Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing the representation of a simple visual stimulus across the cerebellar network

The cerebellum is a highly conserved structure of the vertebrate central nervous system that plays a role in the timing and calibration of motor sequences. Its function is supported by the convergence of fibers from granule cells (GCs) and inferior olive neurons (IONs) onto Purkinje cells (PCs). Theories of cerebellar function postulate that IONs convey error signals to PCs that, paired with the contextual information provided by GCs, can be used as a teaching signal to guide motor learning. Here, we use the larval zebrafish to investigate (i) how sensory representations of the same stimulus vary across GCs and IONs and (ii) how PC activity reflects these two different input streams. We use population calcium imaging to measure the cell responses to flashes of diverse luminance and duration to show that IONs and GCs encode different stimulus properties. First, most GCs show tonic and graded responses, as opposed to IONs, whose activity peaks only at on and off luminance transitions, in agreement with the notion that GCs and IONs encode context and error information, respectively. Secondly, we show that GC activity is patterned over time: some neurons had sustained responses for the entire duration of the stimulus, while in others activity was ramping up with slow time constants. This suggests that, by performing temporal integration, GCs could provide a basis that PCs may use to decode time. Finally, we show how PC activity can be largely reconstructed by a linear combination of granule cells and inferior olive neurons. Together, our observations give support to the notion of an error signal coming from IONs, and provide the first experimental evidence for a temporal patterning of GC activity over many seconds.
An Immune Cell Recirculation-Enabled Microfluidic Array to Study Dynamic Immunotherapeutic Activity in Recapitulated Tumor Microenvironment

The efficacy of immunotherapeutic treatment protocols to enable immune cell mediated treatment of cancer is significantly modulated in the presence of tumor microenvironment (TME) which is a key factor in providing both a physical barrier and immunosuppressive stimuli. Herein, we developed a recirculating, high-throughput microfluidic cell array to capture these crucial players – cytotoxic T cells in circulation, endothelium, and tumor stroma. The system consisted of a three-layered cell array spatially emulating TME, with T cell circulation sustained via fluidic recirculating circuits. This allowed us to study the dynamic TME/circulation system and cancer cell response thereof. The system further revealed that tumor endothelium exhibited a hindrance to T cell infiltration into the breast cancer tumor compartment, which was alleviated when treated with anti-human PD-L1 antibody. The other key stromal component, cancer associated fibroblasts, further attenuated T cell infiltration, and led to reduced apoptosis activity in cancer cells. These results confirm the capability of our tumor-on-a-chip system to recapitulate some key immune cell interactions with the reconstructed TME, along with demon-strating as the feasibility of using this system for high-throughput cancer immunotherapeutic screening.
Regulation of oligodendrocyte lineage development by VAMP2/3 in the developing mouse spinal cord

In the developing and adult CNS, new oligodendrocytes (OLs) are generated from a population of cells known as oligodendrocyte precursor cells (OPCs). As they begin to differentiate, OPCs undergo a series of highly regulated changes to morphology, gene expression, and membrane organization. This stage represents a critical bottleneck in oligodendrogenesis, and the regulatory program that guides it is still not fully understood. Here we show that in vivo toxin-mediated cleavage of the vesicle associated SNARE proteins VAMP2/3 in the OL lineage of both male and female mice impairs the ability of early OLs to mature into functional, myelinating OLs. In the developing mouse spinal cord, many VAMP2/3-deficient OLs appeared to stall in the premyelinating, early OL stage, resulting in an overall loss of both myelin density and OL number. The Src kinase Fyn, a key regulator of oligodendrogenesis and myelination, is highly expressed among premyelinating OLs, but its expression decreases as OLs mature. We found that OLs lacking VAMP2/3 in the spinal cord white matter showed significantly higher expression of Fyn compared to neighboring control cells, potentially due to an extended premyelinating stage. Overall, our results show that functional VAMP2/3 in OL lineage cells is essential for proper myelin formation and plays a major role in controlling the maturation and terminal differentiation of premyelinating OLs. Significance Statement The production of mature oligodendrocytes (OLs) is essential for CNS myelination during development, myelin remodeling in adulthood, and remyelination following injury or in demyelinating disease. Prior to myelin sheath formation, newly formed OLs undergo a series of highly regulated changes during a stage of their development known as the premyelinating, or early OL stage. This stage acts as a critical checkpoint in OL development, and much is still unknown about the dynamic regulatory processes involved. In this study, we show that VAMP2/3, SNARE proteins involved in vesicular trafficking and secretion, play an essential role in regulating premyelinating OL development and are required for healthy myelination in the developing mouse spinal cord.
Neogene climatic fluctuations and poor connectivity with the centres of diversity shaped the Western Palearctic net-winged beetle fauna

Only twenty-two net-winged beetle species (Elateroidea: Lycidae) are known from the Western Palearctic region (WPR), i.e., less than 0.5% of the global lycid diversity and much fewer than from the similar latitudes of East Asia or Northern America. We use the comprehensive distribution data and the molecular phylogeny of ∼400 world lycids, including fourteen European species, to provide a new perspective for understanding the structure and evolution of this group in the WPR. All Mediterranean species represent deeply rooted lineages with relatives in Eastern Asia. These species occur in relictual ranges close to the family’s Pleistocene refugial edge. The phylogeny points to the loss of biological connection with East Asia since the Mid Miocene. A third of WPR species is widespread in Central and Northern Europe, reaching Eastern Asia, some of them possibly younger elements of the European fauna. Unlike relatively high diversity in the Eocene amber, the extant net-winged beetles represent a small fraction of elateroid diversity in the WPR and are generally rare. Therefore, we assume that most WPR species are relics trapped in Mediterranean refugia since the onset of the Plio-Pleistocene cooling and are critically endangered by the ongoing loss of suitable habitats.
SCENIC: An Area and Energy-Efficient CNN-based Hardware Accelerator for Discernable Classification of Brain Pathologies using MRI

Biomedical brain imaging lies at the interface of visual, and spatial neuropathology and neurosurgical intervention. Future treatments are expected to require greater feature detail in imaging as well as underlying mechanisms that will be synergistically advanced. This will be made more accessible via low power embedded devices and cloud platforms through applied deep learning software and hardware analysis. Towards that end, we develop a hardware-software co-design technique referred to as SCENIC (or, Separable Convolution Enabled Non-Invasive Classification) for the identification and classification of glioma brain tumors, using physical tissue features reflected in parameter weighted MRI scan types such as-T1-w, T1-ce, T2-w and FLAIR. The high performance hardware exceeds current accuracy, resource efficiency and time consumption parameters. The proposed SCENIC-CNN Accelerator is synthesized on 45 nm process technology andit can operate at a minimum frequency of 1GHz while maintaining low-power consumption of only 0.36 W and a low chip-area size of 0.431mm2. Our classification accuracy achieves 98.3% in detection of the presence of a tumor pathology and 99.62% within classification of imaging modalities that relate to tissue parameters such as fat content, blood or CSF flow and tissue density. Compared to prominent state-of the-art Convolutional Neural Network (CN) models being designed for biomedical imaging, SCENIC is competitive versus XceptionNet, InceptionV3, ResNet-50, and VGG-16. With model compression techniques, SCENIC requires a memory space of less than 0.265MB. We discuss design methodology as applied to future goals to meet challenging needs to distinguish tumor origins, such as glioma and metastasized tumors, along with other neuropathologies that may be TBI, vascular or developmental. SCENIC will aid impactful, cost-effective, rapid and accurate neurosurgical intervention and treatments.
A low-cost, open-source, and distributed ocean profiling sensor node

Ocean exploration is emerging as a critical field of research for understanding and combating climate change. While coastal waters house vital marine ecosystems, such as coral reefs, they are still vastly unexplored. Sensor nodes in coastal waters can give local communities and researchers relevant data for understanding the relationship between climate change, blue-economy activities, and ecosystem degradation. We present and validate a sensor node that can be opportunistically used by non-industrial fishing boats in order to collectively source ocean profiles containing temperature, light, and acidity levels at multiple depths. We describe the complete system architecture and validate our design with deployments in three different types of water bodies. First, we validate the device’s temperature, light, and depth sensors in a submersion within a controlled pool environment. We then evaluate the software-based power cycling for intermittent data acquisition algorithms through a river deployment. Finally, we demonstrate the complete system’s functionality and possible ways to incorporate acidity sensing with an open ocean deployment. In addition to field testing, we describe and test possible architectures for adding networking capabilities through a WiFi-based system. We conclude that the device’s electrical and mechanical characteristics significantly contribute towards enabling low-cost, community-sourced data acquisition for ocean coastal water profiling.
Mixed-8T: Energy-Efficient Configurable Mixed-VT SRAM Design Techniques for Neural Networks

Artificial Neural Network-based applications such as pattern recognition, image classification etc. consume a significant amount of energy while accessing the memory. Various techniques to reduce these energy demands in SRAM, including heterogeneous and hybrid SRAM designs, have been proposed in earlier works. However, these designs still consume significant energy at higher voltage and suffer from area overhead. Considering the aforementioned issue, we propose 7 different homogeneous Mixed-VT 8T SRAM architectures for neural networks, which overcome these issues. We analyzed the effect of truncation on different neural networks for different datasets and further applied the truncation technique on the SRAM architecture used for ANN. We design the Mixed-$\mathrm{V}_{T}\,$8T SRAM architecture and validate it suitability for 5 different neural networks. Our proposed Mixed$- \mathrm{V}_{T}\,8\mathrm{T}$ SRAM architecture requires maximum of $0.34\times(0.46\times)$ and $0.56\times(0.69\times)$ dynamic energy(leakage power) than Het-6T and Hyb-8T/6T SRAM architecture respectively at 0.5V and maximum of $0.7\times(0.84\times)$ and $0.92\times(0.90\times)$ dynamic energy(leakage power) than Het-6T and Hyb-8T/6T SRAM array respectively at 0.7 V for 6-bit weights of neural networks.
Grace: Low-Cost Time-Synchronized GPIO Tracing for IoT Testbeds

Testbeds have become a vital tool for evaluating and benchmarking applications and algorithms in the Internet of Things (IoT). Testbeds commonly consist of low-power IoT de-vices augmented with observer nodes providing control, logging, and often also power-profiling. Today, the research community operates numerous testbeds, sometimes with hundreds of IoT nodes, to allow for detailed and large-scale evaluation. Most testbeds, however, lack opportunities for tracing distributed program execution with high accuracy in time, for example, via minimally invasive, distributed GPIO tracing. And the ones that do, like Flocklab, are built from custom hardware, which is often too complex, inflexible, or expensive to use for other research groups.This paper closes this gap and introduces Grace, a low-cost, retrofittable, distributed, and time-synchronized GPIO tracing system built from off-the-shelf components, costing less than €20 per node. Grace extends observer nodes in a testbed with (1) time-synchronization via wireless sub-GHz transceivers and (2) logic analyzers for GPIO tracing and logging, enabling time-synchronized GPIO tracing at a frequency of up to 8 MHz. We deploy Grace in a testbed and show that it achieves an average time synchronization error between nodes of 1.53 μs.
A High Voltage Level Shifter for Automotive Buck Converter with a Fast Transient Response

This paper proposes a high voltage level shifter circuit for a buck converter suitable for an automotive application operating with an input supply range of 4-36 V. There is an underlying trade-off between the speed and the power in such level shifter circuits. The conventional high voltage level shifter circuit has a limited speed of operation, large asymmetric propagation delays and a high switching power loss due to a large crowbar current. In the proposed circuit, these limitations are eliminated by strengthening the pull up path during transients. Combining the cross-coupled latching inverters with the assisting pull up transistors, the high voltage stress during transients is also avoided at the internal nodes. The proposed level shifter circuit shows a very fast response with a typical delay of 2 ns making it suitable for a high voltage, high switching frequency application. With a difference of < 2 ns between the rising edge and the falling edge delays, the proposed level shifter minimizes the asymmetry in the responses to a greater extent. Additionally, it consumes nearly zero static power. The dynamic power consumption varies from $122.7 ~\mu \mathrm{W} /$MHz to 1.1 mW/MHz over an input supply range of 4 to 36 V. The circuit is designed in a $0.18~ \mu \mathrm{m}$ high voltage BCD process.
Distinguishing Absorptive Capacity based on the Nature of Knowledge : The Casual Relationships among the Sub-Components of Absorptive Capacity

The purpose of this study is to establish a new distinction regarding absorptive capacity (AC) and to test the causal relationships among its different dimensions. Unlike prior research that divides AC into potential and realized dimensions based on external knowledge-accessing activities, this study provides a new distinction based on both the nature of external knowledge-accessing activities and external knowledge itself. To empirically test the new distinction and causal relationships among each dimension, we collected data from 378 members of 32 research and development (R&D) teams of two leading Japanese manufacturers and analyzed them through partial least squares structural equation modeling. Our findings show that the processes and routines required for marketing knowledge absorption differ from those for technology absorption; however, the former will help organizations better acquire and assimilate external technology. This study deepens the understanding and nature of AC and provides interesting possibilities for further research, such as the reexamination of the effects and antecedents of AC. Further, this study provides managerial contributions by showing the importance of market orientation to managers of R&D organizations, since absorbing market knowledge will promote the effectiveness of external technology search and absorption activities.
Evaluation of the quality of LiDAR data in the varying ambient light

Light detection and ranging (LiDAR) is a widely used sensor for Intelligent transportation systems (ITS). It precisely determines the depth of the objects present around a vehicle. In this paper, the effect of light on the quality of acquired LiDAR data has been presented. The data was captured at different times in a day with varied light conditions. In the early morning and evening, there is partial light. At the night there is no light whereas in the mid-day there is perfect light condition. The data was acquired in the above four timings. On the acquired point cloud data, segmentation of an object, a person in the experiment, was performed. The number of object points and the point density have been observed to examine if light affects the quality of LiDAR data. The results, of the experiments, performed, suggest that the variation of light has little or no effect on the quality of LiDAR data.
A Parameterization for Computing Grid-Averaged Solar Fluxes for Inhomogeneous Marine Boundary Layer Clouds. Part II: Validation Using Satellite Data

Abstract The Independent Pixel approximation (IPA) is one of the simplest methods of computing solar radiative fluxes for inhomogencous clouds. It claims that if p(τ) is a normalized probability density function for cloud optical depth τ and Rpp(τ) is plane-parallel, homogeneous (PPH) albedo, mean cloud albedo can be approximated by integrating p(τ)Rpp(τ) over all τ. The purpose of this study is to assess the ability of the gamma distribution function to represent p(τ) for marine boundary layer clouds and to examine the accuracy of the ensuing gamma IPA albedos. In a separate study, pixel values of τ were inferred from high spatial resolution Landsat imagery of marine boundary layer clouds. The present study utilizes 45 images, each measuring (58 km)2. For each image, a density function pobs(τ) is estimated, and, using the mean τ¯ and variance of τ, a corresponding truncated gamma distribution function pγ(τ) is defined. For a diverse range of clouds, pγ(τ) usually approximate pobs(τ) well. The best result.
Extending Hype Cycle Prediction by Applying Graph Network Analysis

One of the most commonly used technology forecasting methods in the industrial world is Gartner’s technology hype cycle (THC). The THC is a yearly forecast of technology maturity based on a comprehensive assessment of various pieces of information such as news, papers, and research budgets. However, annual updates pose a problem in that it is difficult to utilize the information for management decisions in a timely manner. To deal with this problem, a variety of methods for drawing THCs have been studied. We proposed an approach to draw THC based on an integrated analysis of various information sources, such as papers and patents, by applying graph network analysis. The effectiveness of our approach was demonstrated using display technology as an example. In this study, we confirmed that the same approach can be applied to quantum computer technology to predict technology maturity, demonstrating the versatility of our approach.
A High Gain Vector-Sum Phase Shifter in 28-nm CMOS for 5G communication

This paper presents a high gain vector-sum phase shifter for mmWave communication in a 28 nm CMOS process. The proposed phase shifter consists of an input matching network (IMN) with cascode structure, a coupler type I/Q generator, a variable gain amplifier (VGA), and an output matching network (OBN). The different loaded coupler is utilized in the I/Q generator to obtain a quadrature signal. To increase the gain of the phase shifter, the transistors and capacitors connected with the coupler are optimized. Moreover, the cascode topology is utilized in IMN to implement high isolation as well as enhancement of the gain. The Gilbert cell design is applied to control the I/Q signal in the VGA. The proposed phase shifter achieves a phase shift of 4-bits with high gain in the 32.5 - 36.5 GHz band. The maximum average gain is -1.65 dB at 34.5 GHz, and the root-mean-square (RMS) phase error of 2.24° and RMS gain error of 0.59 dB with power consumption is 27 mW from a 1.8-V supply. The proposed vector-sum phase shifter achieves a high gain with an I/Q generator in Ka-band.
Downlink Throughput Prediction in LTE Cellular Networks Using Time Series Forecasting

Long-Term Evolution (LTE) cellular networks have transformed the mobile business, as users increasingly require various network services such as video streaming, online gaming, and video conferencing. A network planning approach is required for network services to meet user expectations and meet their needs. The User DownLink (UE DL) throughput is considered the most effective Key Performance Indicator (KPI) for measuring the user experience. As a result, the forecast of UE DL throughput is essential in network dimensioning for the network planning team throughout the network design stage. The proposed system employs several KPIs to predict UE DL throughput by combining machine learning and deep learning framework for a time series forecasting rather than the traditional statistical technique based on downlink traffic only. The proposed scheme identifies the most significant KPIs that affect UE DL throughput and provides accurate results based on prediction.
The Dynamics of Innovative Industry Networks during Digitalization : A Case Study of the Game Software Industry in Korea

Digitalization rapidly transforms the network structure of industries. The diffusion of mobile technology has changed not only our daily lives but also business strategies. According to the Sectoral Innovation Systems (SIS) theoretical framework, digitalization is one of the main factors that co-evolves with actors, market or non-market interactions, and institutions. However, most recent research focused more on the life cycle of products or the learning process than on the interactive dynamics among SIS factors. There is limited empirical evidence about the extent to which digital technology co-evolves with industrial networks. This research applies social network analysis (SNA) methodology with the SIS framework, to examine the rate of network evolution and the behavioral changes of firms in the game software industry (GSI) in Korea undergoing digitalization. The GSI network had 1,813 firms with 2,210 directed business transactional links between 2008 and 2017. Major empirical findings are as follows. Firstly, the GSI network structure was highly sparse, with an average density between 0.002 and 0.006 on a scale from 0 to 1. The network diameters varied from 2 to 5. Secondly, the largest component of the GSI network had a dramatic transformation between 2011 and 2014, after the adoption of smartphones in 2010. Digitalization completely transformed the GSI. Finally, the Stochastic Actor-Oriented Model (SAOM) could not confirm a significant role of digitalization on the evolution of the GSI network. However, it indicated a negative influence of knowledge stock in the network evolution.