Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Immune Checkpoint Inhibition on the Risk of Radiation Necrosis Following Stereotactic Radiotherapy for Metastatic Brain Cancer

Purpose/objective Forty percent of cancer patients develop brain metastases (BM) and are often treated with stereotactic radiation (SRS/SRT). Checkpoint inhibitor (CI) use is suspected of increasing the risk of radiation necrosis (RN). Our aim is to determine whether treatment with CI is associated with an increased risk of RN in BM patients treated with SRS/SRT. Methods We retrospectively identified the medical records of BM patients treated with SRS/SRT between 1/2017 and 12/2021 using an institutional database. RN was defined by MRI imaging read by neuroradiologists and/or surgical pathology. V12GY of patients with and without RN was compared using the Mann-Whitney test. The chi-square test was used to see if RN was associated with CI use, histology, particular CI agent used, > 1 course SRS/SRT, SRS/SRT dose, chemotherapy, whole brain radiotherapy (WBRT), age, or sex. Results Two hundred and fifty-nine patients treated with 455 courses of SRS/SRT were analyzed. The most common primary histologies were lung 56% (N=146), breast 14% (N= 37), melanoma 9% (N=24), and renal cancer 7% (N=18). A total of 53.8% (N = no. of patients) were treated with CI. The overall rate of any RN was 21.8% (N=27) in the CI group compared to 14.8% (N=141) in the non-CI group (p=0.174). Mean V12Gy was 15.525 cc and 9.419 cc in patients with and without RN (p=0.02768). Mean number of SRS/SRT courses was 2 and 1.53 for patients with and without RN, and >1 course of SRS/SRT was a predictor of RN (p <0.01). Other features analyzed were not significant. Conclusion RN was higher in the BM patients treated with SRS/SRT receiving CI compared to non-CI patients (21.8%, N=27, versus 14.6%, N= 16), but failed to reach statistical significance. V12Gy and > 1 course of SRS/SRT was associated with RN. Caution should be taken in treating patients with SRS/SRT and CI there might be an increased risk of RN.
A generalized moment approach to sharp bounds for conditional expectations

In this paper, we address the problem of bounding conditional expectations when moment information of the underlying distribution and the random event conditioned upon are given. To this end, we propose an adapted version of the generalized moment problem which deals with this conditional information through a simple transformation. By exploiting conic duality, we obtain sharp bounds that can be used for distribution-free decision-making under uncertainty. Additionally, we derive computationally tractable mathematical programs for distributionally robust optimization (DRO) with side information by leveraging core ideas from ambiguity-averse uncertainty quantification and robust optimization, establishing a moment-based DRO framework for prescriptive stochastic programming.
AllSpark: A Multimodal Spatio-Temporal General Intelligence Model with Thirteen Modalities

For a long time, due to the high heterogeneity in structure and semantics among various spatiotemporal modal data, the joint interpretation of multimodal spatiotemporal data has been an extremely challenging problem. The primary challenge resides in striking a trade-off between the cohesion and autonomy of diverse modalities, and this trade-off exhibits a progressively nonlinear nature as the number of modalities expands. We introduce the Language as Reference Framework (LaRF), a fundamental principle for constructing a multimodal unified model, aiming to strike a trade-off between the cohesion and autonomy among different modalities. We propose a multimodal spatiotemporal general artificial intelligence model, called AllSpark. Our model integrates thirteen different modalities into a unified framework, including 1D (text, code), 2D (RGB, infrared, SAR, multispectral, hyperspectral, tables, graphs, trajectory, oblique photography), and 3D (point clouds, videos) modalities. To achieve modal cohesion, AllSpark uniformly maps diverse modal features to the language modality. In addition, we design modality-specific prompts to guide multi-modal large language models in accurately perceiving multimodal data. To maintain modality autonomy, AllSpark introduces modality-specific encoders to extract the tokens of various spatiotemporal modalities. And modal bridge is employed to achieve dimensional projection from each modality to the language modality. Finally, observing a gap between the model's interpretation and downstream tasks, we designed task heads to enhance the model's generalization capability on specific downstream tasks. Experiments indicate that AllSpark achieves competitive accuracy in modalities such as RGB and trajectory compared to state-of-the-art models.
Auxiliary Network-Enabled Attack Detection and Resilient Control of Islanded AC Microgrid

This paper proposes a cyber-resilient distributed control strategy equipped with attack detection capabilities for islanded AC microgrids in the presence of bounded stealthy cyber attacks affecting both frequency and power information exchanged among neighboring distributed generators (DGs). The proposed control methodology relies on the construction of an auxiliary layer and the establishment of effective inter-layer cooperation between the actual DGs in the control layer and the virtual DGs in the auxiliary layer. This cooperation aims to achieve robust frequency restoration and proportional active power-sharing. It is shown that the in situ presence of a concealed auxiliary layer not only guarantees resilience against stealthy bounded attacks on both frequency and power-sharing but also facilitates a network-enabled attack identification mechanism. The paper provides rigorous proof of the stability of the closed-loop system and derives bounds for frequency and power deviations under attack conditions, offering insights into the impact of the attack signal, control and pinning gains, and network connectivity on the system's convergence properties. The performance of the proposed controllers is illustrated by simulating a networked islanded AC microgrid in a Simulink environment showcasing both attributes of attack resilience and attack detection.
Policy Optimization with Smooth Guidance Rewards Learned from Sparse-Reward Demonstrations

The sparsity of reward feedback remains a challenging problem in online deep reinforcement learning (DRL). Previous approaches have utilized temporal credit assignment (CA) to achieve impressive results in multiple hard tasks. However, many CA methods relied on complex architectures or introduced sensitive hyperparameters to estimate the impact of state-action pairs. Meanwhile, the premise of the feasibility of CA methods is to obtain trajectories with sparse rewards, which can be troublesome in sparse-reward environments with large state spaces. To tackle these problems, we propose a simple and efficient algorithm called Policy Optimization with Smooth Guidance (POSG) that leverages a small set of sparse-reward demonstrations to make reliable and effective long-term credit assignments while efficiently facilitating exploration. The key idea is that the relative impact of state-action pairs can be indirectly estimated using offline demonstrations rather than directly leveraging the sparse reward trajectories generated by the agent. Specifically, we first obtain the trajectory importance by considering both the trajectory-level distance to demonstrations and the returns of the relevant trajectories. Then, the guidance reward is calculated for each state-action pair by smoothly averaging the importance of the trajectories through it, merging the demonstration's distribution and reward information. We theoretically analyze the performance improvement bound caused by smooth guidance rewards and derive a new worst-case lower bound on the performance improvement. Extensive results demonstrate POSG's significant advantages in control performance and convergence speed compared to benchmark DRL algorithms. Notably, the specific metrics and quantifiable results are investigated to demonstrate the superiority of POSG.
Bounded $t$-structures, finitistic dimensions, and singularity categories of triangulated categories

Recently, Amnon Neeman settled a bold conjecture by Antieau, Gepner, and Heller regarding the relationship between the regularity of finite-dimensional noetherian schemes and the existence of bounded $t$-structures on their derived categories of perfect complexes. In this paper, we prove some very general results about the existence of bounded $t$-structures on (not necessarily algebraic or topological) triangulated categories and their invariance under completion. Our general treatment, when specialized to the case of schemes, immediately gives us Neeman's theorem as an application and significantly generalizes another remarkable theorem by Neeman about the equivalence of bounded $t$-structures on the bounded derived categories of coherent sheaves. When specialized to other cases like (not necessarily commutative) rings, nonpositive DG-rings, connective $\mathbb{E}_1$-rings, triangulated categories without models, etc., we get many other applications. Under mild finiteness assumptions, these results give a categorical obstruction, the singularity category in our sense, to the existence of bounded $t$-structures on a triangulated category. The two key tools used in our treatment are the finitistic dimension for a triangulated category (a new concept introduced in the paper) and lifting $t$-structures along completions of triangulated categories.
Generating subspace lattices, their direct products, and their direct powers

In 2008, L\'aszl\'o Z\'adori proved that the lattice Sub$(V)$ of all subspaces of a vector space $V$ of finite dimension at least 3 over a finite field $F$ has a 5-element generating set; in other words, Sub$(V)$ is 5-generated. We prove that the same holds over every 1- or 2-generated field; in particular, over every field that is a finite degree extension of its prime field. Furthermore, let $F$, $t$, $V$, $d\geq 3$, $[d/2]$, and $m$ denote an arbitrary field, the minimum cardinality of a generating set of $F$, a finite dimensional vector space over $F$, the dimension (assumed to be at least $3$) of $V$, the integer part of $d/2$, and the least cardinal such that $m[d^2/4]$ is at least $t$, respectively. We prove that Sub$(V)$ is $(4+m)$-generated but none of its generating sets is of size less than $m$. Moreover, the $k$-th direct power of Sub$(V)$ is $(5+m)$-generated for many positive integers $k$; for all positive integers $k$ if $F$ is infinite. Finally, let $n$ be a positive integer. For $i=1,\dots, n$, let $p_i$ be a prime number or 0, and let $V_i$ be the 3-dimensional vector space over the prime field of characteristic $p_i$. We prove that the direct product of the lattices Sub$(V_1)$, . , Sub$(V_n)$ is 4-generated if and only if each of the numbers $p_1$, . , $p_n$ occurs at most four times in the sequence $p_1$, . , $p_n$. Neither this direct product nor any of the subspace lattices Sub$(V)$ above is 3-generated.
Effects of Palatability and Dispersal Mode on Spatial Patterns of Trees in Oldfields

MYSTER, R. W. (Terrestrial Ecology Division, University of Puerto Rico, San Juan, PR 00936), AND S. T. A. PicyErr (Institute of Ecosystem Studies, Mary Flagler Cary Arboretum, Millbrook, NY 12545). Effects of palatability and dispersal mode on spatial patterns of trees in oldfields. Bull. Torrey Bot. Club 119: 145-151. 1992. -The spatial patterns of tree stems in 480 plots were examined on a series of ten experimental oldfields adjacent to an old growth forest over a 31 -year period. Tree stem density decreased exponentially with distance from the forest edge early in succession. Later in succession the predictive power of the negative exponential model decreased, suggesting that processes such as animal seed dispersal within a field become important. Species palatable to herbivores were denser than unpalatable species close to the forest edge which implies that consumer pressure originates in the field rather than in the forest. Stems of all species were clumped on the scale of the 1 m2 plots. However, stems of bird-dispersed species showed the highest degree of clumping as expected if perches serve as recruitment foci. Results suggest that spatial patterns of tree seedlings in oldfields are significantly influenced by successional age, herbivory, dispersal vector and proximity to forest edge.
On the sense of convergence in the dyadic representation theorem

The dyadic representation of any singular integral operator, as an average of dyadic model operators, has found many applications. While for many purposes it is enough to have such a representation for a"suitable class"of test functions, we show that, under quite general assumptions (essentially minimal ones to make sense of the formula), the representation is actually valid for all pairs $(f,g)\in L^p(\mathbb R^d)\times L^{p'}(\mathbb R^d)$, not just test functions.
Emulating insect brains for neuromorphic navigation

Bees display the remarkable ability to return home in a straight line after meandering excursions to their environment. Neurobiological imaging studies have revealed that this capability emerges from a path integration mechanism implemented within the insect's brain. In the present work, we emulate this neural network on the neuromorphic mixed-signal processor BrainScaleS-2 to guide bees, virtually embodied on a digital co-processor, back to their home location after randomly exploring their environment. To realize the underlying neural integrators, we introduce single-neuron spike-based short-term memory cells with axo-axonic synapses. All entities, including environment, sensory organs, brain, actuators, and the virtual body, run autonomously on a single BrainScaleS-2 microchip. The functioning network is fine-tuned for better precision and reliability through an evolution strategy. As BrainScaleS-2 emulates neural processes 1000 times faster than biology, 4800 consecutive bee journeys distributed over 320 generations occur within only half an hour on a single neuromorphic core.
Reproductive outcome in female wistar rats treated with nhexane, dichloromethane and aqueous ethanol extracts of Cucurbita pepo seed

In developing countries, healthcare challenges and expensive infertility treatments has resulted in resurgent interest in medicinal plants. This study was designed to determine if Curcubita pepo seed can enhance female fertility, by assessing the reproductive outcome in female wistar rats treated with n-hexane (nHE), dichloromethane (DCM) and aqueous ethanol (Aq. Eth) extracts of Curcubita pepo seed. Total of 48 rats randomly grouped into 12 (n=4), were treated for 21 days by oral gavage as follows: A (control) = 0.5ml 20% tween 80 (vehicle); B (positive control) = 10mg/kg clomiphene citrate, C, D&E = 142.86, 285.71 and 428.57 mg/kg nHE; F, G&H = 142.86, 285.71 and 428.57 mg/kg DCM ; and I, J&K =142.86, 285.71 and 428.57 mg/kg Aq.Eth extracts. Group L (positive control 2) = 10mg/kg clomiphene citrate for 8 days. Following treatment, the rats were paired with males for mating, designating the confirmation day as gestational day 0 (GD 0). On GD 20, the animals were laparatomised and reproductive outcome was determined by assessing foetal weight, foetal crown-rump length, litter size, number of implantation and resorption sites. Results showed all extracts had no significant (p>0.05) effect on the reproductive outcome indices. Clomiphene citrate significantly decreased reproductive outcome indices. In conclusion, Cucurbita pepo seed did not enhance the reproductive outcome of treated female rats at the doses and duration used in this study. This finding may serve as a springboard for future studies exploring the effect of C.pepo at different doses or durations.
Ergodic properties of Kantorovich operators

Kantorovich operators are non-linear extensions of Markov operators and are omnipresent in several branches of mathematical analysis. The asymptotic behaviour of their iterates plays an important role even in classical ergodic, potential and probability theories, which are normally concerned with linear Markovian operators, semi-groups, and resolvents. The Kantorovich operators that appear implicitly in these cases, though non-linear, are all positively 1-homogenous. General Kantorovich operators amount to assigning"a cost"to most operations on measures and functions normally conducted"for free"in these classical settings. Motivated by extensions of the Monge-Kantorovich duality in mass transport, the stochastic counterpart of Aubry-Mather theory for Lagrangian systems, weak KAM theory \`a la Fathi-Mather, and ergodic optimization of dynamical systems, we study the asymptotic properties of general Kantorovich operators.
Nonasymptotic Regret Analysis of Adaptive Linear Quadratic Control with Model Misspecification

The strategy of pre-training a large model on a diverse dataset, then fine-tuning for a particular application has yielded impressive results in computer vision, natural language processing, and robotic control. This strategy has vast potential in adaptive control, where it is necessary to rapidly adapt to changing conditions with limited data. Toward concretely understanding the benefit of pre-training for adaptive control, we study the adaptive linear quadratic control problem in the setting where the learner has prior knowledge of a collection of basis matrices for the dynamics. This basis is misspecified in the sense that it cannot perfectly represent the dynamics of the underlying data generating process. We propose an algorithm that uses this prior knowledge, and prove upper bounds on the expected regret after $T$ interactions with the system. In the regime where $T$ is small, the upper bounds are dominated by a term scales with either $\texttt{poly}(\log T)$ or $\sqrt{T}$, depending on the prior knowledge available to the learner. When $T$ is large, the regret is dominated by a term that grows with $\delta T$, where $\delta$ quantifies the level of misspecification. This linear term arises due to the inability to perfectly estimate the underlying dynamics using the misspecified basis, and is therefore unavoidable unless the basis matrices are also adapted online. However, it only dominates for large $T$, after the sublinear terms arising due to the error in estimating the weights for the basis matrices become negligible. We provide simulations that validate our analysis. Our simulations also show that offline data from a collection of related systems can be used as part of a pre-training stage to estimate a misspecified dynamics basis, which is in turn used by our adaptive controller.
Retrieval-Augmented Egocentric Video Captioning

Understanding human actions from videos of first-person view poses significant challenges. Most prior approaches explore representation learning on egocentric videos only, while overlooking the potential benefit of exploiting existing large-scale third-person videos. In this paper, (1) we develop EgoInstructor, a retrieval-augmented multimodal captioning model that automatically retrieves semantically relevant third-person instructional videos to enhance the video captioning of egocentric videos. (2) For training the cross-view retrieval module, we devise an automatic pipeline to discover ego-exo video pairs from distinct large-scale egocentric and exocentric datasets. (3) We train the cross-view retrieval module with a novel EgoExoNCE loss that pulls egocentric and exocentric video features closer by aligning them to shared text features that describe similar actions. (4) Through extensive experiments, our cross-view retrieval module demonstrates superior performance across seven benchmarks. Regarding egocentric video captioning, EgoInstructor exhibits significant improvements by leveraging third-person videos as references.
Generalizing Single-View 3D Shape Retrieval to Occlusions and Unseen Objects

Single-view 3D shape retrieval is a challenging task that is increasingly important with the growth of available 3D data. Prior work that has studied this task has not focused on evaluating how realistic occlusions impact performance, and how shape retrieval methods generalize to scenarios where either the target 3D shape database contains unseen shapes, or the input image contains unseen objects. In this paper, we systematically evaluate single-view 3D shape retrieval along three different axes: the presence of object occlusions and truncations, generalization to unseen 3D shape data, and generalization to unseen objects in the input images. We standardize two existing datasets of real images and propose a dataset generation pipeline to produce a synthetic dataset of scenes with multiple objects exhibiting realistic occlusions. Our experiments show that training on occlusion-free data as was commonly done in prior work leads to significant performance degradation for inputs with occlusion. We find that that by first pretraining on our synthetic dataset with occlusions and then finetuning on real data, we can significantly outperform models from prior work and demonstrate robustness to both unseen 3D shapes and unseen objects.
Refining Pre-Trained Motion Models

Given the difficulty of manually annotating motion in video, the current best motion estimation methods are trained with synthetic data, and therefore struggle somewhat due to a train/test gap. Self-supervised methods hold the promise of training directly on real video, but typically perform worse. These include methods trained with warp error (i.e., color constancy) combined with smoothness terms, and methods that encourage cycle-consistency in the estimates (i.e., tracking backwards should yield the opposite trajectory as tracking forwards). In this work, we take on the challenge of improving state-of-the-art supervised models with self-supervised training. We find that when the initialization is supervised weights, most existing self-supervision techniques actually make performance worse instead of better, which suggests that the benefit of seeing the new data is overshadowed by the noise in the training signal. Focusing on obtaining a"clean"training signal from real-world unlabelled video, we propose to separate label-making and training into two distinct stages. In the first stage, we use the pre-trained model to estimate motion in a video, and then select the subset of motion estimates which we can verify with cycle-consistency. This produces a sparse but accurate pseudo-labelling of the video. In the second stage, we fine-tune the model to reproduce these outputs, while also applying augmentations on the input. We complement this boot-strapping method with simple techniques that densify and re-balance the pseudo-labels, ensuring that we do not merely train on"easy"tracks. We show that our method yields reliable gains over fully-supervised methods in real videos, for both short-term (flow-based) and long-range (multi-frame) pixel tracking.