Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-interest Factorization-heads Attention for Sequential Recommendation

Accurate user interest modeling is vital for recommendation scenarios. One of the effective solutions is the sequential recommendation that relies on click behaviors, but this is not elegant in the video feed recommendation where users are passive in receiving the streaming contents and return skip or no-skip behaviors. Here skip and no-skip behaviors can be treated as negative and positive feedback, respectively. With the mixture of positive and negative feedback, it is challenging to capture the transition pattern of behavioral sequence. To do so, FeedRec has exploited a shared vanilla Transformer, which may be inelegant because head interaction of multi-heads attention does not consider different types of feedback. In this paper, we propose Dual-interest Factorization-heads Attention for Sequential Recommendation (short for DFAR) consisting of feedback-aware encoding layer, dual-interest disentangling layer and prediction layer. In the feedback-aware encoding layer, we first suppose each head of multi-heads attention can capture specific feedback relations. Then we further propose factorization-heads attention which can mask specific head interaction and inject feedback information so as to factorize the relation between different types of feedback. Additionally, we propose a dual-interest disentangling layer to decouple positive and negative interests before performing disentanglement on their representations. Finally, we evolve the positive and negative interests by corresponding towers whose outputs are contrastive by BPR loss. Experiments on two real-world datasets show the superiority of our proposed method against state-of-the-art baselines. Further ablation study and visualization also sustain its effectiveness. We release the source code here: https://github.com/tsinghua-fib-lab/WWW2023-DFAR.
The XPRESS Challenge: Xray Projectomic Reconstruction - Extracting Segmentation with Skeletons

The wiring and connectivity of neurons form a structural basis for the function of the nervous system. Advances in volume electron microscopy (EM) and image segmentation have enabled mapping of circuit diagrams (connectomics) within local regions of the mouse brain. However, applying volume EM over the whole brain is not currently feasible due to technological challenges. As a result, comprehensive maps of long-range connections between brain regions are lacking. Recently, we demonstrated that X-ray holographic nanotomography (XNH) can provide high-resolution images of brain tissue at a much larger scale than EM. In particular, XNH is wellsuited to resolve large, myelinated axon tracts (white matter) that make up the bulk of long-range connections (projections) and are critical for inter-region communication. Thus, XNH provides an imaging solution for brain-wide projectomics. However, because XNH data is typically collected at lower resolutions and larger fields-of-view than EM, accurate segmentation of XNH images remains an important challenge that we present here. In this task, we provide volumetric XNH images of cortical white matter axons from the mouse brain along with ground truth annotations for axon trajectories. Manual voxel-wise annotation of ground truth is a time-consuming bottleneck for training segmentation networks. On the other hand, skeleton-based ground truth is much faster to annotate, and sufficient to determine connectivity. Therefore, we encourage participants to develop methods to leverage skeleton-based training. To this end, we provide two types of ground-truth annotations: a small volume of voxel-wise annotations and a larger volume with skeleton-based annotations. Entries will be evaluated on how accurately the submitted segmentations agree with the ground-truth skeleton annotations.
Shortcut Detection with Variational Autoencoders

For real-world applications of machine learning (ML), it is essential that models make predictions based on well-generalizing features rather than spurious correlations in the data. The identification of such spurious correlations, also known as shortcuts, is a challenging problem and has so far been scarcely addressed. In this work, we present a novel approach to detect shortcuts in image and audio datasets by leveraging variational autoencoders (VAEs). The disentanglement of features in the latent space of VAEs allows us to discover feature-target correlations in datasets and semi-automatically evaluate them for ML shortcuts. We demonstrate the applicability of our method on several real-world datasets and identify shortcuts that have not been discovered before.
Stable Synchronous Propagation of Signals by Feedforward Networks

We analyse the dynamics of networks in which a central pattern generator (CPG) transmits signals along one or more feedforward chains in a synchronous or phase-synchronous manner. Such propagating signals are common in biology, especially in locomotion and peristalsis, and are of interest for continuum robots. We construct such networks as feedforward lifts of the CPG. If the CPG dynamics is periodic, so is the lifted dynamics. Synchrony with the CPG manifests as a standing wave, and a regular phase pattern creates a travelling wave. We discuss Liapunov, asymptotic, and Floquet stability of the lifted periodic orbit and introduce transverse versions of these conditions that imply stability for signals propagating along arbitrarily long chains. We compare these notions to a simpler condition, transverse stability of the synchrony subspace, which is equivalent to Floquet stability when nodes are 1-dimensional.
Size Effect on the Thermal Conductivity of a Type-I Clathrate

Clathrates are a materials class with an extremely low phonon thermal conductivity, which is a key ingredient for a high thermoelectric conversion efficiency. Here, we present a study on the type-I clathrate La1.2Ba6. 8Au5.8Si38. 8□1.4 directed at lowering the phonon thermal conductivity even further by forming mesoscopic wires out of it. Our hypothesis is that the interaction of the low-energy rattling modes of the guest atoms (La and Ba) with the acoustic modes, which originate mainly from the type-I clathrate framework (formed by Au and Si atoms, with some vacancies □), cuts off their dispersion and thereby tilts the balance of phonons relevant for thermal transport to long-wavelength ones. Thus, size effects are expected to set in at relatively long length scales. The structuring was carried out using a top-down approach, where the wires, ranging from 1260 nm to 630 nm in diameter, were cut from a piece of single crystal using a focused ion beam technique. Measurements of the thermal conductivity were performed with a self-heating 3ω technique down to 80 K. Indeed, they reveal a reduction of the room-temperature phonon thermal conductivity by a sizable fraction of ∼40 % for our thinnest wire, thereby confirming our hypothesis.
Temporal Video-Language Alignment Network for Reward Shaping in Reinforcement Learning

Designing appropriate reward functions for Reinforcement Learning (RL) approaches has been a significant problem, especially for complex environments such as Atari games. Utilizing natural language instructions to provide intermediate rewards to RL agents in a process known as reward shaping can help the agent in reaching the goal state faster. In this work, we propose a natural language-based reward shaping approach that maps trajectories from the Montezuma's Revenge game environment to corresponding natural language instructions using an extension of the LanguagE-Action Reward Network (LEARN) framework. These trajectory-language mappings are further used to generate intermediate rewards which are integrated into reward functions that can be utilized to learn an optimal policy for any standard RL algorithms. For a set of 15 tasks from Atari's Montezuma's Revenge game, the Ext-LEARN approach leads to the successful completion of tasks more often on average than the reward shaping approach that uses the LEARN framework and performs even better than the reward shaping framework without natural language-based rewards.
Challenges in Designing Teacher Robots with Motivation Based Gestures

Humanoid robots are increasingly being integrated into learning contexts to assist teaching and learning. However, challenges remain how to design and incorporate such robots in an educational context. As an important part of teaching includes monitoring the motivational and emotional state of the learner and adapting the interaction style and learning content accordingly, in this paper, we discuss the role of gestures displayed by a humanoid robot (i.e., Pepper robot) in a learning and teaching context and present our ongoing research on designing and developing a teacher robot.
Futuristic Variations and Analysis in Fundus Images Corresponding to Biological Traits

Fundus image captures rear of an eye, and which has been studied for the diseases identification, classification, segmentation, generation, and biological traits association using handcrafted, conventional, and deep learning methods. In biological traits estimation, most of the studies have been carried out for the age prediction and gender classification with convincing results. However, the current study utilizes the cutting-edge deep learning (DL) algorithms to estimate biological traits in terms of age and gender together with associating traits to retinal visuals. For the traits association, our study embeds aging as the label information into the proposed DL model to learn knowledge about the effected regions with aging. Our proposed DL models, named FAG-Net and FGC-Net, correspondingly estimate biological traits (age and gender) and generates fundus images. FAG-Net can generate multiple variants of an input fundus image given a list of ages as conditions. Our study analyzes fundus images and their corresponding association with biological traits, and predicts of possible spreading of ocular disease on fundus images given age as condition to the generative model. Our proposed models outperform the randomly selected state of-the-art DL models.
The Capacity Region of Distributed Multi-User Secret Sharing under The Perfect Privacy Condition

We study the distributed multi-user secret sharing (DMUSS) problem under the perfect privacy condition. In a DMUSS problem, multiple secret messages are deployed and the shares are offloaded to the storage nodes. Moreover, the access structure is extremely incomplete, as the decoding collection of each secret message has only one set, and by the perfect privacy condition such collection is also the colluding collection of all other secret messages. The secret message rate is defined as the size of the secret message normalized by the size of a share. We characterize the capacity region of the DMUSS problem when given an access structure, defined as the set of all achievable rate tuples. In the achievable scheme, we assume all shares are mutually independent and then design the decoding function based on the fact that the decoding collection of each secret message has only one set. Then it turns out that the perfect privacy condition is equivalent to the full rank property of some matrices consisting of different indeterminates and zeros. Such a solution does exist if the field size is bigger than the number of secret messages. Finally with a matching converse saying that the size of the secret is upper bounded by the sum of sizes of non-colluding shares, we characterize the capacity region of DMUSS problem under the perfect privacy condition.
Inflationary α-attractor models with singular derivative of potential

A generalization of inflationary α-attractor models (polynomial α-attractor) was recently proposed by Kallosh and Linde, in which the potential involves logarithmic functions of the inflaton so that the derivative of the potential but not potential itself has a singularity. We find that the models can lead to viable inflationary observables even without the pole in the kinetic term. Also, the generalization with a pole order other than two does not significantly change the functional form of the potential. This allows a systematic analysis of the predictions of this class of models. Our models predict larger spectral index ns and tensor-to-scalar ratio r than in the polynomial α-attractor: typically, ns around 0.97–0.98 and r observable by LiteBIRD. Taking advantage of the relatively large ns , we discuss the modification of the potential to produce primordial black holes as the whole dark matter and gravitational waves induced by curvature perturbations detectable by LISA and BBO/DECIGO, while keeping ns in agreement with the Planck/BICEP/Keck data.
Machine Learning for Synthetic Data Generation: a Review

Machine learning heavily relies on data, but real-world applications often encounter various data-related issues. These include data of poor quality, insufficient data points leading to under-fitting of machine learning models, and difficulties in data access due to concerns surrounding privacy, safety, and regulations. In light of these challenges, the concept of synthetic data generation emerges as a promising alternative that allows for data sharing and utilization in ways that real-world data cannot facilitate. This paper presents a comprehensive systematic review of existing studies that employ machine learning models for the purpose of generating synthetic data. The review encompasses various perspectives, starting with the applications of synthetic data generation, spanning computer vision, speech, natural language processing, healthcare, and business domains. Additionally, it explores different machine learning methods, with particular emphasis on neural network architectures and deep generative models. The paper also addresses the crucial aspects of privacy and fairness concerns related to synthetic data generation. Furthermore, this study identifies the challenges and opportunities prevalent in this emerging field, shedding light on the potential avenues for future research. By delving into the intricacies of synthetic data generation, this paper aims to contribute to the advancement of knowledge and inspire further exploration in synthetic data generation.
Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction

We introduce a novel robotic system for improving unseen object instance segmentation in the real world by leveraging long-term robot interaction with objects. Previous approaches either grasp or push an object and then obtain the segmentation mask of the grasped or pushed object after one action. Instead, our system defers the decision on segmenting objects after a sequence of robot pushing actions. By applying multi-object tracking and video object segmentation on the images collected via robot pushing, our system can generate segmentation masks of all the objects in these images in a self-supervised way. These include images where objects are very close to each other, and segmentation errors usually occur on these images for existing object segmentation networks. We demonstrate the usefulness of our system by fine-tuning segmentation networks trained on synthetic data with real-world data collected by our system. We show that, after fine-tuning, the segmentation accuracy of the networks is significantly improved both in the same domain and across different domains. In addition, we verify that the fine-tuned networks improve top-down robotic grasping of unseen objects in the real world.
Illustration of Wing Deformation Effects in Three-Dimensional Flapping Flight

This study numerically investigates the aerodynamic effects of deformation on three-dimensional low aspect ratio wings engaged in hover kinematics. The immersed-boundary finite-volume method is used to solve the Navier–Stokes equations. Deformations are actively prescribed and are represented as deflections about axes that transect the rectangular planform. Two distinct deformation modes associated with tip and root deflection are explored, in addition to a rigid wing counterpart. Variation of the phase between deflection and flapping enables mimicry of a range of wing behavior observed in nature. It is found the introduction of root or tip deflection can increase efficiency by as much as 19.5% or 19.0% respectively. The development of vortical structures about the wing is investigated. It is shown that while the rotational axis of the leading-edge vortex is insensitive to deformation, the shape and orientation of cores of the vortex is modified.
Diagnosing and Rectifying Vision Models using Language

Recent multi-modal contrastive learning models have demonstrated the ability to learn an embedding space suitable for building strong vision classifiers, by leveraging the rich information in large-scale image-caption datasets. Our work highlights a distinct advantage of this multi-modal embedding space: the ability to diagnose vision classifiers through natural language. The traditional process of diagnosing model behaviors in deployment settings involves labor-intensive data acquisition and annotation. Our proposed method can discover high-error data slices, identify influential attributes and further rectify undesirable model behaviors, without requiring any visual data. Through a combination of theoretical explanation and empirical verification, we present conditions under which classifiers trained on embeddings from one modality can be equivalently applied to embeddings from another modality. On a range of image datasets with known error slices, we demonstrate that our method can effectively identify the error slices and influential attributes, and can further use language to rectify failure modes of the classifier.
CLASSIFICATION, CROSS-CLASSIFICATION ANALYSIS AND GENERALIZED BLOCKMODELING

Classification, Analyse classificatoire croisee et modeles en blocs generalises. Les modeles en blocs ont ete etroitement associes avec l'analyse des reseaux sociaux et utilisent souvent des matrices symetriques individus-par-individus ou chaque case decrit un lien entre deux individus. Mais des blocs semblables, et meme equivalents, peuvent etre obtenus par l'analyse classificatoire. La classification croisee -- le croisement cartesien d'une classification des individus avec une classification des variables descriptives -- permet la generalisation de la construction des blocs et identifie aussi les blocs les plus statistiquement significatifs dans l'ensemble des donnees.
A Systematic Performance Analysis of Deep Perceptual Loss Networks Breaks Transfer Learning Conventions

Deep perceptual loss is a type of loss function in computer vision that aims to mimic human perception by using the deep features extracted from neural networks. In recent years, the method has been applied to great effect on a host of interesting computer vision tasks, especially for tasks with image or image-like outputs, such as image synthesis, segmentation, depth prediction, and more. Many applications of the method use pretrained networks, often convolutional networks, for loss calculation. Despite the increased interest and broader use, more effort is needed toward exploring which networks to use for calculating deep perceptual loss and from which layers to extract the features. This work aims to rectify this by systematically evaluating a host of commonly used and readily available, pretrained networks for a number of different feature extraction points on four existing use cases of deep perceptual loss. The use cases of perceptual similarity, super-resolution, image segmentation, and dimensionality reduction, are evaluated through benchmarks. The benchmarks are implementations of previous works where the selected networks and extraction points are evaluated. The performance on the benchmarks, and attributes of the networks and extraction points are then used as a basis for an in-depth analysis. This analysis uncovers insight regarding which architectures provide superior performance for deep perceptual loss and how to choose an appropriate extraction point for a particular task and dataset. Furthermore, the work discusses the implications of the results for deep perceptual loss and the broader field of transfer learning. The results show that deep perceptual loss deviates from two commonly held conventions in transfer learning, which suggests that those conventions are in need of deeper analysis.
First results from a 20-liter prototype dark matter detector with directional sensitivity

Astronomical and cosmological evidence suggests that 27% of the energy content of the universe is in the form of non-baryonic matter referred to as "dark matter. " Weakly interacting massive particles have long been considered attractive candidates for this dark matter and can be found in a wide variety of models of physics beyond the Standard Model. The Dark Matter Time Projection Chamber experiment uses low-pressure gas time projection chambers to search for nuclear recoils caused by interactions between nuclei inside a detector and weakly interacting massive particles in the dark matter halo of the Milky Way galaxy. These detectors are also able to reconstruct the directions of these nuclear recoils, allowing for better rejection of possible background events. This thesis describes the design of a small prototype detector and the strategies used by the DMTPC collaboration to reconstruct events, reject backgrounds, and identify nuclear recoil candidate events. It presents the results of several studies aimed at understanding background events in DMTPC detectors. Finally, this work will present the first results from a nuclear recoil search taken with this detector in a surface laboratory at MIT. Thesis Supervisor: Peter Fisher Title: Professor
Quantifying the energetics of molecular superbubbles in PHANGS galaxies

Star formation and stellar feedback are interlinked processes that redistribute energy and matter throughout galaxies. When young, massive stars form in spatially clustered environments, they create pockets of expanding gas termed superbubbles. As these processes play a critical role in shaping galaxy discs and regulating the baryon cycle, measuring the properties of superbubbles provides important input for galaxy evolution models. With wide coverage and high angular resolution (50-150 pc) of the PHANGS-ALMA $^{12}$CO (2-1) survey, we can now resolve and identify a statistically representative number of superbubbles with molecular gas in nearby galaxies. We identify superbubbles by requiring spatial correspondence between shells in CO with stellar populations identified in PHANGS-HST, and combine the properties of the stellar populations with CO to constrain feedback models and quantify their energetics. We visually identify 325 cavities across 18 PHANGS-ALMA galaxies, 88 of which have clear superbubble signatures (unbroken shells, central clusters, kinematic signatures of expansion). We measure their radii and expansion velocities using CO to dynamically derive their ages and the mechanical power driving the bubbles, which we use to compute the expected properties of the parent stellar populations driving the bubbles. We find consistency between the predicted and derived stellar ages and masses of the stellar populations if we use a supernova blast wave model that injects energy with a coupling efficiency of 10%, whereas continuous models fail to explain stellar ages we measure. Not only does this confirm molecular gas accurately traces superbubble properties, but it also provides key observational constraints for superbubble models. We also find evidence that the bubbles sweep up gas as they expand and speculate that these sites have the potential to host new generations of stars.