Miracle-dz/UniFAR-FTUs huggingface dataset:
Global temporal registration of multiple non-rigid surface sequences

In this paper we consider the problem of aligning multiple non-rigid surface mesh sequences into a single temporally consistent representation of the shape and motion. A global alignment graph structure is introduced which uses shape similarity to identify frames for inter-sequence registration. Graph optimisation is performed to minimise the total non-rigid deformation required to register the input sequences into a common structure. The resulting global alignment ensures that all input sequences are resampled with a common mesh structure which preserves the shape and temporal correspondence. Results demonstrate temporally consistent representation of several public databases of mesh sequences for multiple people performing a variety of motions with loose clothing and hair.
Approximation Ratio of the Min-Degree Greedy Algorithm for Maximum Independent Set on Interval and Chordal Graphs

In this article we prove that the minimum-degree greedy algorithm, with adversarial tie-breaking, is a $(2/3)$-approximation for the Maximum Independent Set problem on interval graphs. We show that this is tight, even on unit interval graphs of maximum degree 3. We show that on chordal graphs, the greedy algorithm is a $(1/2)$-approximation and that this is again tight. These results contrast with the known (tight) approximation ratio of $\frac{3}{\Delta+2}$ of the greedy algorithm for general graphs of maximum degree $\Delta$.
Toward Adaptive Cooperation: Model-Based Shared Control Using LQ-Differential Games

This paper introduces a novel model-based adaptive shared control to allow for the identification and design challenge for shared-control systems, in which humans and automation share control tasks. The main challenge is the adaptive behavior of the human in such shared control interactions. Consequently, merely identifying human behavior without considering automation is insufficient and often leads to inadequate automation design. Therefore, this paper proposes a novel solution involving online identification of the human and the adaptation of shared control using Linear-Quadratic differential games. The effectiveness of the proposed online adaptation is analyzed in simulations and compared with a non-adaptive shared control from the state of the art. Finally, the proposed approach is tested through human-in-the-loop experiments, highlighting its suitability for real-time applications.
Compact 3D Gaussian Splatting For Dense Visual SLAM

Recent work has shown that 3D Gaussian-based SLAM enables high-quality reconstruction, accurate pose estimation, and real-time rendering of scenes. However, these approaches are built on a tremendous number of redundant 3D Gaussian ellipsoids, leading to high memory and storage costs, and slow training speed. To address the limitation, we propose a compact 3D Gaussian Splatting SLAM system that reduces the number and the parameter size of Gaussian ellipsoids. A sliding window-based masking strategy is first proposed to reduce the redundant ellipsoids. Then we observe that the covariance matrix (geometry) of most 3D Gaussian ellipsoids are extremely similar, which motivates a novel geometry codebook to compress 3D Gaussian geometric attributes, i.e., the parameters. Robust and accurate pose estimation is achieved by a global bundle adjustment method with reprojection loss. Extensive experiments demonstrate that our method achieves faster training and rendering speed while maintaining the state-of-the-art (SOTA) quality of the scene representation.
Systematic Management of Variability in UML-based Software Product Lines

This paper presents SMarty, a variability management approach for UML- based software product lines (PL). SMarty is supported by a UML profile, the SMar- tyProfile, and a process for managing variabilities, the SMartyProcess. SMartyProfile aims at representing variabilities, variation points, and variants in UML models by applying a set of stereotypes. SMartyProcess consists of a set of activities that is systematically executed to trace, identify, and control variabilities in a PL based on SMarty. It also identifies variability implementation mechanisms and analyzes specific product configurations. In addition, a more comprehensive application of SMarty is presented using SEI's Arcade Game Maker PL. An evaluation of SMarty and related work are discussed.
Can LLM-Augmented autonomous agents cooperate?, An evaluation of their cooperative capabilities through Melting Pot

As the field of AI continues to evolve, a significant dimension of this progression is the development of Large Language Models and their potential to enhance multi-agent artificial intelligence systems. This paper explores the cooperative capabilities of Large Language Model-augmented Autonomous Agents (LAAs) using the well-known Meltin Pot environments along with reference models such as GPT4 and GPT3.5. Preliminary results suggest that while these agents demonstrate a propensity for cooperation, they still struggle with effective collaboration in given environments, emphasizing the need for more robust architectures. The study's contributions include an abstraction layer to adapt Melting Pot game scenarios for LLMs, the implementation of a reusable architecture for LLM-mediated agent development - which includes short and long-term memories and different cognitive modules, and the evaluation of cooperation capabilities using a set of metrics tied to the Melting Pot's"Commons Harvest"game. The paper closes, by discussing the limitations of the current architectural framework and the potential of a new set of modules that fosters better cooperation among LAAs.
Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation

Sequential recommender systems (SRS) are designed to predict users' future behaviors based on their historical interaction data. Recent research has increasingly utilized contrastive learning (CL) to leverage unsupervised signals to alleviate the data sparsity issue in SRS. In general, CL-based SRS first augments the raw sequential interaction data by using data augmentation strategies and employs a contrastive training scheme to enforce the representations of those sequences from the same raw interaction data to be similar. Despite the growing popularity of CL, data augmentation, as a basic component of CL, has not received sufficient attention. This raises the question: Is it possible to achieve superior recommendation results solely through data augmentation? To answer this question, we benchmark eight widely used data augmentation strategies, as well as state-of-the-art CL-based SRS methods, on four real-world datasets under both warm- and cold-start settings. Intriguingly, the conclusion drawn from our study is that, certain data augmentation strategies can achieve similar or even superior performance compared with some CL-based methods, demonstrating the potential to significantly alleviate the data sparsity issue with fewer computational overhead. We hope that our study can further inspire more fundamental studies on the key functional components of complex CL techniques. Our processed datasets and codes are available at https://github.com/AIM-SE/DA4Rec.
Prioritizing Test Case Generation for Software Testing in High Level Programming Development Environment

Abstract—Software testing is very tedious process and during the software development, testing needs time, efforts and money. Testing and retesting is part of development process and lots of efforts are needed for doing this. Detecting faults and errors in the early stages of development is the main task of any testing team. The entire test suits are written for the same target and the test suits grows as the software evolves over the period of time. Resources are very limited and due to resource constraints like cost, time and money, it is advised to prioritize the execution of test cases so that it can increase chances of early detection of faults in the software development process [1]. In this paper, high level language programming paradigm is considered for the development environment and algorithmic approach of design is considered. In this paper we present a new approach to prioritize test cases of particular software based on the requirements given by the client using high level functional programming language. Running all test cases in a normal Test suite, however, can consume an inordinate amount of time so, its main purpose is to improve rate of fault detection by prioritizing the test cases in a very short span of time and release the updated software to the customer [2]. In this paper a new test case prioritization algorithm, which calculates using data mining technique K-Nearest neighbor, which in turn uses Euclidean distance method approach to prioritize the test cases is proposed. General Terms Software Systems, Software Engineering and Software Testing
Importance of the matriline for genomic imprinting, brain development and behaviour

Mammalian brain development commences during foeto-placental development and is strongly influenced by the epigenetic regulation of imprinted genes. The foetal placenta exerts considerable influence over the functioning of the adult maternal hypothalamus, and this occurs at the same time as the foetus itself is developing a hypothalamus. Thus, the action and interaction of two genomes in one individual, the mother, has provided a template for co-adaptive functions across generations that are important for maternal care and resource transfer, while co-adaptively shaping the mothering capabilities of each subsequent generation. The neocortex is complex, enabling behavioural diversity and cultural learning such that human individuals are behaviourally unique. Retrotransposons may, in part, be epigenetic mediators of such brain diversity. Interestingly some imprinted genes are themselves retrotransposon-derived, and retrotransposon silencing by DNA methylation is thought to have contributed to the evolutionary origins of imprint control regions. The neocortex has evolved to be adaptable and sustain both short-term and long-term synaptic connections that underpin learning and memory. The adapted changes are not themselves inherited, but the predisposing mechanisms for such epigenetic changes are heritable. This provides each generation with the same ability to make new adaptations while constrained by a transgenerational knowledge-based predisposition to preserve others.
Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference

For training an encoder network to perform amortized variational inference, the Kullback-Leibler (KL) divergence from the exact posterior to its approximation, known as the inclusive or forward KL, is an increasingly popular choice of variational objective due to the mass-covering property of its minimizer. However, minimizing this objective is challenging. A popular existing approach, Reweighted Wake-Sleep (RWS), suffers from heavily biased gradients and a circular pathology that results in highly concentrated variational distributions. As an alternative, we propose SMC-Wake, a procedure for fitting an amortized variational approximation that uses likelihood-tempered sequential Monte Carlo samplers to estimate the gradient of the inclusive KL divergence. We propose three gradient estimators, all of which are asymptotically unbiased in the number of iterations and two of which are strongly consistent. Our method interleaves stochastic gradient updates, SMC samplers, and iterative improvement to an estimate of the normalizing constant to reduce bias from self-normalization. In experiments with both simulated and real datasets, SMC-Wake fits variational distributions that approximate the posterior more accurately than existing methods.
Investigating the Benefits of Projection Head for Representation Learning

An effective technique for obtaining high-quality representations is adding a projection head on top of the encoder during training, then discarding it and using the pre-projection representations. Despite its proven practical effectiveness, the reason behind the success of this technique is poorly understood. The pre-projection representations are not directly optimized by the loss function, raising the question: what makes them better? In this work, we provide a rigorous theoretical answer to this question. We start by examining linear models trained with self-supervised contrastive loss. We reveal that the implicit bias of training algorithms leads to layer-wise progressive feature weighting, where features become increasingly unequal as we go deeper into the layers. Consequently, lower layers tend to have more normalized and less specialized representations. We theoretically characterize scenarios where such representations are more beneficial, highlighting the intricate interplay between data augmentation and input features. Additionally, we demonstrate that introducing non-linearity into the network allows lower layers to learn features that are completely absent in higher layers. Finally, we show how this mechanism improves the robustness in supervised contrastive learning and supervised learning. We empirically validate our results through various experiments on CIFAR-10/100, UrbanCars and shifted versions of ImageNet. We also introduce a potential alternative to projection head, which offers a more interpretable and controllable design.
EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes

Self-supervised monocular depth estimation holds significant importance in the fields of autonomous driving and robotics. However, existing methods are typically trained and tested on standard datasets, overlooking the impact of various adverse conditions prevalent in real-world applications, such as rainy days. As a result, it is commonly observed that these methods struggle to handle these challenging scenarios. To address this issue, we present EC-Depth, a novel self-supervised two-stage framework to achieve a robust depth estimation. In the first stage, we propose depth consistency regularization to propagate reliable supervision from standard to challenging scenes. In the second stage, we adopt the Mean Teacher paradigm and propose a novel consistency-based pseudo-label filtering strategy to improve the quality of pseudo-labels, further improving both the accuracy and robustness of our model. Extensive experiments demonstrate that our method achieves accurate and consistent depth predictions in both standard and challenging scenarios, surpassing existing state-of-the-art methods on KITTI, KITTI-C, DrivingStereo, and NuScenes-Night benchmarks.
Learning Fine Pinch-Grasp Skills using Tactile Sensing from A Few Real-world Demonstrations

Imitation learning for robot dexterous manipulation, especially with a real robot setup, typically requires a large number of demonstrations. In this paper, we present a data-efficient learning from demonstration framework which exploits the use of rich tactile sensing data and achieves fine bimanual pinch grasping. Specifically, we employ a convolutional autoencoder network that can effectively extract and encode high-dimensional tactile information. Further, We develop a framework that achieves efficient multi-sensor fusion for imitation learning, allowing the robot to learn contact-aware sensorimotor skills from demonstrations. Our comparision study against the framework without using encoded tactile features highlighted the effectiveness of incorporating rich contact information, which enabled dexterous bimanual grasping with active contact searching. Extensive experiments demonstrated the robustness of the fine pinch grasp policy directly learned from few-shot demonstration, including grasping of the same object with different initial poses, generalizing to ten unseen new objects, robust and firm grasping against external pushes, as well as contact-aware and reactive re-grasping in case of dropping objects under very large perturbations. Furthermore, the saliency map analysis method is used to describe weight distribution across various modalities during pinch grasping, confirming the effectiveness of our framework at leveraging multimodal information.
Decidability of the isomorphism problem between multidimensional substitutive subshifts

An important question in dynamical systems is the classification problem, i.e., the ability to distinguish between two isomorphic systems. In this work, we study the topological factors between a family of multidimensional substitutive subshifts generated by morphisms with uniform support. We prove that it is decidable to check whether two minimal aperiodic substitutive subshifts are isomorphic, under having the same combinatorial structure. The strategy followed in this work consists of giving a complete description of the factor maps between these subshifts. Then, we deduce some interesting consequences on coalescence, automorphism groups, and the number of aperiodic symbolic factors of substitutive subshifts. We also prove other combinatorial results on these substitutions, such as the decidability of defining a subshift, the computability of the recognizability radius, and the conjugacy between substitutions with different supports.
Protein Language Models Expose Viral Mimicry and Immune Escape

Motivation Viruses elude the immune system through molecular mimicry, adopting biophysical characteristics of their host. We adapt protein language models (PLMs) to differentiate between human and viral proteins. Understanding where the immune system and our models make mistakes could reveal viral immune escape mechanisms. Results We applied pretrained deep-learning PLMs to predict viral from human proteins. Our predictors show state-of-the-art results with AUC of 99.7%. We use interpretable error analysis models to characterize viral escapers. Altogether, mistakes account for 3.9% of the sequences with viral proteins being disproportionally misclassified. Analysis of external variables, including taxonomy and functional annotations, indicated that errors typically involve proteins with low immunogenic potential, viruses specific to human hosts, and those using reverse-transcriptase enzymes for their replication. Viral families causing chronic infections and immune evasion are further enriched and their protein mimicry potential is discussed. We provide insights into viral adaptation strategies and highlight the combined potential of PLMs and explainable AI in uncovering mechanisms of viral immune escape, contributing to vaccine design and antiviral research. Availability and implementation Data and results available in https://github.com/ddofer/ProteinHumVir. Contact michall@cc.huji. ac.il
Towards the reality of 3D imaging and display

Up until now 3D television has not taken off as had been anticipated and although many televisions are 3D enabled viewers generally tend to watch without using the 3D option. This must not be taken as a failure of 3D television itself; the fact is that viewers dislike having to wear the special glasses. The next generation of 3D television must be glasses-free (autostereoscopic) and the `Towards the Reality of 3D Imaging and Display' project aims to develop the first viable autostereoscopic television display. There are arguably three technologies that are the main contenders for fulfilling the role; computational multi-later, head tracked and super multiview. In this paper we will describe these and examine the technical challenges and the pros and cons of each approach.
Transfer of Small Resting B Cells into Immunodeficient Hosts Results in the Selection of a Self-renewing Activated B Cell Population

We studied the role of bone marrow B cell production in the renewal of peripheral B cells and the feedback mechanisms that control the entry of newly formed B cells into the peripheral B cell pools. When resting lymph node B cells are injected into B cell–deficient hosts, a fraction of the transferred cells expands and constitutes a highly selected population that survives for prolonged periods of time by continuous cell renewal at the periphery. Although the number of donor B cells recovered is low, a significant fraction shows an activated phenotype, and the serum immunoglobulin (Ig)M levels are as in normal mice. This population of activated B cells is resistant to replacement by a new cohort of B cells and is able to feedback regulate both the entry of newly formed B cells into the peripheral pool and terminal differentiation. These findings suggest that peripheral B cell selection follows the first come, first served rule and that IgM-secreting cells are generated from a pool of stable activated B cells with an independent homeostasis.
Distinct changes to hippocampal and medial entorhinal circuits emerge across the progression of cognitive deficits in epilepsy

Temporal lobe epilepsy (TLE) causes pervasive and progressive memory impairments, yet the specific circuit changes that drive these deficits remain unclear. To investigate how hippocampal-entorhinal dysfunction contributes to progressive memory deficits in epilepsy, we performed simultaneous in vivo electrophysiology in hippocampus (HPC) and medial entorhinal cortex (MEC) of control and epileptic mice 3 or 8 weeks after pilocarpine-induced status epilepticus (Pilo-SE). We found that HPC synchronization deficits (including reduced theta power, coherence, and altered interneuron spike timing) emerged within 3 weeks of Pilo-SE, aligning with early-onset, relatively subtle memory deficits. In contrast, abnormal synchronization within MEC and between HPC-MEC emerged later, by 8 weeks after Pilo-SE, when spatial memory impairment was more severe. Furthermore, a distinct subpopulation of MEC layer 3 excitatory neurons (active at theta troughs) was specifically impaired in epileptic mice. Together, these findings suggest that hippocampal-entorhinal circuit dysfunction accumulates and shifts as cognitive impairment progresses in TLE.
Acetate Synthesis from H2 plus CO2 by Termite Gut Microbes

Gut microbiota from Reticulitermes flavipes termites catalyzed an H2-dependent total synthesis of acetate from CO2. Rates of H2-CO2 acetogenesis in vitro were 1.11 ± 0.37 μmol of acetate g (fresh weight)−1 h−1 (equivalent to 4.44 ± 1.47 nmol termite−1 h−1) and could account for approximately 1/3 of all the acetate produced during the hindgut fermentation. Formate was also produced from H2 + CO2, as were small amounts of propionate, butyrate, and lactate-succinate. However, H2-CO2 formicogenesis seemed largely unrelated to acetogenesis and was believed not to be a significant reaction in situ. Little or no CH4 was formed from H2 + CO2 or from acetate. H2-CO2 acetogenesis was inhibited by O2, KCN, CHCl3, and iodopropane and could be abolished by prefeeding R. flavipes with antibacterial drugs. By contrast, prefeeding R. flavipes with starch resulted in almost complete defaunation but had little effect on H2-CO2 acetogenesis, suggesting that bacteria were the acetogenic agents in the gut. H2-CO2 acetogenesis was also observed with gut microbiota from Prorhinotermes simplex, Zootermopsis angusticollis, Nasutitermes costalis, and N. nigriceps; from the wood-eating cockroach Cryptocercus punctulatus; and from the American cockroach Periplaneta americana. Pure cultures of H2-CO2-acetogenic bacteria were isolated from N. nigriceps, and a preliminary account of their morphological and physiological properties is presented. Results indicate that in termites, CO2 reduction to acetate, rather than to CH4, represents the main electron sink reaction of the hindgut fermentation and can provide the insects with a significant fraction (ca. 1/3) of their principal oxidizable energy source, acetate.