Miracle-dz/UniFAR-FTUs huggingface dataset:
Co-Located MIMO Radar Target Detection in Cluttered and Noisy Environment Based on 2D Block Sparse Recovery

In the co-located multiple input multiple output (MIMO) radar, the matched filter receiver suffers from high range sidelobes, since the waveforms originating from different transmitters cannot be fully orthogonal. Traditional sparse recovery based receivers can suppress the sidelobes by assuming the sparsity of range profiles. However, in most practical situations, the range profile is not sparse due to the presence of clutter. In this paper, an effective co-located MIMO radar target detection algorithm is proposed based on modified smoothed-$l_0$ norm sparse recovery, by exploiting the 2D block sparsity of the range-Doppler profiles of targets corresponding to different transmit-receive pairs. The proposed method can enhance the target detection performance in co-located MIMO radar system, especially for those weak targets which may be overwhelmed by high-level sidelobes of strong targets and clutters, without sacrificing computational efficiency. With a well-designed regularization approach and an adaptive regularization parameter update technique, the proposed method is also robust in noisy environment. Simulation and experimental results validate the effectiveness of the proposed method.
RF-DC Multiplier for RF Energy Harvester based on 32nm and TFET technologies

In this work, we are studying the effect of the technology scaling for different full-wave rectifier topologies using the Cross-Coupled Differential Drive (CCDD) strategy to implement a multiplier. For a conventional CCDD scaling from 90nm to 32nm, the PCE and VCE are maintained the same while a large degradation of the dynamic range and sensitivity are observed. This effect could be slightly limited by using a self-body bias CCDD topology. However, the use of TFET enables to avoid this degradation and provide a large VCE and output voltage for input voltage lower than 300mV. To extend this VCE for input voltage>300mV, we use a CCDD topology increasing the loading drive capability. Interestingly, this resulted not only on increasing the output voltage for large Vin but also demonstrated larger PCE than expected for this topology.
PySStuBs: Characterizing Single-Statement Bugs in Popular Open-Source Python Projects

Single-statement bugs (SStuBs) can have a severe impact on developer productivity. Despite usually being simple and not offering much of a challenge to fix, these bugs may still disturb a developer’s workflow and waste precious development time. However, few studies have paid attention to these simple bugs, focusing instead on bugs of any size and complexity. In this study, we explore the occurrence of SStuBs in some of the most popular open-source Python projects on GitHub, while also characterizing their patterns and distribution. We further compare these bugs to SStuBs found in a previous study on Java Maven projects. We find that these Python projects have different SStuB patterns than the ones in Java Maven projects and identify 7 new SStuB patterns. Our results may help uncover the importance of understanding these bugs for the Python programming language, and how developers can handle them more effectively.
Exploring Approximate Adders for Power-Efficient Harmonics Elimination Hardware Architectures

This paper explores approximate adders (AA) in a harmonic elimination system using Least Mean Square (LMS) filters. We study the impact of the AA Lower-Part-Or Adder (LOA), Error Tolerant Adder (ETA-I), Truncation adder (Trunc), and Copy adder in the system's accuracy, power dissipation, and circuit area. The adders' approximate part varies the approximation level parameter (K) from 1 to 8. The Root-Mean-Square Error and Mean Absolute Error metrics show that the filtering is efficient for all circuits with $K$=4. The results highlight the Copy adder (K=4), using the operand B, as the most efficient adder to be applied in the harmonic elimination system presenting 7.5% less circuit area and 21.8% less power dissipation than the one with precise adders.
Electroencephalography-Based Auditory Attention Decoding: Toward Neurosteered Hearing Devices

People suffering from hearing impairment often have difficulties participating in conversations in so-called cocktail party scenarios where multiple individuals are simultaneously talking. Although advanced algorithms exist to suppress background noise in these situations, a hearing device also needs information about which speaker a user actually aims to attend to. The voice of the correct (attended) speaker can then be enhanced through this information, and all other speakers can be treated as background noise. Recent neuroscientific advances have shown that it is possible to determine the focus of auditory attention through noninvasive neurorecording techniques, such as electroencephalography (EEG). Based on these insights, a multitude of auditory attention decoding (AAD) algorithms has been proposed, which could, combined with appropriate speaker separation algorithms and miniaturized EEG sensors, lead to so-called neurosteered hearing devices. In this article, we provide a broad review and a statistically grounded comparative study of EEG-based AAD algorithms and address the main signal processing challenges in this field.
Code Generation for Room Acoustics Simulations with Complex Boundary Conditions

The software and hardware landscape of high performance computing is expanding faster than computational scientists can take advantage of new frameworks and platforms. In an ideal world, simulation codes would be written once in a high-level manner and achieve high-performance anywhere, but the reality is more complicated. Currently, high-level solutions lack support for sophisticated physical models across different parallel backends. Existing solutions with appropriate support are low-level and, therefore, tied to a specific hardware target.We present an approach that tackles this problem with a modularized separation of concerns: a middle layer separates the management of generating low-level optimized code from a high-level programmable layer. In this paper, we describe how our contributions to this hardware-agnostic, middle-layer language provide functionality for complex room acoustics simulations, a type of Finite Difference Time Domain (FDTD) simulation using stencils which is representative of many other 3D wave models. We show that we are able to develop performance-portable codes for these types of models which leads to performance on par with tuned hand-written implementations. Furthermore, we show how this approach is used to develop both host and device side code for multi-kernel applications, as is required for room acoustics simulations with complex boundaries.
TileSpMV: A Tiled Algorithm for Sparse Matrix-Vector Multiplication on GPUs

With the extensive use of GPUs in modern supercomputers, accelerating sparse matrix-vector multiplication (SpMV) on GPUs received much attention in the last couple of decades. A number of techniques, such as increasing utilization of wide vector units, reducing load imbalance and selecting the best formats, have been developed. However, the 2D spatial sparsity structure has not been well exploited in the existing work for SpMV on GPUs. In this paper, we propose an efficient tiled algorithm called TileSpMV for optimizing SpMV on GPUs through exploiting 2D spatial structure of sparse matrices. We first implement seven warp-level SpMV methods for calculating sparse tiles stored in a variety of formats, and then design a selection method to find the best format and SpMV implementation for each tile. We also adaptively extract nonzeros in the very sparse tiles into a separate matrix to maximize the overall performance. The experimental results show that our method is faster than state-of-the-art SpMV methods such as Merge-SpMV, CSR5 and BSR in most matrices of the full SuiteSparse Matrix Collection and delivers up to 2.61x, 3.96x and 426.59x speedups, respectively.
Timely Redundant Service of Requests by a Sequence of Cluster

The possibilities of increasing the eﬃciency of the redundant service of latency-critical requests by a sequence of nodes of a multilevel cluster have been investigated. The analytic model is proposed, and the eﬀectiveness of the option of redundant request servicing shown for which a copy of the request, executed ﬁrst, is transferred for redundant service to the next cluster level, the remaining copies are destroyed. The novelty of the proposed model is that it allows to taking into account the requirements of not exceeding the maximum permissible total accumulated waiting time for sequential redundant request servicing at all levels of the system. A variant for which a certain number of copies are created during the formation of the request, for each of which a path is predeﬁned as a sequence of nodes of diﬀerent levels involved in maintenance, is considered as a prototype.
Gain-Cell Embedded DRAM Under Cryogenic Operation—A First Study

Operating circuits under cryogenic conditions is effective for a large spectrum of applications. However, the refrigeration requirement for the cooling of cryogenic systems introduces serious issues in terms of power dissipation. Gain-cell embedded dynamic random access memory (GC-eDRAM) is a low-area, logic-compatible embedded memory alternative to static random access memory (SRAM), which has the potential to provide ultralow-power operation under cryogenic conditions due to the lower leakages at these temperatures. In this article, we present the first comparative design exploration of GC-eDRAM under cryogenic conditions performed with transistor models characterized based on actual silicon measurements under temperatures as low as 77 K. Our study shows that the two-transistor (2T)-based GC-eDRAM configurations turn out to be the best solutions for very low-temperature operation. In particular, the 2T mixed GC-eDRAM configurations allow read sensing margin improvements (up to 99%) within the 2T-based configurations while at the same time excel in terms of data retention time (+44%) and power consumption (−27%) when compared to more complex GC-eDRAM topologies. Moreover, even better improvements in terms of area (−73%), leakage power (−97%), retention power (−76%), and energy (−66%) are observed when compared to conventional 6T-SRAM.
Ethanol from photoperiod-sensitive sorghum: A study on biomass structure and process optimization

Cellulosic ethanol made from low cost lignocellulosic biomass has been considered as new generation transportation fuel with economic and environmental advantages. Photoperiodsensitive (PS) sorghum, because of its high biomass yield (2.6 kg dry mass/m 2 ), about 18% of soluble sugar in dry mass, and drought tolerance, is a promising biomass for ethanol production. The overall goals of this study are to develop an efficient approach to convert PS sorghum to ethanol and to understand the structural characteristics of biomass. For increasing the efficiency of biomass conversion, an integrated method, using diluted sulfuric acid pretreatment, has been developed to utilize both the structural polysaccharide (cellulose) and the soluble sugar (sucrose, glucose, and fructose) for fermentation. Response surface methodology was employed to optimize the pretreatment condition for maximizing the cellulose-glucose conversion. Simultaneous enzymatic hydrolysis and yeast fermentation was used for ethanol production. The effects of the buffer concentration, the inoculation dosage and time, and the fermentation temperature were investigated for maximizing ethanol yield. A total conversion efficiency of 77.2% and an ethanol concentration of 2.3% (v/v) were obtained after 72 h fermentation. About 210 kg (~266 Liters) ethanol could be produced from one ton dry mass of PS sorghum under the optimized condition. The structural features of the PS sorghum were studied using techniques including scanning electron microscopy and X-ray diffraction/scattering. Biomass at different botanic locations was investigated. Wide-angle X-ray diffraction (WAXD) study showed that the PS sorghum rind had oriented crystal peaks and the highest degree of crystallinity, whereas the crystalline structures of the inner pith and leaf were less ordered. The results from WAXD suggested that crystalline cellulose was melted at 120 °C before its significant degradation. Both the cellulose crystallinity and the crystal size at the dimension lateral to fiber direction increased as the temperature increased from 120 to 160 °C. The efficiency of enzymatic hydrolysis increased because the protective structure was damaged and most hemicellulose was removed, resulting in the increase in accessible area as suggested by small-angle X-ray scattering result of the increased length of microvoids. The results from WAXD also suggested a simultaneous hydrolysis and crystallization of cellulose by acid. ETHANOL FROM PHOTOPERIOD-SENSITIVE SORGHUM: A STUDY ON BIOMASS STRUCTURE AND PROCESS OPTIMIZATION
UvA-DARE (Digital Academic Repository) Natural variation in wild tomato trichomes; selecting metabolites that contribute to insect resistance using a random forest approach

Background: Plant-produced specialised metabolites are a powerful part of a plant ’ s first line of defence against herbivorous insects, bacteria and fungi. Wild ancestors of present-day cultivated tomato produce a plethora of acylsugars in their type-I/IV trichomes and volatiles in their type-VI trichomes that have a potential role in plant resistance against insects. However, metabolic profiles are often complex mixtures making identification of the functionally interesting metabolites challenging. Here, we aimed to identify specialised metabolites from a wide range of wild tomato genotypes that could explain resistance to vector insects whitefly ( Bemisia tabaci ) and Western flower thrips ( Frankliniella occidentalis) . We evaluated plant resistance , determined trichome density and obtained metabolite profiles of the glandular trichomes by LC-MS (acylsugars) and GC-MS (volatiles). Using a customised Random Forest learning algorithm, we determined the contribution of specific specialised metabolites to the resistance phenotypes observed. Results: The selected wild tomato accessions showed different levels of resistance to both whiteflies and thrips. Accessions resistant to one insect can be susceptible to another. Glandular trichome density is not necessarily a good predictor for plant resistance although the density of type-I/IV trichomes, related to the production of acylsugars, appears to correlate with whitefly resistance. For type VI-trichomes, however, it seems resistance is determined by the specific content of the glands. There is a strong qualitative and quantitative variation in the metabolite profiles between different accessions, even when they are from the same species. Out of 76 acylsugars Conclusions: Whiteflies and thrips are distinctly targeted by certain specialised metabolites found in wild tomatoes. The machine learning approach presented helped to identify features with efficacy toward the insect species studied. These acylsugar metabolites can be targets for breeding efforts towards the selection of insect-resistant cultivars.
A Knowledge Graph Construction Approach for Legal Domain

: Considering that the existing domain knowledge graphs have difficulty in updating data in a timely manner and cannot make use of knowledge sufficiently in the construction process, this paper proposes a legal domain knowledge graph construction approach based on 'China Judgments Online' in order to manage the cases' knowledge contained in it. The construction process is divided into two steps. First, we extract the classification relationships of the cases from structured data. Then, we obtain attribute knowledge of cases from semi-structured data and unstructured data through a relationship extraction model based on an improved cross-entropy loss function. The triples describing knowledge of cases are stored through Neo4j. The accuracy of the proposed approach is verified through experiments and we construct a legal domain knowledge graph which contains more than 4K classification relationships and 12K attribute knowledge to prove its validity.
Physics-based Iterative Projection Complex Neural Network for Phase Retrieval in Lensless Microscopy Imaging

Phase retrieval from intensity-only measurements plays a central role in many real-world imaging tasks. In recent years, deep neural networks based methods emerge and show promising performance for phase retrieval. However, their interpretability and generalization still remain a major challenge. In this paper, we propose to combine the advantages of both model-based alternative projection method and deep neural network for phase retrieval, so as to achieve network interpretability and inference effectiveness simultaneously. Specifically, we unfold the iterative process of the alternative projection phase retrieval into a feed-forward neural network, whose layers mimic the processing flow. The physical model of the imaging process is then naturally embedded into the neural network structure. Moreover, a complex-valued U-Net is proposed for defining image priori for forward and backward projection in dual planes. Finally, we designate physics-based formulation as an untrained deep neural network, whose weights are enforced to fit to the given intensity measurements. In summary, our scheme for phase retrieval is effective, interpretable, physics-based and unsupervised. Experimental results demonstrate that our method achieves superior performance compared with the state-of-the-arts in a practical phase retrieval application—lensless microscopy imaging.
DeepObjStyle: Deep Object-based Photo Style Transfer (Supplementary Material)

Figure 1: Style transfer-1 (content-mismatch). (a) The style image is shown at the top left corner of the content image. The style and content images contain different types of objects. (b) Neural style distorts the structure of the scene. (c) DPS [5] does not maintain the clarity of image features. (d) WCT2 [8] does not maintain the perceptual quality well and creates colored patches. (e) STROTSS [3] distributed image features better, but the output image does not preserve local level features details. (f) Our DeepObjStyle minimizes the effects of the content mismatch and preserve a better structure.
Prior Based Human Completion Supplemental Material

Zibo Zhao1 Wen Liu1 Yanyu Xu2 Xianing Chen1 Weixin Luo1 Lei Jin1 Bohui Zhu4 Tong Liu5 Binqiang Zhao4 Shenghua Gao†1,3 1ShanghaiTech University 2Institute of High Performance Computing, A*STAR 3Shanghai Engineering Research Center of Intelligent Vision and Imaging 4Alibaba Group 5 Taobao {zhaozb,liuwen,chenxn1,luowx,jinlei,gaoshh}@shanghaitech.edu. cn yingmu@taobao.com xu yanyu@ihpc.a-star.edu. sg {bhiui.zbh,binqiang.zhao}@alibaba-inc.com
Towards Explainable Question Answering (XQA)

The increasing rate of information pollution on the Web requires novel solutions to tackle that. Question Answering (QA) interfaces are simpliﬁed and user-friendly interfaces to access information on the Web. However, similar to other AI applications, they are black boxes which do not manifest the details of the learning or reasoning steps for augmenting an answer. The Explainable Question Answering (XQA) system can alleviate the pain of information pollution where it provides trans-parency to the underlying computational model and exposes an interface enabling the end-user to access and validate provenance, validity, context, circulation, interpretation, and feedbacks of information. This position paper sheds light on the core concepts, expectations, and challenges in favor of the following questions (i) What is an XQA system? , (ii) Why do we need XQA? , (iii) When do we need XQA? (iv) How to represent the explanations? (iv) How to evaluate XQA systems?
Applying the Normalized Systems Theory on Microservice Architecture

The thesis is engaged in evolvability of applications build using microservice architecture. It applies the rules defined by the Normalized Systems theory and discusses its impacts. The thesis thoroughly analyses the domain of microservice architecture and identifies the key architectural aspects. The aspects are examined using the Normalized Systems theory and results in a set of guidelines to achieve evolvability of the microservice applications. The viability of proposed guidelines is then discussed.
Robust Consistent Video Depth Estimation Supplementary Material

1. Cellphone videos: We show visual comparisons of our results with Consistent Video Depth (CVD) [4], COLMAP [6], and DeepV2D [9]. 2. Sintel dataset: We use sample videos from the Sintel dataset to validate the two core technical contributions of our work (1) flexible depth deformation for pose optimization and (2) geometry-aware depth filtering. 3. DAVIS dataset: To demonstrate the robustness of our approach, we present the results of all 90 videos from the DAVIS 2017 train and validation dataset.
CALIBRATION AND TESTING OF FRET EFFICIENCY IN A VINCULIN TENSION PROBE By JEREMY

OF THE THESIS . ii ACKNOWLEDGEMENTS . iv TABLE OF CONTENTS . v LIST OF TABLES . vii LIST OF FIGURES . viii CHAPTER 1: BACKGROUND, THEORY, AND IMAGING METHODOLOGY . 1 1.1: An Introduction to FRET and Its Associated Physical Properties . 2 1.1.1: What is FRET? 2 1.1.2: The Uses of FRET . 6 1.2: An Overview of the FRET Probes Used . 7 1.3: An Overview of the Tested Methodology and the Technology Required . 10 1.3.1: Imaging Methodology: 3-Channel Sensitized Emission . 14 1.3.2: Analysis of Data using a lab-developed MATLAB Code . 17 CHAPTER 2: CALIBRATION AND MEASUREMENT IN CONTROL CELL LINES . 22 2.1: Cell Preparation and Transfection . 22 2.2: Trials in Immortalized Baby Mouse Kidney Epithelial Cells . 25 2.2.1: An Overview of Immortalized Baby Mouse Kidney Epithelial Cells . 25 2.2.2: Calibration in Immortalized Baby Mouse Kidney Epithelial Cells . 25 2.2.3: Measurement in Immortalized Baby Mouse Kidney Epithelial Cells. 30 2.3: Trials in Bovine Aortic Endothelial Cells . 33 2.3.1: An Overview of Bovine Aortic Endothelial Cells . 33 vi 2.3.2: Calibration in Bovine Aortic Endothelial Cells . 33 2.3.3: Measurement in Bovine Aortic Endothelial Cells . 36 2.4: Discussion of iBMK and BAEC Results . 40 CHAPTER 3: CALIBRATION AND MEASUREMENT IN NEURONS . 42 3.1: A Background on Neurons and their Associated Properties . 43 3.1.1: A Biological Overview of the Selected Neuron Cell Type . 43 3.1.2: Harvest and Preparation for Testing . 43 3.1.3: Implementation of Protocols . 45 3.2: Calibration in Neurons . 48 3.3: Measurement in Neurons . 52 3.4: Discussion of Results in Neurons . 55 CHAPTER 4: CONCLUSIONS, DISCUSSION, AND FUTURE DIRECTIONS . 58 REFERENCES . 64