Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study of Hypothesis Alignment and its Improvement for Machine Translation System Combination

Recently confusion network decoding shows the best performance in combining outputs from multiple machine translation (MT) systems. However, overcoming different word orders presented in multiple MT systems during hypothesis alignment still remains the biggest challenge to confusion network-based MT system combination. In this paper, we compare four commonly used word alignment methods, namely GIZA++, TER, CLA and IHMM, for hypothesis alignment. Then we propose a method to build the confusion network from intersection word alignment, which utilizes both direct and inverse word alignment between the backbone and hypothesis to improve the reliability of hypothesis alignment. Experimental results demonstrate that the intersection word alignment yields consistent performance improvement for all four word alignment methods on both Chinese-to-English spoken and written language tasks.
Failure analysis of IGBT in intelligent power module

A failure analysis case study of insulated gate bipolar transistor (IGBT) in a intelligent power module (IPM) was introduced. In this paper, the failure mode and mechanism of an IGBT were identified by a series of failure analysis procedures. The high temperature reverse bias stress tests and ion chromatography analysis have been performed to analyze and verify its root failure causes. It indicated that the failure of IGBT chips was caused by the defects on passivation layer and the movable ions were from plastic packaging material and PCBA interface. Those resulted in the inner equipotential rings sustain overvoltage stress, and ultimately leaded to the breakdown of IGBT.
Effect of curriculum on the consolidation of neural network task knowledge

Fundamental to the problem of lifelong machine learning is how to consolidate the knowledge of a learned task within a long-term memory structure (domain knowledge) without the loss of prior knowledge. We investigate the effect of curriculum, i.e., a selection of tasks and the order in which they are learned, on the consolidation of task knowledge. Relevant background material on knowledge transfer and consolidation using multiple task learning (MTL) neural networks is reviewed. A large MTL network is used as the long-term memory structure and task rehearsal overcomes the stability-plasticity problem and the loss of prior knowledge. Experimental results demonstrate that curriculum has a noticeable effect on the accuracy of consolidated knowledge particularly for the first few tasks that are learned. The results also suggest that, for given set of tasks and training examples, the mean accuracy of consolidated domain knowledge converges to the same level regardless of the curriculum. This is caused by the averaging effect of sequential consolidation
A nonlinear optimization algorithm for WindSat wind vector retrievals

WindSat is a space-based polarimetric microwave radiometer designed to demonstrate the capability to measure the ocean surface wind vector using a radiometer. We describe a nonlinear iterative algorithm for simultaneous retrieval of sea surface temperature, columnar water vapor, columnar cloud liquid water, and the ocean surface wind vector from WindSat measurements. The algorithm uses a physically based forward model function for the WindSat brightness temperatures. Empirical corrections to the physically based model are discussed. We present evaluations of initial retrieval performance using a six-month dataset of WindSat measurements and collocated data from other satellites and a numerical weather model. We focus primarily on the application to wind vector retrievals.
CS-Net: Deep Multibranch Network Considering Scene Features for Sharpness Assessment of Remote Sensing Images

Recently, many new results of sharpness assessment for digital images have been achieved, which help to select valuable images from massive images with ragged quality. However, remote sensing images encompass a wide range of scenes with diverse characteristics, and their acquisition is often influenced by blurs and noises. Many commonly used sharpness assessment methods based on uniform metrics face challenges in ensuring both subjective and objective impartiality when applied to remote sensing images. Therefore, a novel method for assessing the sharpness of remote sensing images based on a deep multibranch network considering scene features is proposed. In the method, a multitask module (MTM), comprising scene classification and sharpness assessment tasks, is proposed to comprehensively consider the potential impact of diverse scene characteristics on the assessment of sharpness. The accuracy of sharpness assessment is improved by sharing features that reflect the correlation between the scene and sharpness. To overcome the issue of imbalanced task predictions during the joint training of multiple tasks, a total loss function using gradient balance strategy is designed. In addition, the improved attention module (IAM) and the feature fusion module (FFM) are used to better utilize feature information at different scales. Experimental results obtained from datasets demonstrated that the proposed method can outperform the existing comparable methods and achieve satisfactory results, proving its feasibility and effectiveness.
Comparative Study of Carcass Characteristics and Meat Quality of Local Mediterranean Donkey Breeds

This study aimed to evaluate carcass and meat quality traits in Masri (n = 14) and North African (n = 14) male donkeys, raised in a semi-intensive breeding system, grazing on mountainous areas, with supplementation of 1 kg of barley per day per animal, slaughtered at 5 (±0.48) years old. Compared to Masri, the North African population exhibited higher (p < 0.05) final body weights (181.7 ± 2.1 and 212.5 ± 7.6 kg) and cold carcass weights (101.7 ± 1.3 and 116.2 ± 4.5 kg), but lower (p < 0.05) cold dressing percentages (56.0 ± 0.4 and 54.6 ± 0.4%). Meat quality analyses showed higher (p < 0.05) cooking loss values in Masri meat (43.9 ± 0.8 vs. 39.9 ± 1.2%). Among the meat color parameters, the hue value was higher (p < 0.05) in North African samples (0.42 ± 0.01 vs. 0.39 ± 0.1). Meat from North African donkeys had higher (p < 0.05) dry matter, fat, and protein contents. Meat amino acid analysis revealed abundant levels of lysine, leucine, and methionine, in both populations. Donkey meat from both populations presented a high polyunsaturated fatty acids content, resulting in polyunsaturated fatty acids/saturated fatty acids and omega 6/omega 3 ratios for all breeds close to the recommended values for human health. Atherogenic and thrombogenic indices were also suggested to have positive effects on consumers’ artery health. The characteristics of donkey meat present intriguing nutritional aspects compared to ruminant meat, and its production should be encouraged in the rural development of inner-Mediterranean areas.
Robust non-linear smoothing for vehicle state estimation

This paper presents a robust, non-linear smoothing algorithm and develops the theory behind it. This algorithm is extremely robust to outliers and missing data and handles state-dependent noise. Implementing it is straightforward as it consists mainly of two sub-routines: (a) the Rauch-Tung-Striebel recursions, or Kalman smoother; and (b) a backtracking line search strategy. The computational load grows linearly with the number of data because the algorithm preserves the underlying structure of the problem. Global convergence to a local optimum is guaranteed, under mild assumptions.
Does a New Simple Gaussian Weighting Approach Perform Well in Text Categorization?

A new approach to the Text Categorization problem is here presented. It is called Gaussian Weighting and it is a supervised learning algorithm that, during the training phase, estimates two very simple and easily computable statistics which are: the Presence P, how much a term is present in a category c in the Expressiveness E, how much is present outside c in the rest of the domain. Once the system has learned this information, a Gaussian function is shaped for each term of a category, in order to assign the term a weight that estimates the level of its importance for that particular category. We tested our learning method on the task of single-label classification using the Reuters-21578 benchmark. The outcome of the result was quite impressive: in different experimental setups, we reached a micro-averaged Fl-measure of 0.89, with a peak of 0.899. Moreover, a macro-averaged Recall and Precision was calculated: the former reported a 0.72, the latter a 0.79. These results reach most of the state-of-the-art techniques of machine learning applied to Text Categorization, demonstrating that this new weighting scheme does perform well on this particular task.
Preparation and characterisation of nanoliposomes containing winged bean seeds bioactive peptides

Abstract The aim of this study was to produce and characterise nanosize liposomes containing bioactive peptides with antioxidative and ACE-inhibitory properties, derived from winged bean seeds (WBS) protein. WBS powder was papain-proteolysed, at 70 °C and pH 6.5 for six hours, followed by encapsulation via a solvent-free heating method. The results showed that the WBS proteolysate was successfully incorporated into spherical, unilamellar liposomal particles, with particle diameter, polydispersity index, zeta potential and encapsulation efficiency of 193.3 ± 0.12 nm, 0.4 ± 0.02 (unit less), −70.5 ± 0.30 mV and 27.6 ± 1.17%, respectively. It also demonstrated good storage stability over eight weeks at 4 °C, indicated by slight increment (15.1%) in particle size and a zeta potential only weaker by 17.2% at the end of the study period. These results suggested the feasibility of entrapping water soluble peptides in hydrophobic liposomal system that, upon optimisation, has the potential to act as bioactive food ingredient.
The Association between Cardiovascular Disease Risk and Parental Educational Level in Portuguese Children

The aim of this study was to examine any differences in cardiovascular disease (CVD) risk in Portuguese children split by parental educational level. A cross-sectional school-based study was conducted in 2011 on 359 Portuguese children (202 girls and 157 boys) aged 10 to 17 years (mean age ± SD = 13.9 ± 1.98 years). Height and body mass were assessed to determine body mass index (BMI). Parental education level (PEL) was used as a surrogate for socioeconomic status (SES). Capillary blood sampling was used to determine: Total Cholesterol (TC), Triglycerides (TG), Fasting Glucos (GLUC), High and Low Density Lipoprotein (HDL/LDL). These measurements were combined with measures of systolic blood pressure and cardiorespiratory fitness as z-scores. CVD risk was constructed by summing the z-scores. Analysis of covariance, controlling for BMI, indicated that CVD risk was significantly different across PEL groups (p = 0.01), with CVD risk score being significantly lower in low (p = 0.04) and middle (p = 0.008) PEL groups, compared to high PEL. Moreover, the covariate, BMI was also significant (p = 0.0001, β = 0.023), evidencing a significant positive association between BMI and CVD risk, with higher BMI associated with greater CVD risk. In Portuguese children, significantly greater CVD risk was found for children of high PEL, while higher BMI was associated with greater CVD risk.
Research on the Manufacturing System Production Scheduling Problem Based on the Genetic Algorithm

Scheduling problem of material processing is one of the intellectual scheduling problems and its model is as general as any other practical problem. The current theoretical research result of scheduling problems is mainly about material processing which represents the scheduling problems based on minimum completing time. Genetic Algorithm is a kind of random searching method that imitates creatures’ natural evolution and it has been applied to job shop scheduling in recent years. Taking a job-shop for example, Genetic Algorithm was used to solve the job-shop scheduling problems. The algorithm scheme was proposed, and the proposed genetic algorithm was run. A satisfactory solution could be quickly got.
Incentivizing Calculated Risk-Taking: Evidence from an Experiment with Commercial Bank Loan Officers

This paper uses a series of experiments with commercial bank loan officers to test the effect of performance incentives on risk-assessment and lending decisions. The paper first shows that, while high-powered incentives lead to greater screening effort and more profitable lending, their power is muted by both deferred compensation and the limited liability typically enjoyed by loan officers. Second, the paper presents direct evidence that incentive contracts distort judgment and beliefs, even among trained professionals with many years of experience. Loans evaluated under more permissive incentive schemes are rated significantly less risky than the same loans evaluated under pay-for-performance.
Establishment of cementation parameters of dried waste from evaporation coming from NPP operation

The radioactive wastes generated in Brazil are treated and sent to initial and intermediate storages. The “Project RBMN” proposes the implantation of the Brazilian repository to receive and permanently dispose the low and intermediate level radioactive wastes. The CNEN NN 6.09 standard – Acceptance Criteria for Disposal of Low and Intermediate Radioactive Wastes (LIRW) – establishes the fundamental requirements to accept the wastes packages in the repository. The evaporator concentrate is one of liquid wastes generated in a Nuclear Power Plant (NPP) operation and usually it is cemented directly inside the packing. The objective of this research is to increase the amount of the incorporated waste in each package, using the drying process before the cementation, consequently reducing the volume of the waste to be disposed. Drying and cementation parameters were established in order to scale-up the process aiming at waste products that comply with the requirements of CNEN standard. The cementation of the resulting dry wastes was carried out with different formulations, varying the amount of cement, dry waste and water. These tests were analyzed in order to select the best products, with higher waste incorporation than current process and its complying the requirements of the standard CNEN NN 6.09.
BiFA: Remote Sensing Image Change Detection With Bitemporal Feature Alignment

Despite the success of deep learning-based change detection (CD) methods, their existing insufficiency in temporal (channel and spatial) and multiscale alignment has rendered them insufficient capability in mitigating external factors (illumination changes and perspective differences) arising from different imaging conditions during CD. In this article, a bitemporal feature alignment (BiFA) model is proposed to produce a precise CD map in a lightweight manner by reducing the impact of irrelevant factors. Specifically, for the temporal alignment, the bitemporal interaction (BI) module is proposed to realize the alignment of the bitemporal image channel level. Our intuition is introducing the BI in the feature extraction stage may benefit suppressing the interference, such as illumination changes. Simultaneously, the alignment module based on differential flow field (ADFF) is proposed to explicitly estimate the offset of the bitemporal image and realize their spatial level alignment to mitigate the inadequate registration resulting from different perspectives. Furthermore, for the multiscale alignment, we introduce the implicit neural alignment decoder (IND) to produce more refined prediction maps achieving precise alignment of multiscale features by learning continuous image representations in coordinate space. Our BiFA outperforms other state-of-the-art methods on six datasets (such as the F1-score (F1)/intersection over union (IoU) scores are improved by 2.70%/3.91% and 2.01%/2.94% on Learning, VIsion, and Remote sensing (LEVIR)+-CD and Sun Yat-sen University (SYSU)-CD, respectively) and displays greater robustness in cross-resolution CD. Our code is available at https://github.com/zmoka-zht/BiFA.
Incremental Analysis of Logic Programs

Global analyzers traditionally read and analyze the entire program at once, in a non-incremental way. However, there are many situations which are not well suited to this simple model and which instead require reanalysis of certain parts of a program which has already been analyzed. In these cases, it appears inefficient to perform the analysis of the program again from scratch, as needs to be done with current systems. We describe how the fixpoint algorithms in current generic analysis engines can be extended to support incremental analysis. The possible changes to a program are classified into three types: addition, deletion, and arbitrary change. For each one of these, we provide one or more algorithms for identifying the parts of the analysis that must be recomputed and for performing the actual recomputation. The potential benefits and drawbacks of these algorithms are discussed. Finally, we present some experimental results obtained with an implementation of the algorithms in the PLAI generic abstract interpretation framework. The results show significant benefits when using the proposed incremental analysis algorithms.
Financial constraints, Technology Transfer and Organizational Form ∗ PRELIMINARY AND INCOMPLETE - PLEASE DO NOT QUOTE

In developing countries, transfer and assimilation of technologies developed in more industrialized countries constitute a major source of productivity growth. In this paper, we develop a simple model to show that financial constraints may have an impact on technological knowledge that is transferred from a global buyer to a local supplier. The model predicts that, when capital markets are perfect, high technology transfers are inferred while the supplier is more likely to operate as an independent firm. When the supplier has limited access to credit, technology transfers are reduced and vertical integration may be the preferred organizational form. Empirically, we test whether stronger credit constraints and lower financial development are associated with lower technology transfers, using firm-level data across 17 countries. ∗We thank Thierry Verdier for helpful comments and suggestions. Contacts: juan.carluccio@ens.fr and fally@ens.fr
Development of a genre-dependent TTS system with cross-speaker speaking-style transplantation

One of the biggest challenges in speech synthesis is the production of contextually-appropriate naturally sounding synthetic voices. This means that a Text-To-Speech system must be able to analyze a text beyond the sentence limits in order to select, or even modulate, the speaking style according to a broader context. Our current architecture is based on a two-step approach: text genre identification and speaking style synthesis according to the detected discourse genre. For the final implementation, a set of four genres and their corresponding speaking styles were considered: broadcast news, live sport commentaries, interviews and political speeches. In the final TTS evaluation, the four speaking styles were transplanted to the neutral voices of other speakers not included in the training database. When the transplanted styles were compared to the neutral voices, transplantation was significantly preferred and the similarity to the target speaker was as high as 78%.
The Application of Human Values Through the National Education Philosophy Towards the Development of a Prosperous Nation in Malaysia in the Era of Industrial Revolution 4.0

Despite the challenges of the Industrial Revolution 4.0, human values shape people and societies. Education, the main vehicle for value production, provides it. The Malaysian government's "National Education Philosophy" is a good strategy to infuse the community's academic supremacy with strong human values without getting blown away by quick advancement. Malaysia wants to cultivate and promote healthy human values in all areas of life, including physical, emotional, spiritual, and intellectual. The study examines participants' views on implementing National Education Philosophy-derived human values. It also assesses community respect for these ideals in the context of a prosperous Malaysia. Conducted in the Klang Valley, the pilot survey engaged 112 participants from diverse backgrounds enrolled in tertiary education, utilizing an online survey platform. The study found that human values can create a prosperous life. Physical health, or the body that needs to be maintained to achieve well-being, was found to be a high priority for most respondents, who are also concerned about the environment and a clean life. Most respondents exhibited good emotional stability whether facing their own or others' local or worldwide problems, according to the survey. The survey indicated that respondents' spirituality includes personal demands and rights versus family, society, and country. Among the intellectual aspects, the survey indicated that respondents prioritised epistemology. The study's findings on the complex effects of the Malaysian government's "National Education Philosophy" on participants' perspectives and values provide a foundation for future research on education strategies and the development of comprehensive human values in different societies.
Accuracy of RSS-Based Centroid Localization Algorithms in an Indoor Environment

In this paper, we analyze the accuracy of indoor localization measurement based on a wireless sensor network. The position estimation procedure is based on the received-signal-strength measurements collected in a real indoor environment. Two different classes of low-computational-effort algorithms based on the centroid concept are considered, i.e., the weighted centroid localization method and the relative-span exponential weighted localization method. In particular, different sources of measurement uncertainty are analyzed by means of theoretical simulations and experimental results.
Effect of Virtual Reality on Cognitive Dysfunction in Patients With Brain Tumor

Objective To investigate whether virtual reality (VR) training will help the recovery of cognitive function in brain tumor patients. Methods Thirty-eight brain tumor patients (19 men and 19 women) with cognitive impairment recruited for this study were assigned to either VR group (n=19, IREX system) or control group (n=19). Both VR training (30 minutes a day for 3 times a week) and computer-based cognitive rehabilitation program (30 minutes a day for 2 times) for 4 weeks were given to the VR group. The control group was given only the computer-based cognitive rehabilitation program (30 minutes a day for 5 days a week) for 4 weeks. Computerized neuropsychological tests (CNTs), Korean version of Mini-Mental Status Examination (K-MMSE), and Korean version of Modified Barthel Index (K-MBI) were used to evaluate cognitive function and functional status. Results The VR group showed improvements in the K-MMSE, visual and auditory continuous performance tests (CPTs), forward and backward digit span tests (DSTs), forward and backward visual span test (VSTs), visual and verbal learning tests, Trail Making Test type A (TMT-A), and K-MBI. The VR group showed significantly (p<0.05) better improvements than the control group in visual and auditory CPTs, backward DST and VST, and TMT-A after treatment. Conclusion VR training can have beneficial effects on cognitive improvement when it is combined with computer-assisted cognitive rehabilitation. Further randomized controlled studies with large samples according to brain tumor type and location are needed to investigate how VR training improves cognitive impairment.