Miracle-dz/UniFAR-FTUs huggingface dataset:
The determinants of cross-border equity flows

We apply a new approach to a new panel data set on bilateral gross cross-border equity flows between 14 countries, 1989-96. The remarkably good results have strong implications for theories of asset trade. We find that the geography of information heavily determines the pattern of international transactions. Our model integrates elements of the finance literature on portfolio composition and the international macroeconomics and asset trade literature. Gross asset flows depend on market size in both source and destination country as well as trading costs, in which both information and the transaction technology play a role. The resulting augmented ''gravity'' equation has equity market capitalisation representing market size and distance proxying some informational asymmetries, as well as a variable representing openness of each economy. But other variables explicitly represent information transmission (telephone call traffic and multinational bank branches), an information asymmetry between domestic and foreign investors (degree of insider trading), and the efficiency of transactions (''financial market sophistication''). This equation accounts for almost 70% of the variance of the transaction flows. Dummy variables (adjacency, language, currency or trade bloc, and a ''major financial centre'' effect) do not improve the results, nor does a variable representing destination country stock market returns. The key role of informational asymmetries is confirmed. Our information transmission variables also substantially improve standard gravity equations for trade in goods.
The progeny assessment of dairy sires for milk: the use of contemporary comparisons.

In the Proceedings of this Society two years ago, A. T. G. Macarthur (1954) presented a method for the evaluation of dairy sires under farm conditions. Since that time, considerable use has been made of this technique in the Bureau of Records of the Milk Marketing Board and in this paper we propose to discuss the method critically, in the light of the results obtained on over 1,500 bulls. As the experience we have gained has considerable bearing on the validity of this method, it would perhaps be valuable at the beginning to view the general problem of testing dairy bulls under British conditions. What evidence have we that the use of contemporary comparisons is the best tool that we have available for the assessment of dairy bulls? We must bear in mind here that we have two distinct problems. The first is the assessment of bulls used in A.I. whose daughters have been bred and milked in many herds. The second is the assessment of bulls used in natural service.
Japan Toward the Indochina Sub-Region

The purpose of the paper is to discuss Japanese relations with the Indochina Sub-region and the countries in it. The author starts with a brief overview on the sub-region’s historical background, and then discusses Japan’s historical experiences during the Cold War and post-Cold War periods, as well as the current situation, especially in terms of Japan’s ODA policy and her competition with China in various initiatives for sub-regional cooperation. The author finally refers to some implications for the future development of Japan’s relations with this sub-region.
PointListNet: Deep Learning on 3D Point Lists S UPPLEMENTARY M ATERIAL

The residue-level 3D structure of proteins can be seen as 3D point lists. If the sequence structure is neglected, the amino acids of a protein form a point cloud. Therefore, in Table 1 of the main paper, we compare our method with those 3D point cloud methods (PointNet++ [5], DGCNN [7], Point Transformer [9] and PointMLP [4]) for protein recognition. In this section, we extend those 3D point cloud methods to 4D. Specifically, for PointNet++ and Point Transformer, we integrate the 1D order displacement into modeling, as shown in Table 1. For DGCNN and PointMLP, we follow the original paper to concatenate point order coordinates and features as the input of the network. We conduct the experiments by ourselves.
On the Convergence of IRLS and Its Variants in Outlier-Robust Estimation

Outlier-robust estimation involves estimating some parameters (e.g., 3D rotations) from data samples in the presence of outliers, and is typically formulated as a non-convex and non-smooth problem. For this problem, the classical method called iteratively reweighted least-squares (IRLS) and its variants have shown impressive performance. This paper makes several contributions towards understanding why these algorithms work so well. First, we incorporate majorization and graduated non-convexity (GNC) into the IRLS framework and prove that the resulting IRLS variant is a convergent method for outlier-robust estimation. Moreover, in the robust regression context with a constant fraction of outliers, we prove this IRLS variant converges to the ground truth at a global linear and local quadratic rate for a random Gaussian feature matrix with high probability. Experiments corroborate our theory and show that the proposed IRLS variant converges within 5–10 iterations for typical problem instances of outlier-robust estimation, while state-of-the-art methods need at least 30 iterations. A basic implementation of our method is provided: https://github.com/liangzu/IRLS-CVPR2023
Thematic report : Macroeconomic models including specifically social and environmental aspects Work Package 602 D 602 . 3 : " Thematic report

A significant reduction of the global environmental consequences of European consumption and production activities are the main objective of the policy simulations carried out in this paper. For this purpose three different modelling approaches have been chosen. Two macroeconomic models following the philosophy of consistent stock-flow accounting for the main institutional sectors (households, firms, banks, central bank and government) are used for quantifying the impact of several different policies. These policies comprise classical tax reforms (pricing of resources and emissions) as well as policies aiming at behavioural change in private and public consumption and at technological change (energy and resource efficiency and renewable sources). A Dynamic New Keynesian (DYNK) model is used for a comparison between classical green tax reform and taxing direct and indirect (footprint) energy and resource use of consumers. An important leading principle of the modelling work is the simultaneous treatment of economic (GDP, employment), social (income distribution, unemployment) and environmental issues. The paper shortly describes the different modelling approaches and highlights the most important features for the evaluation of the impacts of different policies. Then the different policy scenarios that are carried out with each model are described. The policy scenarios are not directly comparable between the different models, but show some similarities. The simulation results of the different policy scenarios are then analyzed and discussed. Two important conclusions can be drawn from the simulation results: (i) important trade-offs and synergies exist between the different economic, social and environmental goals (ii) simple policy scenarios mainly putting all the effort in one simple instrument (e.g. tax reform) are not likely to achieve an optimal result. A combination of instruments is most likely to achieve results satisfying the different economic, social and environmental goals. 1. Overview of modelling approaches The stock-flow consistent macroeconomic model Naqvi (2015) presents a macroeconomic model, following the accounting framework presented in Godley and Lavoie (2007). The institutional sectors dealt with in this model comprise: production, households, government, the finance sector and the environment. Economic activities are tracked in two monetary accounts, a balance sheet and a transition flow matrix.
NON-FERMENTING GRAM NEGATIVE BACTERIA: A STUDY ON THEIR PREVALENCE AND ANTI-MICROBIAL SUSCEPTIBILITY PATTERN AMONG PATIENTS ADMITTED IN A TERTIARY CARE HOSPITAL OF BIHAR

Background: Nonfermenting gram-negative bacilli (NFGNB) are a taxonomically diverse group of aerobic, non-sporing, bacilli that either do not utilize glucose as a source of energy or utilize it oxidatively. The aim of the present study was to isolate and identify NFGNB from clinical samples and to assess prevalence and antimicrobial susceptibility profiles in a tertiary care hospital of Eastern India. Materials and Methods: An observational study with cross-sectional design was conducted between January 2022 to December 2022 (1 year) in the Department of Microbiology, JLNMCH, Bhagalpur, and Bihar. All the clinical samples including urine, pus, blood, wound swab and body fluids were received in the laboratory and inoculated on blood and Mac-Conkey agar or CLED agar and incubated aerobically at 37°C for 18 to 24 hours. The isolates which were non-lactose fermenting and showed alkaline change (K/NC) reaction in triple sugar iron agar media were provisionally considered as NFGNB. They were further identified using standard protocols for identification, like gram staining for morphology, hanging drop for motility, pigment production, oxidase test, catalase test etc. Antimicrobial susceptibility test was performed by Kirby-Bauer disc diffusion method using commercially available disc (Hi-Media). Statistical analysis was done by using Excel and SPSS V21. Result: Total 332 NFGNB were isolated from 2157 culture positive clinical samples accounting for an isolation rate of 15.4%. Urine was the most common specimen obtained followed by pus and blood.
Multicategory Support Vector Machines, Theory, and Application to the Classiﬁcation of Microarray Data and Satellite Radiance Data

This is a revision of TR1064, September, 2002. Some initial expository material has been removed at the request of a referee. There are some new results presented concerning the relations between the Bayes rule and other proposed SVM methods for the multicategory case. See especially the paragraph containing Eq (9) on p 8.
A new perspective: Measuring and modeling of landfill methane emissions

Estimating landfill methane emissions at national and global levels is fraught with uncertainties. The goal for the near-term is to improve national and global estimates based on improved models, which more realistically simulate a growing database of field measurements. This would assist regulators and policy makers to more accurately evaluate landfill methane emissions and guide development of national mitigation strategies. This article provides an updated perspective on landfill methane emissions by: (1) discussing recent field measurements and research results; (2) proposing research still needed; and (3) suggesting improved modeling strategies (including regulatory approaches) to assess landfill methane emissions more accurately.
A machine-learning approach to exoplanet spectroscopy : high-precision data analysis of spectrophometric observations of exoplanetary transits and eclipses

The characterization of exoplanetary atmospheres is the new frontier in the field of exoplanets. Transit and eclipse spectroscopy are invaluable sources of information, as they may reveal the chemical composition, the presence of clouds, and the temperature and pressure profiles of the atmospheres of exoplanets. A photometric precision of about one part in 10 is necessary to make statistically significant inferences. The native calibration of current observatories, except Kepler, is not sufficient to reach this precision. In the past, parametric models have been used extensively by most teams to remove instrumental systematics. This approach has caused many debates regarding the use of different parametric choices for the removal of systematic errors. Parametric models decorrelate the systematic noise with the aid of auxiliary information on the instrument: the so-called optical state vectors (OSVs). Such OSVs can include interand intra-pixel position of the star or its spectrum, instrument temperatures and inclinations, and/or other parameters. The choice of the parameters to include in the OSVs is somewhat arbitrary, as is the choice of the functional forms to approximate the dependence of systematic noise on those parameters. The solution to many of the issues deriving from the use of OSVs lies in the use of ‘blind’, non-parametric techniques. Such methods do not require a model for the systematics, and for this reason, they can be applied to any instrument with few changes (if any). In this Thesis, I focus on the Independent Component Analysis (ICA) of multiple time series, which performs a linear transformation of those series into maximally independent components. The use of ICA to detrend instrument systematics in exoplanetary light-curves was first proposed by Waldmann (2012). They experimented with spectroscopic light-curves taken with HST/NICMOS and sequential Kepler observations as input light-curves for the ICA. In this Thesis, I present two novel approaches to detrend single photometric observations in a self-consistent way (pixel-ICA), and scanning-mode spectroscopic observations without mixing the signals at different wavelengths (stripe-ICA). The two techniques that I pioneered extend the applicability of ICA to single observations with different instrument design. Some unsupervised preprocessing steps are also tested. The better performances of these algorithms compared to other ones in the literature are demonstrated over a series of Spitzer and Hubble observations, and synthetic data sets. The (re)analysis of archive and new data with similar techniques will cast new light on the characterization of exoplanets. With unreserved thanks to all those who have helped me along the way.
Prospecting for Practical Methodologies to Examining Links between Governing Boards‟ Performance and Organizational Effectiveness: the Changing Realities

It is worrisome that in Nigeria governing boards are constituted as political settlements for political jobbers and party faithful. This paper therefore, examines difficulties in linking the performance of governing boards to organizational effectiveness. It discusses the complexities inherent in defining and measuring effective performance of either organizations or boards independently of each other, let alone drawing causal links between the two. It reviews qualitative researches that have attempted to establish correlations between board performance and organizational effectiveness, describes typical methodology and summarizes findings. The study raises the concern that CEO and staff performance, perhaps the major intervening variables between board performance and organizational effectiveness, are too often ignored in such research. It also raises questions about some of the commonly used proxy measures of organizational effectiveness. It argues that boards have not commonly assessed their own performance or that of their organizations, because of a lack of standardized best practices or codes, resources, expertise and a readily applied measurement and evaluation framework. To recalibrate the role and importance of governing boards in public sector organizations, this paper offers an evaluation framework relying on the principal-agency theory that might help to effectively serve as model to rejig and revitalize the link between governing boards‟ performance and organizational effectiveness in Nigeria.
Computer vision profiling of neurite outgrowth dynamics reveals spatiotemporal modularity of Rho GTPase signaling

Neurite outgrowth is a prerequisite step to produce the axons and dendrites that wire the adult brain. Neuronal cell migration is crucial for brain morphogenesis. In cell culture, neurite outgrowth is highly dynamic and involves a series of dynamic morphogenetic processes (MPs) such as neurite initiation, elongation, branching, growth cone motility, and collapse (da Silva and Dotti, 2002). Cultured neuronal cells are also highly motile, which might reflect the process of neuronal migration (Nadarajah and Parnavelas, 2002). Specific spatiotemporal signaling networks control the cytoskeletal, trafficking, and adhesion dynamics required for each MP to occur. Because neurite outgrowth and cell migration use overlapping cellular machineries, it is conceivable that some molecular components might regulate both processes. These MPs fluctuate on length and time scales of tens of microns and minutes to hours, which have been missed in steady-state neurite outgrowth measurements, most often at a late differentiation stage. Identification of MP-specific signaling networks thus requires the quantification their morphodynamics. Rho GTPases are key regulators of the cytoskeletal dynamics that regulate neuronal cell morphogenesis (da Silva and Dotti, 2002). Their activity is tightly controlled in time and space by guanine nucleotide-exchange factors (GEFs; Rossman et al., 2005) and GTPase-activating proteins (GAPs; Moon and Zheng, 2003) that activate and deactivate GTPases, respectively. This regulation, as well as coupling of Rho GTPases to specific downstream effectors, dictates their cytoskeletal output at any given subcellular localization (Pertz, 2010). Current models Rho guanosine triphosphatases (GTPases) control the cytoskeletal dynamics that power neurite outgrowth. This process consists of dynamic neurite initiation, elongation, retraction, and branching cycles that are likely to be regulated by specific spatiotemporal signaling networks, which cannot be resolved with static, steady-state assays. We present NeuriteTracker, a computer-vision approach to automatically segment and track neuronal morphodynamics in time-lapse datasets. Feature extraction then quantifies dynamic neurite outgrowth phenotypes. We identify a set of stereotypic neurite outgrowth morphodynamic behaviors in a cultured neuronal cell system. Systematic RNA interference perturbation of a Rho GTPase interactome consisting of 219 proteins reveals a limited set of morphodynamic phenotypes. As proof of concept, we show that loss of function of two distinct RhoA-specific GTPase-activating proteins (GAPs) leads to opposite neurite outgrowth phenotypes. Imaging of RhoA activation dynamics indicates that both GAPs regulate different spatiotemporal Rho GTPase pools, with distinct functions. Our results provide a starting point to dissect spatiotemporal Rho GTPase signaling networks that regulate neurite outgrowth. Computer vision profiling of neurite outgrowth dynamics reveals spatiotemporal modularity of Rho GTPase signaling
Proximate (Co-)Working: Knowledge Spillovers and Social Interactions

We examine the influence of physical proximity on between-start-up knowledge spillovers at one of the largest technology coworking hubs in the United States. Relying on the exogenous assignment of office space to the hub’s 251 start-ups, we find that proximity positively influences knowledge spillovers as proxied by the likelihood of adopting an upstream web technology already used by a peer start-up. This effect is largest for start-ups within close proximity of each other and quickly decays; start-ups more than 20 meters apart on the same floor are indistinguishable from start-ups on different floors. The main driver of the effect appears to be social interactions. Although start-ups in close proximity are most likely to participate in social coworking space events together, knowledge spillovers are greatest between start-ups that socialize but are dissimilar. Ultimately, start-ups that are embedded in environments that have neither too much nor too little diversity perform better but only if they socialize. This paper was accepted by Toby Stuart, entrepreneurship and innovation. Funding: The authors acknowledge funding from the Kauffman Junior Faculty Fellowship and from the Harvard Business School Division of Research and Faculty Development. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022. 03555 .
Visualization of single endogenous polysomes reveals the dynamics of translation in live human cells

nascent image translation of single messenger ribonucleoproteins (mRNPs) Using a of single stochastically on and off the cytoplasm. a ribosome of kilobase elongation rate of Tagging observe that DYNC1H1 polysomes are actively trans-ported by motors, which may deliver the mature protein at appropriate cellular locations. The SunTag should be broadly applicable to study translational regulation in live single cells.
CAMBRIDGE WORKING PAPERS IN ECONOMICS JANEWAY INSTITUTE WORKING PAPERS Do consumption-based asset pricing models explain the dynamics of stock market returns?

We show that three prominent consumption-based asset pricing models - the Bansal-Yaron, Campbell-Cochrane and Cecchetti-Lam-Mark models - cannot explain the dynamic properties of stock market returns. We show this by estimating these models with GMM, deriving ex-ante expected returns from them and then testing whether the difference between realised and expected returns is a martingale difference sequence, which it is not. Mincer-Zarnowitz regressions show that the models’ out-of-sample expected returns are systematically biased. Furthermore, semi-parametric tests of whether the models’ state variables are consistent with the degree of own-history predictability in stock returns suggest that only the Campbell-Cochrane habit variable may be able to explain return predictability, although the evidence on this is mixed
The integration of socio-economic and agricultural surveys by national statistical ofﬁces: The case of the Uganda Harmonized Integrated Survey 1

Since 2009, the Socio-Economic Directorate of the Uganda Bureau of Statistics (UBOS) has been implementing the Uganda National Panel Survey (UNPS). In 2017, the Agriculture and Environment Statistics Directorate (DAES), started implementing the Annual Agricultural Survey (AAS). Similarities and disparities in the design of the two surveys have led, on the one hand, to the duplication of part of the collected information, and, on the other, to the different methodologies inevitably returning different estimates. This has resulted in issues around reconciling and communicating the differences in some agriculture indicators and statistics retrieved through the two different surveys. In integrating the two survey programs under the 50 × 2030 Initiative, UBOS has found a way to improve efﬁciency by avoiding duplication, reducing costs, and aligning methodologies and results. This paper documents the process of integrating the two surveys. On the whole, the extent of methodological, logistical, and institutional integration exempliﬁed by the Uganda Harmonized Integrated Survey (UHIS) may be aspirational in the short term for many NSOs in low-and middle-income countries and can help when establishing long-term strategies and implementation plans to move towards similar goals.
DeapSECURE Computational Training for Cybersecurity: Third-DeapSECURE Computational Training for Cybersecurity: Third-Year Improvements and Impacts Year Improvements and Impacts

The Data-Enabled Advanced Training Program for Cybersecurity Research and Education (DeapSECURE) was introduced in 2018 as a non-degree training consisting of six modules covering a broad range of cyber-infrastructure techniques, including high performance computing, big data, machine learning and advanced cryptography, aimed at reducing the gap between current cybersecurity curricula and requirements needed for advanced research and industrial projects. By its third year, DeapSECURE, like many other educational endeavors, experienced abrupt changes brought by the COVID-19 pandemic. The training had to be retooled to adapt to fully online delivery. Hands-on activities were reformatted to accommodate self-paced learning. In this paper, we describe and assess the third and fourth years of the project and compare them with the first half of the project, which implemented in-person instruction. We also indicate major improvements and present future opportunities for this training program to advance the cybersecurity field.