Miracle-dz/UniFAR-FTUs huggingface dataset:
Semisupervised Learning for a Hybrid Generative/Discriminative Classifier based on the Maximum Entropy Principle

This paper presents a method for designing semisupervised classifiers trained on labeled and unlabeled samples. We focus on a probabilistic semisupervised classifier design for multiclass and single-labeled classification problems and propose a hybrid approach that takes advantage of generative and discriminative approaches. In our approach, we first consider a generative model trained by using labeled samples and introduce a bias correction model, where these models belong to the same model family but have different parameters. Then, we construct a hybrid classifier by combining these models based on the maximum entropy principle. To enable us to apply our hybrid approach to text classification problems, we employed naive Bayes models as the generative and bias correction models. Our experimental results for four text data sets confirmed that the generalization ability of our hybrid classifier was much improved by using a large number of unlabeled samples for training when there were too few labeled samples to obtain good performance. We also confirmed that our hybrid approach significantly outperformed the generative and discriminative approaches when the performance of the generative and discriminative approaches was comparable. Moreover, we examined the performance of our hybrid classifier when the labeled and unlabeled data distributions were different.
Online Domain Adaptation for Semantic Segmentation in Ever-Changing Conditions – Supplementary Material

This document introduces further details on the ECCV 2022 paper - ”Online Domain Adaptation for Semantic Segmentation in Ever-Changing Conditions”. In Section 1, we present results of the OnDA framework on the CityScapes dataset [2] with qualitative samples and a video sequence, in Section 2 we detail the model implementation and hyperparameters, while in Section 3 we further describe the working principle behind Domain Shift Detection. Then we provide additional explanation on some experimental results presented in the paper, in particular we focus on the Standard deviation, Section 4, and the Calibration Error, Section 5. Finally, in Section 6 we expand our Ablation Study and study the impact of the learning rate, the effect of the confidence regularization and Batch Normalization
A Deep Learning-based Fine-grained Hierarchical Learning Approach for Robust Malware Classification

The wide acceptance of Internet of Things (IoT) for both household and industrial applications is accompanied by several security concerns. A major security concern is their probable abuse by adversaries towards their malicious intent. Understanding and analyzing IoT malicious behaviors is crucial, especially with their rapid growth and adoption in wide-range of applications. However, recent studies have shown that machine learning-based approaches are susceptible to adversarial attacks by adding junk codes to the binaries, for example, with an intention to fool those machine learning or deep learning-based detection systems. Realizing the importance of addressing this challenge, this study proposes a malware detection system that is robust to adversarial attacks. To do so, examine the performance of the state-of-the-art methods against adversarial IoT software crafted using the graph embedding and augmentation techniques. In particular, we study the robustness of such methods against two black-box adversarial methods, GEA and SGEA, to generate Adversarial Examples (AEs) with reduced overhead, and keeping their practicality intact. Our comprehensive experimentation with GEA-based AEs show the relation between misclassification and the graph size of the injected sample. Upon optimization and with small perturbation, by use of SGEA, all the IoT malware samples are misclassified as benign. This highlights the vulnerability of current detection systems under adversarial settings. With the landscape of possible adversarial attacks, we then propose DL-FHMC, a fine-grained hierarchical learning approach for malware detection and classification, that is robust to AEs with a capability to detect 88.52% of the malicious AEs.
EnhanceNet : Single Image Super-Resolution Through Automated Texture Synthesis — Supplementary —

In this supplemental, we present some further details on our models and their training procedure, provide additional insights about the influence of the different loss functions to the super-resolution reconstruction, discuss applications and limitations of our approach and show further results and comparisons with other methods. The sections in the supplementary are numbered to match the corresponding sections in the main paper. 4 Additional details on the method 4.2. 3 Patch size of texture matching loss We compute the texture loss LT patch-wise to enforce locally similar textures between Iest and IHR. We found a patch size of 16×16 pixels to result in the best balance between faithful texture generation and the overall perceptual quality of the images. Figure 1 shows ENet-PAT when trained using patches of size 4×4 pixels for the texture matching loss (ENet-PAT-4) and when it is calculated on larger patches of 128×128 pixels (ENet-PAT-128). Using smaller patches leads to artifacts in textured regions while calculating the texture matching loss on too large patches during training leads to artifacts throughout the entire image since the network is trained with texture statistics that are averaged over regions of varying textures, leading to unpleasant results. 4.2. 4 Architecture of the adversarial network Table 1 shows the architecture of our discriminative adversarial network used for the loss term LA. We follow common design patterns [13] and exclusively use convolutional layers with filters of size 3×3 pixels with varying stride lengths to reduce the spatial dimension of the input down to a size of 4×4 pixels where we append two fully connected Output size Layer 128× 128× 3 Input Iest or IHR 128× 128× 32 Conv, lReLU 64× 64× 32 Conv stride 2, lReLU 64× 64× 64 Conv, lReLU 32× 32× 64 Conv stride 2, lReLU 32× 32× 128 Conv, lReLU 16× 16× 128 Conv stride 2, lReLU 16× 16× 256 Conv, lReLU 8× 8× 256 Conv stride 2, lReLU 8× 8× 512 Conv, lReLU 4× 4× 512 Conv stride 2, lReLU
Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization

—Domain generalization (DG) aims to learn from multiple known source domains a model that can generalize well to unknown target domains. The existing DG methods usually exploit the fusion of shared multi-source data for capturing domain invariance and training a generalizable model. However, tremendous data is distributed across lots of places nowadays that can not be shared due to strict privacy policies. A dilemma is thus raised between the generalization learning with shared multi-source data and the privacy protection of real-world sensitive data. In this paper, we introduce a separated domain generalization task with separated source datasets that can only be accessed locally for data privacy protection. We propose a novel solution called Collaborative Semantic Aggregation and Calibration (CSAC) to enable this challenging task. To fully absorb multi-source semantic information while avoiding unsafe data fusion, we conduct data-free semantic aggregation by fusing the models trained on the separated domains layer-by-layer. To address the semantic dislocation problem caused by domain shift, we further design cross-layer semantic calibration with an elaborate attention mechanism to align each semantic level and enhance domain invariance. We unify multi-source semantic learning and alignment in a collaborative way by repeating the semantic aggregation and calibration alternately, keeping each dataset localized, and the data privacy is thus carefully pro-tected. Extensive experiments show the signiﬁcant performance of our method in addressing this challenging task, which is even comparable to the previous DG methods with shared source data.
DoubleField: Bridging the Neural Surface and Radiance Fields for High-ﬁdelity Human Reconstruction and Rendering

View-to-view Transformer We design the view-to-view transformer based on the classical encoder-decoder structure [10]. The number of tokens is equal to the number of views in our transformer. Since only sparse multi-view inputs are involved and the sampling points can be divided into batches during training, our transformer does not suffer from memory issues. 1) The encoder has 1 layer with 8 heads. The size of its inner embedding is 256 and the dimension of Q,K, V for each head is 32. The encoder takes the concatenation of multi-view image features and the direction embedding as inputs and produces the fused features with the same dimension of 256 + (60 + 3). The fused features are concatenated with the positional encoding of query points and then fed into the double MLP to obtain
Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition Supplementary Material

This appendix presents more details of the proposed Attentive Temporal Consistent Network (ATCoN) and is organized as follows: first , we introduce the detailed implementation of ATCoN with specific hyperparameter settings supported by hyperparameter sensitivity analysis. Subsequently , we visualize local temporal features learned by various models to justify the proposed cross-temporal hypothesis . Lastly , we present details of the cross-domain action recognition benchmarks for evaluating ATCoN
Can texture analysis of T2-weighted MRI be used to predict extrathyroidal extension in papillary thyroid carcinoma?

Determining the presence of extrathyroidal extension (ETE) is important for established of different surgical protocol and postoperative patient management in patients with papillary thyroid carcinoma (PTC). The correlation relationship between texture features from T2-weighted imaging (T2WI) and ETE has not been explored extensively. This study aimed to explore the value of T2-weighted magnetic resonance imaging – based whole tumor texture analysis in predict extrathyroidal extension with PTC. In this retrospectively study, 76 patients with pathologically proven PTC were recruited, who received surgical resection and underwent preoperative thyroid magnetic resonance imaging. Based on histo-pathologically findings, patients were classified into ETE and no ETE groups. ETE group was further divided into 2 subgroups (minimal ETE and extensive ETE). Whole-tumor texture analysis was independently performed by 2 radiologists on axial T2WI images. Nine histogram and gray-level co-occurrence matrix (GLCM) texture features were automatically extracted. Univariate and multivariate analysis were performed to determine risk factors associated with ETE. Predictive performance was evaluated by receiver operating characteristic (ROC) analysis. Interobserver agreement, confirmed by intraclass correlation coefficients (ICCs) ranging from 0.78 to 0.89, was excellent for texture analysis between 2 radiologists. T2WI image derived entropy, standard deviation, energy and correlation have significant difference between PTC with and without ETE (all P < .05). Among these, entropy showed the best diagnostic efficiency with the area under ROC curve of 0.837, diagnostic threshold of 5.86, diagnostic sensitivity and specificity of 81.5% and 75.6%, respectively. Additionally, the multivariate analysis revealed that high entropy was an independent risk factor of ETE (odds ratio, OR = 19.348; 95%CI, 4.578-81.760; P = .001). The findings indicate a significant association between texture features of the primary tumor based on T2WI and the presence of ETE in PTC. These results have the potential to help predict ETE preoperatively in patients with PTC, offering valuable insights for clinical decision-making.
Micro-porous PLGA/β-TCP/TPU scaffolds prepared by solvent-based 3D printing for bone tissue engineering purposes

Abstract The 3D printing process of fused deposition modelling is an attractive fabrication approach to create tissue-engineered bone substitutes to regenerate large mandibular bone defects, but often lacks desired surface porosity for enhanced protein adsorption and cell adhesion. Solvent-based printing leads to the spontaneous formation of micropores on the scaffold’s surface upon solvent removal, without the need for further post processing. Our aim is to create and characterize porous scaffolds using a new formulation composed of mechanically stable poly(lactic-co-glycol acid) and osteoconductive β-tricalcium phosphate with and without the addition of elastic thermoplastic polyurethane prepared by solvent-based 3D-printing technique. Large-scale regenerative scaffolds can be 3D-printed with adequate fidelity and show porosity at multiple levels analysed via micro-computer tomography, scanning electron microscopy and N2 sorption. Superior mechanical properties compared to a commercially available calcium phosphate ink are demonstrated in compression and screw pull out tests. Biological assessments including cell activity assay and live-dead staining prove the scaffold’s cytocompatibility. Osteoconductive properties are demonstrated by performing an osteogenic differentiation assay with primary human bone marrow mesenchymal stromal cells. We propose a versatile fabrication process to create porous 3D-printed scaffolds with adequate mechanical stability and osteoconductivity, both important characteristics for segmental mandibular bone reconstruction.
Pareidolias are a function of visuoperceptual impairment

Pareidolias, or the misperception of ambiguous stimuli as meaningful objects, are complex visual illusions thought to be phenomenologically similar to Visual Hallucination (VH). VH are a major predictor of dementia in Parkinson’s Disease (PD) and are included as a core clinical feature in Dementia with Lewy Bodies (DLB). A newly developed Noise Pareidolia Test (NPT) was proposed as a possible surrogate marker for VH in DLB patients as increased pareidolic responses correlated with informant-corroborated accounts of VH. This association could, however, be mediated by visuoperceptual impairment. To understand the drivers of performance on the NPT, we contrasted performances in patient groups that varied both in terms of visuoperceptual ability and rates of VH. N = 43 patients were studied of whom n = 13 had DLB or PD with Dementia (PDD); n = 13 had PD; n = 12 had typical, memory-onset Alzheimer’s Disease (tAD); and n = 5 had Posterior Cortical Atrophy (PCA) due to Alzheimer’s disease. All patient groups reported pareidolias. Within the Lewy body disorders (PD, DLB, PDD), there was no significant difference in pareidolic response rates between hallucinating and non-hallucinating patients. Visuoperceptual deficits and pareidolic responses were most frequent in the PCA group—none of whom reported VH. Regression analyses in the entire patient cohort indicated that pareidolias were strongly predicted by visuoperceptual impairment but not by the presence of VH. These findings suggest that pareidolias reflect the underlying visuoperceptual impairment of Lewy body disorders, rather than being a direct marker for VH.
Highly structured populations of deep-sea copepods associated with hydrothermal vents across the Southwest Pacific, despite contrasting life history traits

Hydrothermal vents are extreme environments, where abundant communities of copepods with contrasting life history traits co-exist along hydrothermal gradients. Here, we discuss how these traits may contribute to the observed differences in molecular diversity and population genetic structure. Samples were collected from vent locations across the globe including active ridges and back-arc basins and compared to existing deep-sea hydrothermal vent and shallow water data, covering a total of 22 vents and 3 non-vent sites. A total of 806 sequences of mtDNA from the Cox1 gene were used to reconstruct the phylogeny, haplotypic relationship and demography within vent endemic copepods (Dirivultidae, Stygiopontius spp.) and non-vent-endemic copepods (Ameiridae, Miraciidae and Laophontidae). A species complex within Stygiopontius lauensis was studied across five pacific back-arc basins at eight hydrothermal vent fields, with cryptic species being restricted to the basins they were sampled from. Copepod populations from the Lau, North Fiji and Woodlark basins are undergoing demographic expansion, possibly linked to an increase in hydrothermal activity in the last 10 kya. Highly structured populations of Amphiascus aff. varians 2 were also observed from the Lau to the Woodlark basins with populations also undergoing expansion. Less abundant harpacticoids exhibit little to no population structure and stable populations. This study suggests that similarities in genetic structure and demography may arise in vent-associated copepods despite having different life history traits. As structured meta-populations may be at risk of local extinction should major anthropogenic impacts, such as deep-sea mining, occur, we highlight the importance of incorporating a trait-based approach to investigate patterns of genetic connectivity and demography, particularly regarding area-based management tools and environmental management plans.
High frequency of carpal tunnel syndrome and associated factors: A cross-sectional study in Peruvian workers from agro-export industry

Carpal tunnel syndrome (CTS) is a peripheral mononeuropathy caused by compression of the median nerve at the wrist and has been reported in workers who perform repetitive movements that involve actions of sustained grasping of vibrating objects. We carried out a cross-sectional analytical study in March 2018 to identify the factors associated with the CTS among workers of agro-export companies in Ica-Peru. CTS confirmation in our study was based on having at least 1 positive screening test (Tinel or Phalen) and a presumptive result of CTS by the Kamath and Stothard Questionnaire. We carried out 4 Poisson regression models to evaluate the factors associated with CTS based on epidemiological and statistical criteria. We enrolled 112 agro-export workers in production (42.0%), packing (35.7%), and administration (22.3%) working areas. The CTS frequency in Peruvian agro-export workers in production, packing, and administrative working area were 78.7%, 45.0%, and 28.0%, respectively. The bivariate analysis found a relationship between the CTS with age, female sex, sports practice, job seniority in the working area (year), and repetitive wrist movements (hours per day). In the multivariate analysis, only job seniority in the working area (year) and repetitive wrist movements maintained their association with CTS. Occupational factors are significantly associated with a high frequency of CTS, such as job seniority in the working area (year) and repetitive wrist movements in agro-export workers. Surveillance programs should be held to prevent, reduce, and monitor workers’ health status.
Wind-driven mid-ocean baroclinic gyres over topography: A circulation equation extending the Sverdrup relation

What is the circulation driven by wind stress in a stratified ocean above topography? This question was answered by Sverdrup (1947) for vertically integrated transport over flat topography only. By applying the ideas and methods of Rhines and Young (1982a,b), a modified form of the Sverdrup transport relation can be derived for the case of stratification and topography in certain circumstances. This circulation equation is, in quasigeostrophic form, J
Does fear of re-injury affect the self-perceived level of lower limb functionality among soccer players with ACL reconstruction?: A cross-sectional study

The fear of re-injury may persist after anterior cruciate ligament (ACL) reconstruction (ACLR) in professional soccer players (PSPs) even after a successful return to sport (RTS). This study aimed to determine the extent of this fear of re-injury and the impact of demographic variables and this fear of re-injury on the lower extremity functional scale (LEFS) scores in PSPs who had completed a successful RTS following ACLR. A cross-sectional survey design was used. Sixty-seven PSPs who had successfully RTS after ACLR, completed a demographic information sheet, the Athlete Fear-Avoidance Questionnaire (AFAQ), and the LEFS. The average AFAQ scores indicated low fear (M = 10.2, SD = 6.7), while high LEFS scores were observed (M = 67.1, SD = 12.4). There were significant strong negative correlations between LEFS and body mass index (BMI; rs [65] = −0.501, P = .001) and AFAQ and BMI (rs [65] = −0.378, P = .001). A hierarchical linear regression analysis found AFAQ to be a significant predictor of LEFS (b = −0.92, s.e. = 0.19, P = .001), with a total variance (adjusted R2) of 32.9%. PSPs’ fear of re-injury post-ACLR significantly reduced their perceived levels of lower limb functionality. This study highlights the need to further explore and develop strategies to manage PSP fear of re-injury to improve sports-related performance post-ACLR.
Identifying price bubbles in copper market: Evidence from a GSADF test approach

This paper uses the test proposed by Generalized Supremum Augmented Dickey-Fuller to identify whether there are multiple bubbles in copper price. The empirical results show that base on market fundamentals, there are seven bubbles existed from January 1980 to March 2023. Through analyses, the first two bubbles can be explained by the demand from Japan by the industry concentration and persistent supply constraint. The third to sixth bubbles are mainly negatively impacted by the global financial crisis and growing demand of China. The last bubble is caused by the economic recovery from Covid-19. The logit regression has stated that aluminum price, copper production, all metals index and GDP have a positive impact on copper bubbles, while China’s copper imports and precious metals price negatively explains copper bubbles. The main contributions are the investigation of the copper price bubbles, its determinants and the different technique of GSADF to detect copper price bubbles. Furthermore, it provides helpful information for those investors to make reasonable investment decisions and thus, avoid potential price risk.
Estimating shadow prices in economies with multiple market failures

Approaches to the estimation of shadow prices generally assume that all but one market function correctly. However, multiple market failures are common in developing countries. We present a theoretical model and an empirical strategy to estimate the shadow price of a subsistence good in an economy where labor markets fail. Our results show that: 1) among subsistence producers, the shadow price of this good must be greater than or equal to the market price, and equal to it for surplus growers; and 2) current methods create biases when the otherwise-perfect-markets assumption is violated. The propositions are tested using a representative survey for rural Mexico. We find that the shadow wage is below that of the market (MXN $93.2/day vs. MXN $132.3/day), and that the shadow price for subsistence corn is over ten times greater than its market price (MXN $32.37/kg vs. MXN $3.19/kg). Unbiased shadow price estimates for subsistence goods help to overcome the limitations of current income poverty measures: their overestimation of the purchasing power of subsistence households and their underestimation of the value of subsistence goods. In rural Mexico, current practice underestimates the population in food poverty by 2%; an additional 9% has income above the poverty line yet fail to meet the utilization dimension of food security.