Miracle-dz/UniFAR-FTUs huggingface dataset:
Membrane proteins synthesized but not processed by isolated maize chloroplasts

One-dimensional maps of proteolytic fragments generated by digestion with Staphylococcus aureus protease in sodium dodecyl sulfate (SDS) were used to identify three polypeptides synthesized by isolated Zea mays chloroplasts. This technique does not depend upon proper incorporation of the newly synthesized polypeptides into a more complex structure for their identification. The only preliminary purification required is electrophoretic separation on SDS-polyacrylamide gels. The pattern of radioactive fragments from labeled proteins which co-migrate with the alpha and beta subunits of chloroplast coupling factor (CF1) corresponds precisely to the pattern of stainable fragments derived from subunits of the purified enzyme. A 34,500-dalton protein is the major membrane-associated product of protein synthesis by isolated maize chloroplasts. From the similarity in the fragments formed by digestion with S. aureus protease, it appears that this radioactive protein is probably a precursor of a 32,000-dalton protein which is a component of the thylakoid. The alpha and beta subunits of CF1 newly synthesized by isolated chloroplasts are not fully extractable by procedures which normally solubilize the enzyme from membranes. The 34,500-dalton protein is not processed to the 32,000-dalton form in any great amount by isolated chloroplasts. A 19,000-dalton fragment of the 32,000-dalton protein is protected from digestion when thylakoids are treated with proteases, while the newly synthesized 34,500-dalton protein is fully susceptible. The isolated chloroplast does not appear to be able to fully integrate these newly made proteins into the membrane structure.
Acoustoelectric non-local spin wave power detector for studying magnon-phonon coupling

We have developed a simple detection scheme to study spin waves excited by surface acoustic wave (SAW) in ferromagnetic thin films. Metallic antennas made of Ta and a ferromagnetic element are placed along the SAW propagation path. The SAW excites spin waves in the ferromagnetic element and induces acoustoelectric current in the antennas, which are detected as a DC voltage. The DC voltage takes an extremum at the spin wave resonance condition, which demonstrates that the antenna acts as a non-local spin wave detector. The antennas placed before and after the ferromagnetic element along the SAW propagation path can probe spin wave excitation from reflected and transmitted SAWs, respectively. Interestingly, we find characteristics of spin wave excitations that are different for the reflected and transmitted SAWs: the former excites spin waves with larger frequency with broader linewidth compared to the latter. The results show that the antennas act as a non-local spin wave power detector and can be used to map out the spin wave spectra in a unique way, providing insights into the magnon-phonon coupling in magnetic nanostructures fabricated on phononic SAW devices.
MARPF: Multi-Agent and Multi-Rack Path Finding

In environments where many automated guided vehicles (AGVs) operate, planning efficient, collision-free paths is essential. Related research has mainly focused on environments with static passages, resulting in space inefficiency. We define multi-agent and multi-rack path finding (MARPF) as the problem of planning paths for AGVs to convey target racks to their designated locations in environments without passages. In such environments, an AGV without a rack can pass under racks, whereas an AGV with a rack cannot pass under racks to avoid collisions. MARPF entails conveying the target racks without collisions, while the other obstacle racks are positioned without a specific arrangement. AGVs are essential for relocating other racks to prevent any interference with the target racks. We formulated MARPF as an integer linear programming problem in a network flow. To distinguish situations in which an AGV is or is not loading a rack, the proposed method introduces two virtual layers into the network. We optimized the AGVs' movements to move obstacle racks and convey the target racks. The formulation and applicability of the algorithm were validated through numerical experiments. The results indicated that the proposed algorithm addressed issues in environments with dense racks.
QCD effects in electroweak $WZjj$ production at current and future hadron colliders

We present new features for the implementation of $WZjj$ production via vector-boson scattering in the framework of the POWHEG BOX program. In particular, previously unavailable semi-leptonic and fully hadronic decay modes of the intermediate vector bosons are provided, and operators of dimension six in an effective-field theory approach to account for physics beyond the Standard Model in the electroweak sector are included. For selected applications phenomenological results are provided to illustrate the capabilities of the new program. The impact of the considered dimension-six operators on experimentally accessible distributions is found to be small for current LHC energies, but enhanced in the kinematic reach of a potential future hadron collider with an energy of 100 TeV. The relevance of fully accounting for spin correlations and off-shell effects in the decay system is explored by a comparison with results obtained with the MadSpin tool that are based on an approximate treatment of the leptonic final state resulting from vector boson scattering processes. For selected semi-leptonic and hadronic decay modes we demonstrate the sensitivity of realistic signal selection procedures on QCD corrections and parton-shower effects.
Empowering Air Travelers: A Chatbot for Canadian Air Passenger Rights

The Canadian air travel sector has seen a significant increase in flight delays, cancellations, and other issues concerning passenger rights. Recognizing this demand, we present a chatbot to assist passengers and educate them about their rights. Our system breaks a complex user input into simple queries which are used to retrieve information from a collection of documents detailing air travel regulations. The most relevant passages from these documents are presented along with links to the original documents and the generated queries, enabling users to dissect and leverage the information for their unique circumstances. The system successfully overcomes two predominant challenges: understanding complex user inputs, and delivering accurate answers, free of hallucinations, that passengers can rely on for making informed decisions. A user study comparing the chatbot to a Google search demonstrated the chatbot's usefulness and ease of use. Beyond the primary goal of providing accurate and timely information to air passengers regarding their rights, we hope that this system will also enable further research exploring the tradeoff between the user-friendly conversational interface of chatbots and the accuracy of retrieval systems.
TT-BLIP: Enhancing Fake News Detection Using BLIP and Tri-Transformer

Detecting fake news has received a lot of attention. Many previous methods concatenate independently encoded unimodal data, ignoring the benefits of integrated multimodal information. Also, the absence of specialized feature extraction for text and images further limits these methods. This paper introduces an end-to-end model called TT-BLIP that applies the bootstrapping language-image pretraining for unified vision-language understanding and generation (BLIP) for three types of information: BERT and BLIP\textsubscript{Txt} for text, ResNet and BLIP\textsubscript{Img} for images, and bidirectional BLIP encoders for multimodal information. The Multimodal Tri-Transformer fuses tri-modal features using three types of multi-head attention mechanisms, ensuring integrated modalities for enhanced representations and improved multimodal data analysis. The experiments are performed using two fake news datasets, Weibo and Gossipcop. The results indicate TT-BLIP outperforms the state-of-the-art models.
Software Analysis Techniques to Approximate Data Centric Direct Measurements

Data centric analysis using direct measurements has been established as a successful performance analysis technique. The information gathered with this technique can be used to address data locality problems and other issues. Existing approaches rely on special hardware support which is needed to negate a ‘skid’ factor. Our approach is viable on hardware where the skid factor is an issue. Prior methods also rely on maintaining runtime information about memory allocation addresses for variables, which may lead to program perturbation. Our approach uses software analysis to eliminate the need for maintaining allocation and free records. We show that by using heuristics our technique can attribute data centric values to program variables and maintain the approximate rank-order found by using traditional techniques.
Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation

This paper focuses on the sim-to-real issue of RGB-D grasp detection and formulates it as a domain adaptation problem. In this case, we present a global-to-local method to address hybrid domain gaps in RGB and depth data and insufficient multi-modal feature alignment. First, a self-supervised rotation pre-training strategy is adopted to deliver robust initialization for RGB and depth networks. We then propose a global-to-local alignment pipeline with individual global domain classifiers for scene features of RGB and depth images as well as a local one specifically working for grasp features in the two modalities. In particular, we propose a grasp prototype adaptation module, which aims to facilitate fine-grained local feature alignment by dynamically updating and matching the grasp prototypes from the simulation and real-world scenarios throughout the training process. Due to such designs, the proposed method substantially reduces the domain shift and thus leads to consistent performance improvements. Extensive experiments are conducted on the GraspNet-Planar benchmark and physical environment, and superior results are achieved which demonstrate the effectiveness of our method.
Memory formation in dense persistent active matter

Protocol-dependent states in structural glasses can encode a disordered, yet retrievable memory. While training such materials is typically done via a global drive, such as external shear, in dense active matter the driving is instead local and spatio-temporally correlated. Here we focus on the impact of such spatial correlation on memory formation. We investigate the mechanical response of a dense amorphous packing of athermal particles, subject to an oscillatory quasistatic driving with a tunable spatial correlation, akin to the instantaneous driving pattern in active matter. We find that the capacity to encode memory can be rendered comparable upon a proper rescaling on the spatial correlation, whereas the efficiency in memory formation increases with motion cooperativity.
On systems of fractional nonlinear partial differential equations

The work considers a system of fractional order partial differential equations. The existence and uniqueness theorems for the classical solution of initial-boundary value problems are proved in two cases: 1) the right-hand side of the equation does not depend on the solution of the problem and 2) it depends on the solution, but at the same time satisfies the classical Lipschitz condition with respect to this variable and an additional condition which guarantees a global existence of the solution. Sufficient conditions are found (in some cases they are necessary) on the initial function and on the right-hand side of the equation, which ensure the existence of a classical solution. In previously known works, linear but more general systems of fractional pseudodifferential equations were considered and the existence of a weak solution was proven in the special classes of distributions.
On high-dimensional classification by sparse generalized Bayesian logistic regression

This work addresses the problem of high-dimensional classification by exploring the generalized Bayesian logistic regression method under a sparsity-inducing prior distribution. The method involves utilizing a fractional power of the likelihood resulting the fractional posterior. Our study yields concentration results for the fractional posterior, not only on the joint distribution of the predictor and response variable but also for the regression coefficients. Significantly, we derive novel findings concerning misclassification excess risk bounds using sparse generalized Bayesian logistic regression. These results parallel recent findings for penalized methods in the frequentist literature. Furthermore, we extend our results to the scenario of model misspecification, which is of critical importance.
Unimodal Multi-Task Fusion for Emotional Mimicry Prediction

In this study, we propose a methodology for the Emotional Mimicry Intensity (EMI) Estimation task within the context of the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild. Our approach leverages the Wav2Vec 2.0 framework, pre-trained on a comprehensive podcast dataset, to extract a broad range of audio features encompassing both linguistic and paralinguistic elements. We enhance feature representation through a fusion technique that integrates individual features with a global mean vector, introducing global contextual insights into our analysis. Additionally, we incorporate a pre-trained valence-arousal-dominance (VAD) module from the Wav2Vec 2.0 model. Our fusion employs a Long Short-Term Memory (LSTM) architecture for efficient temporal analysis of audio data. Utilizing only the provided audio data, our approach demonstrates significant improvements over the established baseline.
BeRGeR: Byzantine-Robust Geometric Routing

We present BeRGeR: the first asynchronous geometric routing algorithm that guarantees delivery of a message despite a Byzantine fault without relying on cryptographic primitives or randomization. The communication graph is a planar embedding that remains three-connected if all edges intersecting the source-target line segment are removed. We prove the algorithm correct and estimate its message complexity.
HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation

Being able to understand visual scenes is a precursor for many downstream tasks, including autonomous driving, robotics, and other vision-based approaches. A common approach enabling the ability to reason over visual data is Scene Graph Generation (SGG); however, many existing approaches assume undisturbed vision, i.e., the absence of real-world corruptions such as fog, snow, smoke, as well as non-uniform perturbations like sun glare or water drops. In this work, we propose a novel SGG benchmark containing procedurally generated weather corruptions and other transformations over the Visual Genome dataset. Further, we introduce a corresponding approach, Hierarchical Knowledge Enhanced Robust Scene Graph Generation (HiKER-SGG), providing a strong baseline for scene graph generation under such challenging setting. At its core, HiKER-SGG utilizes a hierarchical knowledge graph in order to refine its predictions from coarse initial estimates to detailed predictions. In our extensive experiments, we show that HiKER-SGG does not only demonstrate superior performance on corrupted images in a zero-shot manner, but also outperforms current state-of-the-art methods on uncorrupted SGG tasks. Code is available at https://github.com/zhangce01/HiKER-SGG.
Glanceable Data Visualizations for Older Adults: Establishing Thresholds and Examining Disparities Between Age Groups

We present results of a replication study on smartwatch visualizations with adults aged 65 and older. The older adult population is rising globally, coinciding with their increasing interest in using small wearable devices, such as smartwatches, to track and view data. Smartwatches, however, pose challenges to this population: fonts and visualizations are often small and meant to be seen at a glance. How concise design on smartwatches interacts with aging-related changes in perception and cognition, however, is not well understood. We replicate a study that investigated how visualization type and number of data points affect glanceable perception. We observe strong evidence of differences for participants aged 75 and older, sparking interesting questions regarding the study of visualization and older adults. We discuss first steps toward better understanding and supporting an older population of smartwatch wearers and reflect on our experiences working with this population. Supplementary materials are available at https://osf.io/7x4hq/.
Peer provided cell-like networks built out of thin air

The success of Wi-Fi technology as an efficient and low-cost last-mile access solution has enabled massive spontaneous deployments generating storms of beacons all across the globe. Emerging location systems are using these beacons to observe mobility patterns of people through portable or wearable devices and offer use-cases that can help solve critical problems in the developing world. In this paper, we design and develop a novel prototype to organise these spontaneous deployments of Access Points into what we call virtual cells (vcells). We compute virtual cells from a list of Access Points collected from different active scans for a geographical region. We argue that virtual cells can be encoded using Bloom filters to implement the location process. Lastly, we present two illustrative use-cases to showcase the suitability and challenges of the technique.
PE-Planner: A Performance-Enhanced Quadrotor Motion Planner for Autonomous Flight in Complex and Dynamic Environments

The role of a motion planner is pivotal in quadrotor applications, yet existing methods often struggle to adapt to complex environments, limiting their ability to achieve fast, safe, and robust flight. In this letter, we introduce a performance-enhanced quadrotor motion planner designed for autonomous flight in complex environments including dense obstacles, dynamic obstacles, and unknown disturbances. The global planner generates an initial trajectory through kinodynamic path searching and refines it using B-spline trajectory optimization. Subsequently, the local planner takes into account the quadrotor dynamics, estimated disturbance, global reference trajectory, control cost, time cost, and safety constraints to generate real-time control inputs, utilizing the framework of model predictive contouring control. Both simulations and real-world experiments corroborate the heightened robustness, safety, and speed of the proposed motion planner. Additionally, our motion planner achieves flights at more than 6.8 m/s in a challenging and complex racing scenario.
Graph Neural Networks for Learning Equivariant Representations of Neural Networks

Neural networks that process the parameters of other neural networks find applications in domains as diverse as classifying implicit neural representations, generating neural network weights, and predicting generalization errors. However, existing approaches either overlook the inherent permutation symmetry in the neural network or rely on intricate weight-sharing patterns to achieve equivariance, while ignoring the impact of the network architecture itself. In this work, we propose to represent neural networks as computational graphs of parameters, which allows us to harness powerful graph neural networks and transformers that preserve permutation symmetry. Consequently, our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations, predicting generalization performance, and learning to optimize, while consistently outperforming state-of-the-art methods. The source code is open-sourced at https://github.com/mkofinas/neural-graphs.
3D Object Digitization: Majority Interpolation and Marching Cube

In a previous paper, we showed that a 3D object can be digitized without changing the topology if the object is r-regular and if the reconstruction method fulfills certain requirements. In this paper, we give two important examples for such reconstruction methods. First, we introduce majority interpolation, an algorithm to interpolate sampling points at doubled resolution such that topological ambiguities are resolved. Second, we show how the well-known marching cubes algorithm has to be modified such that it is topology preserving. This is the first approach of digitizing 3D objects which guarantees topology preservation for voxel-based or polygonal surface-based reconstructions
Adaptive Multilevel Neural Networks for Parametric PDEs with Error Estimation

To solve high-dimensional parameter-dependent partial differential equations (pPDEs), a neural network architecture is presented. It is constructed to map parameters of the model data to corresponding finite element solutions. To improve training efficiency and to enable control of the approximation error, the network mimics an adaptive finite element method (AFEM). It outputs a coarse grid solution and a series of corrections as produced in an AFEM, allowing a tracking of the error decay over successive layers of the network. The observed errors are measured by a reliable residual based a posteriori error estimator, enabling the reduction to only few parameters for the approximation in the output of the network. This leads to a problem adapted representation of the solution on locally refined grids. Furthermore, each solution of the AFEM is discretized in a hierarchical basis. For the architecture, convolutional neural networks (CNNs) are chosen. The hierarchical basis then allows to handle sparse images for finely discretized meshes. Additionally, as corrections on finer levels decrease in amplitude, i.e., importance for the overall approximation, the accuracy of the network approximation is allowed to decrease successively. This can either be incorporated in the number of generated high fidelity samples used for training or the size of the network components responsible for the fine grid outputs. The architecture is described and preliminary numerical examples are presented.
Fostering co-operative behaviour through social intervention

The emergence and maintenance of co-operation in a society of agents is an important issue and some recent research has explored the role that can be played by a functional model of emotions. For example, it has been shown that the emotions of gratitude and anger can be used to produce co-operative behaviour in a public goods game from agents acting solely in accordance with their current emotional state. The effectiveness of these emotions in producing co-operation depends on the emotional volatility of the agents, which determines the strength of these emotions required to alter behaviour. Often, however, dysfunctional relationships develop, which impairs the performance of the society as a whole. In this paper we explore through a series of computational simulations how interventions by society can be used to correct dysfunctional behaviour. The results of our simulations show that enforcement of co-operative behaviour and education to alter emotional characters can improve overall performance in the dysfunctional cases and that different interventions are appropriate given different initial circumstances.