Miracle-dz/UniFAR-FTUs huggingface dataset:
Bilateral Metastatic Orbital Neuroblastoma with Unusual Clinico-radiologic Manifestation

Neuroblastoma like most primitive neoplasm, primarily affects children under five years. It is uncommon in adolescents and adults. We present a 13-year-old male adolescent with a three-month history of bilateral painful progressive proptosis associated with gradual loss of vision. A contrast-enhanced Computed Tomography scan of the chest and abdomen showed an unusually symmetrical bilateral paravertebral soft tissue masses extending from the cervical to the lumbosacral spines, seamlessly merging with the psoas muscle. A tissue biopsy from the orbital mass reported neuroblastoma. He improved significantly three months following chemotherapy and has been stable during follow up. This case report seeks to demonstrate the many variations in the clinico-radiologic presentation of neuroblastoma and the need for a multidisciplinary approach to patient care, especially in resource-poor setting to ensure good outcome. -
Hybrid Method for the Seismic Vulnerability Assessment of Historic Masonry City Centres

Buildings in historical city centres are particularly vulnerable to seismic events, as observed from past earthquakes in Bhuj (India, 2001), Bam (Iran, 2003), Sichuan (China 2008), L’Aquila (Italy 2009), Gorkha (Nepal 2015), and most recently in Amatrice (Italy 2016). To reduce damage and loss in historic city centres, seismic vulnerability of buildings should be assessed on site with approaches that take into account how parameters related to geometric/structural features, deficiencies and seismic damage impact on seismic failure modes. Currently, an approach that investigates all these parameters in only one consistent procedure for the identification of failure modes is not available, although, there are numerous methods developed to evaluate the seismic vulnerability. This dissertation aims to tackle such a gap by developing a hybrid approach that investigates causes of failure modes through an analytical method and effects of failure modes through a visual inspection tool. The analytical method proposed in the hybrid approach is FaMIVE (Failure Mechanism Identification and Vulnerability Evaluation method), mechanical method that estimates failure modes, capacity and fragility curves by taking into account geometric/structural features and deficiencies of buildings. The second method is LOG-IDEAH (LOGic trees for Identification of Damage due to Earthquakes for Architectural Heritage) web knowledge based system that complements FaMIVE by estimating failure modes through engineering judgements codified in rules developed to interpret seismic damage observed on buildings. The hybrid approach is validated on the city centres of L’Aquila (Italy) damaged by the earthquake in 2009, characterised by typical historic masonry houses. Through the application, the thesis provides a validation approach that is used to refine the framework of the proposed hybrid method and to provide guidance for those facing the challenge of assessing the seismic vulnerability of historic city centres.
Turn-taking in English

In conversation each participant has their own way to get the floor which can be defined as the right to speak. However, there will be only one participant who speaks at a time. Since there will be two or more participants in a conversation, one could not estimate who will take the turn because each participant has the same right to speak. Therefore, each participant has the same opportunity to take the turn. The exchange of turn occurs orderly. The reason is that there are rules which govern their conversation. When someone talks, the others will listen to him and wait for their turns. Those rules are called turn taking system. This paper consists of two chapters. Chaptre one deals with defintions of turn-taking,organization of turn-taking,gender and turn-taking,and formal or informal turn-taking.Chapter two diccuses turn-taking in spoken dialog systems, principled approaches to optimizing turn-taking behavior,and signals in turn-taking. Finally, the conclusion sums up the findings of the study.
Minimality of $ \mathfrak{B} $-free systems in number fields

Let $K$ be a finite extension of $\mathbb{Q}$ and $\mathcal{O}_K$ be its ring of integers. Let $\mathfrak{B}$ be a primitive collection of ideals in $\mathcal{O}_K$. We show that any $\mathfrak{B}$-free system is essentially minimal. Moreoever, the $\mathfrak{B}$-free system is minimal if and only if the characteristic function of $\mathfrak{B}$-free numbers is a Toeplitz sequence. Equivalently, there are no ideal $\mathfrak{d}$ and no infinite pairwise coprime collection of ideals $\mathcal{C}$ such that $\mathfrak{d}\mathcal{C}\subseteq\mathfrak{B}$. Moreover, we find a periodic structure in the Toeplitz case. Last but not least, we describe the restrictions on the cosets of ideals contained in unions of ideals.
Typical perturbation theory: Conditions, accuracy, and comparison with a mesoscopic case.

The perturbation theory based on typicality introduced by Dabelow and Reimann [Phys. Rev. Lett. 124, 120602 (2020)0031-900710.1103/PhysRevLett.124. 120602] and further refined by Dabelow et al. [Phys. Rev. Res. 2, 033210 (2020)2643-156410.1103/PhysRevResearch.2. 033210; J. Stat. Mech. (2021) 0131061742-546810.1088/1742-5468/abd026] provides a powerful tool since it is intended to be applicable to a wide range of scenarios while relying on only a few parameters. Even though the authors present various examples to demonstrate the effectiveness of the theory, the conditions used in its derivation are often not thoroughly checked. It is argued that this is justified (without analytical reasoning) by the robustness of the theory. In the paper at hand, said perturbation theory is tested on three spin-based models. The following criteria are taken into focus: the fulfillment of the conditions, the accuracy of the predicted dynamics, and the relevance of the results with respect to a mesoscopic case.
Transience and anchored isoperimetric dimension of supercritical percolation clusters

We establish several equivalent characterisations of the anchored isoperimetric dimension of supercritical clusters in Bernoulli bond percolation on transitive graphs. We deduce from these characterisations together with a theorem of Duminil-Copin, Goswami, Raoufi, Severo, and Yadin that if $G$ is a transient transitive graph then the infinite clusters of Bernoulli percolation on $G$ are transient for $p$ sufficiently close to $1$. It remains open to extend this result down to the critical probability. Along the way we establish two new cluster repulsion inequalities that are of independent interest.
A new palladium alloy with near-ideal hydrogen storage performance

Hydrogen-based fuels demand high-density storage that can operate under ambient temperatures. Pd and its alloys are the most investigated metal hydrides for hydrogen fuel cell applications. This study presented an alternative Pd alloy for hydrogen storage that can store and release hydrogen at room temperature. The surface of the most studied Pd (110) was modified with Au and Rh so that the hydrogen adsorption energy was 0.49 eV and the release temperature was 365 K. Both values are quite near to the optimum values for the adsorption energy and release temperature of a hydrogen fuel cell in real-world usage.
Building Korean Sign Language Augmentation (KoSLA) Corpus with Data Augmentation Technique

We present an efﬁcient framework of corpus for sign language translation. Aided with a simple but dramatic data augmentation technique, our method converts text into annotated forms with minimum information loss. Sign languages are composed of manual signals, non-manual signals, and iconic features. According to professional sign language interpreters, non-manual signals such as facial expressions and gestures play an important role in conveying exact meaning. By considering the linguistic features of sign language, our proposed framework is a ﬁrst and unique attempt to build a multimodal sign language augmentation corpus (hereinafter referred to as the KoSLA corpus) containing both manual and non-manual modalities. The corpus we built demonstrates conﬁdent results in the hospital context, showing improved performance with augmented datasets. To overcome data scarcity, we resorted to data augmentation techniques such as synonym replacement to boost the efﬁciency of our translation model and available data, while maintaining grammatical and semantic structures of sign language. For the experimental support, we ver-ify the effectiveness of data augmentation technique and usefulness of our corpus by performing a translation task between normal sentences and sign language annotations on two tokenizers. The result was convincing, proving that the BLEU scores with the KoSLA corpus were signiﬁcant.
Constraints on Moon's Orbit 3.2 Billion Years Ago From Tidal Bundle Data

The angular momentum of the Earth‐Moon system was initially dominated by Earth's rotation with a short solar day of around 5 hr duration. Since then, Earth gradually transferred angular momentum through tidal friction to the orbit of the Moon, resulting in an increasing orbital radius and a deceleration of Earth's rotation. Geologic observations of tidal deposits can be used to verify and constrain models of lunar orbital evolution. In this work we reexamine the oldest tidal record suitable for analysis from the Moodies Group, South Africa, with an age of 3.22 billion years. Time frequency analysis of the series of thicknesses of the sandstone‐shale layers yields a periodicity of 15.0 layers, taking into account the possibility of missing laminae. Assuming a mixed tidal system, the duration of two neap‐spring‐neap cycles was 30.0 lunar days for dominant semidiurnal or 30.0 sidereal days for dominant diurnal tides. We derive the relationship between this observation and the past Earth‐Moon distance and re‐visit related published work. We find that the Earth‐Moon distance 3.2 billion years ago was about 70% of today's value. The Archean solar day was around 13 hr long. The ratio of solar to lunar tide‐raising torque controls the leakage of angular momentum from the Earth‐Moon system, but deviation from the assumed ratio of 0.211 results in only moderate changes. A duration of a postulated 21‐hr atmospheric resonance shorter than 200 million years would be consistent with our observation; it would significantly alter the Earth‐Moon distance.
Multitask Learning From Augmented Auxiliary Data for Improving Speech Emotion Recognition

Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems lack generalisation across different conditions. A key underlying reason for poor generalisation is the scarcity of emotion datasets, which is a significant roadblock to designing robust machine learning (ML) models. Recent works in SER focus on utilising multitask learning (MTL) methods to improve generalisation by learning shared representations. However, most of these studies propose MTL solutions with the requirement of meta labels for auxiliary tasks, which limits the training of SER systems. This paper proposes an MTL framework (MTL-AUG) that learns generalised representations from augmented data. We utilise augmentation-type classification and unsupervised reconstruction as auxiliary tasks, which allow training SER systems on augmented data without requiring any meta labels for auxiliary tasks. The semi-supervised nature of MTL-AUG allows for the exploitation of the abundant unlabelled data to further boost the performance of SER. We comprehensively evaluate the proposed framework in the following settings: (1) within corpus, (2) cross-corpus and cross-language, (3) noisy speech, (4) and adversarial attacks. Our evaluations using the widely used IEMOCAP, MSP-IMPROV, and EMODB datasets show improved results compared to existing state-of-the-art methods.
Dual Contrastive Learning for Spatio-temporal Representation

Contrastive learning has shown promising potential in self-supervised spatio-temporal representation learning. Most works naively sample different clips to construct positive and negative pairs. However, we observe that this formulation inclines the model towards the background scene bias. The underlying reasons are twofold. First, the scene difference is usually more noticeable and easier to discriminate than the motion difference. Second, the clips sampled from the same video often share similar backgrounds but have distinct motions. Simply regarding them as positive pairs will draw the model to the static background rather than the motion pattern. To tackle this challenge, this paper presents a novel dual contrastive formulation. Concretely, we decouple the input RGB video sequence into two complementary modes, static scene and dynamic motion. Then, the original RGB features are pulled closer to the static features and the aligned dynamic features, respectively. In this way, the static scene and the dynamic motion are simultaneously encoded into the compact RGB representation. We further conduct the feature space decoupling via activation maps to distill static- and dynamic-related features. We term our method as Dual Contrastive Learning for spatio-temporal Representation (DCLR). Extensive experiments demonstrate that DCLR learns effective spatio-temporal representations and obtains state-of-the-art or comparable performance on UCF-101, HMDB-51, and Diving-48 datasets.
A Baseline for Detecting Out-of-Distribution Examples in Image Captioning

Image captioning research achieved breakthroughs in recent years by developing neural models that can generate diverse and high-quality descriptions for images drawn from the same distribution as training images. However, when facing out-of-distribution (OOD) images, such as corrupted images, or images containing unknown objects, the models fail in generating relevant captions. In this paper, we consider the problem of OOD detection in image captioning. We formulate the problem and suggest an evaluation setup for assessing the model's performance on the task. Then, we analyze and show the effectiveness of the caption's likelihood score at detecting and rejecting OOD images, which implies that the relatedness between the input image and the generated caption is encapsulated within the score.
Regression Metric Loss: Learning a Semantic Representation Space for Medical Images

Regression plays an essential role in many medical imaging applications for estimating various clinical risk or measurement scores. While training strategies and loss functions have been studied for the deep neural networks in medical image classification tasks, options for regression tasks are very limited. One of the key challenges is that the high-dimensional feature representation learned by existing popular loss functions like Mean Squared Error or L1 loss is hard to interpret. In this paper, we propose a novel Regression Metric Loss (RM-Loss), which endows the representation space with the semantic meaning of the label space by finding a representation manifold that is isometric to the label space. Experiments on two regression tasks, i.e. coronary artery calcium score estimation and bone age assessment, show that RM-Loss is superior to the existing popular regression losses on both performance and interpretability. Code is available at https://github.com/DIAL-RPI/Regression-Metric-Loss.
TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Data

Synthetic tabular data generation becomes crucial when real data is limited, expensive to collect, or simply cannot be used due to privacy concerns. However, producing good quality synthetic data is challenging. Several probabilistic, statistical, and generative adversarial networks (GANs) based approaches have been presented for synthetic tabular data generation. Once generated, evaluating the quality of the synthetic data is quite challenging. Some of the traditional metrics have been used in the literature but there is lack of a common, robust, and single metric. This makes it difficult to properly compare the effectiveness of different synthetic tabular data generation methods. In this paper we propose a new universal metric, TabSynDex, for robust evaluation of synthetic data. TabSynDex assesses the similarity of synthetic data with real data through different component scores which evaluate the characteristics that are desirable for"high quality"synthetic data. Being a single score metric, TabSynDex can also be used to observe and evaluate the training of neural network based approaches. This would help in obtaining insights that was not possible earlier. Further, we present several baseline models for comparative analysis of the proposed evaluation metric with existing generative models.
On preservation of set stability under input delays for nonlinear systems with disturbances

— The study demonstrates the preservation of set-input-to-state stability under a small input time delay for compact, invariant sets that contain the origin. A Razumikhin-type theorem that ensures input-to-state stability (ISS) for sets in functional differential equations (FDEs) with disturbances is proved using the nonlinear small-gain theory. These results are essential in fulﬁlling the study’s goal. In comparison to the existing research on set-ISS robustness with respect to small time delays at the input, the results obtained are rather broad, retaining the ISS gain and without any constraints on time delayed states. Illustrative examples are used to show the advantages of the result.
Hennessy-Milner Theorems via Galois Connections

We introduce a general and compositional, yet simple, framework that allows us to derive soundness and expressiveness results for modal logics characterizing behavioural equivalences or metrics (also known as Hennessy-Milner theorems). It is based on Galois connections between sets of (real-valued) predicates on the one hand and equivalence relations/metrics on the other hand and covers a part of the linear-time-branching-time spectrum, both for the qualitative case (behavioural equivalences) and the quantitative case (behavioural metrics). We derive behaviour functions from a given logic and give a condition, called compatibility, that characterizes under which conditions a logically induced equivalence/metric is induced by a fixpoint equation. In particular this framework allows us to derive a new fixpoint characterization of directed trace metrics.
Robust Key-Frame Stereo Visual SLAM with low-threshold Point and Line Features

—In this paper, we develop a robust, efﬁcient visual SLAM system that utilizes spatial inhibition of low threshold, baseline lines, and closed-loop keyframe features. Using ORB- SLAM2, our methods include stereo matching, frame tracking, local bundle adjustment, and line and point global bundle adjustment. In particular, we contribute re-projection in line with the baseline. Fusing lines in the system consume colossal time, and we reduce the time from distributing points to utilizing spatial suppression of feature points. In addition, low threshold key points can be more effective in dealing with low textures. In order to overcome Tracking keyframe redundant problems, an efﬁcient and robust closed-loop tracking key frame is proposed. The proposed SLAM has been extensively tested in KITTI and EuRoC datasets, demonstrating that the proposed system is superior to state-of-the-art methods in various scenarios.
Quantifying the high-dimensionality of quantum devices

We introduce a measure of average dimensionality (or coherence) for high-dimensional quantum devices. This includes sets of quantum measurements, steering assemblages, and quantum channels. For measurements and channels, our measure corresponds to an average compression dimension, whereas for quantum steering we get a semi-device independent quantifier for the average entanglement dimensionality known as the Schmidt measure. We analyse the measure in all three scenarios. First, we show that it can be decided via semi-definite programming for channels and measurements in low-dimensional systems. Second, we argue that the resulting steering measure is a high-dimensional generalisation of the well-known steering weight. Finally, we analyse the behaviour of the measure in the asymptotic setting. More precisely, we show that the asymptotic Schmidt measure of bipartite quantum states is equal to the entanglement cost and show how the recently introduced entanglement of formation for steering assemblages can be related to our measure in the asymptotic case.
Size and depth of monotone neural networks: interpolation and approximation

Monotone functions and data sets arise in a variety of applications. We study the interpolation problem for monotone data sets: The input is a monotone data set with $n$ points, and the goal is to find a size and depth efficient monotone neural network, with non negative parameters and threshold units, that interpolates the data set. We show that there are monotone data sets that cannot be interpolated by a monotone network of depth $2$. On the other hand, we prove that for every monotone data set with $n$ points in $\mathbb{R}^d$, there exists an interpolating monotone network of depth $4$ and size $O(nd)$. Our interpolation result implies that every monotone function over $[0,1]^d$ can be approximated arbitrarily well by a depth-4 monotone network, improving the previous best-known construction of depth $d+1$. Finally, building on results from Boolean circuit complexity, we show that the inductive bias of having positive parameters can lead to a super-polynomial blow-up in the number of neurons when approximating monotone functions.
Development and Validation of Corynebacterium DNA Microarrays

We have developed DNA microarray techniques for studying Corynebacterium glutamicum . A set of 52 C. glutamicum genes encoding enzymes from primary metabolism was ampliﬁed by PCR and printed in triplicate onto glass slides. Total RNA was extracted from cells harvested during the exponential-growth and lysine production phases of a C. glutamicum fermentation. Fluorescently labeled cDNAs were prepared by reverse transcription using random hexamer primers and hybridized to the microarrays. To establish a set of benchmark metrics for this technique, we compared the variability between replicate spots on the same slide, between slides hybridized with cDNAs from the same labeling reaction, and between slides hybridized with cDNAs prepared in separate labeling reactions. We found that the results were both robust and statistically reproducible. Spot-to-spot variability was 3.8% between replicate spots on a given slide, 5.0% between spots on separate slides (though hybridized with identical, labeled cDNA), and 8.1% between spots from separate slides hybridized with samples from separate reverse transcription reactions yielding an average spot to spot variability of 7.1% across all conditions. Furthermore, when we examined the changes in gene expression that occurred between the two phases of the fermentation, we found that results for the majority of the genes agreed with observations made using other methods. These procedures will be a valuable addition to the metabolic engineering toolbox