Miracle-dz/UniFAR-FTUs huggingface dataset:
Modulation of pedunculopontine input to the basal ganglia relieves motor symptoms in Parkinsonian mice

Locomotion, the ability to move, is driven by central pattern generators in the spinal cord. However, its initiation, selection of gait and arrest are controlled by command centers in the brain stem of which the best understood is the mesencephalic locomotor region (MLR). Stimulation of the MLR can initiate or arrest movement, but its neural projections and the neural circuits that mediate these actions are poorly understood. Here we show that glutamatergic neurons in the pedunculopontine nucleus (PPN), a major part of the MLR, project to the substantial nigra pars reticulata (SNr) where they innervate neurons that send afferents to the ventro medial motor thalamus (VM). Stimulation of this input to the SNr in mice slows voluntary and driven movement by modifying gait, with higher frequencies arresting gait. In Parkinsons disease (PD), a neurodegenerative disorder, the cardinal symptoms are bradykinesia, tremor and rigidity. Using an mouse model of PD, we show that inhibition of PPN input to the SNr rescues bradykinesia. This is the first description of a circuit in the mammalian brain that regulates movement and gait selection. Targeting of this circuit may be a more effective modality for treating the motor symptoms of PD.
Click-free imaging of carbohydrate trafficking in live cells using an azido photothermal probe

Real-time tracking of intracellular carbohydrates remains challenging. While click chemistry allows bio-orthogonal tagging with fluorescent probes, the reaction permanently alters the target molecule and only allows a single snapshot. Here, we demonstrate click-free mid-infrared photothermal (MIP) imaging of azide-tagged carbohydrates in live cells. Leveraging the micromolar detection sensitivity for 6-azido-trehalose (TreAz) and the 300-nm spatial resolution of MIP imaging, the trehalose recycling pathway in single mycobacteria, from cytoplasmic uptake to membrane localization, is directly visualized. A peak shift of azide in MIP spectrum further uncovers interactions between TreAz and intracellular protein. MIP mapping of unreacted azide after click reaction reveals click chemistry heterogeneity within a bacterium. Broader applications of azido photothermal probes to visualize the initial steps of the Leloir pathway in yeasts and the newly synthesized glycans in mammalian cells are demonstrated.
Benchmarking clustering, alignment, and integration methods for spatial transcriptomics

Spatial transcriptomics (ST) is advancing our understanding of complex tissues and organisms. However, building a robust clustering algorithm to define spatially coherent regions in a single tissue slice, and aligning or integrating multiple tissue slices originating from diverse sources for essential downstream analyses remain challenging. Numerous clustering, alignment, and integration methods have been specifically designed for ST data by leveraging its spatial information. The absence of benchmark studies complicates the selection of methods and future method development. Here we systematically benchmark a variety of state-of-the-art algorithms with a wide range of real and simulated datasets of varying sizes, technologies, species, and complexity. Different experimental metrics and analyses, like adjusted rand index (ARI), uniform manifold approximation and projection (UMAP) visualization, layer-wise and spot-to-spot alignment accuracy, spatial coherence score (SCS), and 3D reconstruction, are meticulously designed to assess method performance as well as data quality. We analyze the strengths and weaknesses of each method using diverse quantitative and qualitative metrics. This analysis leads to a comprehensive recommendation that covers multiple aspects for users. The code used for evaluation is available on GitHub. Additionally, we provide jupyter notebook tutorials and documentation to facilitate the reproduction of all benchmarking results and to support the study of new methods and new datasets (https://benchmarkst-reproducibility.readthedocs.io/en/latest/).
Competitive assembly resolves the stoichiometry of essential proteins in infectious HIV-1 virions

During assembly on the plasma membrane, HIV-1 virions incorporate Gag-Pol as well as gp120/gp41 trimers. The Pol region consists of protease, reverse transcriptase and integrase precursors which are essential enzymes required for maturation, reverse transcription, and integration of the viral genome in the next host. gp120/gp41 trimers catalyze the fusion of the virion with its next host. Only a fraction of released virions are infectious. The stoichiometry of gp120/gp41 and Gag-Pol proteins in HIV virions was previously measured using cryotomography and ratiometric protein analysis, but what is the stoichiometry of these proteins in infectious virions remained to be determined. Here we developed a method based on competition between infectious HIV backbones with noninfectious mutants and measured 100 ± 10 Gag-Pol and 15 ± 3 gp120/gp41 proteins incorporated in infectious virions assembled in HEK293 cells from NL4.3 HIV-1 backbone. Our measurements are in broad agreement with cryotomography and ratiometric protein analysis and therefore stoichiometry of gp120/gp41 and Gag-Pol in infectious virions is the same as all released virions. With the development of appropriate mutants and infectivity assays, our method is applicable to other infectious viruses. Statement of significance There are 30 million people who have succumbed to the AIDS pandemic with 600,000 additional deaths per year. HIV has an accelerated rate of mutational accumulation with the virus mutating out of neutralizing antibodies within the same patient making development of vaccines challenging. Like most enveloped viruses, only a fraction of released virions are infectious and the question of what selects these virions has remained a mystery. The method developed in this article will allow stoichiometric measurements on infectious virions and therefore allows further studies of causes of infectivity.
Mind Mapping to Explore Farming and Food Systems Interactions

The process of mind mapping to illustrate complex systems has been described in great detail in the book by Buzan (2000) and by others, and there are multiple software programs available to organize the process. This method can be used for taking notes, for summarizing a meeting or seminar, or for making connections and bringing together key interacting elements on a white board or chalk board while a class is in session. We have found this activity especially valuable for students in agroecology who are studying complex farming and food systems, where much of the action results from key relationships and interactions that lead to emergent properties of the system.
Path Loss Models in Dense Urban Areas: A study of Lagos Island, Nigeria

Path loss is a major factor affecting the performance of wireless networks in dense urban areas. This paper investigates the path loss models in Lagos Island, Nigeria, a dense urban area with high-rise buildings and high population density. This paper presents a detailed large-scale 3D ray-tracing investigation of the Lagos Island environment. Path loss analysis was conducted using the Close-In path loss model at 700 MHz for a TR/RX height of 20/2 m. The optimal path loss prediction model for the investigated environment was compared with existing empirical models, and the results show favorable agreement. The Close-in path loss model had a better prediction accuracy with an RMSE of 0.4331 dB, the ECC-33 path loss model achieved an accuracy with the least RMSE of 0.6743 dB. The EGLI path loss prediction model showed a pessimistic performance with the highest RMSE of 2.2496 dB, followed by Hata-Okumura with 1.9606 dB and COST231 extension-to-Hata path loss model with 1.9399 dB. Network service providers can adapt the projected 4G LTE network path loss prediction model to benchmark-related wireless propagation environments. The findings of this study are important for network service providers in Lagos Island and other dense urban areas. The study provides insights into the factors that affect path loss in these areas, and it can help network service providers optimize their transmit power and improve the performance of their wireless networks in areas where 5G is still in development and pilot trials like Nigeria and other parts of developing nations.
Facial Expression Recognition by Photo-Reflective Sensors Considering Time Series and Head Posture

There is a method to recognize facial expressions of Head-Mounted Display (HMD) wearers by machine learning of reflection intensity information from photo-reflective sensors embedded into the interior of an HMD [1]. This study evaluates whether facial expression recognition accuracy can be improved by using a learning model that considers temporal changes in sensor values. We assessed whether facial expression recognition accuracy could be improved by adding the head posture data acquired from the Inertial Measurement Unit (IMU) in the HMD to the discriminator input and performing time-series learning. The experimental results showed that PRS-based facial expression recognition with time-series data was more accurate than without. The multimodal recognition using the reflection intensity and head posture data was slightly more accurate than the discrimination using only the reflection intensity information. It was especially effective for the learning condition without considering the time-series.
Graph-Based Path/Foothold Planning and Quantitative Map Evaluation for Multi-Limbed Climbing Robots

This paper presents a graph-based algorithm for path and foothold planning for multi-limbed climbing robots. For multi-limbed climbing robots locomoting in unstructured environments, path planning and foothold planning are essential because the robots need to move while grasping a limited number of discrete graspable points. In this paper, we present a new graph-based method for obtaining the robot pose path that connects the start and destination points in a minimum number of steps and footholds while following the path when the graspable points distribution and robot configuration are given. The proposed method quantitatively evaluates a given map and planned path in terms of importance level for graspable points as well as path robustness against the loss of graspable points. The proposed algorithm is implemented in a multi-legged robot simulation.
Detection of violent speech against women in Mexican tweets using an active learning approach

In Latin American and Caribbean States the verbal violence against women on social networks, such as Twitter, is a serious threat that has been addressed through the implementation of social norms, public policies, and social movements. Nevertheless, a challenge is the effective and automatic real-time detection of violent tweets. In this sense, traditional machine learning algorithms have been proposed to tackle social issues where the training process is performed in a static manner. However, considering that Twitter is a dynamic environment where a vast of tweets are generated each second, it requires powerful machine learning algorithms that could exploit this pool of unlabeled data to be incorporated into the model through continuous updates. This paper explores an active learning method based on uncertainty sampling, which identifies the most confusing tweets to be labeled by an expert in real-time. This focused selection prioritizes which data can be used to train a multilayer perceptron that can achieve a better performance with fewer training samples. Experimental results show that including new samples yields promising results, increasing the AUC from 0.8712 to 0.8833.
Anticipating Collaborative Trends in Spotify Music using Variants of Graph Neural Networks

In recent years, the music industry has experienced a significant transformation, primarily driven by the emergence of digital streaming platforms, most notably Spotify. This evolution has resulted in a significant shift in how musicians interact, culminating in the emergence of sophisticated musical collabo-ration networks. This research introduces a novel approach for forecasting artist collaborations within the Spotify environment. The approach incorporates various features, including Eigen centrality measures, artist popularity, and genre information. Furthermore, the study leverages the SEAL framework to extract features from sub graphs. To build the predictive model, a range of Graph Neural Networks (GNNs) such as Graph Convolution Network (GCN), Graph Attention Networks (GAT), Graph Isomorphism Network (GIN), and GraphSage are leveraged. Additionally, the research explores the concept of ensemble modeling to enhance the predictive power of the link prediction model. Comparing the ensemble approach with and without the SEAL framework, we achieve an accuracy of 0.795 and 0.957, respectively.
Bounds and extremal graphs for monitoring edge-geodetic sets in graphs

A monitoring edge-geodetic set, or simply an MEG-set, of a graph $G$ is a vertex subset $M \subseteq V(G)$ such that given any edge $e$ of $G$, $e$ lies on every shortest $u$-$v$ path of $G$, for some $u,v \in M$. The monitoring edge-geodetic number of $G$, denoted by $meg(G)$, is the minimum cardinality of such an MEG-set. This notion provides a graph theoretic model of the network monitoring problem. In this article, we compare $meg(G)$ with some other graph theoretic parameters stemming from the network monitoring problem and provide examples of graphs having prescribed values for each of these parameters. We also characterize graphs $G$ that have $V(G)$ as their minimum MEG-set, which settles an open problem due to Foucaud \textit{et al.} (CALDAM 2023), and prove that some classes of graphs fall within this characterization. We also provide a general upper bound for $meg(G)$ for sparse graphs in terms of their girth, and later refine the upper bound using the chromatic number of $G$. We examine the change in $meg(G)$ with respect to two fundamental graph operations: clique-sum and subdivisions. In both cases, we provide a lower and an upper bound of the possible amount of changes and provide (almost) tight examples.
Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders

Inference for Variational Autoencoders (VAEs) consists of learning two models: (1) a generative model, which transforms a simple distribution over a latent space into the distribution over observed data, and (2) an inference model, which approximates the posterior of the latent codes given data. The two components are learned jointly via a lower bound to the generative model's log marginal likelihood. In early phases of joint training, the inference model poorly approximates the latent code posteriors. Recent work showed that this leads optimization to get stuck in local optima, negatively impacting the learned generative model. As such, recent work suggests ensuring a high-quality inference model via iterative training: maximizing the objective function relative to the inference model before every update to the generative model. Unfortunately, iterative training is inefficient, requiring heuristic criteria for reverting from iterative to joint training for speed. Here, we suggest an inference method that trains the generative and inference models independently. It approximates the posterior of the true model a priori; fixing this posterior approximation, we then maximize the lower bound relative to only the generative model. By conventional wisdom, this approach should rely on the true prior and likelihood of the true model to approximate its posterior (which are unknown). However, we show that we can compute a deterministic, model-agnostic posterior approximation (MAPA) of the true model's posterior. We then use MAPA to develop a proof-of-concept inference method. We present preliminary results on low-dimensional synthetic data that (1) MAPA captures the trend of the true posterior, and (2) our MAPA-based inference performs better density estimation with less computation than baselines. Lastly, we present a roadmap for scaling the MAPA-based inference method to high-dimensional data.
Measuring Non-Typical Emotions for Mental Health: A Survey of Computational Approaches

Analysis of non-typical emotions, such as stress, depression and engagement is less common and more complex compared to that of frequently discussed emotions like happiness, sadness, fear, and anger. The importance of these non-typical emotions has been increasingly recognized due to their implications on mental health and well-being. Stress and depression impact the engagement in daily tasks, highlighting the need to understand their interplay. This survey is the first to simultaneously explore computational methods for analyzing stress, depression, and engagement. We discuss the most commonly used datasets, input modalities, data processing techniques, and information fusion methods used for the computational analysis of stress, depression and engagement. A timeline and taxonomy of non-typical emotion analysis approaches along with their generic pipeline and categories are presented. Subsequently, we describe state-of-the-art computational approaches for non-typical emotion analysis, including a performance summary on the most commonly used datasets. Following this, we explore the applications, along with the associated challenges, limitations, and future research directions.
Laying the Foundation First? Investigating the Generalization from Atomic Skills to Complex Reasoning Tasks

Current language models have demonstrated their capability to develop basic reasoning, but struggle in more complicated reasoning tasks that require a combination of atomic skills, such as math word problem requiring skills like arithmetic and unit conversion. Previous methods either do not improve the inherent atomic skills of models or not attempt to generalize the atomic skills to complex reasoning tasks. In this paper, we first propose a probing framework to investigate whether the atomic skill can spontaneously generalize to complex reasoning tasks. Then, we introduce a hierarchical curriculum learning training strategy to achieve better skill generalization. In our experiments, we find that atomic skills can not spontaneously generalize to compositional tasks. By leveraging hierarchical curriculum learning, we successfully induce generalization, significantly improve the performance of open-source LMs on complex reasoning tasks. Promisingly, the skill generalization exhibit effective in cross-dataset and cross-domain scenarios. Complex reasoning can also help enhance atomic skills. Our findings offer valuable guidance for designing better training strategies for complex reasoning tasks.
USimAgent: Large Language Models for Simulating Search Users

Due to the advantages in the cost-efficiency and reproducibility, user simulation has become a promising solution to the user-centric evaluation of information retrieval systems. Nonetheless, accurately simulating user search behaviors has long been a challenge, because users' actions in search are highly complex and driven by intricate cognitive processes such as learning, reasoning, and planning. Recently, Large Language Models (LLMs) have demonstrated remarked potential in simulating human-level intelligence and have been used in building autonomous agents for various tasks. However, the potential of using LLMs in simulating search behaviors has not yet been fully explored. In this paper, we introduce a LLM-based user search behavior simulator, USimAgent. The proposed simulator can simulate users' querying, clicking, and stopping behaviors during search, and thus, is capable of generating complete search sessions for specific search tasks. Empirical investigation on a real user behavior dataset shows that the proposed simulator outperforms existing methods in query generation and is comparable to traditional methods in predicting user clicks and stopping behaviors. These results not only validate the effectiveness of using LLMs for user simulation but also shed light on the development of a more robust and generic user simulators.
Strategizing against Q-learners: A Control-theoretical Approach

In this paper, we explore the susceptibility of the Q-learning algorithm (a classical and widely used reinforcement learning method) to strategic manipulation of sophisticated opponents in games. We quantify how much a strategically sophisticated agent can exploit a naive Q-learner if she knows the opponent's Q-learning algorithm. To this end, we formulate the strategic actor's problem as a Markov decision process (with a continuum state space encompassing all possible Q-values) as if the Q-learning algorithm is the underlying dynamical system. We also present a quantization-based approximation scheme to tackle the continuum state space and analyze its performance both analytically and numerically.
Biophysics Informed Pathological Regularisation for Brain Tumour Segmentation

Recent advancements in deep learning have significantly improved brain tumour segmentation techniques; however, the results still lack confidence and robustness as they solely consider image data without biophysical priors or pathological information. Integrating biophysics-informed regularisation is one effective way to change this situation, as it provides an prior regularisation for automated end-to-end learning. In this paper, we propose a novel approach that designs brain tumour growth Partial Differential Equation (PDE) models as a regularisation with deep learning, operational with any network model. Our method introduces tumour growth PDE models directly into the segmentation process, improving accuracy and robustness, especially in data-scarce scenarios. This system estimates tumour cell density using a periodic activation function. By effectively integrating this estimation with biophysical models, we achieve a better capture of tumour characteristics. This approach not only aligns the segmentation closer to actual biological behaviour but also strengthens the model's performance under limited data conditions. We demonstrate the effectiveness of our framework through extensive experiments on the BraTS 2023 dataset, showcasing significant improvements in both precision and reliability of tumour segmentation.
Hyper-CL: Conditioning Sentence Representations with Hypernetworks

While the introduction of contrastive learning frameworks in sentence representation learning has significantly contributed to advancements in the field, it still remains unclear whether state-of-the-art sentence embeddings can capture the fine-grained semantics of sentences, particularly when conditioned on specific perspectives. In this paper, we introduce Hyper-CL, an efficient methodology that integrates hypernetworks with contrastive learning to compute conditioned sentence representations. In our proposed approach, the hypernetwork is responsible for transforming pre-computed condition embeddings into corresponding projection layers. This enables the same sentence embeddings to be projected differently according to various conditions. Evaluation on two representative conditioning benchmarks, namely conditional semantic text similarity and knowledge graph completion, demonstrates that Hyper-CL is effective in flexibly conditioning sentence representations, showcasing its computational efficiency at the same time. We also provide a comprehensive analysis of the inner workings of our approach, leading to a better interpretation of its mechanisms.