Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Future Reference Patterns for Efficient Cache Replacement Decisions

This study proposes a cache replacement policy technique to increase the cache hit rate. This policy can improve the efficiency of cache management and performance. Heuristic cache replacement policies are mechanisms that are designed empirically in advance to determine what needs to be replaced. This study explains why the heuristic policy does not achieve a high accuracy for certain patterns of data. A machine learning method is proposed to predict the blocks that need to be requested in the future to prevent erroneous decisions. The core operation of the proposed method is that when a cache miss occurs, the machine learning model predicts a future block reference sequence that is based on the block reference sequence of the input sequence. The predicted block is added to the prediction buffer and the predicted block is removed from the non-access buffer if it exists in the non-access buffer. After filling the prediction buffer, the conventional replacement policy can be replaced with a time complexity of O(1) by replacing the block with a non-access buffer. The proposed method improves the least recently used (LRU) algorithm by 77%, the least frequently used (LFU) algorithm by 65%, and the adaptive replacement cache (ARC) by 77% and shows a hit rate similar to that of state-of-the-art research. The proposed method reinforces the existing heuristic policy and enables a consistent performance for LRU- and LFU-friendly workloads.
Comprehensive Evaluation Method of Contractor Selection for Construction Enterprise

There are many subjective factors in traditional contractor selection of construction enterprises,and researches mainly focus on the price and qualification of contractors.To overcome these shortcomings,a comprehensive evaluation index system of contractor selection is built from the perspective of complex system.It includes indexes of social influence,technological capability,engineering management ability,market exploitation ability,financial ability,and resources management ability,which make the evaluation system more reasonable and scientific.Meanwhile,the entropy weight method is introduced to calculate the weight of index,and it eliminates the subjective influence of human beings.
Gendered Taxes: The Interaction of Tax Policy with Gender Equality

This paper provides an overview of the relation between tax policy and gender equality, covering labor, capital and wealth, as well as consumption taxes. It considers implicit and explicit gender biases and corrective taxation. On labor taxes, we discuss the well-established findings on female labor supply and present new empirical work on the impact of household taxation. We also analyze the impact of progressivity on pay gaps and labor supply. On capital and wealth taxation, we discuss the implications of lower effective capital income taxation on the personal income tax burden gap across genders. We show that countries with relatively low female shares of capital income and wealth also tend to tax property and inheritances particularly lightly. On consumption taxes, we cover taxes on feminine hygiene products and excise taxes, which we assess in relation to externalities and differences in consumption patterns across genders. RECOMMENDED CITATION: Coelho, Maria, Aieshwarya Davis, Alexander Klemm, and Carolina Osorio Buitron, 2022, “Gendered Taxes: The Interaction of Tax Policy with Gender Equality,” IMF Working Paper No. 22/26. JEL Classification Numbers: H20, H23, J16.
How Do We Assess Energy Availability and RED-S Risk Factors in Para Athletes?

Low energy availability (LEA) is considered to be the underlying cause of a number of maladaptations in athletes, including impaired physiological function, low bone mineral density (BMD), and hormonal dysfunction. This is collectively referred to as ‘Relative Energy Deficiency in Sport’ (RED-S). LEA is calculated through assessment of dietary energy intake (EI), exercise energy expenditure (EEE) and fat-free mass (FFM). The incidence of LEA in Paralympic athletes is relatively unknown; however, there are legitimate concerns that Para athletes may be at even higher risk of LEA than able-bodied athletes. Unfortunately, there are numerous issues with the application of LEA assessment tools and the criterion for diagnosis within the context of a Para population. The calculation of EEE, in particular, is limited by a distinct lack of published data that cover a range of impairments and activities. In addition, for several RED-S-related factors, it is difficult to distinguish whether they are truly related to LEA or a consequence of the athlete’s impairment and medical history. This narrative review outlines deficits and complexities when assessing RED-S and LEA in Para athletes, presents the information that we do have, and provides suggestions for future progress in this important area of sports nutrition.
Recognition of Upper Limb Action Intention Based on IMU

Using motion information of the upper limb to control the prosthetic hand has become a hotspot of current research. The operation of the prosthetic hand must also be coordinated with the user’s intention. Therefore, identifying action intention of the upper limb based on motion information of the upper limb is key to controlling the prosthetic hand. Since a wearable inertial sensor bears the advantages of small size, low cost, and little external environment interference, we employ an inertial sensor to collect angle and angular velocity data during movement of the upper limb. Aiming at the action classification for putting on socks, putting on shoes and tying shoelaces, this paper proposes a recognition model based on the Dynamic Time Warping (DTW) algorithm of the motion unit. Based on whether the upper limb is moving, the complete motion data are divided into several motion units. Considering the delay associated with controlling the prosthetic hand, this paper only performs feature extraction on the first motion unit and the second motion unit, and recognizes action on different classifiers. The experimental results reveal that the DTW algorithm based on motion unit bears a higher recognition rate and lower running time. The recognition rate reaches as high as 99.46%, and the average running time measures 8.027 ms. In order to enable the prosthetic hand to understand the grasping intention of the upper limb, this paper proposes a Generalized Regression Neural Network (GRNN) model based on 10-fold cross-validation. The motion state of the upper limb is subdivided, and the static state is used as the sign of controlling the prosthetic hand. This paper applies a 10-fold cross-validation method to train the neural network model to find the optimal smoothing parameter. In addition, the recognition performance of different neural networks is compared. The experimental results show that the GRNN model based on 10-fold cross-validation exhibits a high accuracy rate, capable of reaching 98.28%. Finally, the two algorithms proposed in this paper are implemented in an experiment of using the prosthetic hand to reproduce an action, and the feasibility and practicability of the algorithm are verified by experiment.
The predictive utility of patient-reported outcomes and performance status for survival in metastatic lung cancer patients treated with chemoimmunotherapy

Background Atezolizumab, an immune checkpoint inhibitor, in combination with chemotherapy (chemoimmunotherapy) has become a first-line treatment option for metastatic non-small cell lung cancer (NSCLC). Patient-reported outcomes (PROs) are self-reported measures that have shown promise in their predictive value for survival. However, there have been no studies that have assessed the prognostic performance of PROs in an advanced NSCLC cohort initiating first-line atezolizumab based chemoimmunotherapy. Methods This study used individual-participant data (IPD) from the IMpower130, IMpower131 and IMpower150 clinical trials. Cox proportional hazards regression was utilized to determine the association between pre-treatment PROs with overall survival (OS) and progression free survival (PFS). The prediction performance of PROs was assessed using the C-statistic. For the PRO measure identified as the most predictive of survival, an exploratory analysis comparing the predictive performance against Eastern Cooperative Oncology Group Performance Status (ECOG-PS) was conducted. Results Patient-reported physical function, fatigue, appetite loss, pain, role function, global health status, social function, dyspnoea, constipation, nausea and vomiting, insomnia, emotional function, cognitive function, and financial difficulty were statistically associated with OS (P<0.05). Physical function (c=0.62), fatigue (c=0.61), and appetite loss (c=0.60) were the most predictive variables for OS. Patient-reported physical function (c=0.60) also had higher predictive performance than physician-defined ECOG-PS (c=0.57). Conclusions In patients with advanced NSCLC who received first line atezolizumab based therapy, pre-treatment PROs were prognostic for survival outcomes. Patient-reported physical function had higher predictive performance compared to physician-defined ECOG-PS. These results suggest PROs have significant worth in clinical practice and research trials of ICIs as a stratification factors.
SALINITY RESISTANCE AND ABILITY OF N2-FIXING RHIZOBACTERIA ISOLATES TO IMPROVE RICE SEEDLING GROWTH UNDER SALINITY STRESS

Salinity is the main limiting factor that inhibits the growth of rice plants significantly. N2-Fixing Rhizobacteria halotolerant able to increase the growth of cave rice plants to support plant productivity. This study was conducted to test PA and KP2 isolates to grow and increase Inpari rice plant growth at salinity stress (0, 4, 8, and 12 dSm-1). This study used GT (Growth time) with three replications to identify isolates growth ability on salinity stress as first stages. The second stage was to determine isolates ability to improve rice seedling growth under salinity stress using a randomized block design with twelve treatments and three replications. The result showed that all isolate could grow and tolerate salinity stress up to 12 dSm-1 and growth faster under salinity stress after 48 hours from inoculation. PA isolate grew 42.92 % while KP2 isolate was 57.80 % faster at 12 dSm-1 compared to non-saline condition. The result also showed that KP2 isolate give the best performance at 4 dSm-1 significantly increasing plant height 50.45%, plant height 183.88% compare to without inoculation on the same level salinity. Therefore, that ideal salinity for KP2 isolates to improve rice seedling growth under salinity stress was 4 dSm-1.
Cerato-platanin protein 1 is not critical for Fusarium graminearum growth and aggressiveness, but its overexpression provides an edge to Fusarium head blight in wheat

Abstract Fusarium graminearum is a mycotoxin-producing pathogen involved in Fusarium head blight (FHB) disease of cereals, and is responsible for compromising grain yield and quality.Over the course of infection, the fungus secretes various metabolites and proteins, of which only a handful have been fully characterized in the context of FHB. We report herein the role of a secreted cerato-platanin protein (FGSG_10212, FgCPP1) in the growth and aggressiveness of F. graminearum. Pathogen strains in which FgCPP1 was disrupted (Δfgcpp1) or overexpressed (FgCPP1-OX1) did not differ from the wild-type with respect to macroconidial germination or mycelial growth, nor in sensitivity to chemicals targeting fungal cell walls. Host—plant interaction studies were conducted including disease assays in wheat and host-cell localization of FgCPP1 in tobacco cells. Consistent with previous reports for FgCPP1 disruption mutants of F. graminearum, no difference was observed among FgCPP1-OX1, Δfgcpp1 and the wild-type in their ability to cause disease spread in point-inoculated wheat spikes. Spray inoculation of the wheat spikes with FgCPP1-OX1 resulted in an increase in diseased spikelets as early as 3 days post-inoculation in each of the three wheat genotypes assessed; however, this increase was not observed when the disease assay was repeated in the cultivar ‘Penhold’ with an additional FgCPP1-OX transformant, FgCPP1-OX2. FgCPP1-OX1 also resulted in increased levels of deoxynivalenol accumulation in axenic culture, as well as in spray-inoculated wheat spikes. We propose that FgCPP1 is a minor contributor to pathogen aggressiveness in wheat.
Gender-Adversarial Networks for Face Privacy Preserving

Privacy concerns over face recognition systems have attracted extensive attention in various fields. For gender privacy-preserving work, there are two key challenges: 1) privacy, i.e., confusing gender classifiers and 2) utility, i.e., maintaining its face verification performance. To address both issues, this article develops a novel gender-adversarial network, referred to as Gender-AN, to impart gender privacy to face images. Gender-AN employs an attribute-independent encoder–decoder GAN-based network to perturb the input face image, training with the assistance of the proper facial attributes. The perturbed image is then able to obfuscate gender classifiers while maintaining identity discriminability. To optimize the generator, a multitask-based loss function is utilized, which includes attribute manipulation loss, face matcher loss, adversarial loss, and reconstruction loss functions. This optimization facilitates our model to achieve the generalization, verification preserve, and natural appearance, simultaneously. Extensive experiments confirm the effectiveness of the proposed model in enhancing gender privacy and preserving face verification utility.
Improving visualization of cAMP gradients using algorithmic modelling

A ubiquitous second messenger molecule, cAMP is responsible for orchestrating many different cellular functions through a variety of pathways. Fӧrster resonance energy transfer (FRET) probes have been used to visualize cAMP spatial gradients in pulmonary microvascular endothelial cells (PMVECs). However, FRET probes have inherently low signal-to-noise ratios; multiple sources of noise can obscure accurate visualization of cAMP gradients using a hyperspectral imaging system. FRET probes have also been used to measure cAMP gradients in 3D; however, it can be difficult to differentiate between true FRET signals and noise. To further understand the effects of noise on experimental data, a model was developed to simulate cAMP gradients under experimental conditions. The model uses a theoretical cAMP heatmap generated using finite element analysis. This heatmap was converted to simulate the FRET probe signal that would be detected experimentally with a hyperspectral imaging system. The signal was mapped onto an image of unlabeled PMVECs. The result was a time lapse model of cAMP gradients obscured by autofluorescence, as visualized with FRET probes. Additionally, the model allowed the simulated expression level of FRET signal to be varied. This allowed accurate attribution of signal to FRET and autofluorescence. Comparing experimental data to the model results at different levels of FRET efficiency has allowed improved understanding of FRET signal specificity and how autofluorescence interferes with FRET signal detection. In conclusion, this model can more accurately determine cAMP gradients in PMVECs. This work was supported by NIH award P01HL066299, R01HL58506 and NSF award 1725937.
On the Energy Efciency of IEEE 802.11 WLANs

Understanding the energy consumption of wireless interfaces is critical to provide guidelines for the design and implementation of new protocols or interfaces. In this work we analyze the energy performance of an IEEE 802.11 WLAN. Our contributions are as follows: i )w e present an accurate analytical model that is able to predict the energy consumption, ii )w e present an approximate model that sacri ces accuracy for the sake of analytical tractability, iii )b ased on this simpli ed analysis, we derive the optimal conguration to maximize energy efciency of a WLAN, and iv) nally, we also analyze the trade- off between throughput and energy efciency that IEEE 802.11 imposes. While most of these results consider a homogeneous WLAN scenario where all stations share the same energy features, we also discuss the case of heterogeneous environments, where different devices show different power consumption char- acteristics.
Enhanced-bandwidth PIFA Antenna with a Slot on Ground Plane

In order to overcome the shortcoming of narrow bandwidth for conventional planar inverted-F antenna (PIFA), a novel bandwidth enhancement approach for internal multi-band handset antennas is proposed in this paper. In particular, a rectangular slot with an appropriate location and a proper size is introduced into the ground plane of the system printed circuit board to improve the bandwidths of handset antennas in both the low (GSM850/GSM900) and high (DCS1800/PCS1900) bands. The in∞uences of the slot with difierent locations and sizes on antenna bandwidth are investigated. A prototype that can cover the frequency bands of GSM850, GSM900, DCS1800 and PCS1900 simultaneously was fabricated and measured. Good agreement between the measurement and simulation results is achieved. Nowadays, modern wireless communication technologies are in the process of rapid development. Multi-system applications have been used explosively. Owing to this, it bas been a necessity to design antennas with the characteristics of multiband and wideband for mobile terminals. At present, planar inverted-F antenna (PIFA) is being adopted extensively as handset antennas because of its advantages of compact structure, low proflle, easy fabrication, low manufacturing cost and easy integration with portable devices. However, a major disadvantage of the PIFA antenna is its narrow impedance bandwidth. Hence, it is desirable to flnd methods that can enhance the bandwidths of the PIFA antennas. The bandwidth of the PIFA antenna described in (1) is broadened by using a tapered-type radiating patch. Multi-layer patches and a folded stub are introduced into the internal PIFA to improve its bandwidth (2). The bandwidth enhancement approaches mentioned above both focus on making modiflcations on the radiating elements of PIFA antennas. There are also other bandwidth improvement methods concentrating on making modiflcations on the system ground plane of mobile terminals. For example, a T-shaped ground plane is reported to broaden the bandwidth of the PIFA antenna (3). A slotted ground plane for handset devices is described to lower the proflle and improve the bandwidth of PIFA antennas (4,5). The bandwidth of the PIFA antenna designed in (6) is enhanced by using quarter-wavelength wavetraps. In this paper, a novel technique that is used to broaden the impedance bandwidth of traditional multi-band PIFA antennas is presented. In particular, a rectangular slot is introduced into the ground plane of mobile chassis. By varying the location and size of the slot, the equivalent length of the ground plane can be adjusted to the optimal lengths of the low and high bands, which thus enhance the bandwidths of PIFA antennas for both the low and high bands. The in∞uences of difier- ent slot locations and sizes on the bandwidths of the low and high bands are studied and analyzed. Based on this study, a compact internal quad-band (GSM850/GSM900/ DCS1800/PCS1900) PIFA antenna with only a volume of 20£40£6mm 3 was constructed and measured. The measurement results agree well with the simulation ones.
Accurate Physical Property Predictions via Deep Learning

Neural networks and deep learning have been successfully applied to tackle problems in drug discovery with increasing accuracy over time. There are still many challenges and opportunities to improve molecular property predictions with satisfactory accuracy even further. Here, we proposed a deep-learning architecture model, namely Bidirectional long short-term memory with Channel and Spatial Attention network (BCSA), of which the training process is fully data-driven and end to end. It is based on data augmentation and SMILES tokenization technology without relying on auxiliary knowledge, such as complex spatial structure. In addition, our model takes the advantages of the long- and short-term memory network (LSTM) in sequence processing. The embedded channel and spatial attention modules in turn specifically identify the prime factors in the SMILES sequence for predicting properties. The model was further improved by Bayesian optimization. In this work, we demonstrate that the trained BSCA model is capable of predicting aqueous solubility. Furthermore, our proposed method shows noticeable superiorities and competitiveness in predicting oil–water partition coefficient, when compared with state-of-the-art graphs models, including graph convoluted network (GCN), message-passing neural network (MPNN), and AttentiveFP.
Toward Knowledge Extraction in Classification of Volcano-Seismic Events: Visualizing Hidden States in Recurrent Neural Networks

Understanding how deep hierarchical models build their knowledge is a key issue in the usage of artificial intelligence to interpret the reality behind data. Depending on the discipline and models used, such knowledge may be represented in ways that are more or less intelligible for humans, limiting further improvements on the performance of the existing models. In order to delve into the characterization and modeling of volcano-seismic signals, this article emphasizes the idea of deciphering what and how recurrent neural networks (RNNs) model, and how this knowledge can be used to improve data interpretation. The key to accomplishing these objectives is both analyzing the hidden state dynamics associated with their hidden units as well as pruning/trimming based on the specialization of neurons. In this article, we process, analyze, and visualize the hidden states activation maps of two RNN architectures when managing different types of volcano-seismic events. As a result, the class-dependent discriminative behavior of most active neurons is analyzed, thereby increasing the comprehension of the detection and classification tasks. A representative dataset from the deception island volcano (Antarctica), containing volcano-tectonic earthquakes, long period events, volcanic tremors, and hybrid events, is used to train the models. Experimental analysis shows how neural activity and its associated specialization skills change depending on the architecture chosen and the type of event analyzed.
UPed: A Quadruped Robot Platform to Study Directional Leg Compliance

This paper presents UPed, a quadruped robot platform, for studying passive, directional, leg compliance in robots. This work was motivated by the benefits provided by passively and directionally compliant legs in humans and animals. UPed uses under-actuated legs based on the bent-knee model which is capable of changing the direction and magnitude of the leg compliance by adjusting the knee angle and distal leg stiffness. Pneumatic pistons were chosen as variable compliance elements, enabling the leg compliance to be adjusted during locomotion. The platform was validated experimentally by evaluating how the robot's pitch rate and cost of transport changed with variations in leg compliance and knee angle. Results of these experiments indicated that significant performance improvements are possible by selecting appropriate values of compliance and knee angle. They also demonstrated a unique capability to explore adjustable directional compliance and other parameters in robot legs on a physical robotic system. UPed has the potential to facilitate significant performance improvements of legged robots by enabling additional in-depth studies on directional passive leg compliance.