Miracle-dz/UniFAR-FTUs huggingface dataset:
Supervised Learning: No Loss No Cry

Supervised learning requires the specification of a loss function to minimise. While the theory of admissible losses from both a computational and statistical perspective is well-developed, these offer a panoply of different choices. In practice, this choice is typically made in an \emph{ad hoc} manner. In hopes of making this procedure more principled, the problem of \emph{learning the loss function} for a downstream task (e.g., classification) has garnered recent interest. However, works in this area have been generally empirical in nature. In this paper, we revisit the {\sc SLIsotron} algorithm of Kakade et al. (2011) through a novel lens, derive a generalisation based on Bregman divergences, and show how it provides a principled procedure for learning the loss. In detail, we cast {\sc SLIsotron} as learning a loss from a family of composite square losses. By interpreting this through the lens of \emph{proper losses}, we derive a generalisation of {\sc SLIsotron} based on Bregman divergences. The resulting {\sc BregmanTron} algorithm jointly learns the loss along with the classifier. It comes equipped with a simple guarantee of convergence for the loss it learns, and its set of possible outputs comes with a guarantee of agnostic approximability of Bayes rule. Experiments indicate that the {\sc BregmanTron} substantially outperforms the {\sc SLIsotron}, and that the loss it learns can be minimized by other algorithms for different tasks, thereby opening the interesting problem of \textit{loss transfer} between domains.
Unsupervised deep clustering for predictive texture pattern discovery in medical images

Predictive marker patterns in imaging data are a means to quantify disease and progression, but their identification is challenging, if the underlying biology is poorly understood. Here, we present a method to identify predictive texture patterns in medical images in an unsupervised way. Based on deep clustering networks, we simultaneously encode and cluster medical image patches in a low-dimensional latent space. The resulting clusters serve as features for disease staging, linking them to the underlying disease. We evaluate the method on 70 T1-weighted magnetic resonance images of patients with different stages of liver steatosis. The deep clustering approach is able to find predictive clusters with a stable ranking, differentiating between low and high steatosis with an F1-Score of 0.78.
Machine learning approaches for identifying prey handling activity in otariid pinnipeds

Systems developed in wearable devices with sensors onboard are widely used to collect data of humans and animals activities with the perspective of an on-board automatic classification of data. An interesting application of these systems is to support animals' behaviour monitoring gathered by sensors' data analysis. This is a challenging area and in particular with fixed memories capabilities because the devices should be able to operate autonomously for long periods before being retrieved by human operators, and being able to classify activities onboard can significantly improve their autonomy. In this paper, we focus on the identification of prey handling activity in seals (when the animal start attaching and biting the prey), which is one of the main movement that identifies a successful foraging activity. Data taken into consideration are streams of 3D accelerometers and depth sensors values collected by devices attached directly on seals. To analyse these data, we propose an automatic model based on Machine Learning (ML) algorithms. In particular, we compare the performance (in terms of accuracy and F1score) of three ML algorithms: Input Delay Neural Networks, Support Vector Machines, and Echo State Networks. We attend to the final aim of developing an automatic classifier on-board. For this purpose, in this paper, the comparison is performed concerning the performance obtained by each ML approach developed and its memory footprint. In the end, we highlight the advantage of using an ML algorithm, in terms of feasibility in wild animals' monitoring.
Conjoined Dirichlet Process

Author(s): Ngo, Michelle N; Pluta, Dustin S; Ngo, Alexander N; Shahbaba, Babak | Abstract: Biclustering is a class of techniques that simultaneously clusters the rows and columns of a matrix to sort heterogeneous data into homogeneous blocks. Although many algorithms have been proposed to find biclusters, existing methods suffer from the pre-specification of the number of biclusters or place constraints on the model structure. To address these issues, we develop a novel, non-parametric probabilistic biclustering method based on Dirichlet processes to identify biclusters with strong co-occurrence in both rows and columns. The proposed method utilizes dual Dirichlet process mixture models to learn row and column clusters, with the number of resulting clusters determined by the data rather than pre-specified. Probabilistic biclusters are identified by modeling the mutual dependence between the row and column clusters. We apply our method to two different applications, text mining and gene expression analysis, and demonstrate that our method improves bicluster extraction in many settings compared to existing approaches.
Small world is not enough: Criteria for network choice and conclusiveness of simulations

Most agent-based models include a social network that describes the structure of interactions within the artificial population. Because of the dramatic impact of this structure on the simulated dynamics, modelers create this network for it to match criteria of plausibility (e.g. the small-world property). Networks are actually created by one network generator compliant with these criteria, like the Watts-Strogatz algorithm in the case of small-world networks. However, this practice comes to study the model's dynamics over the specific networks generated by one algorithm instead of the dynamics over the class of networks of interest, possibly inducing a strong bias in results. We identify three problematics related to this bias: (i) representativity of a network generator to a class of networks, (ii) conclusiveness of simulations over a class of networks and (iii) the gain in conclusiveness when refining the criteria for network choice. We propose an experimental protocol and instantiate it on small-world networks for epidemics, opinion and culture dynamics. We show that (i) Watts-Strogatz networks are not representative of small-world networks (ii) simulation results over small-worlds are arguably inconclusive, and (iii) even small-world networks having the same size, density, transitivity and average path length do not lead to coherent results. Beyond questioning the relevance of simulation results obtained from artificial networks, this research also constitute one more argument for the exploration of other approaches that are not solely focused on networks' statistical properties.
A Translational Three-Degrees-of-Freedom Parallel Mechanism With Partial Motion Decoupling and Analytic Direct Kinematics

According to the topological design theory and the method of parallel mechanism (PM) based on position and orientation characteristic (POC) equations, this paper studied a three-degrees-of-freedom (3-DOF) translational PM that has three advantages, i.e., (i) it consists of three fixed actuated prismatic joints, (ii) the PM has analytic solutions to the direct and inverse kinematic problems, and (iii) the PM is of partial motion decoupling property. First, the main topological characteristics, such as the POC, degree-of-freedom, and coupling degree, were calculated for kinematic modeling. Thanks to these properties, the direct and inverse kinematic problems can be readily solved. Further, the conditions of the singular configurations of the PM were analyzed, which corresponds to its partial motion decoupling property.
Multi-object Monocular SLAM for Dynamic Environments

In this paper, we tackle the problem of multibody SLAM from a monocular camera. The term multibody, implies that we track the motion of the camera, as well as that of other dynamic participants in the scene. The quintessential challenge in dynamic scenes is unobservability: it is not possible to unambiguously triangulate a moving object from a moving monocular camera. Existing approaches solve restricted variants of the problem, but the solutions suffer relative scale ambiguity (i.e., a family of infinitely many solutions exist for each pair of motions in the scene). We solve this rather intractable problem by leveraging single-view metrology, advances in deep learning, and category-level shape estimation. We propose a multi pose-graph optimization formulation, to resolve the relative and absolute scale factor ambiguities involved. This optimization helps us reduce the average error in trajectories of multiple bodies over real-world datasets, such as KITTI [1]. To the best of our knowledge, our method is the first practical monocular multi-body SLAM system to perform dynamic multi-object and ego localization in a unified framework in metric scale.
RePose: Learning Deep Kinematic Priors for Fast Human Pose Estimation

We propose a novel efficient and lightweight model for human pose estimation from a single image. Our model is designed to achieve competitive results at a fraction of the number of parameters and computational cost of various state-of-the-art methods. To this end, we explicitly incorporate part-based structural and geometric priors in a hierarchical prediction framework. At the coarsest resolution, and in a manner similar to classical part-based approaches, we leverage the kinematic structure of the human body to propagate convolutional feature updates between the keypoints or body parts. Unlike classical approaches, we adopt end-to-end training to learn this geometric prior through feature updates from data. We then propagate the feature representation at the coarsest resolution up the hierarchy to refine the predicted pose in a coarse-to-fine fashion. The final network effectively models the geometric prior and intuition within a lightweight deep neural network, yielding state-of-the-art results for a model of this size on two standard datasets, Leeds Sports Pose and MPII Human Pose.
DropCluster: A structured dropout for convolutional networks

Dropout as a regularizer in deep neural networks has been less effective in convolutional layers than in fully connected layers. This is due to the fact that dropout drops features randomly. When features are spatially correlated as in the case of convolutional layers, information about the dropped pixels can still propagate to the next layers via neighboring pixels. In order to address this problem, more structured forms of dropout have been proposed. A drawback of these methods is that they do not adapt to the data. In this work, we introduce a novel structured regularization for convolutional layers, which we call DropCluster. Our regularizer relies on data-driven structure. It finds clusters of correlated features in convolutional layer outputs and drops the clusters randomly at each iteration. The clusters are learned and updated during model training so that they adapt both to the data and to the model weights. Our experiments on the ResNet-50 architecture demonstrate that our approach achieves better performance than DropBlock or other existing structured dropout variants. We also demonstrate the robustness of our approach when the size of training data is limited and when there is corruption in the data at test time.
Different Types of Voice User Interface Failures May Cause Different Degrees of Frustration

We report on an investigation into how different types of failures in a voice user interface (VUI) affects user frustration. To this end, we conducted a pilot user study ($n=10$) and a main user study ($n=30$), both with a simple voice-operated calendar application that we built using the Alexa Skills Kit. In our pilot study, we identified three major failure types as perceived by the users, namely, Reason Unknown, Speech Misrecognition, and Utterance Pattern Match Failure, along with more fine-grained failure types from the developer's viewpoint such as Intent Pattern Match Failure and Intent Misclassification. Then, in our main study, we set up three user tasks that were designed to each induce a specific failure type, and collected user frustration ratings for each task. Our main findings are: (a)Users may be relatively tolerant to user-perceived Speech Misrecognition, and not so to user-perceived Reason Unknown and Utterance Mattern Match Failures; (b)Regarding the relationship between developer-perceived and user-perceived failure types, 68.8\% of developer-perceived Intent Misclassification instances caused user-perceived Reason Unkown failures. From (a) and (b), a practical design implication would be to try to prevent Intent Misclassification from happening by carefully crafting the utterance patterns for each intent.
The Bloom Tree

We introduce a data structure that allows for efficient (probabilistic) presence proofs and non-probabilistic absence proofs in a bandwidth efficient and secure way. The Bloom tree combines the idea of Bloom filters with that of Merkle trees. Bloom filters are used to verify the presence, or absence of elements in a set. In the case of the Bloom tree, we are interested to verify and transmit the presence, or absence of an element in a secure and bandwidth efficient way to another party. Instead of sending the whole Bloom filter to check for the presence, or absence of an element, the Bloom tree achieves efficient verification by using a compact Merkle multiproof.
Learning to Recommend With Multiple Cascading Behaviors

Most existing recommender systems leverage user behavior data of one type only, such as the purchase behavior in E-commerce that is directly related to the business Key Performance Indicator (KPI) of conversion rate. Besides the key behavioral data, we argue that other forms of user behaviors also provide valuable signal, such as views, clicks, adding a product to shopping carts and so on. They should be taken into account properly to provide quality recommendation for users. In this work, we contribute a new solution named short for Neural Multi-Task Recommendation (NMTR) for learning recommender systems from user multi-behavior data. We develop a neural network model to capture the complicated and multi-type interactions between users and items. In particular, our model accounts for the cascading relationship among different types of behaviors (e.g., a user must click on a product before purchasing it). To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task. Extensive experiments on two real-world datasets demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data. Further analysis shows that modeling multiple behaviors is particularly useful for providing recommendation for sparse users that have very few interactions.
An Empirical Assessment of Security Risks of Global Android Banking Apps

Mobile banking apps, belonging to the most security-critical app category, render massive and dynamic transactions susceptible to security risks. Given huge potential financial loss caused by vulnerabilities, existing research lacks a comprehensive empirical study on the security risks of global banking apps to provide useful insights and improve the security of banking apps. Since data-related weaknesses in banking apps are critical and may directly cause serious financial loss, this paper first revisits the state-of-the-art available tools and finds that they have limited capability in identifying data-related security weaknesses of banking apps. To complement the capability of existing tools in data-related weakness detection, we propose a three-phase automated security risk assessment system, named Ausera, which leverages static program analysis techniques and sensitive keyword identification. By leveraging Ausera, we collect 2,157 weaknesses in 693 real-world banking apps across 83 countries, which we use as a basis to conduct a comprehensive empirical study from different aspects, such as global distribution and weakness evolution during version updates. We find that apps owned by subsidiary banks are always less secure than or equivalent to those owned by parent banks. In addition, we also track the patching of weaknesses and receive much positive feedback from banking entities so as to improve the security of banking apps in practice. We further find that weaknesses derived from outdated versions of banking apps or third-party libraries are highly prone to being exploited by attackers. To date, we highlight that 21 banks have confirmed the weaknesses we reported (including 126 weaknesses in total). We also exchange insights with 7 banks, such as HSBC in UK and OCBC in Singapore, via in-person or online meetings to help them improve their apps. We hope that the insights developed in this paper will inform the communities about the gaps among multiple stakeholders, including banks, academic researchers, and third-party security companies.
AN UNUSUAL TUMOUR PRESENTING IN THE LUNGS

A 66-year-old Fijian woman presented with a 12-hour history of haemoptysis of greater than 400 ml. Over the previous 4–6 months she had blood-streaked sputum, lethargy and weight loss. CT scan of the chest showed multiple lesions within the lung fields, including a large mass in the left hemithorax partly accounting for the white-out seen on chest X-ray (Fig. 1). She was found to have lesions in the liver, spleen, pelvis and subcutis. The main lung mass had been biopsied 9 years previously and was said to be a carcinoid tumour (Fig. 2 and 3). PATHOLOGY Core biopsies of a mass in the abdominal wall were received and were compared with the histological sections of the original biopsy. Microscopically, the lesions were identical. The tumour was composed of sheets, tubules and wellformed rosettes of small cells with ovoid nuclei and inconspicuous eosinophilic cytoplasm. Occasional nuclear grooves were noted. There were scattered mitotic figures ( < 2/10 HPF) and necrosis was absent.
The Examination of Muscle Balance in Dancers

The aim was to examine the anthropometric parameters of representatives of different dance styles, the strength and extensibility of trunk muscles, and whether low back pain influences performance during a show or stage appearance. According to our hypothesis, low back pain and deviations in the functional conditions of the spine are common in dancers aged 9-18, and can have a negative effect in their performance on stage. In addition, we assume that weakness can be experienced in the strength of the trunk muscles, their extensibility is not appropriate even in spite of regular training. 92 dancers (average age: 14,15± 2,95 years) participated in the examination: 30 ballet dancers, 32 ballroom dancers, and 30 hip-hop dancers. The anthropometric data was measured with OMRON KaradaScan body composition monitor, the level of the negative effect of low back pain on stage performance was measured by visual analogue scale, and the strength and extensibility was measured with a Kraus-Weber test. On the basis of the anthropologic evaluation, we received a significant difference concerning each examined parameters (body weight: p=0,000, height: p= 0,000, muscle mass: p= 0,001, BMI: p=0,000, body fat percentage: p=0,000), the reason of which was the fact that the different dance styles have different physical requirements for the dancers. In accordance with data measured by the visual analogue scale, no significant difference was received (p=0,104) between the examined groups, although the hip-hop dancers reported the highest percentage (10,54%) of the occurrence of low back pain that negatively affects their stage performance. On the basis of the Kraus-Weber test, in the case of both abdominal muscle exercises the hip-hop group had significantly better performance (K-W A.: p=0,016, K-W B.: p=0,001) than the other two groups. Concerning muscle extensibility, (K-W F.: p= 0,030) the ballet group had significantly better results. In the case of all three examined groups it can be claimed that despite regular training, low back complaints occur at a very young age, which also have negative effects on stage performance.
Melanoma in the Eyes of Mechanobiology

Skin is the largest organ of the human body with several important functions that can be impaired by injury, genetic or chronic diseases. Among all skin diseases, melanoma is one of the most severe, which can lead to death, due to metastization. Mechanotransduction has a crucial role for motility, invasion, adhesion and metastization processes, since it deals with the response of cells to physical forces. Signaling pathways are important to understand how physical cues produced or mediated by the Extracellular Matrix (ECM), affect healthy and tumor cells. During these processes, several molecules in the nucleus and cytoplasm are activated. Melanocytes, keratinocytes, fibroblasts and the ECM, play a crucial role in melanoma formation. This manuscript will address the synergy among melanocytes, keratinocytes, fibroblasts cells and the ECM considering their mechanical contribution and relevance in this disease. Mechanical properties of melanoma cells can also be influenced by pigmentation, which can be associated with changes in stiffness. Mechanical changes can be related with the adhesion, migration, or invasiveness potential of melanoma cells promoting a high metastization capacity of this cancer. Mechanosensing, mechanotransduction, and mechanoresponse will be highlighted with respect to the motility, invasion, adhesion and metastization in melanoma cancer.
Integrated Analysis of Large-Scale Omics Data Revealed Relationship Between Tissue Specificity and Evolutionary Dynamics of Small RNAs in Maize (Zea mays)

The evolutionary dynamics and tissue specificity of protein-coding genes are well documented in plants. However, the evolutionary consequences of small RNAs (sRNAs) on tissue-specific functions remain poorly understood. Here, we performed integrated analysis of 195 deeply sequenced sRNA libraries of maize B73, representing more than 10 tissues, and identified a comprehensive list of 419 maize microRNA (miRNA) genes, 271 of which were newly discovered in this study. We further characterized the evolutionary dynamics and tissue specificity of miRNA genes and corresponding miRNA isoforms (isomiRs). Our analysis revealed that tissue specificity of isomiR events tends to be associated with miRNA gene abundance and suggested that the frequencies of isomiR types are affected by the local genomic regions. Moreover, genome duplication (GD) events have dramatic effect on evolutionary dynamics of maize miRNA genes, and the abundance divergence for tissue-specific miRNA genes is associated with GD events. Further study indicated that duplicate miRNA genes with tissue-specific expression patterns, such as miR2275a, a phased siRNA (phasiRNA) trigger, contribute to phenotypic traits in maize. Additionally, our study revealed the expression preference of 21- and 24-nt phasiRNAs in relation to tissue specificity. This large-scale sRNAomic study depicted evolutionary implications of tissue-specific maize sRNAs, which coordinate genome duplication, isomiR modification, phenotypic traits and phasiRNAs differentiation.