Miracle-dz/UniFAR-FTUs huggingface dataset:
Exact Inference in DSGE Models

Inference in Dynamic Stochastic General Equilibrium [ DSGE ] models is challenging for various by now recognized reasons, most of which relate to their identiﬁcation. Recent identiﬁcation-robust methods have been proposed yet their properties remain asymptotic. Macroeconomic series are rather limited, which suggest that ﬁnite sample improvements are still required in this literature. This paper proposes ﬁnite sample conﬁdence set estimation methods for any DSGE that can be simulated once a ﬁnite dimensional parameter vector is speciﬁed. Four econometric principles are interacted to obtain exactness: (i) duality principles linking exact tests to exact conﬁdence intervals via test inversion; (ii) the Monte Carlo test principle; (iii) the Indirect Inference principle; and (iv) multivariate distance principles. The statistics we consider evaluate discrepancies between the parameters of a relevant reduced form from their DSGE-restricted population counterpart, which we propose to obtain by simulation. Rather than minimizing these discrepancies (which would make sense from a rather restrictive point estimation perspective) we view model-to-data matching as a search for the ”insigniﬁcant” discrepancies. We derive exact p -values to gauge whether discrepancies are signiﬁcant or not, which use simulation at two stages: the ﬁrst to approximate population parameters and the second to simulate the associated statistics under the null. We derive exact p -values despite the dependence induced by the ﬁrst stage. Indirect Inference is one of the most suited econometric principles that can formally connect calibration and inference in macroeconomics. Our set-estimation approach via insigniﬁcant DSGE-to-Data distances formally builds a sensitivity analysis into the matching exercise. Simulations based on a New Keynesian model show that the method holds real and concrete promise. An empirical analysis further illustrates the usefulness of our method that admits empty sets as a possible solution. Empty sets suggest speciﬁcation problems. We ﬁnd that the considered model is rejected by real data.
Banff or Simputation? Assessing Alternative Approaches to the Imputation of Missing and Erroneous Data on BEA’s Multinational Enterprise Surveys

The U.S. Bureau of Economic Analysis (BEA) employs automated data editing and imputation systems to process a subset of its multinational enterprise (MNE) surveys. Until recently, all of the auto-editing systems used at BEA were built around the Banff system for data editing and imputation produced by Statistics Canada, which runs in SAS. To enhance BEA’s flexibility, BEA researchers have explored the feasibility of creating auto-editing systems built around software other than Banff, focusing in particular on a set of R packages created by researchers at Statistics Netherlands. Since previous research has established that Banff produces highly accurate imputations on BEA’s MNE surveys, a key question is whether these R packages produce imputations that are as accurate as those produced by Banff. Among these R packages, the Simputation package is responsible for almost all of the imputation functionality. This project employs a simulation-based approach to assess the relative accuracy of the imputations produced by Banff and Simputation, using data collected by two different MNE survey instruments. The simulation results indicate that Simputation is sufficiently accurate to make the Statistics Netherlands R packages a viable alternative to Banff. However, the results differ by survey instrument, suggesting that Simputation might produce more accurate imputations when the instrument collects relatively few data items and that Banff might be more accurate for forms that are longer and more complex.
FACTORS INFLUENCING FIXED INCOME MUTUAL FUND PERFORMANCE

A mutual fund is one of the investment instruments currently developing in Indonesia. There are many types of mutual funds, such as money market funds, fixed-income funds, equity funds, and mixed funds. When an investor invests their money in mutual funds, the investor has to be able to pick the best mutual funds based on mutual funds' performance and their risk profile. There are a lot of factors to determine mutual funds' performance. This research aims to analyze the effect of fund age, fund size, expenditure ratio, and past performance on fixed income mutual funds performance starting from 2016 – 2019. The performance of fixed income mutual funds is measured with the Sharpe method. Multiple regression results analyzed data from the research. The research results show that fund age has a negative and significant effect on fixed income mutual funds. Meanwhile, fund size, expenditure ratio, and past performance don’t significantly affect the performance of fixed-income mutual funds. This research provides investors insight into being more selective in choosing mutual fund products, especially fixed income mutual funds. Furthermore, portfolio managers might use this study to assess the performance of the mutual funds they manage. So that the mutual funds they manage might have a higher future performance. The study contributes to a better understanding of the factors to consider when investing in fixed income mutual funds.
Bulletin of Electrical Engineering and Informatics

Received Jul 30, 2021 Revised Jul 1, 2022 Accepted Jul 5, 2022 In the new-normal era, public services must make various adjustments to keep the community safe during the COVID-19 pandemic. The Public Service Mall is an initiative to put several public services offices in a centralized location. However, it will create a crowd of people who want access to public service. This paper evaluates multi-tenant models with the rapid adaptation of cloud computing technology for all organizations' shapes and sizes, focusing on multi-tenants and multi-services, where each tenant might have multiple services to offer. We also proposed a multi-tenant architecture that can serve queues in several places to prevent the spread of COVID-19 due to the crowd of people in public places. The design of multitenants and multi-services applications should consider various aspects such as security, database, data communication, and user interface. We designed and built the "QuAntri'' business logic to simplify the process for multiservices in each tenant. The developed system is expected to improve tenants' performance and reduce the crowd in the public service. We compared our agile method for system development with some of the previous multi-tenant architectures. Our experiments showed that our method overall is better than the referenced model-view-controller (MVC), model-view-presenter (MVP), and model-model-view-presenter (M-MVP).
IIIb study in patients with relapsing multiple sclerosis : 96-week results-1 a ( Rebif ® New Formulation ) in a Phase β Safety and immunogenicity of a new formulation of interferon

http://msj.sagepub. com/content/15/2/219 The online version of this article can be found at: DOI: 10.1177/1352458508097299 2009 15: 219 originally published online 28 August 2008 Mult Scler behalf of the Rebif® New Formulation Study Group G Giovannoni, O Barbarash, F Casset-Semanaz, J King, L Metz, G Pardo, J Simsarian, PS Sørensen, B Stubinski and on IIIb study in patients with relapsing multiple sclerosis: 96-week results -1a (Rebif® New Formulation) in a Phase β Safety and immunogenicity of a new formulation of interferon
Least Squares Approach to the Rayliegh-Ritz Method

In this paper the author will attempt to present the Rayliegh-Ritz method of approximating solutions to Sturm-Louiville BVPs using linear algebra. Another approach to this problem involves using the calculus of variations. However, the author feels more insight can be obtained by using a least squares approach by de ﬁ ning a norm using the di ﬀ erential equation itself.
IoBT-OS: Optimizing the Sensing-to-Decision Pipeline for the Internet of Battlefield Things

—Recent concepts in defense herald an increasing degree of automation of future military systems, with an emphasis on accelerating sensing-to-decision pipelines at the tactical edge, reducing their network communication footprint, and improving the inference quality of intelligent components in the loop. These requirements pose resource management challenges, calling for operating-system-like constructs that optimize the use of limited computational resources at the tactical edge. This paper describes these challenges and presents IoBT-OS , an operating system for the Internet of Battlefield Things that aims to optimize decision latency, improve decision accuracy, and reduce corresponding resource demands on computational and network components. A simple case-study with initial evaluation results is shown from a target tracking application scenario.
SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation

A few-shot generative model should be able to generate data from a novel distribution by only observing a limited set of examples. In few-shot learning the model is trained on data from many sets from distributions sharing some underlying properties such as sets of characters from different alphabets or objects from different categories. We extend current latent variable models for sets to a fully hierarchical approach with an attention-based point to set-level aggregation and call our method SCHA-VAE for Set-Context-Hierarchical-Aggregation Variational Autoencoder. We explore likelihood-based model comparison, iterative data sampling, and adaptation-free out-of-distribution generalization. Our results show that the hierarchical formulation better captures the intrin-sic variability within the sets in the small data regime. This work generalizes deep latent variable approaches to few-shot learning, taking a step toward large-scale few-shot generation with a formulation that readily works with current state-of-the-art deep generative models.
DeltaZ: An Accessible Compliant Delta Robot Manipulator for Research and Education

This paper presents the DeltaZ robot, a centimeter-scale, low-cost, delta-style robot that allows for a broad range of capabilities and robust functionalities. The DeltaZ robot is 3D-printed from soft and rigid materials with a design that is easy to assemble and maintain, and lowers the barriers to utilize. Functionality of the robot stems from its three translational degrees of freedom and a closed form kinematic solution which makes manipulation problems more intuitive compared to many other manipulators. Moreover, the low cost of the robot presents an opportunity to democratize manipulators for research and education settings. We describe how the robot can be used as a reinforcement learning bench-mark. Open-source 3D-printable designs and code for building and using the robot are available to the public.
CaTT-KWS: A Multi-stage Customized Keyword Spotting Framework based on Cascaded Transducer-Transformer

Customized keyword spotting (KWS) has great potential to be deployed on edge devices to achieve hands-free user experience. However, in real applications, false alarm (FA) would be a serious problem for spotting dozens or even hundreds of keywords, which drastically affects user experience. To solve this problem, in this paper, we leverage the recent advances in transducer and transformer based acoustic models and propose a new multi-stage customized KWS framework named Cascaded Transducer-Transformer KWS (CaTT-KWS), which includes a transducer based keyword detector, a frame-level phone predictor based force alignment module and a transformer based decoder. Specifically, the streaming transducer module is used to spot keyword candidates in audio stream. Then force alignment is implemented using the phone posteriors predicted by the phone predictor to finish the first stage keyword verification and refine the time boundaries of keyword. Finally, the transformer decoder further verifies the triggered keyword. Our proposed CaTT-KWS framework reduces FA rate effectively without obviously hurting keyword recognition accuracy. Specifically, we can get impressively 0.13 FA per hour on a challenging dataset, with over 90% relative reduction on FA comparing to the transducer based detection model, while keyword recognition accuracy only drops less than 2%.
Robot’s Gendering Trouble: A Scoping Review of Gendering Humanoid Robots and Its Effects on HRI

The discussion around gendering humanoid robots has gained more traction in the last few years. To lay the basis for a full comprehension of how robots’ “gender” has been understood within the Human–Robot Interaction (HRI) community—i.e., how it has been manipulated, in which contexts, and which effects it has yielded on people’s perceptions and interactions with robots—we performed a scoping review of the literature. We identified 553 papers relevant for our review retrieved from 5 different databases. The final sample of reviewed papers included 35 papers written between 2005 and 2021, which involved a total of 3902 participants. In this article, we thoroughly summarize these papers by reporting information about their objectives and assumptions on gender (i.e., definitions and reasons to manipulate gender), their manipulation of robots’ “gender” (i.e., gender cues and manipulation checks), their experimental designs (e.g., demographics of participants, employed robots), and their results (i.e., main and interaction effects). The review reveals that robots’ “gender” does not affect crucial constructs for the HRI, such as likability and acceptance, but rather bears its strongest effect on stereotyping. We leverage our different epistemological backgrounds in Social Robotics and Gender Studies to provide a comprehensive interdisciplinary perspective on the results of the review and suggest ways to move forward in the field of HRI.
CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution

Mobile devices run deep learning models for various purposes, such as image classification and speech recognition. Due to the resource constraints of mobile devices, researchers have focused on either making a lightweight deep neural network (DNN) model using model pruning or generating an efficient code using compiler optimization. Surprisingly, we found that the straightforward integration between model compression and compiler auto-tuning often does not produce the most efficient model for a target device. We propose CPrune, a compiler-informed model pruning for efficient target-aware DNN execution to support an application with a required target accuracy. CPrune makes a lightweight DNN model through informed pruning based on the structural information of subgraphs built during the compiler tuning process. Our experimental results show that CPrune increases the DNN execution speed up to 2.73x compared to the state-of-the-art TVM auto-tune while satisfying the accuracy requirement.
Learning node embeddings via summary graphs: a brief theoretical analysis

Graph representation learning plays an important role in many graph mining applications, but learning embeddings of large-scale graphs remains a problem. Recent works try to improve scalability via graph summarization -- i.e., they learn embeddings on a smaller summary graph, and then restore the node embeddings of the original graph. However, all existing works depend on heuristic designs and lack theoretical analysis. Different from existing works, we contribute an in-depth theoretical analysis of three specific embedding learning methods based on introduced kernel matrix, and reveal that learning embeddings via graph summarization is actually learning embeddings on a approximate graph constructed by the configuration model. We also give analysis about approximation error. To the best of our knowledge, this is the first work to give theoretical analysis of this approach. Furthermore, our analysis framework gives interpretation of some existing methods and provides great insights for future work on this problem.
A Prolog assisted search for new simple Lie algebras

We describe some recent computer investigations with the `Constraint Logic Programming over Finite Domains' -- CLP(FD) -- library in the Prolog programming environment to search for new simple Lie algebras over the field $\GF(2)$ of $2$ elements. Motivated by a paper of Grishkov et. al., we specifically look for those with a `thin decomposition', and we settle one of their conjectures. We extrapolate from our results the existence of two new infinite families of simple Lie algebras, in addition to finding seven new sporadic examples in dimension $31$. We also better contextualise some previously discovered simple algebras, putting them into families which do not seem to have ever appeared in the literature, and give an updated table of those currently known.
Building African Voices

Modern speech synthesis techniques can produce natural-sounding speech given sufficient high-quality data and compute resources. However, such data is not readily available for many languages. This paper focuses on speech synthesis for low-resourced African languages, from corpus creation to sharing and deploying the Text-to-Speech (TTS) systems. We first create a set of general-purpose instructions on building speech synthesis systems with minimum technological resources and subject-matter expertise. Next, we create new datasets and curate datasets from"found"data (existing recordings) through a participatory approach while considering accessibility, quality, and breadth. We demonstrate that we can develop synthesizers that generate intelligible speech with 25 minutes of created speech, even when recorded in suboptimal environments. Finally, we release the speech data, code, and trained voices for 12 African languages to support researchers and developers.
A Robust Decision Making Framework for Optimal Strategy Selection in Warfare under Model Uncertainty

In this paper is presented a framework for treating uncertainty in optimal decision problems occuring in combat situations, in order to robustly select the optimal strategy. A stochastic version of the popular Lanchester's aimed-fire model is considered as the underlying combat system describing the combet dynamics, and upon this an optimal decision rule for allocating forces is constructed. This approach results to a very extendable optimal decision framework, where the optimal strategy is chosen by simultaneously treating robustly uncertainty regarding critical combat parameters.
Autonomous Drug Design with Multi-Armed Bandits

Recent developments in artificial intelligence and automation support a new drug design paradigm: autonomous drug design. Under this paradigm, generative models can provide suggestions on thousands of molecules with specific properties, and automated laboratories can potentially make, test and analyze molecules with minimal human supervision. However, since still only a limited number of molecules can be synthesized and tested, an obvious challenge is how to efficiently select among provided suggestions in a closed-loop system. We formulate this task as a stochastic multi-armed bandit problem with multiple plays, volatile arms and similarity information. To solve this task, we adapt previous work on multi-armed bandits to this setting, and compare our solution with random sampling, greedy selection and decaying-epsilon-greedy selection strategies. According to our simulation results, our approach has the potential to perform better exploration and exploitation of the chemical space for autonomous drug design.
Multimodal 4DVarNets for the Reconstruction of Sea Surface Dynamics From SST-SSH Synergies

The space-time reconstruction of sea surface dynamics from satellite observations is a challenging inverse problem due to the associated irregular sampling. Satellite altimetry provides direct observation of the sea surface height (SSH), which relates to the divergence-free component of sea surface currents. The associated sampling pattern prevents operational schemes from retrieving fine-scale dynamics, typically below 10 days. By contrast, other satellite sensors provide higher-resolution observations of sea surface tracers such as sea surface temperature (SST). Multimodal inversion schemes then arise as appealing approaches. Though theoretical evidence supports the existence of an explicit relationship between SST and sea surface dynamics under specific dynamical regimes, the generalization to the variety of upper ocean dynamical regimes is complex. Here, we investigate this issue from a physics-informed learning perspective. We introduce a trainable multimodal inversion scheme for the reconstruction of sea surface dynamics from multisource satellite-derived observations, namely satellite-derived SSH and SST data. The proposed multimodal 4DVarNet schemes combine a variational formulation involving trainable observation and a priori terms with a trainable gradient-based solver. An observing system simulation experiment (OSSE) for a Gulf stream region supports the relevance of our approach compared with state-of-the-art schemes. We report a relative improvement greater than 60% compared with the operational altimetry product in terms of root mean square error (MSE) and resolved space-time scales. We discuss further the potential and the limitations of the proposed approach for the reconstruction and forecasting of geophysical dynamics from irregularly-sampled satellite observations.
An Empirical Evaluation of k-Means Coresets

Coresets are among the most popular paradigms for summarizing data. In particular, there exist many high performance coresets for clustering problems such as $k$-means in both theory and practice. Curiously, there exists no work on comparing the quality of available $k$-means coresets. In this paper we perform such an evaluation. There currently is no algorithm known to measure the distortion of a candidate coreset. We provide some evidence as to why this might be computationally difficult. To complement this, we propose a benchmark for which we argue that computing coresets is challenging and which also allows us an easy (heuristic) evaluation of coresets. Using this benchmark and real-world data sets, we conduct an exhaustive evaluation of the most commonly used coreset algorithms from theory and practice.
Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots

While motion planning of locomotion for legged robots has shown great success, motion planning for legged robots with dexterous multi-finger grasping is not mature yet. We present an efficient motion planning framework for simultaneously solving locomotion (e.g., centroidal dynamics), grasping (e.g., patch contact), and contact (e.g., gait) problems. To accelerate the planning process, we propose distributed optimization frameworks based on Alternating Direction Methods of Multipliers (ADMM) to solve the original large-scale Mixed-Integer NonLinear Programming (MINLP). The resulting frameworks use Mixed-Integer Quadratic Programming (MIQP) to solve contact and NonLinear Programming (NLP) to solve nonlinear dynamics, which are more computationally tractable and less sensitive to parameters. Also, we explicitly enforce patch contact constraints from limit surfaces with micro-spine grippers. We demonstrate our proposed framework in the hardware experiments, showing that the multi-limbed robot is able to realize various motions including free-climbing at a slope angle 45° with a much shorter planning time.