Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing NILC Extractions of the Thermal Sunyaev–Zel’Dovich Effect with Deep Learning

All-sky maps of the thermal Sunyaev–Zel’dovich effect (SZ) tend to suffer from systematic features arising from the component-separation techniques used to extract the signal. In this work, we investigate one of these methods, known as needlet internal linear combination (NILC), and test its performance on simulated data. We show that NILC estimates are strongly affected by the choice of the spatial localization parameter (Γ), which controls a bias-variance trade-off. Typically, NILC extractions assume a fixed value of Γ over the entire sky, but we show there exists an optimal Γ that depends on the SZ signal strength and local contamination properties. Then we calculate the NILC solutions for multiple values of Γ and feed the results into a neural network to predict the SZ signal. This extraction method, which we call Deep-NILC, is tested against a set of validation data, including recovered radial profiles of resolved systems. Our main result is that Deep-NILC offers significant improvements over choosing fixed values of Γ.
Approximating maximum-size properly colored forests

In the Properly Colored Spanning Tree problem, we are given an edge-colored undirected graph and the goal is to find a properly colored spanning tree, i.e., a spanning tree in which any two adjacent edges have distinct colors. The problem is interesting not only from a graph coloring point of view, but is also closely related to the Degree Bounded Spanning Tree and (1,2)-Traveling Salesman problems, two classical questions that have attracted considerable interest in combinatorial optimization and approximation theory. Previous work on properly colored spanning trees has mainly focused on determining the existence of such a tree and hence has not considered the question from an algorithmic perspective. We propose an optimization version called Maximum-size Properly Colored Forest problem, which aims to find a properly colored forest with as many edges as possible. We consider the problem in different graph classes and for different numbers of colors, and present polynomial-time approximation algorithms as well as inapproximability results for these settings. Our proof technique relies on the sum of matching matroids defined by the color classes, a connection that might be of independent combinatorial interest. We also consider the Maximum-size Properly Colored Tree problem, which asks for the maximum size of a properly colored tree not necessarily spanning all the vertices. We show that the optimum is significantly more difficult to approximate than in the forest case, and provide an approximation algorithm for complete multigraphs.
Adaptive Control for Triadic Human-Robot-FES Collaboration in Gait Rehabilitation: A Pilot Study

The hybridisation of robot-assisted gait training and functional electrical stimulation (FES) can provide numerous physiological benefits to neurological patients. However, the design of an effective hybrid controller poses significant challenges. In this over-actuated system, it is extremely difficult to find the right balance between robotic assistance and FES that will provide personalised assistance, prevent muscle fatigue and encourage the patient's active participation in order to accelerate recovery. In this paper, we present an adaptive hybrid robot-FES controller to do this and enable the triadic collaboration between the patient, the robot and FES. A patient-driven controller is designed where the voluntary movement of the patient is prioritised and assistance is provided using FES and the robot in a hierarchical order depending on the patient's performance and their muscles' fitness. The performance of this hybrid adaptive controller is tested in simulation and on one healthy subject. Our results indicate an increase in tracking performance with lower overall assistance, and less muscle fatigue when the hybrid adaptive controller is used, compared to its non adaptive equivalent. This suggests that our hybrid adaptive controller may be able to adapt to the behaviour of the user to provide assistance as needed and prevent the early termination of physical therapy due to muscle fatigue.
EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models

This work introduces EE-Tuning, a lightweight and economical solution to training/tuning early-exit large language models (LLMs). In contrast to the common approach of full-parameter pre-training, EE-Tuning augments any pre-trained (and possibly fine-tuned) standard LLM with additional early-exit layers that are tuned in a parameter-efficient manner, which requires significantly less computational resources and training data. Our implementation of EE-Tuning achieves outstanding training efficiency via extensive performance optimizations, as well as scalability due to its full compatibility with 3D parallelism. Results of systematic experiments validate the efficacy of EE-Tuning, confirming that effective early-exit LLM inference can be achieved with a limited training budget. In hope of making early-exit LLMs accessible to the community, we release the source code of our implementation of EE-Tuning at https://github.com/pan-x-c/EE-LLM.
Benefits of Transformer: In-Context Learning in Linear Regression Tasks with Unstructured Data

In practice, it is observed that transformer-based models can learn concepts in context in the inference stage. While existing literature, e.g., \citet{zhang2023trained,huang2023context}, provide theoretical explanations on this in-context learning ability, they assume the input $x_i$ and the output $y_i$ for each sample are embedded in the same token (i.e., structured data). However, in reality, they are presented in two tokens (i.e., unstructured data \cite{wibisono2023role}). In this case, this paper conducts experiments in linear regression tasks to study the benefits of the architecture of transformers and provides some corresponding theoretical intuitions to explain why the transformer can learn from unstructured data. We study the exact components in a transformer that facilitate the in-context learning. In particular, we observe that (1) a transformer with two layers of softmax (self-)attentions with look-ahead attention mask can learn from the prompt if $y_i$ is in the token next to $x_i$ for each example; (2) positional encoding can further improve the performance; and (3) multi-head attention with a high input embedding dimension has a better prediction performance than single-head attention.
Role of filler lanthanide ions on lattice dynamics of phosphide skutterudites RFe$_4$P$_{12}$ (R = La, Ce, and Pr) from first principles

Phosphide skutterudites primarily show promise for thermoelectric applications due to their chemical stability at high temperatures and relatively low cost. Ion doping and band gap engineering have been used to enhance their typically poor thermoelectric performance, opening avenues for practical applications. Herein, we report a comparative lattice dynamics study on the impact of filler and temperature on the structural and vibrational properties of RFe$_4$P$_{12}$ (R = La, Ce, and Pr) skutterudites. Calculations are performed within the quasi-harmonic approximation, and the results are critically compared against experimental data and other \textit{ab initio} calculations. We found gaps between the heat-carrying acoustic and optical modes, a-o gaps, of approximately 4, -2, and 0.01\,cm$^{-1}$ for La, Ce, and Pr compounds, respectively. These results suggest a filler-induced reduction in the a-o gap is attributed to the softening of the optical modes instead of the conventionally considered upward shift of acoustic modes proposed in the \textit{rattling} scenario. The distinct softening of the optical modes is rationalized by the stiffening of chemical bonds between the filler and host lattice.
On uniform recurrence for hyperbolic automorphisms of the $2$-dimensional torus

We are interested in studying sets of the form \[ \mathcal{U}(\alpha) := \left\{ x\in X: \ \exists M=M(x) \geq 1 \text{ such that } \forall N\geq M, \ \exists n\leq N \text{ such that } d(T^nx, x) \leq |\lambda|^{-\alpha N} \right\} \] where $(X,T,d)$ is our metric dynamical system and $|\lambda|>1$. Although a lot of results exist for the one dimensional case, not as many are known for systems in higher dimensions and especially in the hyperbolic case. We consider $X=\mathbb{T}^2$, $T(x) = Ax \pmod{1}$, where $A$ is a hyperbolic, area preserving, $2\times 2$ matrix with integer entries and $\lambda$ is the eigenvalue of $A$ of modulus larger than $1$ and we explicitly calculate the Hausdorff dimension of this set.
Self-supervised learning of video representations from a child's perspective

Children learn powerful internal models of the world around them from a few years of egocentric visual experience. Can such internal models be learned from a child's visual experience with highly generic learning algorithms or do they require strong inductive biases? Recent advances in collecting large-scale, longitudinal, developmentally realistic video datasets and generic self-supervised learning (SSL) algorithms are allowing us to begin to tackle this nature vs. nurture question. However, existing work typically focuses on image-based SSL algorithms and visual capabilities that can be learned from static images (e.g. object recognition), thus ignoring temporal aspects of the world. To close this gap, here we train self-supervised video models on longitudinal, egocentric headcam recordings collected from a child over a two year period in their early development (6-31 months). The resulting models are highly effective at facilitating the learning of action concepts from a small number of labeled examples; they have favorable data size scaling properties; and they display emergent video interpolation capabilities. Video models also learn more robust object representations than image-based models trained with the exact same data. These results suggest that important temporal aspects of a child's internal model of the world may be learnable from their visual experience using highly generic learning algorithms and without strong inductive biases.
CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration

LiDARs are widely used for mapping and localization in dynamic environments. However, their high cost limits their widespread adoption. On the other hand, monocular localization in LiDAR maps using inexpensive cameras is a cost-effective alternative for large-scale deployment. Nevertheless, most existing approaches struggle to generalize to new sensor setups and environments, requiring retraining or fine-tuning. In this paper, we present CMRNext, a novel approach for camera-LIDAR matching that is independent of sensor-specific parameters, generalizable, and can be used in the wild for monocular localization in LiDAR maps and camera-LiDAR extrinsic calibration. CMRNext exploits recent advances in deep neural networks for matching cross-modal data and standard geometric techniques for robust pose estimation. We reformulate the point-pixel matching problem as an optical flow estimation problem and solve the Perspective-n-Point problem based on the resulting correspondences to find the relative pose between the camera and the LiDAR point cloud. We extensively evaluate CMRNext on six different robotic platforms, including three publicly available datasets and three in-house robots. Our experimental evaluations demonstrate that CMRNext outperforms existing approaches on both tasks and effectively generalizes to previously unseen environments and sensor setups in a zero-shot manner. We make the code and pre-trained models publicly available at http://cmrnext.cs. uni-freiburg.de .
Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusters

Unsupervised learning has gained prominence in the big data era, offering a means to extract valuable insights from unlabeled datasets. Deep clustering has emerged as an important unsupervised category, aiming to exploit the non-linear mapping capabilities of neural networks in order to enhance clustering performance. The majority of deep clustering literature focuses on minimizing the inner-cluster variability in some embedded space while keeping the learned representation consistent with the original high-dimensional dataset. In this work, we propose soft silhoutte, a probabilistic formulation of the silhouette coefficient. Soft silhouette rewards compact and distinctly separated clustering solutions like the conventional silhouette coefficient. When optimized within a deep clustering framework, soft silhouette guides the learned representations towards forming compact and well-separated clusters. In addition, we introduce an autoencoder-based deep learning architecture that is suitable for optimizing the soft silhouette objective function. The proposed deep clustering method has been tested and compared with several well-studied deep clustering methods on various benchmark datasets, yielding very satisfactory clustering results.
Polarization-induced stress in the noisy voter model

A new model for the dynamics of opinion formation is proposed and analysed at the mean-field level. It can be regarded as a generalization of the noisy voter model in which agents update their binary states by copying others and by an intrinsic mechanism affected by the degree of polarization in the system. It also takes into account whether the agents enhance or reduce their intrinsic mechanism upon increasing polarization. Four phases or shapes of the steady-state probability of a fraction of agents in a given state are found (unimodal, bimodal, W and M). In the unimodal (resp. bimodal) phase, the copying (resp. intrinsic) mechanism is globally dominant, while in the W (resp. M) phase the copying (resp. intrinsic) mechanism is the relevant one close to the consensus states while it reduces its influence as approaching coexistence. In the thermodynamic limit, the bimodal and W phases disappear, while the unimodal and M phases prevail. The theoretical results, obtained analytically from the master equation, and the numerical simulations are in good agreement.
Re-evaluation of Zospeumschaufussi von Frauenfeld, 1862 and Z.suarezi Gittenberger, 1980, including the description of two new Iberian species using Computer Tomography (CT) (Eupulmonata, Ellobioidea, Carychiidae)

Abstract The present study aims to clarify the confused taxonomy of Z.schaufussi von Frauenfeld, 1862 and Zospeumsuarezi Gittenberger, 1980. Revision of Iberian Zospeum micro snails is severely hindered by uncertainties regarding the identity of the oldest Iberian Zospeum species, Z.schaufussi von Frauenfeld, 1862. In this paper, we clarify its taxonomic status by designating a lectotype from the original syntype series and by describing its internal and external shell morphology. Using SEM-EDX, we attempt to identify the area of the type locality cave more precisely than “a cave in Spain”. The shell described and illustrated by Gittenberger (1980) as Z.schaufussi appears not to be conspecific with the lectotype shell, and is considered a separate species, Z.gittenbergeri Jochum, Prieto & De Winter, sp. n. Zospeumsuarezi was described from various caves in NW Spain. Study of the type material reveals that these shells are not homogenous in shell morphology. The holotype shell of Z.suarezi is imaged here for the first time. The paratype shell, illustrated by Gittenberger (1980) from a distant, second cave, is described as Zospeumpraetermissum Jochum, Prieto & De Winter, sp. n. The shell selected here as lectotype of Z.schaufussi, was also considered a paratype of Z.suarezi by Gittenberger (1980). Since this specimen is morphologically very similar to topotypic shells of Z.suarezi, the latter species is considered a junior synonym of Z.schaufussi (syn. n.). The internal shell morphology of all these taxa is described and illustrated using X-ray Micro Computer Tomography (Micro-CT).
Multiple variants of the type VII secretion system in Gram-positive bacteria

Type VII secretion systems (T7SS) are found in bacteria across the Bacillota and Actinomycetota phyla and have been well described in Staphylococcus aureus, Bacillus subtilis and pathogenic mycobacteria. The T7SS from Actinomycetota and Bacillota share two common components, a membrane-bound EccC/EssC ATPase and EsxA, a small helical hairpin protein of the WXG100 family. However, they also have additional phylum-specific components, and as a result they are termed the T7SSa (Actinomycetota) and T7SSb (Bacillota), respectively. Here we identify additional organisations of the T7SS across these two phyla and describe eight additional T7SS subtypes which we have named T7SSc – T7SSj. T7SSd is found exclusively in Actinomycetota including the Olselnella and Bifodobacterium genus, whereas the other seven are found only in Bacillota. All of the novel subtypes contain the canonical ATPase (TsxC) and the WXG100-family protein (TsxA). Most of them also contain a small ubiquitin-related protein, TsxB, related to the T7SSb EsaB/YukB component. Protein kinases, phosphatases and forkhead associated (FHA) proteins are often encoded in the novel T7SS gene clusters. Candidate substrates of these novel T7SS subtypes include LXG-domain and RHS proteins. Predicted substrates are frequently encoded alongside genes for additional small WXG100-related proteins that we speculate serve as co-secretion partners. Collectively our findings reveal unexpected diversity in the T7SS in Gram-positive bacteria.
A general platform for targeting MHC-II antigens via a single loop

Class-II major histocompatibility complexes (MHC-IIs) are central to the communications between CD4+ T cells and antigen presenting cells (APCs), but intrinsic structural features associated with MHC-II make it difficult to develop a general targeting system with high affinity and antigen specificity. Here, we introduce a protein platform, Targeted Recognition of Antigen-MHC Complex Reporter for MHC-II (TRACeR-II), to enable the rapid development of peptide-specific MHC-II binders. TRACeR-II has a small helical bundle scaffold and uses an unconventional mechanism to recognize antigens via a single loop. This unique antigen-recognition mechanism renders this platform highly versatile and amenable to direct structural modeling of the interactions with the antigen. We demonstrate that TRACeR-II binders can be rapidly evolved across multiple alleles, while computational protein design can produce specific binding sequences for a SARS-CoV-2 peptide of unknown complex structure. TRACeR-II sheds light on a simple and straightforward approach to address the MHC peptide targeting challenge, without relying on combinatorial selection on complementarity determining region (CDR) loops. It presents a promising basis for further exploration in immune response modulation as well as a broad range of theragnostic applications.
Scale-Dependent Coding of the Hippocampus in Relational Memory

Memory, woven into the very fabric of consciousness, serves as a time-traveling vessel in the mind, using detailed recollections not just for nostalgic reflection but as an abstract map for charting unknown futures. Here, we employed fMRI to investigate how the hippocampus (HPC) encodes detailed experiences into abstract knowledge (i.e., formation) and uses this knowledge for decision making (i.e., utilization) when human participants learned and then utilized spatial-temporal relations in a relational memory task. We found a functional gradient along the anterior-posterior axis of the HPC, characterized by representational similarity and functional connectivity with the autobiographical network. Here, the posterior HPC was more actively engaged in memory formation, whereas the anterior HPC was predominantly involved in memory utilization. Our computational modeling of relational memory further established a causal link between this functional gradient and the HPC’s well-documented anatomical gradient, as optimal task performance arose from a combination of a fine-grained representation of past experiences by the posterior HPC and a coarser representation of abstract knowledge for future planning by the anterior HPC. This scale-dependent coding scheme led to the emergence of grid-like, heading direction-like, and place-like units in our neural network model, analogous to those discovered in biological brains. Taken together, our study revealed the HPC’s functional gradient in representing relational memory, and further connected it to the anatomical gradient of place cells, supporting a unified framework where both spatial and episodic memory rely on relational representations that integrate spatial localization with temporal continuity.
Ataxia Telangiectasia patient-derived neuronal and brain organoid models reveal mitochondrial dysfunction and oxidative stress

Ataxia Telangiectasia (AT) is a rare genetic disorder caused by mutations in the ATM gene and is characterized by oxidative stress, premature ageing, and progressive neurodegeneration of the cerebellum. The molecular mechanisms driving the neurological defects AT remain unclear, mainly due to lack of human neuronal models. Here, we use AT patient-derived pluripotent stem cells (iPSCs) and iPSC-derived neurons and brain organoids to comprehensively explore mitochondrial dysfunction, oxidative stress, and senescence phenotypes. We identified mislocalisation of mitochondria, a prevailing reduction in mitochondrial membrane potential, and increased oxidative stress in AT patient-derived iPSC and neuronal cultures that was restored by ATM gene correction. Cortical brain organoids from AT patients also display extensive oxidative stress, increased levels of senescence, and impaired neuronal function that could be counteracted with antioxidant treatment. Transcriptomic analysis identified disruptions in regulatory networks related to mitochondrial function and maintenance, including alterations in the PARP/SIRT signalling axis and dysregulation of key mitophagy and mitochondrial fission-fusion processes. Our study reveals that progressive mitochondrial dysfunction and aberrant ROS production are hallmarks of AT, and lead us to conclude that ATM is a master regulator of mitochondrial homeostasis.
Suspension Electrospinning of Decellularized Extracellular Matrix

Decellularized extracellular matrices (dECM) have strong regenerative potential as tissue engineering scaffolds; however, current clinical options for dECM are limited to freeze-drying its native form into sheets. Electrospinning is a versatile scaffold fabrication technique that allows control of macro- and microarchitecture. It remains challenging to electrospin dECM; which has led researchers to either blend it with synthetic materials or use enzymatic digestion to fully solubilize the dECM. Both strategies reduce the innate bioactivity of dECM and limit its regenerative potential. Herein, we developed a new suspension electrospinning method to fabricate a pure dECM scaffold that retains its innate bioactivity. Systematic investigation of suspension parameters was used to identify critical rheological properties required to instill “spinnability,” including homogenization, concentration, and particle size. Homogenization enhanced particle interaction to impart the requisite elastic behavior to withstand electrostatic drawing without breaking. A direct correlation between concentration and viscosity was observed that altered fiber morphology; whereas, particle size had minimal impact on suspension properties and fiber morphology. The versatility of this new method was demonstrated by electrospinning dECM with three common decellularization techniques (Abraham, Badylak, Luo) and tissue origins (intestinal submucosa, heart, skin). Bioactivity retention after electrospinning was confirmed using cell proliferation, angiogenesis, and macrophage assays. Collectively, these findings provide a framework for researchers to electrospin dECM for diverse tissue engineering applications.