Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis

Artificial intelligence (AI) in healthcare, especially in medical imaging, faces challenges due to data scarcity and privacy concerns. Addressing these, we introduce Med-DDPM, a diffusion model designed for 3D semantic brain MRI synthesis. This model effectively tackles data scarcity and privacy issues by integrating semantic conditioning. This involves the channel-wise concatenation of a conditioning image to the model input, enabling control in image generation. Med-DDPM demonstrates superior stability and performance compared to existing 3D brain imaging synthesis methods. It generates diverse, anatomically coherent images with high visual fidelity. In terms of dice score accuracy in the tumor segmentation task, Med-DDPM achieves 0.6207, close to the 0.6531 accuracy of real images, and outperforms baseline models. Combined with real images, it further increases segmentation accuracy to 0.6675, showing the potential of our proposed method for data augmentation. This model represents the first use of a diffusion model in 3D semantic brain MRI synthesis, producing high-quality images. Its semantic conditioning feature also shows potential for image anonymization in biomedical imaging, addressing data and privacy issues. We provide the code and model weights for Med-DDPM on our GitHub repository (https://github.com/mobaidoctor/med-ddpm/) to support reproducibility.
Hierarchically branched diffusion models leverage dataset structure for class-conditional generation

Class-labeled datasets, particularly those common in scientific domains, are rife with internal structure, yet current class-conditional diffusion models ignore these relationships and implicitly diffuse on all classes in a flat fashion. To leverage this structure, we propose hierarchically branched diffusion models as a novel framework for class-conditional generation. Branched diffusion models rely on the same diffusion process as traditional models, but learn reverse diffusion separately for each branch of a hierarchy. We highlight several advantages of branched diffusion models over the current state-of-the-art methods for class-conditional diffusion, including extension to novel classes in a continual-learning setting, a more sophisticated form of analogy-based conditional generation (i.e. transmutation), and a novel interpretability into the generation process. We extensively evaluate branched diffusion models on several benchmark and large real-world scientific datasets spanning many data modalities.
TrajFlow: Learning Distributions over Trajectories for Human Behaviour Prediction

: Predicting the future behavior of human road users remains an open challenge for the development of risk-aware autonomous vehicles. An important aspect of this challenge is effectively capturing the uncertainty inherent to human behavior. This paper proposes an approach for probabilistic trajectory prediction based on normalizing ﬂows, which provides an analytical expression of the learned distribution. We reformulate the problem of capturing distributions over trajectories into capturing distributions over abstracted trajectory features using an autoencoder, simplifying the learning task of the normalizing ﬂows. TrajFlow improves the calibration of the learned distributions while achieving predictive performance on par with or superior to state-of-the-art methods on the ETH/UCY and the rounD data set.
Relation Extraction from Scientific Texts in Russian with Limited Training Data

In this paper, we address the task of extracting semantic relations between entities in scientific articles in Russian, with a focus on scientific terms as entities. We present a dataset that includes annotated abstracts of scientific articles in Russian. This dataset was used to train and test models and develop an algorithm for the automatic extraction of semantic relations. We conducted experiments and compared one zero-shot and one few-shot approach for relation extraction: one based on the perplexity score and the other based on the use of prototype vectors of relations. Our results show that both methods can achieve reasonable performance, demonstrating the potential of zero-shot and few-shot approaches for relation extraction in scientific texts in Russian. The developed tool and annotated dataset are publicly available and could be valuable resources for other researchers 1 .
LaTeX Rainbow: Universal LaTeX to PDF Document Semantic & Layout Annotation Framework

Machine Learning models in the field of Information Extraction for Scientific Publications require high-quality labeled data. The large amount of easily accessible L A TEX source code is a treasure trove of high-quality labeled data. However, existing datasets comprised of document collections and PDF extraction tools have limitations: (1) The hierarchical structure of papers is lost because labeling is done in terms of pages rather than documents; (2) The reading order is not extracted, which potentially muddles the extracted contextual structure; (3) Papers included in the datasets are not likely to be up-to-date.To address these challenges, we propose L A T E X Rainbow, a framework that bridges L A TEX to PDF that can automatically annotate and extract semantic and layout information from L A T E X source code. This framework extends existing annotation methods by taking into account the properties of different existing approaches. It can produce token-level semantic structure annotations, preserve the paper’s reading order, and extract the table of contents, i.e., the article’s section structure. L A T E X Rain-bow enables anyone to extend their datasets with the latest documents. The project is open-sourced on GitHub 1 for community contributions and use.
An Investigation of Warning Erroneous Chat Translations in Cross-lingual Communication

Machine translation models are still inappropriate for translating chats, despite the popularity of translation software and plug-in applications. The complexity of dialogues poses significant challenges and can hinder cross-lingual communication. Instead of pursuing a flawless translation system, a more practical approach would be to issue warning messages about potential mistranslations to reduce confusion. However, it is still unclear how individuals perceive these warning messages and whether they benefit the crowd. This paper tackles to investigate this question and demonstrates the warning messages’ contribution to making chat translation systems effective.
WAMP: Writing, Annotation, and Marking Platform

Creating annotated corpora has always been one of the major challenges for grammatical error correction (GEC). Annotated corpora are critical since they enable us to evaluate the accuracy of GEC systems by providing gold-standard references. In this paper, we propose a web-based annotation tool – Writing, Annotation, and Marking Platform (WAMP) – that tackles this issue of generating annotated corpora by allowing annotators to annotate essays with ease and export the resulting annotated essays for use in GEC research. The source code and a demo video of WAMP are publicly available at: https://github.com/nusnlp/wamp .
RECESS: Resource for Extracting Cause, Effect, and Signal Spans

Causality expresses the relation between two arguments, one of which represents the cause and the other the effect (or consequence). Causal relations are fundamental to human decision making and reasoning, and extracting them from natural language texts is crucial for building effective natural language understanding models. However, the scarcity of annotated corpora for causal relations poses a challenge in the development of such tools. Thus, we created Resource for Extracting Cause, Effect, and Signal Spans (RECESS), a comprehensive corpus annotated for causality at different levels, including Cause, Effect, and Signal spans. The corpus contains 3,767 sentences, of which, 1,982 are causal sentences that contain a total of 2,754 causal relations. We report base-line experiments on two natural language tasks (Causal Sentence Classification, and Cause-Effect-Signal Span Detection), and establish initial benchmarks for future work. We conduct an in-depth analysis of the corpus and the properties of causal relations in text. RECESS is a valuable resource for developing and evaluating causal relation extraction models, benefiting researchers working on topics from information retrieval to natural language understanding and inference.
Rethinking Response Evaluation from Interlocutor’s Eye for Open-Domain Dialogue Systems

Open-domain dialogue systems have started to engage in continuous conversations with humans. Those dialogue systems are required to be adjusted to the human interlocutor and evaluated in terms of their perspective. However, it is questionable whether the current automatic evaluation methods can approximate the interlocutor's judgments. In this study, we analyzed and examined what features are needed in an automatic response evaluator from the interlocutor's perspective. The first experiment on the Hazumi dataset revealed that interlocutor awareness plays a critical role in making automatic response evaluation correlate with the interlocutor's judgments. The second experiment using massive conversations on X (formerly Twitter) confirmed that dialogue continuity prediction can train an interlocutor-aware response evaluator without human feedback while revealing the difficulty in evaluating generated responses compared to human responses.
Temporal Tides of Emotional Resonance: A Novel Approach to Identify Mental Health on Social Media

Identifying mental health conditions using user-generated text on social media paved the way for automated computational methods for mental health surveillance on social media. Infer-ring the accurate state of mental health markers requires understanding the user’s emotions and history of mental health conditions for identifying the mental health landscape of at-risk users. Existing works have exploited social media data by using users’ most recent posts; however, previous works ignored users’ emotional historical activity on social media, indicating their mental state over time. In this work, we address this gap and present a novel emotion-and time-aware architecture that jointly learns social media users’ emotional historical context and temporal posting irregularities for mental health surveillance on social media. We conduct classification experiments to identify social media users with mental conditions, i.e., (i) healthy users and users affected with depression and (ii) healthy users and users prone to self-harm. Experimental results demonstrate proposed method outperforms recent competitive methods, demonstrating the importance of capturing the user’s temporal emotional spectrum and time-aware emotional context through historical posts for social media users’ mental health surveillance.
Practical Tools from Domain Adaptation for Designing Inclusive, Equitable, and Robust Generative AI

Generative language technologies have become integral to everyday communication, shaping social interactions and informing critical decision-making processes in areas such as recruitment, healthcare, and education. However, they often struggle to grasp the "long tail" of data distributions — concepts less frequently observed during training — which could have significant repercussions. These models may marginalize underrepresented groups by failing to comprehend preferred communication styles, such as code-switching, or perpetuating societal biases like gender bias. Sectors like health-care, education, and law, requiring personalization and exhibiting nuanced linguistic features, are also particularly affected when pre-trained models misconstrue or overlook "long tail" data concepts. While methods like distillation of smaller language models, active learning, and other bias mitigation strategies can augment traditional training techniques, a careful statistical analysis is essential for their effective application. This tutorial offers a comprehensive examination of how to develop equitable, robust, and inclusive language technologies using statistical tools from Domain Adaptation (DA) that catalyze positive social change. We will delve into strategies for bias mitigation, explore how to measure bias, and examine open problems in creating culturally-grounded and inclusive language technologies. Accompanying code notebooks and packages will be provided. 1
Computational basis of hierarchical and counterfactual information processing

Cognitive theories attribute humans’ unparalleled capacity in solving complex multistage decision problems to distinctive hierarchical and counterfactual reasoning strategies. Here, we used a combination of human psychophysics and behaviorally-constrained neural network modeling to understand the computational basis of these cognitive strategies. We first developed a multi-stage decision-making task that humans solve using a combination of hierarchical and counterfactual processing. We then used a series of hypothesis-driven behavioral experiments to systematically dissect the potential computational constraints that underlie these strategies. One experiment revealed that humans have limited capacity for parallel processing. Another indicated that counterfactuals do not fully compensate for this limitation because of working memory limits. A third experiment revealed that the degree to which humans use counterfactuals depends on the fidelity of their working memory. Next, we asked whether the strategies humans adopt are computationally rational; i.e., optimal under these constraints. To do so, we analyzed the behavior of a battery of task-optimized recurrent neural networks (RNNs) that were subjected to one or more of these constraints. Remarkably, only RNNs that were subjected to all these constraints behaved similarly to humans. Further analysis of the RNNs revealed that what cognitive theories posit as distinctive strategies such as hierarchical and counterfactual are subdivisions in a continuum of computationally rational solutions that includes optimal, counterfactual, postdictive, and hierarchical.
Multiplexing of temporal and spatial information in the lateral entorhinal cortex

Episodic memory involves the processing of spatial and temporal aspects of personal experiences. The lateral entorhinal cortex (LEC) plays an essential role in subserving memory. However, the specific mechanism by which LEC integrates spatial and temporal information remains elusive. Here, we recorded LEC neurons while rats performed foraging and shuttling behaviors on one-dimensional, linear or circular tracks. Unlike open-field foraging tasks, many LEC cells displayed spatial firing fields in these tasks and demonstrated selectivity for traveling directions. Furthermore, some LEC neurons displayed changes in the firing rates of their spatial rate maps during a session, a phenomenon referred to as rate remapping. Importantly, this temporal modulation was consistent across sessions, even when the spatial environment was altered. Notably, the strength of temporal modulation was found to be greater in LEC compared to other brain regions, such as the medial entorhinal cortex (MEC), CA1, and CA3. Thus, the spatial rate mapping observed in LEC neurons may serve as a coding mechanism for temporal context, allowing for flexible multiplexing of spatial and temporal information.
Chemokine expression profile of an innate granuloma

Granulomas are defined by the presence of organized layers of immune cells that include macrophages. Granulomas are often characterized as a way for the immune system to contain an infection and prevent its dissemination. We recently established a mouse infection model where Chromobacterium violaceum induces the innate immune system to form granulomas in the liver. This response successfully eradicates the bacteria and returns the liver to homeostasis. Here, we sought to characterize the chemokines involved in directing immune cells to form the distinct layers of a granuloma. We use spatial transcriptomics to investigate the spatial and temporal expression of all CC and CXC chemokines and their receptors within this granuloma response. The expression profiles change dynamically over space and time as the granuloma matures and then resolves. To investigate the importance of monocyte-derived macrophages in this immune response, we studied the role of CCR2 during C. violaceum infection. Ccr2−/− mice had negligible numbers of macrophages, but large numbers of neutrophils, in the C. violaceum-infected lesions. In addition, lesions had abnormal architecture resulting in loss of bacterial containment. Without CCR2, bacteria disseminated and the mice succumbed to the infection. This indicates that macrophages are critical to form a successful innate granuloma in response to C. violaceum. Impact statement A successful innate granuloma requires CCR2 to organize the macrophage ring, and without CCR2, mice succumb to Chromobacterium violaceum infection.
Functionally distinct dopamine domains in the hippocampus

Numerous studies have identified dopamine signaling in the hippocampus as necessary for certain types of learning and memory. Since dopamine in the striatum is strongly tied to rewards, dopamine in the hippocampus is thought to reinforce reward learning. Despite the critical influence of dopamine on hippocampal function, little is known about dopamine release in the hippocampus or the specific ways dopamine can influence hippocampal function. Based on the functional complexity of hippocampal circuitry, we hypothesized the existence of multiple dopamine signaling domains. Using optical dopamine sensors, two-photon imaging, and head-fixed behaviors, we identified two functionally and spatially distinct dopamine domains in the hippocampus. The “superficial” domain (cell somata and apical dendrites) showed reward-related dopamine transients early in Pavlovian conditioning but were replaced by “deep” domain transients (basal dendritic layer) with experience. These two domains also play distinct roles in a hippocampal-dependent, goal-directed virtual reality task where mice use exploratory licks to discover the location of a hidden reward zone. Here, positive dopamine ramps appeared in the superficial domain as mice approached the reward zone, similar to those seen in the striatum. At the same time, the deep domain showed strong reward-related transients. These results reveal small-scale, anatomically segregated, dopamine domains in the hippocampus. Furthermore dopamine domain activity had temporal-specificity for different phases of behavior. Finally, the subcellular scale of dopamine domains suggests specialized postsynaptic pathways for processing and integrating functionally distinct dopaminergic influences.
Quantifying the Uncertainty in Historical and Future Simulations of Northern Hemisphere Spring Snow Cover

AbstractProjections of twenty-first-century Northern Hemisphere (NH) spring snow cover extent (SCE) from two climate model ensembles are analyzed to characterize their uncertainty. Phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble exhibits variability resulting from both model differences and internal climate variability, whereas spread generated from a Canadian Earth System Model–Large Ensemble (CanESM-LE) experiment is solely a result of internal variability. The analysis shows that simulated 1981–2010 spring SCE trends are slightly weaker than observed (using an ensemble of snow products). Spring SCE is projected to decrease by −3.7% ± 1.1% decade−1 within the CMIP5 ensemble over the twenty-first century. SCE loss is projected to accelerate for all spring months over the twenty-first century, with the exception of June (because most snow in this month has melted by the latter half of the twenty-first century). For 30-yr spring SCE trends over the twenty-first century, int.
A Computational Study on the Activation of Neural Transmission in Deep Brain Stimulation

—Deep brain stimulation is an established treat-ment for neurodegenerative movement disorders such as Parkinson’s disease that mitigates symptoms by overwrit-ing pathological signals from the central nervous system to the motor system. Nearly all computational models of DBS, directly or indirectly, associate clinical improvements with the extent of fibre activation in the vicinity of the stimulating electrode. However, it is not clear how such activation modulates information transmission. Here, we use the exact cable equation for straight or curved axons and show that DBS segregates the signalling pathways into one of the three communicational modes: complete information blockage, uni-, and bi-directional transmission. Furthermore, all these modes respond to the stimulating pulse in an asynchronous but frequency-locked fashion. Asynchrony depends on the geometry of the axon, its placement and orientation, and the stimulation protocol. At the same time, the electrophysiology of the nerve determines frequency-locking. Such a trimodal response challenges the notion of activation as a binary state and studies that correlate it with the DBS outcome. Importantly, our work suggests that a mechanistic understanding of DBS action relies on distinguishing between these three modes of information transmission.
New Understanding of Kuqaia Sporomorph

The sporamorph Kuqaia is an Acritarch fossil similar to animal shell or Gastropoda,which has been commonly used in formation classifications and correlations due to its properties of short geochronology,extensive distribution and easy to recognition.In the past,it was generally considered to be fairly limited in horizon distribution such as Jurassic Yangxia formation,Kangsu for-mation,lower part of Yangye formation in Tarim basin and Jurassic Sangonghe formation and corresponding downhole intervals in Junggar basin,and belong to late or middle-late period of Early Jurassic.The recent research indicates that Kuqaia originated in early period of Early Jurassic,flourished in late or middle-late period of Early Jurassic and may be distributed to Middle Juras-sic.
Corporate Governance and Market Performance: Empirical Evidence from Nigerian Banks

: The need to manage banks effectively and efficiently has become more important given the role they play in allocating scarce resources among competing uses. However, the efficacy of mechanisms employed by the boards of banks to protect and promote the interests of stakeholders has been questioned by the dozens of bankruptcies and collapse of banks around the world. In an effort to address this problem, this study empirically examined the relationships among corporate governance mechanisms and bank performance in Nigeria. The study employed panel regression analysis comprising pooled effects, fixed effects and random effects estimators on a sample of 13 out of an average of 21 banks listed on the Nigerian Stock Exchange (NSE) for the period 2005 through 2015. Consistent with earlier studies, this study found evidence of a negative and significant influence of board composition on market price per share (MPS) and a positive and significant influence of gender diversity on market capitalization (LMC). Bank age is also found to relate negatively with performance. In contrast however, the study did not find any empirical evidence to support the hypotheses that the size of the board and that of the bank affect performance as measured by MPS and LMC. In view of these evidences and in line with the agency theory, sufficient evidences have emerged that minimising the appointment of outside directors is necessary to reduce them, the board size and the high cost of maintaining directors in order to achieve superior performance. In addition, policies that will encourage more participation of female directors on the board by way of enforcing gender quotas should be encouraged by the banks and the regulatory agencies.