Miracle-dz/UniFAR-FTUs huggingface dataset:
One-stage Modality Distillation for Incomplete Multimodal Learning

Learning based on multimodal data has attracted increasing interest recently. While a variety of sensory modalities can be collected for training, not all of them are always available in development scenarios, which raises the challenge to infer with incomplete modality. To address this issue, this paper presents a one-stage modality distillation framework that unifies the privileged knowledge transfer and modality information fusion into a single optimization procedure via multi-task learning. Compared with the conventional modality distillation that performs them independently, this helps to capture the valuable representation that can assist the final model inference directly. Specifically, we propose the joint adaptation network for the modality transfer task to preserve the privileged information. This addresses the representation heterogeneity caused by input discrepancy via the joint distribution adaptation. Then, we introduce the cross translation network for the modality fusion task to aggregate the restored and available modality features. It leverages the parameters-sharing strategy to capture the cross-modal cues explicitly. Extensive experiments on RGB-D classification and segmentation tasks demonstrate the proposed multimodal inheritance framework can overcome the problem of incomplete modality input in various scenes and achieve state-of-the-art performance.
. (2020). Co‐designing a virtual world with young people to deliver social cognition therapy in early psychosis. Early Intervention in Psychiatry, 14(1), 37-43.

Aims: Co-design implies genuine partnership in the generation of knowledge between service users and researchers. Service user involvement in research has been encouraged in government policy, but it is rarely achieved, especially at trial initial stages. Co-designed with service users, we adapted existing manualised social cognition intervention for people with a first episode of psychosis to a virtual world environment
A Simple Method for the Performance Analysis of Fluid Antenna Systems Under Correlated Nakagami-m Fading

By recognizing the tremendous flexibility of the emerging fluid antenna system (FAS), which allows dynamic reconfigurability of the location of the antenna within a given space, this letter investigates the performance of a single-antenna FAS over spatially correlated Nakagami- ${m}$ fading channels. Specifically, simple and highly accurate closed-form approximations for the cumulative density function of the FAS channel and the outage probability of the proposed system are obtained by employing a novel asymptotic matching method, which is an improved version of the well-known moment matching. With this method, the outage probability can be computed simply without incurring complex multi-fold integrals, thus requiring negligible computational effort. Finally, the accuracy of the proposed approximations is validated, and it is shown that the FAS can meet or even exceed the performance attained by the conventional maximal ratio combining (MRC) technique.
Drug-induced liver injury associated with elexacaftor/tezacaftor/ivacaftor from the FDA Adverse Event Reporting System (FAERS)

Introduction: The efficacy and safety of elexacaftor/tezacaftor/ivacaftor (ETI) have been established in prospective clinical trials. Liver function test elevations were observed in a greater proportion of patients receiving ETI compared with placebo; however, the relatively small number of patients and short duration of study preclude detection of rare but clinically significant associations with drug-induced liver injury (DILI). To address this gap, we assessed the real-world risk of DILI associated with ETI through data mining of the FDA Adverse Event Reporting System (FAERS). Methods: Disproportionality analyses were conducted on FAERS data from the fourth quarter of 2019 through the third quarter of 2022. Comparative patient demographics, onset time and outcomes for ETI-DILI were also obtained. Results: 452 reports of DILI associated with ETI were found, representing 2.1% of all adverse event reports for ETI. All disproportionality measures were significant for ETI-DILI at p < 0.05; the reporting odds ratio (ROR) was comparable to that of drugs classified by FDA as "Most-DILI concern". The most notable demographic finding was a male majority for ETI-DILI compared to a female majority for non ETI-DILI. Median ETI-DILI onset time was 50.5 days, and hospitalization was the second most common complication. Conclusion: Using FAERS data, ETI was found to be disproportionality associated with DILI. Future research is needed to investigate the hepatotoxic mechanisms and assess potential mitigation strategies for ETI-induced hepatotoxicity.
Replacing softmax with ReLU in Vision Transformers

Previous research observed accuracy degradation when replacing the attention softmax with a point-wise activation such as ReLU. In the context of vision transformers, we find that this degradation is mitigated when dividing by sequence length. Our experiments training small to large vision transformers on ImageNet-21k indicate that ReLU-attention can approach or match the performance of softmax-attention in terms of scaling behavior as a function of compute.
Novel bayesian multiscale methods for image denoising using alpha - stable distributions

Ο απώτeρος σκοπός της έρeυνας που παρουσιάζeται σe αυτή τη διδακτορική διατριβή eίναι η διάθeση στην κοινότητα των κλινικών eπιστημόνων μeθόδων οι οποίeς να παρέχουν την καλύτeρη δυνατή πληροφορία για να γίνeι μια σωστή ιατρική διάγνωση. Οι eικόνeς υπeρήχων προσβάλλονται eνδογeνώς από θόρυβο, ο οποίος οφeίλeται στην διαδικασία δημιουργίας των eικόνων μέσω ακτινοβολίας που χρησιμοποιeί σύμφωνeς κυματομορφές. Eίναι σημαντικό πριν τη διαδικασία ανάλυσης της eικόνας να γίνeται απάλeιψη του θορύβου μe κατάλληλο τρόπο ώστe να διατηρeίται η υφή της eικόνας, η οποία βοηθά στην διάκριση eνός ιστού από έναν άλλο. Κύριος στόχος της διατριβής αυτής υπήρξe η ανάπτυξη νέων μeθόδων καταστολής του θορύβου σe ιατρικές eικόνeς υπeρήχων στο πeδίο του μeτασχηματισμού κυματιδίων. Αρχικά αποδeίξαμe μέσω eκτeνών πeιραμάτων μοντeλοποίησης, ότι τα δeδομένα που προκύπτουν από τον διαχωρισμό των eικόνων υπeρήχων σe υποπeριοχές συχνοτήτων πeριγράφονται eπακριβώς από μη-γκαουσιανές κατανομές βαρέων ουρών, όπως eίναι οι άλφα-eυσταθeίς κατανομές. Κατόπιν, αναπτύξαμe Μπeϋζιανούς eκτιμητές που αξιοποιούν αυτή τη στατιστική πeριγραφή. Πιο συγκeκριμένα, χρησιμοποιήσαμe το άλφα-eυσταθές μοντέλο για να σχeδιάσουμe eκτιμητές eλάχιστου απόλυτου λάθος και μέγιστης eκ των υστέρων πιθανότητας για άλφα-eυσταθή σήματα αναμeμeιγμένα μe μη-γκαουσιανό θόρυβο. Οι eπeξeργαστές αφαίρeσης θορύβου που προέκυψαν eπeνeργούν κατά μη-γραμμικό τρόπο στα δeδομένα και συσχeτίζουν μe βέλτιστο τρόπο αυτή την μη-γραμμικότητα μe τον βαθμό κατά τον οποίο τα δeδομένα eίναι μη-γκαουσιανά. Συγκρίναμe τις τeχνικές μας μe κλασσικά φίλτρα καθώς και σύγχρονeς μeθόδους αυστηρού και μαλακού κατωφλίου eφαρμόζοντάς τeς σe πραγματικές ιατρικές eικόνeς υπeρήχων και ποσοτικοποιήσαμe την απόδοση που eπιτeύχθηκe. Τέλος, δeίξαμe ότι οι προτeινόμeνοι eπeξeργαστές μπορούν να βρουν eφαρμογές και σe άλλeς πeριοχές eνδιαφέροντος και eπιλέξαμe ως eνδeικτικό παράδeιγμα την πeρίπτωση eικόνων ραντάρ συνθeτικής διατομής.
HM-Conformer: A Conformer-based audio deepfake detection system with hierarchical pooling and multi-level classification token aggregation methods

Audio deepfake detection (ADD) is the task of detecting spoofing attacks generated by text-to-speech or voice conversion systems. Spoofing evidence, which helps to distinguish between spoofed and bona-fide utterances, might exist either locally or globally in the input features. To capture these, the Conformer, which consists of Transformers and CNN, possesses a suitable structure. However, since the Conformer was designed for sequence-to-sequence tasks, its direct application to ADD tasks may be sub-optimal. To tackle this limitation, we propose HM-Conformer by adopting two components: (1) Hierarchical pooling method progressively reducing the sequence length to eliminate duplicated information (2) Multi-level classification token aggregation method utilizing classification tokens to gather information from different blocks. Owing to these components, HM-Conformer can efficiently detect spoofing evidence by processing various sequence lengths and aggregating them. In experimental results on the ASVspoof 2021 Deepfake dataset, HM-Conformer achieved a 15.71% EER, showing competitive performance compared to recent systems.
About discrete subgroups of full groups of measure preserving equivalence relations

In this note we study countable subgroups of the full group of a measure preserving equivalence relation. We provide various constraints on the group structure, the nature of the action, and on the measure of fixed point sets, that imply that the subgroup topology is not discrete. We mention various conjectures about discrete subgroups of full groups.
Data Distribution Bottlenecks in Grounding Language Models to Knowledge Bases

Language models (LMs) have already demonstrated remarkable abilities in understanding and generating both natural and formal language. Despite these advances, their integration with real-world environments such as large-scale knowledge bases (KBs) remains an underdeveloped area, affecting applications such as semantic parsing and indulging in"hallucinated"information. This paper is an experimental investigation aimed at uncovering the robustness challenges that LMs encounter when tasked with knowledge base question answering (KBQA). The investigation covers scenarios with inconsistent data distribution between training and inference, such as generalization to unseen domains, adaptation to various language variations, and transferability across different datasets. Our comprehensive experiments reveal that even when employed with our proposed data augmentation techniques, advanced small and large language models exhibit poor performance in various dimensions. While the LM is a promising technology, the robustness of the current form in dealing with complex environments is fragile and of limited practicality because of the data distribution issue. This calls for future research on data collection and LM learning paradims.
On the formation of singularities for the slightly supercritical NLS equation with nonlinear damping

We consider the focusing, mass-supercritical NLS equation augmented with a nonlinear damping term. We provide sufficient conditions on the nonlinearity exponents and damping coefficients for finite-time blow-up. In particular, singularities are formed for focusing and dissipative nonlinearities of the same power, provided that the damping coefficient is sufficiently small. Our result thus rigorously proves the non-regularizing effect of nonlinear damping in the mass-supercritical case, which was suggested by previous numerical and formal results. We show that, under our assumption, the damping term may be controlled in such a way that the self-similar blow-up structure for the focusing NLS is approximately retained even within the dissipative evolution. The nonlinear damping contributes as a forcing term in the equation for the perturbation around the self-similar profile, that may produce a growth over finite time intervals. We estimate the error terms through a modulation analysis and a careful control of the time evolution of total momentum and energy functionals.
How to Handle Different Types of Out-of-Distribution Scenarios in Computational Argumentation? A Comprehensive and Fine-Grained Field Study

The advent of pre-trained Language Models (LMs) has markedly advanced natural language processing, but their efficacy in out-of-distribution (OOD) scenarios remains a significant challenge. Computational argumentation (CA), modeling human argumentation processes, is a field notably impacted by these challenges because complex annotation schemes and high annotation costs naturally lead to resources barely covering the multiplicity of available text sources and topics. Due to this data scarcity, generalization to data from uncovered covariant distributions is a common challenge for CA tasks like stance detection or argument classification. This work systematically assesses LMs' capabilities for such OOD scenarios. While previous work targets specific OOD types like topic shifts or OOD uniformly, we address three prevalent OOD scenarios in CA: topic shift, domain shift, and language shift. Our findings challenge the previously asserted general superiority of in-context learning (ICL) for OOD. We find that the efficacy of such learning paradigms varies with the type of OOD. Specifically, while ICL excels for domain shifts, prompt-based fine-tuning surpasses for topic shifts. To sum up, we navigate the heterogeneity of OOD scenarios in CA and empirically underscore the potential of base-sized LMs in overcoming these challenges.
Title: The mechanical function of the tibialis posterior muscle and its tendon during locomotion

The tibialis posterior (TP) muscle is believed to provide mediolateral stability of the subtalar joint during the stance phase of walking as it actively lengthens to resist pronation at foot contact and then actively shortens later in stance to contribute to supination. Because of its anatomical structure of short muscle fibers and long series elastic tissue, we hypothesised that TP would be a strong candidate for energy storage and return. We investigated the potential elastic function of the TP muscle and tendon through simultaneous measurements of muscle fascicle length (ultrasound), muscle tendon unit length (musculoskeletal modeling) and muscle activation (intramuscular electromyography). In early stance, TP fascicles actively shortened as the entire muscle-tendon unit lengthened, resulting in the absorption of energy through stretch of the series elastic tissue. Energy stored in the tendinous tissue from early stance was maintained during mid-stance, although a small amount of energy may have been absorbed via minimal shortening in the series elastic elements and lengthening of TP fascicles. A significant amount of shortening occurred in both the fascicles and muscle-tendon unit in late stance, as the activation of TP decreased and power was generated. The majority of the shortening was attributable to shortening of the tendinous tissue. We conclude that the tendinous tissue of TP serves two primary functions during walking: 1) to buffer the stretch of its fascicles during early stance and 2) to enhance the efficiency of the TP through absorption and return of elastic strain energy.
SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems

Membership inference attacks allow adversaries to determine whether a particular example was contained in the model's training dataset. While previous works have confirmed the feasibility of such attacks in various applications, none has focused on speaker recognition (SR), a promising voice-based biometric recognition technique. In this work, we propose SLMIA-SR, the first membership inference attack tailored to SR. In contrast to conventional example-level attack, our attack features speaker-level membership inference, i.e., determining if any voices of a given speaker, either the same as or different from the given inference voices, have been involved in the training of a model. It is particularly useful and practical since the training and inference voices are usually distinct, and it is also meaningful considering the open-set nature of SR, namely, the recognition speakers were often not present in the training data. We utilize intra-similarity and inter-dissimilarity, two training objectives of SR, to characterize the differences between training and non-training speakers and quantify them with two groups of features driven by carefully-established feature engineering to mount the attack. To improve the generalizability of our attack, we propose a novel mixing ratio training strategy to train attack models. To enhance the attack performance, we introduce voice chunk splitting to cope with the limited number of inference voices and propose to train attack models dependent on the number of inference voices. Our attack is versatile and can work in both white-box and black-box scenarios. Additionally, we propose two novel techniques to reduce the number of black-box queries while maintaining the attack performance. Extensive experiments demonstrate the effectiveness of SLMIA-SR.
Transforming growth factor‐β, Interleukin‐23 and interleukin‐1β modulate TH22 response during active multidrug‐resistant tuberculosis

We previously reported that patients with multidrug‐resistant tuberculosis (MDR‐TB) showed low systemic and Mtb‐induced Th22 responses associated to high sputum bacillary load and severe lung lesions suggesting that Th22 response could influence the ability of these patients to control bacillary growth and tissue damage. In MDR‐TB patients, the percentage of IL‐22+ cells inversely correlates with the proportion of senescent PD‐1+ T cells. Herein, we aimed to evaluate the pathways involved on the regulation of systemic and Mtb‐induced Th22 response in MDR‐TB and fully drug‐susceptible TB patients (S‐TB) and healthy donors. Our results show that while IL‐1β and IL‐23 promote Mtb‐induced IL‐22 secretion and expansion of IL‐22+ cells, TGF‐β inhibits this response. Systemic and in vitro Mtb‐induced Th22 response inversely correlates with TGF‐β amounts in plasma and in PBMC cultures respectively. The number of circulating PD‐1+ T cells directly correlates with plasmatic TGF‐β levels and blockade of PD‐1/PD‐L1 signalling enhances in vitro Mtb‐induced expansion of IL‐22+ cells. Thus, TGF‐β could also inhibit Th22 response through upregulation of PD‐1 expression in T cells. Higher percentage of IL‐23+ monocytes was observed in TB patients. In contrast, the proportion of IL‐1β+ monocytes was lower in TB patients with bilateral lung cavities (BCC) compared to those patients with unilateral cavities (UCC). Interestingly, TB patients with BCC showed higher plasmatic and Mtb‐induced TGF‐β secretion than patients with UCC. Thus, high TGF‐β secretion and subtle differences in IL‐23 and IL‐1β expression could diminish systemic and in vitro Mtb‐induced Th22 response along disease progression in TB patients.
TAZ facilitates breast tumor growth by promoting an immune‐suppressive tumor microenvironment

The core Hippo pathway module consists of a tumour‐suppressive kinase cascade that inhibits the transcriptional coactivators Yes‐associated protein (YAP) and WW domain‐containing transcription regulator protein 1 (WWTR1; also known as TAZ). When the Hippo pathway is downregulated, as often occurs in breast cancer, YAP/TAZ activity is induced. To elaborate the roles of TAZ in triple‐negative breast cancer (TNBC), we depleted Taz in murine TNBC 4T1 cells, using either CRISPR/Cas9 or small hairpin RNA (shRNA). TAZ‐depleted cells and their controls, harbouring wild‐type levels of TAZ, were orthotopically injected into the mammary fat pads of syngeneic BALB/c female mice, and mice were monitored for tumour growth. TAZ depletion resulted in smaller tumours compared to the tumours generated by control cells, in line with the notion that TAZ functions as an oncogene in breast cancer. Tumours, as well as their corresponding in vitro cultured cells, were then subjected to gene expression profiling by RNA sequencing (RNA‐seq). Interestingly, pathway analysis of the RNA‐seq data indicated a TAZ‐dependent enrichment of ‘Inflammatory Response’, a pathway correlated with TAZ expression levels also in human breast cancer tumours. Specifically, the RNA‐seq analysis predicted a significant depletion of regulatory T cells (Tregs) in TAZ‐deficient tumours, which was experimentally validated by the staining of tumour sections and by quantitative cytometry by time of flight (CyTOF). Strikingly, the differences in tumour size were completely abolished in immune‐deficient mice, demonstrating that the immune‐modulatory capacity of TAZ is critical for its oncogenic activity in this setting. Cytokine array analysis of conditioned medium from cultured cells revealed that TAZ increased the abundance of a small group of cytokines, including plasminogen activator inhibitor 1 (Serpin E1; also known as PAI‐1), CCN family member 4 (CCN4; also known as WISP‐1) and interleukin‐23 (IL‐23), suggesting a potential mechanistic explanation for its in vivo immunomodulatory effect. Together, our results imply that TAZ functions in a non‐cell‐autonomous manner to modify the tumour immune microenvironment and dampen the anti‐tumour immune response, thereby facilitating tumour growth.
A Bayesian Approach to Robust Inverse Reinforcement Learning

We consider a Bayesian approach to offline model-based inverse reinforcement learning (IRL). The proposed framework differs from existing offline model-based IRL approaches by performing simultaneous estimation of the expert's reward function and subjective model of environment dynamics. We make use of a class of prior distributions which parameterizes how accurate the expert's model of the environment is to develop efficient algorithms to estimate the expert's reward and subjective dynamics in high-dimensional settings. Our analysis reveals a novel insight that the estimated policy exhibits robust performance when the expert is believed (a priori) to have a highly accurate model of the environment. We verify this observation in the MuJoCo environments and show that our algorithms outperform state-of-the-art offline IRL algorithms.
Learning in the Dark: Privacy-Preserving Machine Learning using Function Approximation

Over the past few years, a tremendous growth of machine learning was brought about by a significant increase in adoption and implementation of cloud-based services. As a result, various solutions have been proposed in which the machine learning models run on a remote cloud provider and not locally on a user's machine. However, when such a model is deployed on an untrusted cloud provider, it is of vital importance that the users' privacy is preserved. To this end, we propose Learning in the Dark -- a hybrid machine learning model in which the training phase occurs in plaintext data, but the classification of the users' inputs is performed directly on homomorphically encrypted ciphertexts. To make our construction compatible with homomorphic encryption, we approximate the ReLU and Sigmoid activation functions using low-degree Chebyshev polynomials. This allowed us to build Learning in the Dark -- a privacy-preserving machine learning model that can classify encrypted images with high accuracy. Learning in the Dark preserves users' privacy since it is capable of performing high accuracy predictions by performing computations directly on encrypted data. In addition to that, the output of Learning in the Dark is generated in a blind and therefore privacy-preserving way by utilizing the properties of homomorphic encryption.