NOMINAL EXCHANGE RATE MISALIGNMENT: IS IT PARTICULARLY IMPORTANT TO AGRICULTURAL TRADE?

This paper examines whether exchange rate misalignment negatively affects agricultural trade, compared to other industry sectors. Nominal exchange rate misalignment is obtained from the percentage deviation of real exchange rates from their long-run equilibrium based on the theory of purchasing power parity. In order to explore this issue, a bilateral trade matrix involving trade flows between 10 developed countries is constructed. Using panel data analysis, a gravity model is estimated for 4 industry sectors over the period 1974-1999. The study finds that over-valuation (under-valuation) of the nominal exchange rate negatively (positively) affects export performance of the agricultural sector in particular. In the large-scale manufacturing sectors considered in this paper, exports are not significantly affected by exchange rate misalignment.exchange rate misalignment, agricultural trade, gravity model, International Relations/Trade,

Transformed Distribution Matching for Missing Value Imputation

We study the problem of imputing missing values in a dataset, which has important applications in many domains. The key to missing value imputation is to capture the data distribution with incomplete samples and impute the missing values accordingly. In this paper, by leveraging the fact that any two batches of data with missing values come from the same data distribution, we propose to impute the missing values of two batches of samples by transforming them into a latent space through deep invertible functions and matching them distributionally. To learn the transformations and impute the missing values simultaneously, a simple and well-motivated algorithm is proposed. Our algorithm has fewer hyperparameters to fine-tune and generates high-quality imputations regardless of how missing values are generated. Extensive experiments over a large number of datasets and competing benchmark algorithms show that our method achieves state-of-the-art performance.Comment: ICML 2023 camera-ready version, https://openreview.net/forum?id=WBWb1FU8i

Campus Sustainability Appraisal in Nigeria: Setting up Sustainable Attributes for Higher Educational Institutions

Sustainable campus development has gained the attention of several policymakers and urban planners within the past decades with different campuses across the world claiming to be sustainable or have adopted initiatives of becoming sustainable. The different tools for assessing sustainability in higher education cannot be utilised in all institutions across the globe due to factors such as regional variation. This paper established and formalised a systematic approach to comprehensively review sustainability indicators identified in 13 campus sustainability assessment tools. Thereafter, Twitter social media and an online big data analysis tool were utilised in selecting environmental-based sustainability indicators for higher educational institutions in Nigeria. The rise in the use of social media amongst tertiary institution stakeholders ensures that a better understanding of environmental challenges can be derived from the perspectives of these stakeholders. The findings from the comprehensive review of the selected 13 tools reveal that there are variations in the sets of their sustainability indicators and selection process. None of the tools have compatible indicators for campus sustainability appraisal and none of the tools utilised social media and big data technology to arrive at the adopted set of indicators for their appraisal framework, threshold, and rating. We identified energy, environment, transport, infrastructure, waste, and water as the major categories for sustainability indicators in Nigeria. The current research gap identified from literature strongly justifies the purpose of this study that setup sustainability indicators that are peculiar to tertiary institutions in Nigeria that will bring about an appraisal framework and also give room for campuses to compare their sustainability performance and interchange of standard practices

Labeled Subgraph Entropy Kernel

In recent years, kernel methods are widespread in tasks of similarity measuring. Specifically, graph kernels are widely used in fields of bioinformatics, chemistry and financial data analysis. However, existing methods, especially entropy based graph kernels are subject to large computational complexity and the negligence of node-level information. In this paper, we propose a novel labeled subgraph entropy graph kernel, which performs well in structural similarity assessment. We design a dynamic programming subgraph enumeration algorithm, which effectively reduces the time complexity. Specially, we propose labeled subgraph, which enriches substructure topology with semantic information. Analogizing the cluster expansion process of gas cluster in statistical mechanics, we re-derive the partition function and calculate the global graph entropy to characterize the network. In order to test our method, we apply several real-world datasets and assess the effects in different tasks. To capture more experiment details, we quantitatively and qualitatively analyze the contribution of different topology structures. Experimental results successfully demonstrate the effectiveness of our method which outperforms several state-of-the-art methods.Comment: 9 pages,5 figure

Signaling Network Assessment of Mutations and Copy Number Variations Predicts Breast Cancer Subtype-specific Drug Targets

Individual cancer cells carry a bewildering number of distinct genomic alterations i.e., copy number variations and mutations, making it a challenge to uncover genomic-driven mechanisms governing tumorigenesis. Here we performed exome-sequencing on several breast cancer cell lines which represent two subtypes, luminal and basal. We integrated this sequencing data, and functional RNAi screening data (i.e., for identifying genes which are essential for cell proliferation and survival), onto a human signaling network. Two subtype-specific networks were identified, which potentially represent core-signaling mechanisms underlying tumorigenesis. Within both networks, we found that genes were differentially affected in different cell lines; i.e., in some cell lines a gene was identified through RNAi screening whereas in others it was genomically altered. Interestingly, we found that highly connected network genes could be used to correctly classify breast tumors into subtypes based on genomic alterations. Further, the networks effectively predicted subtype-specific drug targets, which were experimentally validated.Comment: 4 figs, more related papers at http://www.cancer-systemsbiology.org, appears in Cell Reports, 201

Performance analysis of quantum repeaters enabled by deterministically generated photonic graph states

By encoding logical qubits into specific types of photonic graph states, one can realize quantum repeaters that enable fast entanglement distribution rates approaching classical communication. However, the generation of these photonic graph states requires a formidable resource overhead using traditional approaches based on linear optics. Overcoming this challenge, a number of new schemes have been proposed that employ quantum emitters to deterministically generate photonic graph states. Although these schemes have the potential to significantly reduce the resource cost, a systematic comparison of the repeater performance among different encodings and different generation schemes is lacking. Here, we quantitatively analyze the performance of quantum repeaters based on two different graph states, i.e. the tree graph states and the repeater graph states. For both states, we compare the performance between two generation schemes, one based on a single quantum emitter coupled to ancillary matter qubits, and one based on a single quantum emitter coupled to a delayed feedback. We identify the optimal scheme at different system parameters. Our analysis provides a clear guideline on the selection of the optimal generation scheme for graph-state-based quantum repeaters, and lays out the parameter requirements for future experimental realizations of different schemes.Comment: 17 pages, 8 figure

Must I retire? : Optional retirement as a solution to ageing workforce

Like many developed societies, Hong Kong is suffering from a rapid ageing population, especially when taking into account the imminent retirement tide of the baby-boomers. According to the Census and Statistics Department, the proportion of the ageing population is projected to rise sharply from 13% in 2011 to 30% in 2041, making Hong Kong a Hyper-aged Society. Since most organisations require their older employees to leave the workforce when these employees are reaching retirement age, the experienced, skillful, and still able-to-work manpower will be completely wasted, which severely lower the city\u27s productivity due to a shrinking labour force. In addition, with the group of ageing workforce gradually leaving the market in the future, the demands on social security and other related expenses are expected to increase

Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach

Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging. Pre-processing approaches, such as missing trace or shot interpolation, are typically employed to enhance seismic data in such cases. Recently, deep learning has been used to address the trace interpolation problem at the expense of large amounts of training data to adequately represent typical seismic events. Nonetheless, state-of-the-art works have mainly focused on trace reconstruction, with little attention having been devoted to shot interpolation. Furthermore, existing methods assume regularly spaced receivers/sources failing in approximating seismic data from real (irregular) surveys. This work presents a novel shot gather interpolation approach which uses a continuous coordinate-based representation of the acquired seismic wavefield parameterized by a neural network. The proposed unsupervised approach, which we call coordinate-based seismic interpolation (CoBSI), enables the prediction of specific seismic characteristics in irregular land surveys without using external data during neural network training. Experimental results on real and synthetic 3D data validate the ability of the proposed method to estimate continuous smooth seismic events in the time-space and frequency-wavenumber domains, improving sparsity or low rank-based interpolation methods