Miracle-dz/UniFAR-FTUs huggingface dataset:
Seer: Language Instructed Video Prediction with Latent Diffusion Models

Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video prediction (TVP) is an essential task to facilitate general robot policy learning. To tackle this task and empower robots with the ability to foresee the future, we propose a sample and computation-efficient model, named \textbf{Seer}, by inflating the pretrained text-to-image (T2I) stable diffusion models along the temporal axis. We enhance the U-Net and language conditioning model by incorporating computation-efficient spatial-temporal attention. Furthermore, we introduce a novel Frame Sequential Text Decomposer module that dissects a sentence's global instruction into temporally aligned sub-instructions, ensuring precise integration into each frame of generation. Our framework allows us to effectively leverage the extensive prior knowledge embedded in pretrained T2I models across the frames. With the adaptable-designed architecture, Seer makes it possible to generate high-fidelity, coherent, and instruction-aligned video frames by fine-tuning a few layers on a small amount of data. The experimental results on Something Something V2 (SSv2), Bridgedata and EpicKitchens-100 datasets demonstrate our superior video prediction performance with around 480-GPU hours versus CogVideo with over 12,480-GPU hours: achieving the 31% FVD improvement compared to the current SOTA model on SSv2 and 83.7% average preference in the human evaluation.
Non-commutative resolutions for Segre products and Cohen-Macaulay rings of hereditary representation type

We study commutative Cohen-Macaulay rings whose Cohen-Macaulay representation theory are controlled by representations of quivers, which we call hereditary representation type. Based on tilting theory and cluster tilting theory, we construct some commutative Cohen-Macaulay rings of hereditary representation type. First we give a general existence theorem of cluster tilting module or non-commutative crepant resolutions on the Segre product of two commutative Gorenstein rings whenever each factor has such an object. As an application we obtain three examples of Gorenstein rings of hereditary representation type coming from Segre products of polynomial rings. Next we introduce extended numerical semigroup rings which generalize numerical semigroup rings and form a class of one-dimensional Cohen-Macaulay non-domains, and among them we provide one family of Gorenstein rings of hereditary representation type. Furthermore, we discuss a $4$-dimensional non-Gorenstein Cohen-Macaulay ring whose representations are still controlled by a finite dimensional hereditary algebra. We show that it has a unique $2$-cluster tilting object, and give a complete classification of rigid Cohen-Macaulay modules, which turns out to be only finitely many.
Variational Inference for Longitudinal Data Using Normalizing Flows

This paper introduces a new latent variable generative model able to handle high dimensional longitudinal data and relying on variational inference. The time dependency between the observations of an input sequence is modelled using normalizing flows over the associated latent variables. The proposed method can be used to generate either fully synthetic longitudinal sequences or trajectories that are conditioned on several data in a sequence and demonstrates good robustness properties to missing data. We test the model on 6 datasets of different complexity and show that it can achieve better likelihood estimates than some competitors as well as more reliable missing data imputation. A code is made available at \url{https://github.com/clementchadebec/variational_inference_for_longitudinal_data}.
The embedding theorem in Hurwitz-Brill-Noether Theory

We generalize the Embedding Theorem of Eisenbud-Harris from classical Brill-Noether theory to the setting of Hurwitz-Brill-Noether theory. More precisely, in classical Brill-Noether theory, the embedding theorem states that a general linear series of degree d and rank r on a general curve of genus g is an embedding if r is at least 3. If \(f \colon C \to \mathbb{P}^1\) is a general cover of degree k, and L is a line bundle on C, recent work of the authors shows that the splitting type of \(f_* L\) provides the appropriate generalization of the pair (r, d) in classical Brill--Noether theory. In the context of Hurwitz-Brill-Noether theory, the condition that r is at least 3 is no longer sufficient to guarantee that a general such linear series is an embedding. We show that the additional condition needed to guarantee that a general linear series |L| is an embedding is that the splitting type of \(f_* L\) has at least three nonnegative parts. This new extra condition reflects the unique geometry of k-gonal curves, which lie on scrolls in \(\mathbb{P}^r\).
Many-body effects in the out-of-equilibrium dynamics of a composite bosonic Josephson junction

The out-of-equilibrium many-body quantum dynamics of an interacting Bose gas trapped in a one-dimensional composite double-well potential is studied by solving the many-body Schr\"odinger equation numerically accurately by employing the multiconfigurational time-dependent Hartree for bosons (MCTDHB) method. The composite double-well is formed by merging two deformed harmonic wells having a hump at their centre. We characterised the dynamics by the time evolution of survival probability, fragmentation, and many-particle position and momentum variances. Our study demonstrates the prominent role played by the higher orbitals in the dynamics and thereby highlighted the necessity of a many-body technique like MCTDHB which can take into account all the relevant orbitals for the accurate description of complex many-body dynamics. Further, we showed that the universality of fragmentation with respect to the number of particles corresponding to a particular interaction strength is also exhibited by the higher-order orbitals. Therefore, it is a robust phenomenon not limited to systems that can be described by two orbitals only.
Switching the Magnetization in Quantum Antiferromagnets

The orientation of the order parameter of quantum magnets can be used to store information in a dense and efficient way. Switching this order parameter corresponds to writing data. To understand how this can be done, we study a precessional reorientation of the sublattice magnetization in an (an)isotropic quantum antiferromagnet induced by an applied magnetic field. We use a description including the leading quantum and thermal fluctuations, namely Schwinger boson mean-field theory, because this theory allows us to describe both ordered phases and the phases in between them, as is crucial for switching. An activation energy has to be overcome requiring a minimum applied field $h_\text{t}$ which is given essentially by the spin gap. It can be reduced significantly for temperatures approaching the N\'eel temperature facilitating switching. The time required for switching diverges when the field approaches $h_\text{t}$ which is the signature of an inertia in the magnetization dynamics. The temporal evolution of the magnetization and of the energy reveals signs of dephasing. The switched state has lost a part of its coherence because the magnetic modes do not evolve in phase.
Text-to-Image Diffusion Models are Zero-Shot Classifiers

The excellent generative capabilities of text-to-image diffusion models suggest they learn informative representations of image-text data. However, what knowledge their representations capture is not fully understood, and they have not been thoroughly explored on downstream tasks. We investigate diffusion models by proposing a method for evaluating them as zero-shot classifiers. The key idea is using a diffusion model's ability to denoise a noised image given a text description of a label as a proxy for that label's likelihood. We apply our method to Stable Diffusion and Imagen, using it to probe fine-grained aspects of the models' knowledge and comparing them with CLIP's zero-shot abilities. They perform competitively with CLIP on a wide range of zero-shot image classification datasets. Additionally, they achieve state-of-the-art results on shape/texture bias tests and can successfully perform attribute binding while CLIP cannot. Although generative pre-training is prevalent in NLP, visual foundation models often use other methods such as contrastive learning. Based on our findings, we argue that generative pre-training should be explored as a compelling alternative for vision-language tasks.
Unsupervised Adaptation from Repeated Traversals for Autonomous Driving

For a self-driving car to operate reliably, its perceptual system must generalize to the end-user's environment -- ideally without additional annotation efforts. One potential solution is to leverage unlabeled data (e.g., unlabeled LiDAR point clouds) collected from the end-users' environments (i.e. target domain) to adapt the system to the difference between training and testing environments. While extensive research has been done on such an unsupervised domain adaptation problem, one fundamental problem lingers: there is no reliable signal in the target domain to supervise the adaptation process. To overcome this issue we observe that it is easy to collect unsupervised data from multiple traversals of repeated routes. While different from conventional unsupervised domain adaptation, this assumption is extremely realistic since many drivers share the same roads. We show that this simple additional assumption is sufficient to obtain a potent signal that allows us to perform iterative self-training of 3D object detectors on the target domain. Concretely, we generate pseudo-labels with the out-of-domain detector but reduce false positives by removing detections of supposedly mobile objects that are persistent across traversals. Further, we reduce false negatives by encouraging predictions in regions that are not persistent. We experiment with our approach on two large-scale driving datasets and show remarkable improvement in 3D object detection of cars, pedestrians, and cyclists, bringing us a step closer to generalizable autonomous driving.
The Shapes of Stellar Spectra

Stellar atmospheres separate the hot and dense stellar interiors from the emptiness of space. Radiation escapes from the outermost layers of a star, carrying direct physical information. Underneath the atmosphere, the very high opacity keeps radiation thermalized and resembling a black body with the local temperature. In the atmosphere the opacity drops, and radiative energy leaks out, which is redistributed in wavelength according to the physical processes by which matter and radiation interact, in particular photoionization. In this article, I will evaluate the role of photoionization in shaping the stellar energy distribution of stars. To that end, I employ simple, state-of-the-art plane-parallel model atmospheres and a spectral synthesis code, dissecting the effects of photoionization from different chemical elements and species, for stars of different masses in the range of 0.3 to 2 M$_{\odot}$. I examine and interpret the changes in the observed spectral energy distributions of the stars as a function of the atmospheric parameters. The photoionization of atomic hydrogen and H$^-$ are the most relevant contributors to the continuum opacity in the optical and near-infrared regions, while heavier elements become important in the ultraviolet region. In the spectra of the coolest stars (spectral types M and later), the continuum shape from photoionization is no longer recognizable due to the accumulation of lines, mainly from molecules. These facts have been known for a long time, but the calculations presented provide an updated quantitative evaluation and insight into the role of photoionization on the structure of stellar atmospheres.
Thermal switching field distribution of a single domain particle for field-dependent attempt frequency

We present an analytical derivation of the switching field distribution (SFD) at finite temperature for a single domain particle from the Neel-Brown model in the presence of a linearly swept magnetic field. By considering the field dependence of the attempt frequency f0 in the rate equation, we find enhancement of coercivity compared to models using constant f0. The contribution of thermal fluctuations to the standard deviation of the switching field HC derived here reaches values of 10% HC. Considering this contribution, which has been neglected in previous work, is important for the correct interpretation of measurements of switching field distributions.
Structure and interactions of biological helices

Helices are essential building blocks of living organisms, be they molecular fragments of proteins -helices , macromolecules DNA and collagen , or multimolecular assemblies microtubules and viruses . Their interactions are involved in packing of meters of genetic material within cells and phage heads, recognition of homologous genes in recombination and DNA repair, stability of tissues, and many other processes. Helical molecules form a variety of mesophases in vivo and in vitro. Recent structural studies, direct measurements of intermolecular forces, single-molecule manipulations, and other experiments have accumulated a wealth of information and revealed many puzzling physical phenomena. It is becoming increasingly clear that in many cases the physics of biological helices cannot be described by theories that treat them as simple, unstructured polyelectrolytes. The present article focuses on the most important and interesting aspects of the physics of structured macromolecules, highlighting various manifestations of the helical motif in their structure, elasticity, interactions with counterions, aggregation, and polyand mesomorphic transitions.
Honeycomb: Ordered Key-Value Store Acceleration on an FPGA-Based SmartNIC

In-memory ordered key-value stores are an important building block in modern distributed applications. We present Honeycomb, a hybrid software-hardware system for accelerating read-dominated workloads on ordered key-value stores that provides linearizability for all operations including scans. Honeycomb stores a B-Tree in host memory. It executes <sc>put</sc>, <sc>update</sc> and <sc>delete</sc> on a CPU. At the same time, it offloads <sc>scan</sc> and <sc>get</sc> onto an FPGA-based SmartNIC. This approach enables large stores and simplifies the FPGA implementation but raises the challenge of data access and synchronization across the slow PCIe bus. We describe how Honeycomb overcomes this challenge with careful data structure design, caching, request parallelism with out-of-order execution, wait-free read operations, and fast synchronization between the CPU and the FPGA. For read-heavy YCSB workloads, Honeycomb increases the throughput of a state-of-the-art ordered key-value store by at least <inline-formula><tex-math notation="LaTeX">$1.8\times$</tex-math><alternatives><mml:math display="inline"><mml:mn>1.8</mml:mn><mml:mo>×</mml:mo></mml:math><inline-graphic xlink:href="liu-ieq1-3345173.gif"/></alternatives></inline-formula>. For scan-heavy workloads inspired by cloud storage, Honeycomb increases the throughput by more than <inline-formula><tex-math notation="LaTeX">$2\times$</tex-math><alternatives><mml:math display="inline"><mml:mn>2</mml:mn><mml:mo>×</mml:mo></mml:math><inline-graphic xlink:href="liu-ieq2-3345173.gif"/></alternatives></inline-formula>. The cost-performance, which is more important for large-scale deployments, is improved by at least <inline-formula><tex-math notation="LaTeX">$1.5\times$</tex-math><alternatives><mml:math display="inline"><mml:mn>1.5</mml:mn><mml:mo>×</mml:mo></mml:math><inline-graphic xlink:href="liu-ieq3-3345173.gif"/></alternatives></inline-formula> on these workloads.
Robust Risk-Aware Option Hedging

The objectives of option hedging/trading extend beyond mere protection against downside risks, with a desire to seek gains also driving agent's strategies. In this study, we showcase the potential of robust risk-aware reinforcement learning (RL) in mitigating the risks associated with path-dependent financial derivatives. We accomplish this by leveraging a policy gradient approach that optimises robust risk-aware performance criteria. We specifically apply this methodology to the hedging of barrier options, and highlight how the optimal hedging strategy undergoes distortions as the agent moves from being risk-averse to risk-seeking. As well as how the agent robustifies their strategy. We further investigate the performance of the hedge when the data generating process (DGP) varies from the training DGP, and demonstrate that the robust strategies outperform the non-robust ones.
Time-Domain Speech Enhancement Assisted by Multi-Resolution Frequency Encoder and Decoder

Time-domain speech enhancement (SE) has recently been intensively investigated. Among recent works, DEMUCS [1] introduces multi-resolution STFT loss to enhance performance. However, some resolutions used for STFT contain non-stationary signals, and it is challenging to learn multi-resolution frequency losses simultaneously with only one output. For better use of multi-resolution frequency information, we supplement multiple spectrograms in different frame lengths into the time-domain encoders. They extract stationary frequency information in both narrowband and wideband. We also adopt multiple decoder outputs, each of which computes its corresponding resolution frequency loss. Experimental results show that (1) it is more effective to fuse stationary frequency features than non-stationary features in the encoder, and (2) the multiple outputs consistent with the frequency loss improve performance. Experiments on the Voice-Bank dataset show that the proposed method obtained a 0.14 PESQ improvement.
Identification of Negative Transfers in Multitask Learning Using Surrogate Models

Multitask learning is widely used in practice to train a low-resource target task by augmenting it with multiple related source tasks. Yet, naively combining all the source tasks with a target task does not always improve the prediction performance for the target task due to negative transfers. Thus, a critical problem in multitask learning is identifying subsets of source tasks that would benefit the target task. This problem is computationally challenging since the number of subsets grows exponentially with the number of source tasks; efficient heuristics for subset selection do not always capture the relationship between task subsets and multitask learning performances. In this paper, we introduce an efficient procedure to address this problem via surrogate modeling. In surrogate modeling, we sample (random) subsets of source tasks and precompute their multitask learning performances. Then, we approximate the precomputed performances with a linear regression model that can also predict the multitask performance of unseen task subsets. We show theoretically and empirically that fitting this model only requires sampling linearly many subsets in the number of source tasks. The fitted model provides a relevance score between each source and target task. We use the relevance scores to perform subset selection for multitask learning by thresholding. Through extensive experiments, we show that our approach predicts negative transfers from multiple source tasks to target tasks much more accurately than existing task affinity measures. Additionally, we demonstrate that for several weak supervision datasets, our approach consistently improves upon existing optimization methods for multitask learning.
Feature Tracks are not Zero-Mean Gaussian

In state estimation algorithms that use feature tracks as input, it is customary to assume that the errors in feature track positions are zero-mean Gaussian. Using a combination of calibrated camera intrinsics, ground-truth camera pose, and depth images, it is possible to compute ground-truth positions for feature tracks extracted using an image processing algorithm. We find that feature track errors are not zero-mean Gaussian and that the distribution of errors is conditional on the type of motion, the speed of motion, and the image processing algorithm used to extract the tracks.
Forecasting a Large Number of Tropical Cyclone Intensities around Japan Using a High-Resolution Atmosphere–Ocean Coupled Model

AbstractThis work quantifies the benefits of using a high-resolution atmosphere–ocean coupled model in tropical cyclone (TC) intensity forecasts in the vicinity of Japan. To do so, a large number of high-resolution calculations were performed by running the Japan Meteorological Agency (JMA) nonhydrostatic atmospheric mesoscale model (AMSM) and atmosphere–ocean coupled mesoscale model (CMSM). A total of 281 3-day forecasts were compiled for 34 TCs from April 2009 to September 2012 for each model. The performance of these models is compared with the JMA global atmospheric spectral model (GSM) that is used for the operational TC intensity guidance. The TC intensities are better predicted by CMSM than the other models. The improvement rates in CMSM relative to GSM and AMSM generally increase with increasing forecast time (FT). CMSM is better than GSM and AMSM by 27.4% and 21.3% at FT = 48 h in terms of minimum sea level pressure, respectively. Regarding the maximum wind speed, CMSM is better than GSM and AMSM.
Infrared regularization and finite size dynamics of entanglement entropy in Schwarzschild black hole

In this paper, infrared regularization of semi-infinite entangling regions and island formation for regions of finite size in the eternal Schwarzschild black hole are considered. We analyze whether the complementarity property and pure state condition of entanglement entropy can be preserved in the given approximation. We propose a special regularization that satisfies these two properties. With regard to entangling regions of finite size, we derive two fundamental types of them, which we call"mirror-symmetric"(MS) and"asymmetric"(AS). For MS regions, we discover a discontinuous evolution of the entanglement entropy of Hawking radiation due to finite lifetime of the island. The entanglement entropy of matter for semi-infinite regions in two-sided Schwarzschild black hole does not follow the Page curve. The lifetime of AS regions is bounded from above due to the phenomenon that we call"Cauchy surface breaking". Shortly before this breaking, the island configuration becomes non-symmetric. For both types of finite regions, there is a critical size, below which the island never dominates. For regions smaller than some other critical size, the island does not emerge. Finally, we show that the island prescription does not help to solve the information paradox for certain finite regions.