaches for identifying the cellular targets of natural products.

Covering: 2010 up to 2016. Deconvoluting the mode of action of natural products and drugs remains one of the biggest challenges in chemistry and biology today. Chemical proteomics is a growing area of chemical biology that seeks to design small molecule probes to understand protein function. In the context of natural products, chemical proteomics can be used to identify the protein binding partners or targets of small molecules in live cells. Here, we highlight recent examples of chemical probes based on natural products and their application for target identification. The review focuses on probes that can be covalently linked to their target proteins (either via intrinsic chemical reactivity or via the introduction of photocrosslinkers), and can be applied "in situ" - in living systems rather than cell lysates. We also focus here on strategies that employ a click reaction, the copper-catalysed azide-alkyne cycloaddition reaction (CuAAC), to allow minimal functionalisation of natural product scaffolds with an alkyne or azide tag. We also discuss 'competitive mode' approaches that screen for natural products that compete with a well-characterised chemical probe for binding to a particular set of protein targets. Fuelled by advances in mass spectrometry instrumentation and bioinformatics, many modern strategies are now embracing quantitative proteomics to help define the true interacting partners of probes, and we highlight the opportunities this rapidly evolving technology provides in chemical proteomics. Finally, some of the limitations and challenges of chemical proteomics approaches are discussed

DeVLBert: Learning Deconfounded Visio-Linguistic Representations

In this paper, we propose to investigate the problem of out-of-domain visio-linguistic pretraining, where the pretraining data distribution differs from that of downstream data on which the pretrained model will be fine-tuned. Existing methods for this problem are purely likelihood-based, leading to the spurious correlations and hurt the generalization ability when transferred to out-of-domain downstream tasks. By spurious correlation, we mean that the conditional probability of one token (object or word) given another one can be high (due to the dataset biases) without robust (causal) relationships between them. To mitigate such dataset biases, we propose a Deconfounded Visio-Linguistic Bert framework, abbreviated as DeVLBert, to perform intervention-based learning. We borrow the idea of the backdoor adjustment from the research field of causality and propose several neural-network based architectures for Bert-style out-of-domain pretraining. The quantitative results on three downstream tasks, Image Retrieval (IR), Zero-shot IR, and Visual Question Answering, show the effectiveness of DeVLBert by boosting generalization ability.Comment: 10 pages, 4 figures, to appear in ACM MM 2020 proceeding

Reports of the AAAI 2019 spring symposium series

Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must "build machines that make sense of what goes on in their environment," a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data; self-driving cars have already been involved in fatalities, including a pedestrian; and yet machine learning is unable to explain the contexts within which it operates

Parental educational level and cardiovascular disease risk factors in schoolchildren in large urban areas of Turkey: Directions for public health policy

BACKGROUND: It is widely accepted that the development of atherosclerosis starts at an early age. However, there are very few studies evaluating the prevalence of the common clinical and behavioral cardiovascular disease (CVD) risk factors among children, especially in developing countries. The aim of the present cross-sectional survey was to evaluate the distribution of blood lipid profile and various behavioral (i.e. dietary habits, physical activity status) factors related to CVD risk and its relationships to paternal (PEL) and maternal educational level (MEL) among primary schoolchildren in Turkey. METHODS: In three major metropolises in Turkey (Istanbul, Ankara and Izmir), a random sample of 1044 children aged 12 and 13 years old was examined. ANOVA was applied to evaluate the tested hypothesis, after correcting for multiple comparisons (Tukey correction). RESULTS: After controlling for energy and fat intake, physical activity status and Body Mass Index (BMI), it was found that mostly PEL had a significant positive effect for most of the subgroups examined (Lower vs. Higher and Medium vs. Higher) on TC and HDL-cholesterol and a negative effect on TC/HDL ratio for both genders. Furthermore, both boys and girls with higher PEL and MEL were found to have higher energy intake derived from fat and protein than their counterparts with Medium and Lower PEL and MEL, while the opposite was observed for the percentage of energy derived from carbohydrates. CONCLUSIONS: Our study provides indications for a possible association between an adverse lipid profile, certain dietary patterns and Higher PEL and MEL among schoolchildren in Turkey. These findings underline the possible role of social status, indicated by the degree of education of both parents, in developing certain health behaviors and health indices among Turkish children and provide some guidance for Public Health Policy