Miracle-dz/UniFAR-FTUs huggingface dataset:
Women in Political Party Recruitment in Japan: A Comparative Study of the Liberal Democratic Party (LDP) and the Constitutional Democratic Party (CDP)

This research will shed light on the role of women in political parties in Japan. Japanis still in 125th position out of 146 countries in the World Economic Forum's Global Gender Gap Index (GGI) Report 2023. This puts Japan at the bottom among developed countries. Even though various policies related to gender equality have been taken by the Japanese government. Politics as an instrument of democracy should ideally play a role in the promotion and campaign of gender equality in Japan. The ruling LDP (Liberal Democratic Party) and the opposition CDP (Constitutional Democratic Party) also played the issue of gender equality as part of efforts to respond to women's public. The response of political parties can certainly be traced to how political parties recruit and place women in party and parliamentary structures. This is the purpose of this study, to find out how the LDP and CDP recruit and candidacy women in their respective parties as an effort to realize gender equality. Using qualitative methods and party recruitment perspectives, it was found that both LDP and CDP provide space for women's involvement in their political parties. Although the LDP in the recruitment of women still uses a conservative and exclusive approach, the CDP is more progressive and inclusive. And also both, the LDP and CDP both consider the capital and political motivations of each female candidate. However, neither of them has made the affirmative quota policy effective within his party
A lightweight streaming layer for multicore execution

As multicore architectures gain widespread use, it becomes increasingly important to be able to harness their additional processing power to achieve higher performance. However, exploiting parallel cores to improve single-program performance is difficult from a programmer's perspective because most existing programming languages dictate a sequential method of execution. Stream programming, which organizes programs by independent filters communicating over explicit data channels, exposes useful types of parallelism that can be exploited. However, there is still the burden of mapping high-level stream programs to specific multicore architectures. The complexities of each architecture's underlying details makes it difficult to schedule the execution of a stream program with high performance. In this paper, we present the specifications for an intermediate layer between the stream program and the target architecture. This multicore streaming layer (MSL) provides a common level of abstraction that facilitates efficient execution of stream programs by making it easier for compilers to manage computation, and by providing automatic orchestration and optimization of communication when appropriate. We implemented a framework for one such instance of the MSL targeted to the Cell processor and the StreamIt language and achieved greater than 88% utilization on all benchmarks with relatively small amounts of code. The framework can also be applied to other architectures and stream programming languages to enhance generality and portability.
Energy-Distortion Exponents in Lossy Transmission of Gaussian Sources Over Gaussian Channels

Lossy transmission of Gaussian sources over energy-limited Gaussian point-to-point and broadcast channels is studied under the infinite bandwidth regime, i.e., when the number of channel uses is unlimited. Using previously known asymptotic achievability and converse results, the energy-distortion exponent, defined as the rate of decay of the square-error distortion as the available energy-to-noise ratio increases without bound, is completely characterized for both the point-to-point and broadcast channel cases. Turning then to the scenario of zero-delay transmission, where outage events with arbitrarily small probability are allowed, it is shown that the same energy-distortion exponent as in the infinite-delay case can be achieved in all the studied scenarios.
TBA*: Time-Bounded A*

Real-time heuristic search algorithms are used for planning by agents in situations where a constant-bounded amount of deliberation time is required for each action regardless of the problem size. Such algorithms interleave their planning and execution to ensure real-time response. Furthermore, to guarantee completeness, they typically store improved heuristic estimates for previously expanded states. Although subsequent planning steps can benefit from updated heuristic estimates, many of the same states are expanded over and over again. Here we propose a variant of the A* algorithm, Time-Bounded A* (TBA*), that guarantees real-time response. In the domain of path-finding on videogame maps TBA* expands an order of magnitude fewer states than traditional real-time search algorithms, while finding paths of comparable quality. It reaches the same level of performance as recent state-of-the-art real-time search algorithms but, unlike these, requires neither state-space abstractions nor pre-computed pattern databases.
Discovering Predictive Association Rules

Association rule algorithms can produce a very large number of output patterns. This has raised questions of whether the set of discovered rules "overfit" the data because all the patterns that satisfy some constraints are generated (the Bonferroni effect). In other words, the question is whether some of the rules are "false discoveries" that are not statistically significant. We present a novel approach for estimating the number of "false discoveries" at any cutoff level. Empirical evaluation shows that on typical datasets the fraction of rules that may be false discoveries is very small. A bonus of this work is that the statistical significance measures we compute are a good basis for ordering the rules for presentation to users, since they correspond to the statistical "surprise" of the rule. We also show how to compute confidence intervals for the support and confidence of an association rule, enabling the rule to be used predictively on future data.
Pollution and Sustainability Indices for Small and Medium Wastewater Treatment Plants in the Southwest of Spain

Two indices have been defined in this work to measure the pollution load that a Wastewater Treatment Plant (WWTP) receives and the efficiency of the process it carries out from the overall perspective of pollution removal and energy efficiency. The aim is to provide two performance parameters which could be used to characterize both the working conditions of a WWTP and how well it treats the pollution it receives. In this way, they may be used to compare the behavior of different plants in a certain geographical area. Forty-five WWTPs in the Southwest of Spain were studied to find out the most significant information that is to be used to identify and define both indices. Seven of the variables recorded were used to define each one. Their values were calculated for the whole set of plants studied. The results showed that most of the plants received a low pollution load. Only six of them suffered from a high pollution load, probably because of farms and food industries located in their surroundings. Only three plants showed a poor efficiency, while 23 of them presented a very good one.
Building a High-performance Message-passing System for MEMORY CHANNEL Clusters

DEVICE INTERFACE ABSTRACT DEVICE INTERFACE Figure 14 Digital MPI Architecture Table 5 MPI Latency Comparison MPI Implementation Transport Platform Latency MPICH 1.0. 10 Sockets FDDI DEC 3000/800 350 ms MPICH 1.0. 10 Shared Memory AlphaServer 2100 4/233 30 ms Digital MPI V1.0 MEMORY CHANNEL 1.0 AlphaServer 2100 4/233 16 ms Digital MPI V1.0 MEMORY CHANNEL 1.5 AlphaServer 4100 5/300 6.9 ms Digital MPI V1.0 Shared Memory AlphaServer 2100 4/233 9.5 ms Digital MPI V1.0 Shared Memory AlphaServer 4100 5/300 5.2 ms
Stochastic Pre-Classification for Software Defined Firewalls

Firewalls are ubiquitous security functions and exist in almost all network connected devices whether protecting host stacks or providing transient packet filtering. Firewall performance, which is a key ingredient for network performance, can be greatly degraded by traffic crafted to exploit its filtering algorithms. These attacks can greatly reduce the Quality of Service (QoS) received by existing authorized flows in the firewall. This paper proposes a novel architecture that decouples this linkage between authorized flow QoS and adversarial traffic, marginalizing disruption caused by unauthorized flows, and ultimately improving overall performance of software defined firewalls. We show substantial improvements in throughput, packet loss, and latency over baseline software defined firewalls with varying ratios of attack traffic. All results are obtained using the cycle accurate architecture simulator gem5, and Internet packet traces obtained from 10 Gbps interfaces of core Internet routers.
On-line Learning of Mutually Orthogonal Subspaces for Face Recognition by Image Sets

We address the problem of face recognition by matching image sets. Each set of face images is represented by a subspace (or linear manifold) and recognition is carried out by subspace-to-subspace matching. In this paper, 1) a new discriminative method that maximises orthogonality between subspaces is proposed. The method improves the discrimination power of the subspace angle based face recognition method by maximizing the angles between different classes. 2) We propose a method for on-line updating the discriminative subspaces as a mechanism for continuously improving recognition accuracy. 3) A further enhancement called locally orthogonal subspace method is presented to maximise the orthogonality between competing classes. Experiments using 700 face image sets have shown that the proposed method outperforms relevant prior art and effectively boosts its accuracy by online learning. It is shown that the method for online learning delivers the same solution as the batch computation at far lower computational cost and the locally orthogonal method exhibits improved accuracy. We also demonstrate the merit of the proposed face recognition method on portal scenarios of multiple biometric grand challenge.
Performance of hashing-based schemes for Internet load balancing

Load balancing is a key technique for improving Internet performance. Effective use of load balancing requires good traffic distribution schemes. We study the performance of several hashing schemes for distributing traffic over multiple links while preserving the order of packets within a flow. Although hashing-based load balancing schemes have been proposed in the past, this is the first comprehensive study of their performance using real traffic traces. We evaluate five direct hashing methods and one table-based hashing method. We find that hashing using a 16-bit CRC over the five-tuple gives excellent load balancing performance. Further, load-adaptive table-based hashing using the exclusive OR of the source and destination IP addresses achieves comparable performance to the 16-bit CRC. Table-based hashing can also distribute traffic load according to unequal weights. We also report on four other schemes with poor to moderate performance.
ASSESSMENT OF RADIATION EXPOSURE IN MANNED MISSIONS TO MARS FOR THREE PROFILES

The biggest risks in a mission to Mars are the long periods of time with the lack of gravity, the psychological effects due to isolation, the risk of contamination by diseases in confined space, and exposure to high doses of radiation. It is recognized that the latter poses the greatest scientific and technological challenge to a viable mission. In this work, we present estimates of the equivalent dose in an astronaut for three mission-to-Mars profiles proposed by NASA. For this, we performed Monte Carlo simulations of the energy deposited in the ICRU sphere taking into account the main radiation sources in space using the Geant4 simulation toolkit. The results show that the introduction of 10 cm equivalent Al shielding significantly reduces the equivalent dose, although our estimates are still above the dose limits adopted by NASA. These results show, however, that the values are in the range for optimization in terms of shielding solutions, as well as the choice of the most appropriate mission trajectories to minimize the dose to astronauts.
Augmenting Field Data for Testing Systems Subject to Incremental Requirements Changes

When testing data processing systems, software engineers often use real-world data to perform system-level testing. However, in the presence of new data requirements, software engineers may no longer benefit from having real-world data with which to perform testing. Typically, new test inputs complying with the new requirements have to be manually written. We propose an automated model-based approach that combines data modelling and constraint solving to modify existing field data to generate test inputs for testing new data requirements. The approach scales in the presence of complex and structured data, thanks to both the reuse of existing field data and the adoption of an innovative input generation algorithm based on slicing the model into parts. We validated the scalability and effectiveness of the proposed approach using an industrial case study. The empirical study shows that the approach scales in the presence of large amounts of structured and complex data. The approach can produce, within a reasonable time, test input data that is over ten times larger in size than the data generated with constraint solving only. We also demonstrate that the generated test inputs achieve more code coverage than the test cases implemented by experienced software engineers.
Using Deep Learning for Pulmonary Nodule Detection & Diagnosis

This study uses a revolutionary image recognition method, deep learning, for the classification of potentially malignant pulmonary nodules. Deep learning is based on deep neural networks. We report results of our initial findings and compare performance of deep neural nets using a combination of different network topologies and optimization parameters. Classification accuracy, sensitivity and specificity of the network performance are assessed for each of the four topologies.
Power Side-Channel Analysis and Mitigation for Neural Network Accelerators based on Memristive Crossbars

The modern trend of exploring Artificial Intelligence (AI) in various industries, such as big data, edge computing, automobile, and medical applications, has increased tremendously. As functionalities grow, energy-efficient hardware for AI devices becomes crucial. To address that, Computation-in-Memory (CiM) using Non-Volatile Memories (NVMs) offers a promising solution. However, security is also an important concern in this computation paradigm. In this work, we analyze the vulnerability for power side-channel attacks on Multiply-Accumulate (MAC) operations implemented in CiM architecture based on emerging NVMs. Our results show that peripheral devices such as Analog-to-Digital Converters (ADCs) leak much more sensitive information than the crossbar itself because of its significant power consumption. Therefore, we propose a circuit-level countermeasure based on hiding for the ADCs of memristive CiM architecture to mitigate the power attacks. The efficiency of our proposed countermeasure is shown by both attacks and leakage assessment methodologies using a maximum of one million measurement traces.
Effect of Light Acclimation on the Organization of Photosystem II Super- and Sub-Complexes in Arabidopsis thaliana

To survive under highly variable environmental conditions, higher plants have acquired a large variety of acclimation responses. Different strategies are used to cope with changes in light intensity with the common goal of modulating the functional antenna size of Photosystem II (PSII). Here we use a combination of biochemical and biophysical methods to study these changes in response to acclimation to high light (HL). After 2 h of exposure, a decrease in the amount of the large PSII supercomplexes is observed indicating that plants are already acclimating to HL at this stage. It is also shown that in HL the relative amount of antenna proteins decreases but this decrease is far less than the observed decrease of the functional antenna size, suggesting that part of the antenna present in the membranes in HL does not transfer energy efficiently to the reaction center. Finally, we observed LHCII monomers in all conditions. As the solubilization conditions used do not lead to monomerization of purified LHCII trimers, we should conclude that a population of LHCII monomers exists in the membrane. The relative amount of LHCII monomers strongly increases in plants acclimated to HL, while no changes in the trimer to monomer ratio are observed upon short exposure to stress.
Functional Role of PPARs in Ruminants: Potential Targets for Fine-Tuning Metabolism during Growth and Lactation

Characterization and biological roles of the peroxisome proliferator-activated receptor (PPAR) isotypes are well known in monogastrics, but not in ruminants. However, a wealth of information has accumulated in little more than a decade on ruminant PPARs including isotype tissue distribution, response to synthetic and natural agonists, gene targets, and factors affecting their expression. Functional characterization demonstrated that, as in monogastrics, the PPAR isotypes control expression of genes involved in lipid metabolism, anti-inflammatory response, development, and growth. Contrary to mouse, however, the PPARγ gene network appears to controls milk fat synthesis in lactating ruminants. As in monogastrics, PPAR isotypes in ruminants are activated by long-chain fatty acids, therefore, making them ideal candidates for fine-tuning metabolism in this species via nutrients. In this regard, using information accumulated in ruminants and monogastrics, we propose a model of PPAR isotype-driven biological functions encompassing key tissues during the peripartal period in dairy cattle.
Deep Learning Collision Aware Transmission Scheduling for Dense LPWANs

The rapid growth of the Internet-of-things (IoT) in recent years has led to the development of new access technologies targeting low-power wide area networks (LPWANs). LPWAN is a promising solution for long-range and low-power IoT and machine-to-machine (M2M) communication applications. However, practical LPWAN deployments suffer from intense collisions due to uncoordinated transmissions, given the dense deployment of devices and wide coverage area. Therefore, this paper proposes a deep learning-based collision aware transmission priority scheduling technique (CA-PST) to mitigate high packet collision rate in ultra-dense wireless networks. The proposed CA-PST is applied to the low-range wide area network (LoRaWAN) platform where the LoRa gateway configures the nodes to a particular transmission protocol classes based on the predicted number of packet collisions. Aided by unsupervised learning clustering, the nodes located at higher transmission priority clusters will be allowed to communicate with the gateway using class C and therefore avoid packet collisions. Whereas nodes in lower transmission priority clusters communicate with the gateway using class A. Therefore, the CA-PST offers a fair trade-off between the packet delivery rate (PDR) and total energy consumption. Extensive simulation results show that using CA-PST can significantly improve the LPWAN reliability in terms of PDR in ultra-dense networks.
Temporal Variations of the Mole Fraction, Carbon, and Hydrogen Isotope Ratios of Atmospheric Methane in the Hudson Bay Lowlands, Canada

We have conducted simultaneous measurements of the mole fraction and carbon and hydrogen isotope ratios (δ13C and δD) of atmospheric methane (CH4) at Churchill (58°44′N, 93°49′W) in the northern part of the Hudson Bay Lowlands (HBL), Canada, since 2007. Compared with the measurements at an Arctic baseline monitoring station, Ny‐Ålesund, Svalbard (78°55′N, 11°56′E), CH4 mole fraction is generally higher and δ13C and δD are lower at Churchill due to regional biogenic CH4 emissions. Clear seasonal cycles in the CH4 mole fraction, δ13C, and δD are observable at Churchill, and their seasonal phases in summer are earlier by approximately 2 weeks than those at Ny‐Ålesund. Using the one‐box model analysis, the phase difference is ascribed to the different seasonal influence of CH4 emissions from boreal wetlands on the two sites. Short‐term CH4 variations are also observed at Churchill throughout the year. The analysis of the observed isotopic signatures of atmospheric CH4 confirmed that the short‐term CH4 variations are mainly produced by biogenic CH4 released from the HBL wetlands in summer and by fossil fuel CH4 transported over the Arctic in winter. Forward simulations of an atmospheric chemistry‐transport model, with wetland CH4 fluxes prescribed by a process‐based model, show unrealistically high CH4 mole fractions at Churchill in summer, suggesting that CH4 emissions assigned to the HBL wetlands are overestimated. Our best estimate of the HBL CH4 emissions is 2.7 ± 0.3 Tg CH4 yr−1 as an average of 2007–2013, consistent with recent estimations by inverse modeling studies.
Declarative specification of software architectures

Scaling formal methods to large, complex systems requires methods of modeling systems at high levels of abstraction. In this paper, we describe such a method for specifying system requirements at the software architecture level. An architecture represents a way breaking down a system into a set of interconnected components. We use architecture theories to specify the behavior of a system in terms of the behavior of its components via a collection of axioms. The axioms describe the effects and limits of component variation and the assumptions a component can make about the environment provided by the architecture. As a result of the method the verification of the basic architecture can be separated from the verification of the individual component instantiations. We present an example of using architecture theories to model the task coordination architecture of a multi-threaded plan execution system.
Exploring Machine Learning for Thread Characterization on Heterogeneous Multiprocessors

We introduce a thread characterization method that explores hardware performance counters and machine learning techniques to automate estimating workload execution on heterogeneous processors. We show that our characterization scheme achieves higher accuracy when predicting performance indicators, such as instructions per cycle and last-level cache misses, commonly used to determine the mapping of threads to processor types at runtime. We also show that support vector regression achieves higher accuracy when compared to linear regression, and has very low (1%) overhead. The results presented in this paper can provide a foundation for advanced investigations and interesting new directions in intelligent thread scheduling and power management on multiprocessors.