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1. A Circuit for Simultaneous Reception of Data and Power using a Solar Cell

   https://doi.org/10.1109/TGCN.2021.3087008  

   Kadirvelu, Sindhubala ; Leon-Salas, Walter D. ; Fan, Xiaozhe ; Kim, Jongseok ; Peleato, Borja ; Mohammadi, Saeed ; Vijayalakshmi, B. ( June 2021 , IEEE Transactions on Green Communications and Networking)

   null (Ed.)

   This paper presents a circuit for simultaneous reception of optical power and data using a solar cell. The circuit employs a switched-inductor boost DC-DC converter for energy harvesting and a low-power thresholding receiver for data reception. The thresholding data receiver comprises a current-sense resistor that monitors the current output of the solar cell, an instrumentation amplifier, a band-pass filter and a comparator. A system-level analysis of an optical communication system employing the proposed circuit is presented along with a circuit-level analysis and implementation. As a proof-of-concept, the proposed circuit for simultaneous power and data reception is implemented using off-the-shelf components and tested using a custom-built test setup. Measurement results, including harvested power, electronic noise and bit error rate (BER), are reported for a GaAs solar cell and a red LED light source. Results show that 223 μW of power are harvested by the DC-DC converter at a distance of 32.5 cm and a radiated power of 9.3 mW. At a modulation depth of 50% and a transmission speed of 2.5 kbps, a BER of 1.008×10^-3 is achieved. Measurement results reveal that the proposed solution exhibits a trade-off between harvested power, transmission speed and BER. 

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2. Exploring Algorithm Building through Designing and Making Kinetic Sculpture

   Huang, J. ( June 2023 , International Conference of the Learning Sciences (ICLS))

   Algorithm building, creating a step-by-step procedure to carry out a solution, is a challenging concept for youth to learn and practice. Kinetic sculpture is a novel context for examining how students may learn algorithms through designing and making. As part of a larger study, we collected and analyzed a total of 18 student pre- and post-tests on computational thinking, physical computing, and arts. To examine how students build algorithms in the process of designing and making a kinetic sculpture, we analyze two vignettes from two small groups in a STEAM-based workshop. Findings show that while designing and building kinetic sculpture, students learned computational thinking and applied algorithms by incorporating inputs, outputs, and variables during the process. This study offers a springboard to investigate how students create and apply algorithms in designing and making kinetic sculpture and provides empirical evidence on how students learn algorithms in a STEAM learning context. 

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3. A computational interpretation of compact closed categories: reversible programming with negative and fractional types

   https://doi.org/10.1145/3434290  

   Chen, Chao-Hong ; Sabry, Amr ( January 2021 , Proceedings of the ACM on Programming Languages)

   null (Ed.)

   Compact closed categories include objects representing higher-order functions and are well-established as models of linear logic, concurrency, and quantum computing. We show that it is possible to construct such compact closed categories for conventional sum and product types by defining a dual to sum types, a negative type, and a dual to product types, a fractional type. Inspired by the categorical semantics, we define a sound operational semantics for negative and fractional types in which a negative type represents a computational effect that ``reverses execution flow'' and a fractional type represents a computational effect that ``garbage collects'' particular values or throws exceptions. Specifically, we extend a first-order reversible language of type isomorphisms with negative and fractional types, specify an operational semantics for each extension, and prove that each extension forms a compact closed category. We furthermore show that both operational semantics can be merged using the standard combination of backtracking and exceptions resulting in a smooth interoperability of negative and fractional types. We illustrate the expressiveness of this combination by writing a reversible SAT solver that uses backtracking search along freshly allocated and de-allocated locations. The operational semantics, most of its meta-theoretic properties, and all examples are formalized in a supplementary Agda package. 

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4. Resurrection of Amolops nepalicus Yang, 1991 (Amphibia: Anura: Ranidae), with Comments on the Record of A. cf. afghanus in Nepal and China and the Validity of Two Other Junior Synonyms of A. marmoratus (Blyth, 1855)

   https://doi.org/10.11646/zootaxa.4819.1.7  

   WANG, KAI ; BHATTARAI, SANTOSH ; WU, YUNHE ; CHE, JING ; SILER, CAMERON D. ( July 2020 , Zootaxa)

   null (Ed.)

   The taxonomic validity of the controversial taxon, Amolops nepalicus Yang, 1991, is evaluated based on the examination of its holotype, along with topotypic A. marmoratus (Blyth, 1855) and A. afghanus (Günther, 1858), and other related congeners. Morphological comparison shows the holotype of A. nepalicus differs from its senior synonym A. marmoratus and all recognized species, supporting its valid species status. We provide a detailed redescription of the holotype based on a robust morphometric dataset and expanded comparisons with recognized congeners from the Pan-Himalayas and Southeast Asia. In addition, we discuss the remaining suspicious records of A. cf. afghanus in Nepal and China based on available specimens, and comment on the validity of two remaining junior synonyms of A. marmoratus, namely Ixalus argus Annandale, 1912 and Rana latopalmata Boulenger, 1882, suggesting that they may represent subjective synonyms of A. gerbillus (Annandale, 1912) and A. panhai Matsui and Nabhitabhata, 2006, respectively. 

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5. Data Transformation Acceleration using Deterministic Finite-State Transducers

   https://doi.org/10.1109/BigData55660.2022.10020756  

   Nourian, Marziyeh ; Nguyen, Tri ; Chien, Andrew A. ; Becchi, Michela ( December 2022 , 2022 IEEE International Conference on Big Data (Big Data))

   Data transformation tasks are a critical and costly part of many data processing and analytics applications. A simple computing model that can efficiently represent data transformation and be mapped to different platforms can provide programmers with the flexibility o f u sing different data representations and allow for exploiting different platforms, including general-purpose processors and accelerators.We propose extended Deterministic Finite State Transducers (DFST+s), a computing model that enables the compact expression of data transformations (a significantly terser expression compared to the DFSTs model, a traditional computational abstraction for data transformation), aiding their correct and efficient implementation. We define the TF ORM language to facilitate expressing the DFST+, and the TFORM virtual machine to enable a further compact expression, leading to a high performance and portable implementation. We propose two TFORM VM execution models and evaluate them using a variety of data transformations (from Apache Parquet file format and sparse matrices). Our results show both effective portability across CPU and a hardware accelerator, and performance increases of 1.7× and 11.7× geometric mean, respectively, over a custom CPU implementation of the same transformations. 

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