Miracle-dz/UniFAR-FTUs huggingface dataset:
Endogenous Production Networks and Non-Linear Monetary Transmission

I develop a tractable sticky-price model, where input-output linkages are formed endogenously. The model delivers cyclical properties of networks that are consistent with those I estimate using sectoral and firm-level data, conditional on identified real and nominal shocks. A novel source of state dependence in nominal rigidities arises: whenever firms optimally choose to connect to more suppliers, pricing complementarities and monetary non-neutrality strengthen, and vice versa. As a result, the model jointly rationalizes observed non-linearities associated with monetary transmission. First, the model produces cycle dependence: the magnitude of real GDP’s response to a monetary shock is procyclical. This occurs because in expansions the level of productivity is high, encouraging cost-minimizing firms to connect to more suppliers, which makes pricing decisions more co-ordinated and monetary non-neutrality stronger. Second, there is path dependence: nonneutrality of real GDP is higher following previous periods of loose monetary policy. The latter happens as under nominal rigidities higher supply of money makes prices charged by suppliers cheap relative to the cost of in-house labor, encouraging more connections and strengthening pricing complementarities. Third, there is size dependence: larger monetary expansions make the production network denser and hence have a disproportionally larger effect on GDP than smaller expansions; on the other hand, larger monetary contractions break the network and have a disproportionally smaller effect. Such size dependence holds even under fully time-dependent pricing. JEL Classification: C67, E23, E52.
Vertical Data Processing for Mining Big Data: A Predicate Tree Approach

Time is a critical factor in processing a very large volume of data a.k. a ‘Big Data’. Many existing data mining algorithms (supervised and unsupervised) become futile because of the ubiquitous use of horizontal processing i.e. row-by-row processing of stored data. Processing time for big data is further exacerbated by its high dimensionality (# of features) and high cardinality (# of records). To address this processing-time issue, we proposed a vertical approach with predicate trees (pTree). Our approach structures data into columns of bit slices, which range from few to hundreds and are processed vertically i.e. column by column. We tested and compared our vertical approach to traditional (horizontal) approach using three basic Boolean operations namely addition, subtraction and multiplication with 10 data sizes. The length of data size ranged from half a billion bits to 5 billion bits. The results are analyzed w.r. t processing speed time and speed gain for both the approaches. The result shows that our vertical approach outperformed the traditional approach for all Boolean operations (add, subtract and multiply) across all data sizes and results in speed-gain between 24% to 96%. We concluded from our results that our approach being in data-mining ready format is best suited to apply to operations involving complex computations in big data application to achieve significant speed gain.
Effects of superhero embodiment in virtual reality on exertion capabilities

Studies into Virtual Reality (VR) and embodied cognition reveal that VR is capable of altering one’s behavior, body schema and body image. This study aims to determine whether the particular trait of “super strength” commonly associated with superheroes can be induced in males by using Virtual Reality. Specifically, it is hypothesized that embodiment in the body of Superman through VR leads to increased exertion capabilities. In an affordance test, male participants were asked to estimate how long they thought they would be able to perform a planking exercise. They then had to perform this exercise for as long as possible. This was done three times: setting up the baseline, after being embodied in an Average Body (AB), and after being embodied in a Superman Body (SB). The order of the AB and SB conditions alternated per participant. Sense of presence was measured through a questionnaire after each condition. No significant effects that support the hypothesis were found within the affordance test results, but a significant relation in support of the hypothesis was found between average presence and the change score of SB Exertion. Further experimentation is needed in order to confirm this effect, see whether it exists in females, and to come to definitive conclusions about the effects of embodiment on exertion capabilities.
A Bilingual Advantage for Children with Autism: Effect of a Bilingual Education on Set Shifting in Children with Autism Spectrum Disorder

The proposed study will examine the effect of an early bilingual school environment on the set shifting abilities of children with autism spectrum disorder (ASD). More specifically, it will evaluate how an English-French bilingual education program affects the set shifting abilities of children with ASD compared to a monolingual English education program. Set shifting will be measured by the Dimensional Change Card Sort (DCCS) task both before and after the respective education programs. I hypothesize that there will be a main effect of both time point and education program on set shifting abilities such that (a) set shifting abilities will improve from pre-instruction to post-instruction, and (b) those who receive a bilingual education will outperform those who receive a monolingual education on set shifting ability overall. I also hypothesize that these main effects will be qualified by an interaction, such that (c) bilingual classroom instruction will result in better set shifting abilities particularly at post-instruction when compared to monolingual classroom instruction. If future results from this proposed study do indeed suggest that the proposed hypotheses are correct, it would be interesting to further explore what this would mean for a possible restructuring of current programs in place for the education of children with autism such as the Applied Behavior Analysis (ABA) program.
A Novel Technique for Blackhole Discernment Employing Big Data Analytics in IoT Networks

– Internet of Things is connection of equipment, machine, software and everything around us to make smart things which have unique IP address to distinguish from other things. In Internet of Things everyday objects have capabilities to identify, sense and process the data so they can communicate with each other generating huge volumes of data. But data security is the major issue as when communication takes place the data can be altered through malicious attacks taking place on network while the data is being transmitted from different devices. So, in this paper a novel totalitarian approach is proposed for detection of blackhole attack which drops the packets being transmitted in the network leading to hacking and loss of data. So, in order to eliminate this DoS attack from the network proposed technique can be used where occurrence of malicious infected path results in initiation of request sent by any local node to all of its neighbor’s node for verifying the presence of the malicious path in their routing tables which has a high percentage of usage. We have used the approach of a modified routing table along with analysis of the threshold value generated by nodes which generates huge volume of big data and variation in amount of data generated by malicious nodes we can identify suspicious links before the attack is initiated and starts disturbing within the network. This is done to establish legitimacy of the node and on the corroboration of a malicious attack a blacklisting message is broadcasted to exclude the colluder nodes from the network. Results and analysis of this technique is been done on the basis of packet delivery ratio, throughput, power consumption and congestion control. Along with this comparative analysis of this technique is done with the other existing techniques proposed by various renowned researchers for identifying these blackhole attacks taking place in IoT Networks. The proposed technique makes use of a modified routing table and utilizes Big Data analytics to accurately ascertain the blackhole nodes in the IoT network. The detection and isolation of colluder nodes is being done accurately with minimalistic consumption of resources as opposed to some of the existing techniques, which may require high power consumption, synchronization, as well as other resources thereby making these techniques resource intensive. Since this technique makes use of multivariate parameter, it is more resilient to faults. Our approach also aims to alleviate the security and privacy concerns associated with big data thereby being a potential flag winner for the organizations.
Appendix A: Notional HRI Interface Redesign

Actions Analyze incoming scientific data Supervise state of AUVs to predict potential mission critical issues Take direct control of individual AUVs to avoid potential problems (e.g., missed regions, collisions) using waypoints and operational controls Anchors: coordinate operator-AUV data relay AUVs: navigate between planned waypoints AUVs: collect seismic imaging data AUVs: respond to updated operator commands
The influence of intranasal peste des petits ruminants vaccine application methods on the induction of immune responses in goats: clinicopathological and immunohistochemical findings

Intranasal administration of peste des petits ruminants (PPR) vaccine may induce mucosal and systemic immune responses in goats but little is known about the influence of the vaccine application methods. This study reports the influence of two different intranasal vaccine application methods on the immune responses in goats following intranasal PPR vaccination. Twenty, male west African dwarf goats were divided into 4 groups (n=5). Groups A and B were vaccinated intranasally (IN) with live attenuated PPR vaccine (Nigeria 75/1) by either nasal dropper (Group A) or nasal spray (Group B) methods and compared with the subcutaneous route of vaccination (Group C) and unvaccinated goats (Group D) for 28 days. PPR blocking ELISA based on the H-antigen demonstrated high-titres of PPRV-specific antibodies in all vaccinated goats with peak percentage inhibitions of 79.3% (day 14); 69.8% (day 21) and 86.6% (day 21) for IN-Drop; IN-Spray and Subcutaneous vaccination groups, respectively. Histomorphological assessment of the lungs showed the development of bronchus-associated lymphoid tissues (BALT) in Group B (INSpray). Immunohistochemistry showed PPR viral antigens in the lymphoid cells of the BALT in Group B and in the spleen and mediastinal lymph nodes of all the vaccinated goats after 28 days of vaccination. This study shows that the choice of application methods for intranasal PPR vaccine delivery may affect the induction of PPR-specific systemic immune responses. IN Drop method may result in an earlier peak in the antibody titer but the IN-Spray method holds greater potential for pulmonary protection against PPR induced pulmonary disease.
EE 367 / CS 448I Computational Imaging 2022 Notes: Noise, Denoising, and Image Reconstruction with Noise

This document serves as a supplement to the material discussed in class. The document is not meant to be a comprehensive review of noise in imaging or denoising. It is supposed to give a concise, yet insightful summary of Gaussian and Poisson-distributed noise, denoising strategies, and deconvolution in the presence of Gaussian or Poisson-distributed noise. In
exdqlm : An R Package for Estimation and Analysis of Flexible Dynamic Quantile Linear Models

We present the R package exdqlm as a tool for dynamic quantile regression. The main focus of the package is to provide a framework for Bayesian inference and forecasting of ﬂexible dynamic quantile linear models (exDQLMs). exDQLMs utilize a new family of error distributions for parametric quantile regression, the extended asymmetric Laplace (exAL), a generalization of the asymmetric Laplace (AL) which is commonly used in parametric quantile regression. Estimation of a dynamic quantile linear model, which utilizes the AL, is included in the package as a special case. Non-time-varying quantile regression models are also a special case of our methods. Further, routines for estimation of a nonlinear relationship between the response and a given input variable at a speciﬁed quantile via a transfer function model are available. The software provides the choice of two diﬀerent algorithms for posterior inference: Markov chain Monte Carlo (MCMC), and importance sampling variational Bayes (ISVB). While MCMC provides an eﬃcient sample-based exploration of the posterior distribution, the approximation obtained by the ISVB algorithm enables fast inference for long time series at a fraction of the memory and computational costs of the MCMC. A routine for forecasting the dynamic quantile is available in the package, as well as several quantitative and visual diagnostics for model evaluation. We illustrate the implementation of the functions and algorithms in the exdqlm package with a step-by-step guide for the analysis of several real data sets.
Network Externalities and Cross-Platform App Development in Mobile Platforms

The benefits consumers get from a mobile phone depend on the software apps available to use on that phone. These indirect network effects are especially important since apps are specific to a platform (e.g., iOS or Android) and are often not available on all platforms at once. We measure the cost to a platform of having delayed entry of some apps, and study the effectiveness of cross-platform development frameworks that allow developers to build an app on one platform and distribute on another. To do so, we develop a model of demand for both handsets and apps that takes into account the heterogeneous indirect network effects between apps and handsets as well as developer entry decisions. We estimate the model using a unique dataset of mobile phone sales and app downloads. We find that delayed developer entry cost the Android platform over $400M in phone sales in 2013 and 2014. We also find that cross-platform frameworks are unable to steer developers towards developing on Android first unless they can generate near-native quality iOS apps.
A Comparison of Interpolation Methods in Fast Fluid Dynamics /Author=Leonard, Eric; Qiao, Hongtao; Nabi, Saleh /CreationDate=October 19, 2021 /Subject=Multi-Physical Modeling

This paper examines the use of OpenFOAM’s cellPoint and cellPointWallModified interpolation routines in fast fluid dynamics (FFD). In addition, an arrangement of OpenFOAM’s data structures is proposed which speeds up FFD and an adjustment to FFD which incorporates the Boussinesq approximation is discussed. The findings of this paper show that the use of the cellPointWallModified interpolation routine in FFD produces results closer to reference data in higher Reynolds number flows than the use of the cellPoint interpolation routine, a result which highlights the important role that interpolation plays in the accuracy of FFD. International High Performance Buildings Conference 2021 c © 2021 MERL. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Mitsubishi Electric Research Laboratories, Inc. 201 Broadway, Cambridge, Massachusetts 02139
The Impact of Mobility Models on Geographic Routing in Multi-Hop Wireless Networks and Extensions – A Survey

– Multi-hop Wireless Networks (MWNs) emerge as an enabling communication technology, evolving rapidly due to the accelerating advancements and creating potential network applications that significantly improve the quality of life. Pure general-purpose MANET laid the theoretical foundation for MWNs, and many extensions are successfully deployed in commercial networks. This article surveys geographical routing protocols and mobility models applicable to MWNs and their recently proposed extensions. Mobility is a significant factor that profoundly impacts the performance of multi-hop geographical routing. This study analyzes various mobility models that significantly influence the performance of geographical routing protocols based on the characteristics and behavior of various network extensions. This survey investigates the primary challenges in designing geographical routing for various mobility models that notably impact the routing performance for a particular network extension. It also explores the enormous potential of geographical routing protocols under each extension and adequately addressing the routing and mobility-related issues. The essential factors that impact geographical routing, the freshness of location information, and the adaptive location update are examined extensively for various network extensions. Finally, the survey concludes with future research challenges and directions.
Addressing Multi-core Timing Interference using Co-Runner Locking

This paper presents a task synchronization mechanism, called co-runner locking, to address the timing interference problem in multi-core real-time systems. It prevents certain subsets of tasks from executing simultaneously on different cores in order to avoid large performance penalties from inter-core interference. We provide the general properties of the co-runner locking mechanism and discuss the runtime control policies that determine the execution order of tasks in a co-runner-locking relationship. For schedulability analysis, we derive a response-time test that upper-bounds the delay from co-runner locking and the slowdown imposed by permitted co-runners by combining two new analytic approaches: job-oriented and load-oriented. In evaluation, we demonstrate that the co-runner locking mechanism is an effective alternative to address the "one-out-of-m" problem and brings about a significant improvement in real-time taskset schedulability.
L2S: A Framework for Synthesizing the Most Probable Program under a Specification

In many scenarios, we need to find the most likely program that meets a specification under a local context, where the local context can be an incomplete program, a partial specification, natural language description, and so on. We call such a problem program estimation. In this article, we propose a framework, LingLong Synthesis Framework (L2S), to address this problem. Compared with existing work, our work is novel in the following aspects. (1) We propose a theory of expansion rules to describe how to decompose a program into choices. (2) We propose an approach based on abstract interpretation to efficiently prune off the program sub-space that does not satisfy the specification. (3) We prove that the probability of a program is the product of the probabilities of choosing expansion rules, regardless of the choosing order. (4) We reduce the program estimation problem to a pathfinding problem, enabling existing pathfinding algorithms to solve this problem. L2S has been applied to program generation and program repair. In this article, we report our instantiation of this framework for synthesizing conditional expressions (L2S-Cond) and repairing conditional statements (L2S-Hanabi). The experiments on L2S-Cond show that each option enabled by L2S, including the expansion rules, the pruning technique, and the use of different pathfinding algorithms, plays a major role in the performance of the approach. The default configuration of L2S-Cond correctly predicts nearly 60% of the conditional expressions in the top 5 candidates. Moreover, we evaluate L2S-Hanabi on 272 bugs from two real-world Java defects benchmarks, namely Defects4J and Bugs.jar. L2S-Hanabi correctly fixes 32 bugs with a high precision of 84%. In terms of repairing conditional statement bugs, L2S-Hanabi significantly outperforms all existing approaches in both precision and recall.
Programming Assignment 4: DCGAN, StyleGAN, and DQN

You will work in a group of 3–4 students for this assignment. This should be the same group as in the last assignment. If not, please discuss with the course staff. Remember that the course staff is happy to help with problems you run into. For help, read all Piazza posts and ask questions (that have not already been addressed), attend office hours, or meet with any course staff member either at the prearranged office hour time or at a mutually satisfactory time you arrange.
Eating Out: Fast Food Consumption and its Impact on Health of Young Generation of Karachi

This paper has measured the impacts of fast food on the health of the young generation. In today’s age where both the genders are working as bread earners of the family, people prefer fast foods because of the convenience factor and eating fast food is affecting and influencing the health of the young generation. The core population, who consume fast food the most frequently is teenage youth. The study is based on value expectancy theory and attempts to develop and test a theory-based questionnaire that identifies factor relating to fast food consumption and impact on health. In this qualitative study, data is collected by interviews with seven persons. The predetermine open-ended questions had been made to investigate the thoughts of respondents and indicate the key meanings that each participant had described his or her experiences. After conducting interviews from participants then identified relevant themes and are further discussed in the results. All findings are supported by the literature review that is related to this study. Moreover, the qualitative study ends with some recommendations to the young generation.
Knowledge distillation from multispectral Images for fish freshness estimation

Fish quality is primarily effected by the number of days elapsed since harvesting, while bad storage conditions can also lead to quality degradation similar to the impact time. Existing approaches require laboratory testing, a laborious and timeconsuming process. In this work, we investigate technologies for quantifying fish quality though the development of deep learning models for analyzing imagery of fish. We first demonstrate that such a quantification is possible, to a certain degree, from multispectral images provided a sufficient number of training examples is available. Given that, we explore how knowledge distillation can be utilized for achieving similar fish quality estimation accuracy, but instead of using high-end multispectral imaging systems, using off-the-shelf RGB cameras. Experimental evaluation on individuals from the Mullus Marbatus family demonstrates that the proposed methodology constitutes a valid approach.
Deep Grammar Optimization for Submessagae Structure of Network Protocol Parsers

—Network protocols may contain a structure where a single packet contains a header information and a set of multiple differing submessage elements. We propose an approach that will automatically detect this structure through the protocol’s grammar and generate optimized parser code to decode it efﬁciently. This paper analyzes how the proposed optimization performs as part of an Intrusion Detection System, parsing trafﬁc from the industry standard RTPS network protocol. We present a robust interface between the parser and packet analyzer provided at a deep level for each submessage to improve system performance.