Miracle-dz/UniFAR-FTUs huggingface dataset:
Coloring Graphs While Avoiding Monochromatic Cycles

We consider the problem of deciding whether a given directed graph can be vertex partitioned into two acyclic subgraphs. Applications of this problem include testing rationality of collective consumption behavior, a subject in microeconomics. We prove that the problem is NP-complete, even for oriented graphs and argue that the existence of a constant-factor approximation algorithm is unlikely for an optimization version which maximizes the number of vertices that can be colored using two colors while avoiding monochromatic cycles. We present three exact algorithms, namely an integer-programming algorithm based on cycle identi fication, a backtracking algorithm, and a branch-and-check algorithm. We compare these three algorithms both on real-life instances and on randomly generated graphs. We find that for the latter set of graphs, every algorithm solves instances of considerable size within few seconds; however, the CPU time of the integer-programming algorithm increases with the number of vertices in the graph while that of the two other procedures does not. For real-life instances, the integer-programming algorithm solves the largest instance in about a half hour while the branch-and-check algorithm takes about ten minutes and the backtracking algorithm less than fi ve minutes. Finally, for every algorithm, we also study empirically the transition from a high to a low probability of a YES answer as function of the number of arcs divided by the number of vertices.
Bias-variance, regularization, instability and stabilization

This chapter is concerned with some of the fundamentals in predicting numerical outcomes - known in statistics as regression. Here is a road map: Using the test set definition of mean-squared prediction error, we will see that the prediction error can be decomposed into two major components - bias and variance. The idea of regularization is to construct a sequence of predictors that begin with high variance-low bias and go to low variance-high bias. Then the problem becomes selecting the member of this sequence having the lowest bias-variance sum. How well we are able to do this turns out to depend on the stability of the method for constructing the predictors. Many methods, including decision trees and neural nets are unstable. But unstable procedures can be stabilized leading to significant improvements in accuracy.
Boost Verification Results by Bridging the Hardware/Software Testbench Gap

—Today’s complex designs increasingly include at least one, and often more, embedded processors. Given software’s increasing role in the overall design functionality, it has become increasingly important to leverage the embedded processors in verifying hardware/software interactions during system-level verification. This paper presents a UVM-based package for software-driven verification, and presents applications of this package that enable more-comprehensive system-level verification.
PATH LOSS MEASUREMENT AND PREDICTION IN OUTDOOR FRUIT ORCHARD FOR WIRELESS SENSOR NETWORK AT 2.4 GHZ BAND

This work describes the path loss of radio propagation for wireless sensor network in the outdoor fruit orchard which is one of the common agriculture environments. The measurement was conducted in the jackfruit orchard in the 2.45 GHz band. Unlike other studies conducted in the fruit orchard environments, the variation of path loss over the relative angles between the plant rows and the line-of-sight direction from the transmitter to the receiver is identified. The equivalent vegetation obstruction model is proposed as the function of the equivalent number of trees along the line-of-sight to better represent the angular path loss variation. This leads to the proposal of the path loss prediction approach at any point in the fruit orchard by using a few measurement efforts. This work also introduces the Monte Carlo simulation using the numerical electromagnetic scattering computation called hybrid T -matrix method to evaluate the relative angular vegetation loss of a single tree that is used as the input to determine the equivalent number of trees. The evaluation results suggest that it can further reduce the measurement workload required for the proposed path loss prediction approach.
Towards GPU-driven Code Execution for Distributed Deep Learning

—Modern state-of-the-art deep learning (DL) appli- cations tend to scale out to a large number of parallel GPUs. Unfortunately, we observe that the collective communication overhead across GPUs is often the key limiting factor of performance for distributed DL. It under-utilizes the networking bandwidth by frequent transfers of small data chunks, which also incurs a substantial I/O overhead on GPU that interferes with computation on GPU. The root cause lies in the inefficiency of CPU-based communication event handling as well as the inability to control the GPU’s internal DMA engine with GPU threads. To address the problem, we propose a GPU-driven code execution system that leverages a GPU-controlled hardware DMA engine for I/O offloading. Our custom DMA engine pipelines multiple DMA requests to support efficient small data transfer while it eliminates the I/O overhead on GPU cores. Unlike existing GPU DMA engines initiated only by CPU, we let GPU threads to directly control DMA operations, which leads to a highly efficient system where GPUs drive their own execution flow and handle communication events autonomously without CPU intervention. Our prototype DMA engine achieves a line-rate from a message size as small as 8KB (3.87x better throughput) with only 4. 32µs of communication latency (9.1x faster) while it incurs little interference with computation on GPU, achieving 1.82x higher all-reduce throughput in a real training workload.
Machine Learning-Guided Stimulus Generation for Functional Veriﬁcation

As chip complexity continues to grow, simulation-based functional veriﬁcation is becoming a bottleneck for design turnaround time. This problem can potentially be mitigated by machine learning-guided (ML-guided) stimulus generation that attains veriﬁcation coverage with considerably reduced simulations. Although there can be several different ways for realizing such generation, related previous works mostly restrict their attention to only one or two and there lacks a systematic study. Additionally, a large portion of the previous work was produced in the early years of ML and thus the recent progress in ML technology is largely missing in this ﬁeld of study. In our work, we study ML-guided stimulus generation at multiple hierarchies, along with the development of new techniques and exploitation of leading-edge ML technology. Experimental results indicate that test-level optimization, which is coarse-grained, is not always effective. The cases difﬁcult for test-level optimization can be well solved by our transaction-level optimization, which is ﬁne-grained. Our ternary classiﬁcation technique is shown to be superior to the conventional binary classiﬁcation. Overall, our techniques lead to about 70% reduction in veriﬁcation time (total CPU-compute time) even when model training and data processing time are counted.
Tencent-MVSE: A Large-Scale Benchmark Dataset for Multi-Modal Video Similarity Evaluation

Multi-modal video similarity evaluation is important for video recommendation systems such as video de-duplication, relevance matching, ranking, and diversity control. However, there still lacks a benchmark dataset that can support supervised training and accurate evaluation. In this paper, we propose the Tencent-MVSE dataset, which is the first benchmark dataset for the multi-modal video similarity evaluation task. The Tencent-MVSE dataset contains video pairs similarity annotations, and diverse metadata including Chinese title, automatic speech recognition (ASR) text, as well as human-annotated categories/tags. We provide a simple baseline with a multi-modal Transformer architecture to perform supervised multi-modal video similarity evaluation. We also explore pre-training strategies to make use of the unpaired data. The whole dataset as well as our baseline will be released to promote the development of the multi-modal video similarity evaluation. The dataset has been released in https://tencent-mvse.github. io/.
Learning Neuro-Symbolic World Models for Text-Based Game Playing Agents

Text-based games serve as important benchmarks for agents with natural language capabilities. To enable such agents, we are interested in the problem of learning useful world models. Our assumption is that such a world model is best represented by a logical form which underlies the structure of these games. We propose to tackle this problem by leveraging the expressivity of recent neuro-symbolic architectures, specifically the Logical Neural Networks (LNN). Here, we describe a method that can learn neuro-symbolic world models on the TextWorld-Commonsense set of games. We then show that planning on this learned world model results in optimal actions in the game world.
Design and Verification of a Multichip Coherence Protocol

- This paper describes an industrial experience of the development of a multichip coherence interconnect protocol (OCI). The protocol specification was formulated as extended state transition tables. We used formal as well as simulation tools in different protocol development’s phases like discovery, design, modeling, validation and maintenance. A number of design bugs as well as holes (missing features) were found in these phases. Furthermore, the protocol models were used to validate micro-architectural protocols and constraints. The tools and processes developed were employed in the design of two major multi-billion transistors chips and helped us in achieving a quicker convergence of verification work by saving many months of effort
Adaptive Test Generation for Fast Functional Coverage Closure

Coverage-driven test generation (CDG) is a well-studied approach to accelerate design veriﬁcation. However, existing CDG methods rely on veriﬁcation experts to extrapolate coverage-to-input relationships or use costly data-driven techniques to learn these dependencies, both of which introduce signiﬁcant overhead to the veriﬁcation process. In this work, we propose CDG 4CDG, a low-overhead framework that automatically extracts the coverage dependency graph associated with a design, and leverages the dependency information for adaptive test stimuli generation. The test generation is performed by maximum likelihood estimation method in CDG 4CDG that uses the dependency graph as prior information and adaptively changes the distribution of the random input stimuli to exercise the design more comprehensively towards higher coverage. We integrated CDG 4CDG with standard CRV ﬂow. Our evaluations on open-source as well as large-scale real-world designs of a complex block in tensor processing unit (TPU) show that our approach signiﬁcantly speeds up the coverage convergence with no human effort required.
Improvement in object segmentation techniques over a live video sequence with optimal inference run-time (Using Deep Neural Networks)

—This paper deals with the improvement of segmentation techniques used for detecting objects in video sequences. A lot of work has been done in the past which have shown good segmentation results on still images. Here, I have tried to propose an extension of Mask R-CNN[7] for segmentation on video images with some inputs from OSVOS architecture[2] which is used to segment an object along the entire video given the mask of only one single labeled sample and the classification of all the pixels of video sequence into foreground and background given the annotation reference of one or more frames.
Extending UVM Register Abstraction Layer for Verification of Register Access via Serial Bus Interface

—UVM Register Abstraction Layer is a convenient way to model the registers within a hardware design. As the Serial Peripheral Interface (SPI) is used to access register space of the device, the support of bit-resolution access level and complex buffer access mechanism is required. These features are not covered by standard UVM documentation. The proposed solutions can be applied to some other serial bus interfaces and can be easily extended to overcome additional challenges which might be caused by serial access. All proposed solutions support passive operation, which is a critical requirement for the verification process, as it provides reusability.
Optimizing Random Test Constraints Using Machine Learning Algorithms

- A staple of modern verification is constrained random simulation, which involves generation of random transaction streams controlled through a set of adjustable constraints. One of the major challenges of verification is finding the right combinations of constraints to produce the most stressful tests with the widest variety of random stimulus. With typical nightly and weekly regressions generating billions of simulation cycles in tens of thousands of tests, it is impossible for a human to process all available data. Verification engineers therefore use various aggregation and approximation methodologies, such as code and functional coverage, to gain insight into regression results. However, the fields of machine learning and data mining excel at exactly these kinds of problems by finding patterns in this vast repository of data and extrapolating insights to guide us in the best direction. This paper presents a methodology in which coverage results and machine learning algorithms are used to generate tests most likely to find new bugs in a design.
IP-XACT based SoC Interconnect Verification Automation

-In complex modern Systems on Chip (SoCs), verifying its backbone interconnect with large numbers of bus masters and slaves is one of the most significant challenges. It requires verifying various interface protocols, conversions of different transaction types, and large combinational transaction paths. To cope with this problem, we used Verification Intellectual Property (VIP) for each protocol and a verification platform for interconnect. However, configuring each VIP to connect them with entire master and slave interfaces is a cumbersome manual process. It is also very challenging to update its configuration to comply with frequent specification changes that accommodate market changes. By automatically generating these configurations for the interconnect verification environment using IP-XACT, we can dramatically reduce the time and the effort required for SoC backbone interconnect verification.
Computing Wasserstein-$p$ Distance Between Images with Linear Cost

When the images are formulated as discrete measures, computing Wasserstein-p distance between them is challenging due to the complexity of solving the corresponding Kantorovich's problem. In this paper, we propose a novel algorithm to compute the Wasserstein-p distance between discrete measures by restricting the optimal transport (OT) problem on a subset. First, we define the restricted OT problem and prove the solution of the restricted problem converges to Kantorovich's OT solution. Second, we propose the SparseSinkhorn algorithm for the restricted problem and provide a multi-scale algorithm to estimate the subset. Finally, we implement the proposed algorithm on CUDA and illustrate the linear computational cost in terms of time and memory requirements. We compute Wasserstein-p distance, estimate the transport mapping, and transfer color between color images with size ranges from $64\times 64$ to $1920\times 1200$. (Our code is available at https://github.com/ucascnic/CudaOT)
Fingerprint Recognition Based on Compressive Sensing using Gabor Filters

-Fingerprint Recognition is a widely used Biometric Identification mechanism. Up till now different techniques have been proposed for having satisfactory Fingerprint Identification. The widely used minutiae-based representation did not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures could not be completely characterized by minutiae. The proposedcompressive sensing using Gaborfilter algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint and compress it produce a compact fixed length Finger Code. The proposed compressive sensing using Gaborfilter algorithmis more accurate than the minutiae based algorithm.
Transverse Fresnel-Fizeau drag effects in strongly dispersive media (11 pages)

A light beam normally incident upon an uniformly moving dielectric medium is, in general, subject to bendings due to a transverse Fresnel-Fizeau light drag effect. In most familiar dielectrics, the magnitude of this bending effect is very small and hard to detect. Yet, the effect can be dramatically enhanced in strongly dispersive media where slow group velocities in the m/s range have been recently observed taking advantage of the electromagnetically induced transparency effect. In addition to the usual downstream drag that takes place for positive group velocities, we discuss a significant anomalous upstream drag which is expected to occur for negative group velocities. Furthermore, for sufficiently fast speeds of the medium, higher-order dispersion terms are found to play an important role and to be responsible for light propagation along curved paths or the restoration of the time and space coherence of an incident noisy beam. The physics underlying this class of slow-light effects is thoroughly discussed.
Avaliação de quarta geração: revisão integrativa sobre sua aplicação na pesquisa em saúde

O presente estudo objetivou identificar na produção cientí­fica existente, como vem sendo utilizado o referencial metodológicoavaliação de quarta geração nas pesquisas em saúde. Trata-se de uma revisão integrativa a partir de publicações levantadas nas bases de dados PubMed, Banco de Dados em Enfermagem, Literatura Latino-Americana e do Caribe em Ciências da Saúde e Scientific Electronic Library Online, no perí­odo de 2005 a 2015. Utilizou-se a combinação dos descritores avaliação de quarta geração e avaliação qualitativa, em todos os idiomas, totalizando 27 estudos analisados. O processo de análise permitiu a construção de duas categorias temáticas: Potencialidades e desfechos da avaliação de quarta geração e Limitações da metodologia atreladas com estratégias no campo da pesquisa. Observou-se predomí­nio de estudos brasileiros na área de saúde mental e escassez de estudos internacionais. Aspectos positivos foram encontrados devido í escolha do referencial metodológico, principalmente no que se refere em dar voz aos grupos de interesses estudados. As publicações evidenciam a relevância do rigor metodológico a ser seguido quando se trabalha com a avaliação de quarta geração, uma vez que é necessário adotar os critérios estabelecidos pelos autores deste referencial.
Multiplicative or compensatory advantage? Multigenerational contribution to grandchildren’s educational success in the Soviet and the post-Soviet contexts

ABSTRACT Drawing on retrospective data from the Estonian Family and Fertility Survey, this article examines the impact of grandfathers, who reached adulthood in the Estonian Republic before World War II, on their grandchildren’s educational attainment in the late Soviet and post-Soviet Estonia. The article argues that despite the Sovietization policies, the high social position of grandfathers had a positive effect on their grandchildren’s educational attainment, net of parental education and resources. Our results show that the multiplication effect (i.e. the advantage of having highly educated parents is strengthened by grandparents’ resources) prevails over the compensatory one (i.e. the use of advantageous grandparents’ resources to overcome shortage of parental resources), suggesting that social hierarchies and advantages of the pre-Soviet period contribute to the overall and increasing intergenerational inequality in the late Soviet and post-Soviet Estonian society. This conclusion is also supported by finding that respondents with persistently high (across two familial generations and political regimes) social background have the highest probability to attain higher education, while offspring of parents characterized by the loss of grandparents’ high pre-WWII status has very low (and practically non-different from that of descendants of persistently low social background) probability to attain higher education.
Local L\'evy Models and their Volatility Smile

We propose a class of equity models whose volatility, L\'evy measure, and killing rate all have local stochastic state-dependence. In this framework we find a closed form solution for the price of any European-style option. Additionally, for a certain sub-class of models, we find an exact expression for the induced implied volatility smile. To illustrate our framework, we perform specific computations for hybrid CEV/L\'evy model (which we christen as the "C\'EV" model).
Towards supervised real-time human activity recognition on embedded equipment

In recent years, real-time human activity recognition (HAR) has reached importance due to its applications in various domains such as assistive services for the elderly in smart buildings, monitoring, well-being, comfort and security. Various techniques, researched within the image processing and computer vision communities, have been established to recognize human activities in real-time, but all of them are based on wearable sensors and there is no much attention for ambient sensor based approaches. In the literature, deep learning (DL) is one of effective and cost-efficient supervised learning model and different architectures haves been investigated for real-time HAR. However, it still struggles with the quality of data as well as hardware implementation issues.This paper presents two contributions. Firstly, an intensive analysis of DL architectures and its characteristics along with their limitations in the framework of real time HAR are investigated. Secondly, existing hardware architectures and related challenges in this field are highlighted (adaptation of DL architectures towards microcontrollers, difficulty to provide a smart home with numerous sensors and trends regarding cloud-bases approaches). Then, new research directions and solutions around the real-time data quality assessment, the study of main performance factors for DL on microcontrollers, the concept of minimal sensors set up for the employment of IoT devices and the distributed intelligence are suggested to solve them respectively and to improve this field.