Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiwavelength Pulsations and Surface Temperature Distribution in the Middle-aged Pulsar B1055–52

We present a detailed study of the X-ray emission from PSR B1055–52 using XMM-Newton observations from 2019 and 2000. The phase-integrated X-ray emission from this pulsar is poorly described by existing models of neutron star atmospheres. Instead, we confirm that, similar to other middle-aged pulsars, the best-fitting spectral model consists of two blackbody components, with substantially different temperatures and emitting areas, and a nonthermal component characterized by a power law. Our phase-resolved X-ray spectral analysis using this three-component model reveals variations in the thermal emission parameters with the pulsar’s rotational phase. These variations suggest a nonuniform temperature distribution across the neutron star’s surface, including the cold thermal component and probable hot spot(s). Such a temperature distribution can be caused by external and internal heating processes, likely a combination thereof. We observe very high pulse fractions, 60%–80% in the 0.7–1.5 keV range, dominated by the hot blackbody component. This could be related to temperature nonuniformity and potential beaming effects in an atmosphere. We find indication of a second hot spot that appears at lower energies (0.15–0.3 keV) than the first hot spot (0.5–1.5 keV) in the X-ray light curves and is offset by about half a rotation period. This finding aligns with the nearly orthogonal rotator geometry suggested by radio observations of this interpulse pulsar. If the hot spots are associated with polar caps, a possible explanation for their temperature asymmetry could be an offset magnetic dipole and/or an additional toroidal magnetic field component in the neutron star crust.
Magnetic Dirac systems: Violation of bulk-edge correspondence in the zigzag limit

We consider a Dirac operator with constant magnetic field defined on a half-plane with boundary conditions that interpolate between infinite mass and zigzag. By a detailed study of the energy dispersion curves we show that the infinite mass case generically captures the profile of these curves, which undergoes a continuous pointwise deformation into the topologically different zigzag profile. Moreover, these results are applied to the bulk-edge correspondence. In particular, by means of a counterexample, we show that this correspondence does not always hold true in the zigzag case.
Enhancing Object Detection Performance for Small Objects through Synthetic Data Generation and Proportional Class-Balancing Technique: A Comparative Study in Industrial Scenarios

Object Detection (OD) has proven to be a significant computer vision method in extracting localized class information and has multiple applications in the industry. Although many of the state-of-the-art (SOTA) OD models perform well on medium and large sized objects, they seem to under perform on small objects. In most of the industrial use cases, it is difficult to collect and annotate data for small objects, as it is time-consuming and prone to human errors. Additionally, those datasets are likely to be unbalanced and often result in an inefficient model convergence. To tackle this challenge, this study presents a novel approach that injects additional data points to improve the performance of the OD models. Using synthetic data generation, the difficulties in data collection and annotations for small object data points can be minimized and to create a dataset with balanced distribution. This paper discusses the effects of a simple proportional class-balancing technique, to enable better anchor matching of the OD models. A comparison was carried out on the performances of the SOTA OD models: YOLOv5, YOLOv7 and SSD, for combinations of real and synthetic datasets within an industrial use case.
Scale Invariant Extension of the Standard Model: A Nightmare Scenario in Cosmology

Inflationary observables of a classically scale invariant model, in which the origin of the Planck mass and the electroweak scale including the right-handed neutrino mass is chiral symmetry breaking in a QCD-like hidden sector, are studied. Despite a three-field inflation the initial-value-dependence is strongly suppressed thanks to a river-valley like potential. The model predicts the tensor-to-scalar ratio $r$ of cosmological perturbations smaller than that of the $R^2$ inflation, i.e., $ 0.0044 \gsim r \gsim 0.0017$ for e-foldings between $50$ and $60$: The model will be consistent even with a null detection at LiteBird/CMB-S4. We find that the non-Gaussianity parameter $f_{NL}$ is $O(10^{-2})$, the same size as that of single-field inflation. The dark matter particles are the lightest Nambu-Goldstone bosons associated with chiral symmetry breaking, which are decay products of one of the inflatons and are heavier than $10^9$ GeV with a strongly suppressed coupling with the standard model, implying that the dark matter will be unobservable in direct as well as indirect measurements.
Efficient Paths for Local Counterdiabatic Driving

Local counterdiabatic driving (CD) provides a feasible approach for realizing approximate reversible/adiabatic processes like quantum state preparation using only local controls and without demanding excessively long protocol times. However, in many instances getting high accuracy of such CD protocols requires engineering very complicated new controls or pulse sequences. In this work, we describe a systematic method for altering the adiabatic path by adding extra local controls along which performance of local CD protocols is enhanced. We then show that this method provides dramatic improvement in the preparation of non-trivial GHZ ground states of several different spin systems with both short-range and long-range interactions.
Multi-modal News Understanding with Professionally Labelled Videos (ReutersViLNews)

While progress has been made in the domain of video-language understanding, current state-of-the-art algorithms are still limited in their ability to understand videos at high levels of abstraction, such as news-oriented videos. Alternatively, humans easily amalgamate information from video and language to infer information beyond what is visually observable in the pixels. An example of this is watching a news story, where the context of the event can play as big of a role in understanding the story as the event itself. Towards a solution for designing this ability in algorithms, we present a large-scale analysis on an in-house dataset collected by the Reuters News Agency, called Reuters Video-Language News (ReutersViLNews) dataset which focuses on high-level video-language understanding with an emphasis on long-form news. The ReutersViLNews Dataset consists of long-form news videos collected and labeled by news industry professionals over several years and contains prominent news reporting from around the world. Each video involves a single story and contains action shots of the actual event, interviews with people associated with the event, footage from nearby areas, and more. ReutersViLNews dataset contains videos from seven subject categories: disaster, finance, entertainment, health, politics, sports, and miscellaneous with annotations from high-level to low-level, title caption, visual video description, high-level story description, keywords, and location. We first present an analysis of the dataset statistics of ReutersViLNews compared to previous datasets. Then we benchmark state-of-the-art approaches for four different video-language tasks. The results suggest that news-oriented videos are a substantial challenge for current video-language understanding algorithms and we conclude by providing future directions in designing approaches to solve the ReutersViLNews dataset.
Modeling the Kinematics of Central and Satellite Galaxies Using Normalizing Flows

Galaxy clustering contains information on cosmology, galaxy evolution, and the relationship between galaxies and their dark matter hosts. On small scales, the detailed kinematics of galaxies within their host halos determines the galaxy clustering. In this paper, we investigate the dependence of the central and satellite galaxy kinematics on $\boldsymbol{\theta}$, the intrinsic host halo properties (mass, spin, concentration), cosmology ($\Omega_{\textrm{m}}$, $\sigma_8$), and baryonic feedback from active galactic nuclei and supernovae ($A_{\rm AGN1}$, $A_{\rm AGN2}$, $A_{\rm SN1}$, $A_{\rm SN2}$). We utilize 2,000 hydrodynamic simulations in CAMELS run using IllustrisTNG and SIMBA galaxy formation models. Focusing on central and satellite galaxies with $M>10^9M_\ast$, we apply neural density estimation (NDE) with normalizing flows to estimate their $p(\Delta r|\boldsymbol{\theta})$ and $p(\Delta v|\boldsymbol{\theta})$, where $\Delta r$ and $\Delta v$ are the magnitudes of the halo-centric spatial and velocity offsets. With NDE, we accurately capture the dependence of galaxy kinematics on each component of $\boldsymbol{\theta}$. For central galaxies, we identify significant spatial and velocity biases dependent on halo mass, concentration, and spin. For satellite distributions, we find significant deviations from an NFW profile and evidence that they consist of distinct orbiting and infalling populations. However, we find no significant dependence on $\boldsymbol{\theta}$ besides a weak dependence on host halo spin. For both central and satellite galaxies, there is no significant dependence on cosmological parameters and baryonic feedback. These results provide key insights for improving the current halo occupation distribution (HOD) models. This work is the first in a series that will re-examine and develop HOD frameworks for improved modeling of galaxy clustering at smaller scales.
Session-level Normalization and Click-through Data Enhancement for Session-based Evaluation

Since a user usually has to issue a sequence of queries and examine multiple documents to resolve a complex information need in a search session, researchers have paid much attention to evaluating search systems at the session level rather than the single-query level. Most existing session-level metrics evaluate each query separately and then aggregate the query-level scores using a session-level weighting function. The assumptions behind these metrics are that all queries in the session should be involved, and their orders are fixed. However, if a search system could make the user satisfied with her first few queries, she may not need any subsequent queries. Besides, in most real-world search scenarios, due to a lack of explicit feedback from real users, we can only leverage some implicit feedback, such as users' clicks, as relevance labels for offline evaluation. Such implicit feedback might be different from the real relevance in a search session as some documents may be omitted in the previous query but identified in the later reformulations. To address the above issues, we make two assumptions about session-based evaluation, which explicitly describe an ideal session-search system and how to enhance click-through data in computing session-level evaluation metrics. Based on our assumptions, we design a session-level metric called Normalized U-Measure (NUM). NUM evaluates a session as a whole and utilizes an ideal session to normalize the result of the actual session. Besides, it infers session-level relevance labels based on implicit feedback. Experiments on two public datasets demonstrate the effectiveness of NUM by comparing it with existing session-based metrics in terms of correlation with user satisfaction and intuitiveness. We also conduct ablation studies to explore whether these assumptions hold.
Backpropagation Through Agents

A fundamental challenge in multi-agent reinforcement learning (MARL) is to learn the joint policy in an extremely large search space, which grows exponentially with the number of agents. Moreover, fully decentralized policy factorization significantly restricts the search space, which may lead to sub-optimal policies. In contrast, the auto-regressive joint policy can represent a much richer class of joint policies by factorizing the joint policy into the product of a series of conditional individual policies. While such factorization introduces the action dependency among agents explicitly in sequential execution, it does not take full advantage of the dependency during learning. In particular, the subsequent agents do not give the preceding agents feedback about their decisions. In this paper, we propose a new framework Back-Propagation Through Agents (BPTA) that directly accounts for both agents' own policy updates and the learning of their dependent counterparts. This is achieved by propagating the feedback through action chains. With the proposed framework, our Bidirectional Proximal Policy Optimisation (BPPO) outperforms the state-of-the-art methods. Extensive experiments on matrix games, StarCraftII v2, Multi-agent MuJoCo, and Google Research Football demonstrate the effectiveness of the proposed method.
Reference-Dependent Asset Pricing with a Stochastic Consumption-Dividend Ratio

We study a discrete-time consumption-based capital asset pricing model under expectations-based reference-dependent preferences. More precisely, we consider an endowment economy populated by a representative agent who derives utility from current consumption and from gains and losses in consumption with respect to a forward-looking, stochastic reference point. First, we consider a general model in which the agent's preferences include both contemporaneous gain-loss utility, that is, utility from the difference between current consumption and previously held expectations about current consumption, and prospective gain-loss utility, that is, utility from the difference between intertemporal beliefs about future consumption. A semi-closed form solution for equilibrium asset prices is derived for this case. We then specialize to a model in which the agent derives contemporaneous gain-loss utility only, obtaining equilibrium asset prices in closed form. Extensive numerical experiments show that, with plausible values of risk aversion and loss aversion, our models can generate equity premia that match empirical estimates. Interestingly, the models turn out to be consistent with some well-known empirical facts, namely procyclical variation in the price-dividend ratio and countercyclical variation in the conditional expected equity premium and in the conditional volatility of the equity premium. Furthermore, we find that prospective gain-loss utility is necessary for the model to predict reasonable values of the price-dividend ratio.
Cavity optomechanical liquid level meter using a twin-microbottle resonator

Cavity optomechanical devices can be made to have good compatibility with optical fiber technology by utilizing fiber-based waveguides and cavities and can be used in high-performance optical sensor applications. Such optomechanical microsensors have a great potential for exploring the properties of liquids, such as density, viscosity, and masses of included nanoparticles. However, as yet, there is no cavity optomechanical architecture that can be used to sense the liquid's shape, e.g., liquid level. In this paper, we report a demonstration of a liquid-level meter using a twin-microbottle resonator that can make measurements at arbitrary positions and depths in the liquid. The twin-microbottle resonator has a maximum diameter of 68 $\mu$m and length of 800 $\mu$m. By immersing one part of it in water and keeping the other part in air, the mechanical radial breathing mode can be read out sensitively while maintaining a high optical quality factor of the optical whispering gallery mode regardless of the water immersion. This high mechanical displacement sensitivity provides a frequency resolution that is high enough to measure the mechanical frequency shift due to the water immersion and resolves the water level to 2.6$\pm$0.9 pm. This unique liquid-level meter based on a highly sensitive cavity optomechanical setup can be used to detect tiny fluctuations of various air-liquid and liquid-liquid interfaces.
Full-Stack Optimization for CAM-Only DNN Inference

The accuracy of neural networks has greatly improved across various domains over the past years. Their ever-increasing complexity, however, leads to prohibitively high energy demands and latency in von Neumann systems. Several computing-in-memory (CIM) systems have recently been proposed to overcome this, but trade-offs involving accuracy, hardware reliability, and scalability for large models remain a challenge. Additionally, for some CIM designs, the activation movement still requires considerable time and energy. This paper explores the combination of algorithmic optimizations for ternary weight neural networks and associative processors (APs) implemented using racetrack memory (RTM). We propose a novel compilation flow to optimize convolutions on APs by reducing their arithmetic intensity. By leveraging the benefits of RTM-based APs, this approach substantially reduces data transfers within the memory while addressing accuracy, energy efficiency, and reliability concerns. Concretely, our solution improves the energy efficiency of ResNet-18 inference on ImageNet by 7.5x compared to crossbar in-memory accelerators while retaining software accuracy.
New Approximate Stochastic Dominance Approaches for Enhanced Indexation Models

In this paper, we discuss portfolio selection strategies for Enhanced Indexation (EI), which are based on stochastic dominance relations. The goal is to select portfolios that stochastically dominate a given benchmark but that, at the same time, must generate some excess return with respect to a benchmark index. To achieve this goal, we propose a new methodology that selects portfolios using the ordered weighted average (OWA) operator, which generalizes previous approaches based on minimax selection rules and still leads to solving linear programming models. We also introduce a new type of approximate stochastic dominance rule and show that it implies the almost Second-order Stochastic Dominance (SSD) criterion proposed by Lizyayev and Ruszczynski (2012). We prove that our EI model based on OWA selects portfolios that dominate a given benchmark through this new form of stochastic dominance criterion. We test the performance of the obtained portfolios in an extensive empirical analysis based on real-world datasets. The computational results show that our proposed approach outperforms several SSD-based strategies widely used in the literature, as well as the global minimum variance portfolio.
From Understanding to Utilization: A Survey on Explainability for Large Language Models

Explainability for Large Language Models (LLMs) is a critical yet challenging aspect of natural language processing. As LLMs are increasingly integral to diverse applications, their"black-box"nature sparks significant concerns regarding transparency and ethical use. This survey underscores the imperative for increased explainability in LLMs, delving into both the research on explainability and the various methodologies and tasks that utilize an understanding of these models. Our focus is primarily on pre-trained Transformer-based LLMs, such as LLaMA family, which pose distinctive interpretability challenges due to their scale and complexity. In terms of existing methods, we classify them into local and global analyses, based on their explanatory objectives. When considering the utilization of explainability, we explore several compelling methods that concentrate on model editing, control generation, and model enhancement. Additionally, we examine representative evaluation metrics and datasets, elucidating their advantages and limitations. Our goal is to reconcile theoretical and empirical understanding with practical implementation, proposing exciting avenues for explanatory techniques and their applications in the LLMs era.
Contrastive Learning in Distilled Models

Natural Language Processing models like BERT can provide state-of-the-art word embeddings for downstream NLP tasks. However, these models yet to perform well on Semantic Textual Similarity, and may be too large to be deployed as lightweight edge applications. We seek to apply a suitable contrastive learning method based on the SimCSE paper, to a model architecture adapted from a knowledge distillation based model, DistilBERT, to address these two issues. Our final lightweight model DistilFace achieves an average of 72.1 in Spearman's correlation on STS tasks, a 34.2 percent improvement over BERT base.
On the Efficacy of Text-Based Input Modalities for Action Anticipation

Although the task of anticipating future actions is highly uncertain, information from additional modalities help to narrow down plausible action choices. Each modality provides different environmental context for the model to learn from. While previous multi-modal methods leverage information from modalities such as video and audio, we primarily explore how text inputs for actions and objects can also enable more accurate action anticipation. Therefore, we propose a Multi-modal Anticipative Transformer (MAT), an attention-based video transformer architecture that jointly learns from multi-modal features and text captions. We train our model in two-stages, where the model first learns to predict actions in the video clip by aligning with captions, and during the second stage, we fine-tune the model to predict future actions. Compared to existing methods, MAT has the advantage of learning additional environmental context from two kinds of text inputs: action descriptions during the pre-training stage, and the text inputs for detected objects and actions during modality feature fusion. Through extensive experiments, we evaluate the effectiveness of the pre-training stage, and show that our model outperforms previous methods on all datasets. In addition, we examine the impact of object and action information obtained via text and perform extensive ablations. We evaluate the performance on on three datasets: EpicKitchens-100, EpicKitchens-55 and EGTEA GAZE+; and show that text descriptions do indeed aid in more effective action anticipation.
NeRF-AD: Neural Radiance Field with Attention-based Disentanglement for Talking Face Synthesis

Talking face synthesis driven by audio is one of the current research hotspots in the fields of multidimensional signal processing and multimedia. Neural Radiance Field (NeRF) has recently been brought to this research field in order to enhance the realism and 3D effect of the generated faces. However, most existing NeRF-based methods either burden NeRF with complex learning tasks while lacking methods for supervised multimodal feature fusion, or cannot precisely map audio to the facial region related to speech movements. These reasons ultimately result in existing methods generating inaccurate lip shapes. This paper moves a portion of NeRF learning tasks ahead and proposes a talking face synthesis method via NeRF with attention-based disentanglement (NeRF-AD). In particular, an Attention-based Disentanglement module is introduced to disentangle the face into Audio-face and Identity-face using speech-related facial action unit (AU) information. To precisely regulate how audio affects the talking face, we only fuse the Audio-face with audio feature. In addition, AU information is also utilized to supervise the fusion of these two modalities. Extensive qualitative and quantitative experiments demonstrate that our NeRF-AD outperforms state-of-the-art methods in generating realistic talking face videos, including image quality and lip synchronization. To view video results, please refer to https://xiaoxingliu02.github. io/NeRF-AD.