Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation of desmoglein 1 and 3 immunohistochemistry with autoantibody levels and clinical severity in pemphigus

Pemphigus is a chronic potentially fatal autoimmune bullous disorder. Pemphigus vulgaris (PV) and pemphigus foliaceus (PF) are the two common subtypes. PV is the most common and aggressive type characterized by oral mucosal erosions and cutaneous lesions. PF presents with blisters on the scalp, face, and upper trunk, and spares the mucosae. Direct immunofluorescence (DIF) is the gold standard for diagnosis. Immunohistochemistry (IHC) is an emerging alternate diagnostic tool. In this study, our objectives were to identify the staining patterns of desmoglein 1 (dsg 1) and desmoglein 3 (dsg 3) IHC and to correlate the same with autoantibody levels and clinical severity in patients with PV and PF.
Syn-eruptive incision of Koko Crater, Oahu, Hawaii by condensed steam and hot cohesive debris flows: a re-interpretation of the type locality of "surge-eroded U-shaped channels"

Phreatomagmatic fall, low-concentration PDC deposits and remobilized equivalents dominate the products of craters (tuff cones/rings) of Koko fissure, south-east Oahu. At Koko crater, Fisher (1977) described "U-shaped" channels, which he interpreted as due to erosion by low-concentration PDCs (surges), with minor modification by stream and debris flows. Similar channels on tuff cones and rings elsewhere in the world have been interpreted as "surge-eroded" by subsequent authors. However, no evidence for erosion by PDCs was observed during recent fieldwork, which suggested rather the following model. An important observation is that initial incision is always correlated with the emplacement of vesiculated ash layers (derived from Hanauma Bay), and is only very rarely associated with other facies. Incision of the vesiculated ash by run-off generated an initial and widespread network of sinuous, narrow (<15 cm) and shallow (<15 cm) rills. The strong correlation of rills with vesiculated ash and the lack of obvious water-escape structures in these ashes implies that run-off was mostly derived from associated steam-rich plumes. Initial steam and rain-fed incision was probably also enhanced in these very fine-grained cohesive deposits as a consequence of lowered infiltration rates. The rill network developed locally into deeper channels (i.e. gullies) during steam and rain-fed run-off, and by significant erosion during emplacement of vesiculated (hot) debris flows, derived from remobilized vesiculated ash. Pyroclastic density currents from Hanuama Bay traveling laterally across the flank of Koko Crater, perpendicular to the gully axes, provided the bulk of the gully fills, but gave rise to little or no modification of their margins. Rill and gully development by rainfall alone could explain similar examples of incision of low-concentration PDC deposits elsewhere in the world, but the possibility of steam-fed rills and erosion by hot debris flows should be considered. Low-concentration PDCs do not seem to be able to erode their substrate in all cases.
What Can Facial Movements Reveal? Depression Recognition and Analysis Based on Optical Flow Using Bayesian Networks

Recent evidence have demonstrated that facial expressions could be a valid and important aspect for depression recognition. Although various works have been achieved in automatic depression recognition, it is a challenge to explore the inherent nuances of facial expressions that might reveal the underlying differences between depressed patients and healthy subjects under different stimuli. There is a lack of an undisturbed system that monitors depressive patients’ mental states in various free-living scenarios, so this paper steps towards building a classification model where data collection, feature extraction, depression recognition and facial actions analysis are conducted to infer the differences of facial movements between depressive patients and healthy subjects. In this study, we firstly present a plan of dividing facial regions of interest to extract optical flow features of facial expressions for depression recognition. We then propose facial movements coefficients utilising discrete wavelet transformation. Specifically, Bayesian Networks equipped with construction of Pearson Correlation Coefficients based on discrete wavelet transformation is learnt, which allows for analysing movements of different facial regions. We evaluate our method on a clinically validated dataset of 30 depressed patients and 30 healthy control subjects, and experiments results obtained the accuracy and recall of 81.7%, 96.7%, respectively, outperforming other features for comparison. Most importantly, the Bayesian Networks we built on the coefficients under different stimuli may reveal some facial action patterns of depressed subjects, which have a potential to assist the automatic diagnosis of depression.
The art of the float

For more than 2000 years, artists have exploited cast shadows to influence how objects appear to be positioned in a scene. A contact cast shadow can anchor an object to the ground and a detached cast shadow can make an object appear to float. However, there is a period of approximately 1000 years when there were virtually no cast shadows in art. How were states of contact versus floating depicted by artists without cast shadows? Here, we survey various techniques used by artists to anchor relative position with and without cast shadows. We then conduct experimental tests of the hypothesized surface attraction principles that underlie these techniques. In the absence of cast shadows, an object (a wooden box) was often seen as resting on a surface as long as that surface offered information about ground orientation and support (a tiled floor). When the ground surface was ambiguous and cloud-like (1/f noise), the box was more likely to be seen to float. The presence of cast shadows made the box appear to contact the ground whether it was well-defined or ambiguous. Both shadows and surface support also increased the accuracy with which participants detected when the box was tilted up from the ground. These results indicate that artists long ago discovered the important power of support relationships to anchor objects to surfaces in the absence of shadows.
Dual-Branch Temperature Scaling Calibration for Long-Tailed Recognition

The calibration for deep neural networks is currently receiving widespread attention and research. Miscalibration usually leads to overconfidence of the model. While, under the condition of long-tailed distribution of data, the problem of miscalibration is more prominent due to the different confidence levels of samples in minority and majority categories, and it will result in more serious overconfidence. To address this problem, some current research have designed diverse temperature coefficients for different categories based on temperature scaling (TS) method. However, in the case of rare samples in minority classes, the temperature coefficient is not generalizable, and there is a large difference between the temperature coefficients of the training set and the validation set. To solve this challenge, this paper proposes a dual-branch temperature scaling calibration model (Dual-TS), which considers the diversities in temperature parameters of different categories and the non-generalizability of temperature parameters for rare samples in minority classes simultaneously. Moreover, we noticed that the traditional calibration evaluation metric, Excepted Calibration Error (ECE), gives a higher weight to low-confidence samples in the minority classes, which leads to inaccurate evaluation of model calibration. Therefore, we also propose Equal Sample Bin Excepted Calibration Error (Esbin-ECE) as a new calibration evaluation metric. Through experiments, we demonstrate that our model yields state-of-the-art in both traditional ECE and Esbin-ECE metrics.
Regret Lower Bounds in Multi-agent Multi-armed Bandit

Multi-armed Bandit motivates methods with provable upper bounds on regret and also the counterpart lower bounds have been extensively studied in this context. Recently, Multi-agent Multi-armed Bandit has gained significant traction in various domains, where individual clients face bandit problems in a distributed manner and the objective is the overall system performance, typically measured by regret. While efficient algorithms with regret upper bounds have emerged, limited attention has been given to the corresponding regret lower bounds, except for a recent lower bound for adversarial settings, which, however, has a gap with let known upper bounds. To this end, we herein provide the first comprehensive study on regret lower bounds across different settings and establish their tightness. Specifically, when the graphs exhibit good connectivity properties and the rewards are stochastically distributed, we demonstrate a lower bound of order $O(\log T)$ for instance-dependent bounds and $\sqrt{T}$ for mean-gap independent bounds which are tight. Assuming adversarial rewards, we establish a lower bound $O(T^{\frac{2}{3}})$ for connected graphs, thereby bridging the gap between the lower and upper bound in the prior work. We also show a linear regret lower bound when the graph is disconnected. While previous works have explored these settings with upper bounds, we provide a thorough study on tight lower bounds.
Venous Thromboembolism during Pregnancy and Postpartum: Study in a Tertiary Hospital

Result: The overall incidence of symptomatic VTE among the study subjects was 0.28% (25/9,041 deliveries), representing an incidence of Deep Venous Thrombosis (DVT) and Pulmonary Thrombo Embolism (PTE) of 0.22% and 0.05%, respectively. During the pregnancy and postpartum periods, 19 and 6 VTE cases, respectively, were observed. The risk of DVT was possibly the highest in the first trimester. All the objectively confirmed PTEs typically presented postpartum or within 26 days of delivery. Further, 96% of VTE cases had one or more large anamnestic risk factor(s). The effect was small, but the incidence of VTE (0.40% vs 0.14%) decreased by 65% during the post-implementation period.
Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes

The large amount of time clinicians spend sifting through patient notes and documenting in electronic health records (EHRs) is a leading cause of clinician burnout. By proactively and dynamically retrieving relevant notes during the documentation process, we can reduce the effort required to find relevant patient history. In this work, we conceptualize the use of EHR audit logs for machine learning as a source of supervision of note relevance in a specific clinical context, at a particular point in time. Our evaluation focuses on the dynamic retrieval in the emergency department, a high acuity setting with unique patterns of information retrieval and note writing. We show that our methods can achieve an AUC of 0.963 for predicting which notes will be read in an individual note writing session. We additionally conduct a user study with several clinicians and find that our framework can help clinicians retrieve relevant information more efficiently. Demonstrating that our framework and methods can perform well in this demanding setting is a promising proof of concept that they will translate to other clinical settings and data modalities (e.g., labs, medications, imaging).
Effekter av olika sätt att producera livsmedel – en inventering av jämförelser mellan ekologisk och konventionell produktion

Ekologisk produktion har totalt sett en mer positiv, eller mindre negativ, inverkan pa miljon. Det pekar fyra omfattande inventeringar, avseende 18 europeiska lander (inklusive Sverige), som jamfor ekologisk och konventionell produktion pa. Att en okad omfattning av den ekologiska produktionen utan tvivel skulle minska de negativa miljoeffekterna och resursanvandningen i jordbruket ar den slutsats som dras i alla de fyra inventeringarna. Bilden som ges av genomforda studier ar dock i nagra avseenden inte entydig. Detta galler till exempel kvavelackage, samt fosfor- och kaliumforsorjning. Inom en del omraden, framfor allt livsmedelskvalitet, djurhalsa och klimatforandringar, kravs ocksa mer forskning for att kunna gora en tillforlitlig jamforelse. For vissa faktorer ar dessutom skillnaderna sma. En allman iakttagelse vad galler effektivitet ar att den ekologiska produktionen utnyttjar mer mark medan den konventionella produktionen utnyttjar mer energi och andra externa insatser. Jordbruksproduktion ar en mycket komplex verksamhet som ar beroende av lika komplexa ekosystem. Forandringar som gors i ett sadant system medfor ofelbart ett antal oavsedda effekter vid sidan om de avsedda.
Topological Properties of Almost Abelian Groups

An almost Abelian Lie group is a non-Abelian Lie group with a codimension 1 Abelian subgroup. We show that all discrete subgroups of complex simply connected almost Abelian groups are finitely generated. The topology of connected almost Abelian Lie groups is studied by expressing each connected almost Abelian Lie group as a quotient of its universal covering group by a discrete normal subgroup. We then prove that no complex connected almost Abelian group is compact, and give conditions for the compactness of connected subgroups of such groups. Towards studying the homotopy type of complex connected almost Abelian groups, we investigate the maximal compact subgroups of such groups.
Improving Audio-Visual Speech Recognition by Lip-Subword Correlation Based Visual Pre-training and Cross-Modal Fusion Encoder

In recent research, slight performance improvement is observed from automatic speech recognition systems to audio-visual speech recognition systems in end-to-end frameworks with low-quality videos. Unmatching convergence rates and specialized input representations between audio-visual modalities are considered to cause the problem. In this paper, we propose two novel techniques to improve audio-visual speech recognition (AVSR) under a pre-training and fine-tuning training framework. First, we explore the correlation between lip shapes and syllable-level subword units in Mandarin through a frame-level subword unit classification task with visual streams as input. The fine-grained subword labels guide the network to capture temporal relationships between lip shapes and result in an accurate alignment between video and audio streams. Next, we propose an audio-guided Cross-Modal Fusion Encoder (CMFE) to utilize main training parameters for multiple cross-modal attention layers to make full use of modality complementarity. Experiments on the MISP2021-AVSR data set show the effectiveness of the two proposed techniques. Together, using only a relatively small amount of training data, the final system achieves better performances than state-of-the-art systems with more complex front-ends and back-ends. The code is released at 1.
KernelWarehouse: Towards Parameter-Efficient Dynamic Convolution

Dynamic convolution learns a linear mixture of $n$ static kernels weighted with their sample-dependent attentions, demonstrating superior performance compared to normal convolution. However, existing designs are parameter-inefficient: they increase the number of convolutional parameters by $n$ times. This and the optimization difficulty lead to no research progress in dynamic convolution that can allow us to use a significant large value of $n$ (e.g., $n>100$ instead of typical setting $n<10$) to push forward the performance boundary. In this paper, we propose $KernelWarehouse$, a more general form of dynamic convolution, which can strike a favorable trade-off between parameter efficiency and representation power. Its key idea is to redefine the basic concepts of"$kernels$"and"$assembling$ $kernels$"in dynamic convolution from the perspective of reducing kernel dimension and increasing kernel number significantly. In principle, KernelWarehouse enhances convolutional parameter dependencies within the same layer and across successive layers via tactful kernel partition and warehouse sharing, yielding a high degree of freedom to fit a desired parameter budget. We validate our method on ImageNet and MS-COCO datasets with different ConvNet architectures, and show that it attains state-of-the-art results. For instance, the ResNet18|ResNet50|MobileNetV2|ConvNeXt-Tiny model trained with KernelWarehouse on ImageNet reaches 76.05%|81.05%|75.52%|82.51% top-1 accuracy. Thanks to its flexible design, KernelWarehouse can even reduce the model size of a ConvNet while improving the accuracy, e.g., our ResNet18 model with 36.45%|65.10% parameter reduction to the baseline shows 2.89%|2.29% absolute improvement to top-1 accuracy.
ConservationBots: Autonomous Aerial Robot for Fast Robust Wildlife Tracking in Complex Terrains

Radio tagging and tracking are fundamental to understanding the movements and habitats of wildlife in their natural environments. Today, the most widespread, widely applicable technology for gathering data relies on experienced scientists armed with handheld radio‐telemetry equipment to locate low‐power radio transmitters attached to wildlife from the ground. Although aerial robots can transform labor‐intensive conservation tasks, the realization of autonomous systems for tackling task complexities under real‐world conditions remains a challenge. We developed ConservationBots—small aerial robots for tracking multiple, dynamic, radio‐tagged wildlife. The aerial robot achieves robust localization performance and fast task completion times—significant for energy‐limited aerial systems while avoiding close encounters with potential, counterproductive disturbances to wildlife. Our approach overcomes the technical and practical problems posed by combining a lightweight sensor with new concepts: (i) planning to determine both trajectory and measurement actions guided by an information‐theoretic objective, which allows the robot to strategically select near‐instantaneous range‐only measurements to achieve faster localization, and time‐consuming sensor rotation actions to acquire bearing measurements and achieve robust tracking performance; (ii) a bearing detector more robust to noise; and (iii) a tracking algorithm formulation robust to missed and false detections experienced in real‐world conditions. We conducted extensive studies: simulations built upon complex signal propagation over high‐resolution elevation data on diverse geographical terrains; field testing; studies with wombats (Lasiorhinus latifrons; nocturnal, vulnerable species dwelling in underground warrens) and tracking comparisons with a highly experienced biologist to validate the effectiveness of our aerial robot and demonstrate the significant advantages over the manual method.
Independent Distribution Regularization for Private Graph Embedding

Learning graph embeddings is a crucial task in graph mining tasks. An effective graph embedding model can learn low-dimensional representations from graph-structured data for data publishing benefiting various downstream applications such as node classification, link prediction, etc. However, recent studies have revealed that graph embeddings are susceptible to attribute inference attacks, which allow attackers to infer private node attributes from the learned graph embeddings. To address these concerns, privacy-preserving graph embedding methods have emerged, aiming to simultaneously consider primary learning and privacy protection through adversarial learning. However, most existing methods assume that representation models have access to all sensitive attributes in advance during the training stage, which is not always the case due to diverse privacy preferences. Furthermore, the commonly used adversarial learning technique in privacy-preserving representation learning suffers from unstable training issues. In this paper, we propose a novel approach calledPrivate Variational Graph AutoEncoders (PVGAE) with the aid of independent distribution penalty as a regularization term. Specifically, we split the original variational graph autoencoder (VGAE) to learn sensitive and non-sensitive latent representations using two sets of encoders. Additionally, we introduce a novel regularization to enforce the independence of the encoders. We prove the theoretical effectiveness of regularization from the perspective of mutual information. Experimental results on three real-world datasets demonstrate that PVGAE outperforms other baselines in private embedding learning regarding utility performance and privacy protection.
Realizing Non-Physical Actions through Hermitian-Preserving Map Exponentiation

Quantum mechanics features a variety of distinct properties such as coherence and entanglement, which could be explored to showcase potential advantages over classical counterparts in information processing. In general, legitimate quantum operations must adhere to principles of quantum mechanics, particularly the requirements of complete positivity and trace preservation. Nonetheless, non-physical maps, especially Hermitian-preserving maps, play a crucial role in quantum information science. To date, there exists no effective method for implementing these non-physical maps with quantum devices. In this work, we introduce the Hermitian-preserving map exponentiation algorithm, which can effectively realize the action of an arbitrary Hermitian-preserving map by encoding its output into a quantum process. We analyze the performances of this algorithm, including its sample complexity and robustness, and prove its optimality in certain cases. When combined with algorithms such as the Hadamard test and quantum phase estimation, it allows for the extraction of information and generation of states from outputs of Hermitian-preserving maps, enabling various applications. Utilizing positive but not completely positive maps, this algorithm provides exponential advantages in entanglement detection and quantification compared to protocols based on single-copy operations. In addition, it facilitates the recovery of noiseless quantum states from multiple copies of noisy states by implementing the inverse map of the corresponding noise channel, offering an intriguing approach to handling quantum errors. Our findings present a pathway for systematically and efficiently implementing non-physical actions with quantum devices, thereby boosting the exploration of potential quantum advantages across a wide range of information processing tasks.
ΣIGMA: Scale-Invariant Global Sparse Shape Matching

We propose a novel mixed-integer programming (MIP) formulation for generating precise sparse correspondences for highly non-rigid shapes. To this end, we introduce a projected Laplace-Beltrami operator (PLBO) which combines intrinsic and extrinsic geometric information to measure the deformation quality induced by predicted correspondences. We integrate the PLBO, together with an orientation-aware regulariser, into a novel MIP formulation that can be solved to global optimality for many practical problems. In contrast to previous methods, our approach is provably invariant to rigid transformations and global scaling, initialisation-free, has optimality guarantees, and scales to high resolution meshes with (empirically observed) linear time. We show state-of-the-art results for sparse non-rigid matching on several challenging 3D datasets, including data with inconsistent meshing, as well as applications in mesh-to-point-cloud matching.