Miracle-dz/UniFAR-FTUs huggingface dataset:
Imaging DNA damage response by γH2AX in vivo predicts treatment response to Lutetium-177 radioligand therapy and suggests senescence as a therapeutically desirable outcome

Rationale: An effective absorbed dose response relationship is yet to be established for Lutetium-177 based radionuclide therapies such as 177Lu-DOTATATE and 177Lu-PSMA. The inherent biological heterogeneity of neuroendocrine and prostate cancers may make the prospect of establishing cohort-based dose-response relationships unobtainable. Instead, an individual-based approach, monitoring the dose-response within each tumor could provide the necessary metric to monitor treatment efficacy. Methods: We developed a dual isotope SPECT imaging strategy to monitor the change over time in the relationship between 177Lu-DOTATATE and 111In-anti-γH2AX-TAT, a modified radiolabelled antibody that allows imaging of DNA double strand breaks, in mice bearing rat pancreatic cancer xenografts. The dynamics of γH2AX foci, apoptosis and senescence following exposure to 177Lu-DOTATATE was further investigated in vitro and in ex vivo tumor sections. Results: The change in slope of the 111In-anti-γH2AX-TAT to 177Lu signal between days 5 and 7 was found to be highly predictive of survival (r = 0.955, P < 0.0001). This pivotal timeframe was investigated further in vitro: clonogenic survival correlated with the number of γH2AX foci at day 6 (r = -0.995, P < 0.0005). While there was evidence of continuously low levels of apoptosis, delayed induction of senescence in vitro appeared to better account for the γH2AX response to 177Lu. The induction of senescence was further investigated by ex vivo analysis and corresponded with sustained retention of 177Lu within tumor regions. Conclusions: Dual isotope SPECT imaging can provide individualized tumor dose-responses that can be used to predict lutetium-177 treatment efficacy. This bio-dosimeter metric appears to be dependent upon the extent of senescence induction and suggests an integral role that senescence plays in lutetium-177 treatment efficacy.
Risk factors for fall among the elderly with diabetes mellitus type 2 in Jeddah, Saudi Arabia, 2022: a cross-sectional study

Background: Diabetes mellitus type 2 is a major chronic condition that is considered common among elderly people, with multiple potential complications that could contribute to falls. However, this concept is not well understood; thus, the aim of this study is to estimate the prevalence of falls among diabetes patients. Methods: In this observational cross-sectional study, 309 diabetic patients aged 60 years or more who visited the primary healthcare centers of the Ministry of National Guard – Health Affairs in Jeddah were chosen via convenience sampling method. To collect the data, a structured Fall Risk Assessment questionnaire and Fall Efficacy Score scale were used. Results: The mean age of the participants was estimated to be 68.5 (SD: 7.4) years. Among the participants, 48.2% have fallen before, and 63.1% of them suffered falls in the past 12 months. The results showed that gait problems were independently associated with a higher likelihood of falls among elderly patients [odds ratio (OR)=1.98; 95% CI: 1.08–3.62; P=0.026]. Based on the linear regression analysis, we identified the following risk factors for lower falls efficacy: having gait problems (β=12.50; 95% CI: 7.38–17.6; P<0.001), balance difficulties (β=6.58; 95% CI: 1.35–11.8; P=0.014), and neurological/cognitive impairments (β=9.62; 95% CI: 3.89–15.4; P=0.001), as well as having poor sleep quality (β=8.11, 95% CI: 3.32–12.9; P<0.001). Conclusion: This paper suggests that diabetes mellitus is an independent fall risk factor among the elderly. Therefore, identifying such patients as being at higher risk and prompt referral to a specialist falls clinic is recommended.
Meta-learning approaches for few-shot learning: A survey of recent advances

Despite its astounding success in learning deeper multi-dimensional data, the performance of deep learning declines on new unseen tasks mainly due to its focus on same-distribution prediction. Moreover, deep learning is notorious for poor generalization from few samples. Meta-learning is a promising approach that addresses these issues by adapting to new tasks with few-shot datasets. This survey first briefly introduces meta-learning and then investigates state-of-the-art meta-learning methods and recent advances in: (I) metric-based, (II) memory-based, (III), and learning-based methods. Finally, current challenges and insights for future researches are discussed.
Designing a 3D gestural interface to support user interaction with time-oriented data as immersive 3D radar charts

The design of intuitive three-dimensional user interfaces is vital for interaction in virtual reality, allowing to effectively close the loop between a human user and the virtual environment. The utilization of 3D gestural input allows for useful hand interaction with virtual content by directly grasping visible objects, or through invisible gestural commands that are associated with corresponding features in the immersive 3D space. The design of such interfaces remains complex and challenging. In this article, we present a design approach for a three-dimensional user interface using 3D gestural input with the aim to facilitate user interaction within the context of Immersive Analytics. Based on a scenario of exploring time-oriented data in immersive virtual reality using 3D Radar Charts, we implemented a rich set of features that is closely aligned with relevant 3D interaction techniques, data analysis tasks, and aspects of hand posture comfort. We conducted an empirical evaluation $$(n=12)$$ ( n = 12 ) , featuring a series of representative tasks to evaluate the developed user interface design prototype. The results, based on questionnaires, observations, and interviews, indicate good usability and an engaging user experience. We are able to reflect on the implemented hand-based grasping and gestural command techniques, identifying aspects for improvement in regard to hand detection and precision as well as emphasizing a prototype’s ability to infer user intent for better prevention of unintentional gestures.
Zwicky Transient Facility and Globular Clusters: The Period–Luminosity and Period–Wesenheit Relations for SX Phoenicis Variables in the gri Band

SX Phoenicis (SXP) variables are short-period pulsating stars that exhibit a period–luminosity (PL) relation. We derived the gri-band PL and extinction-free period–Wesenheit (PW) relations, as well as the period-color and reddening-free period-Q-index relations for 47 SXP stars located in 21 globular clusters, using the optical light curves taken from Zwicky Transient Facility. These empirical relations were derived for the first time in the gri filters except for the g-band PL relation. We used our gi-band PL and PW relations to derive a distance modulus to Crater II dwarf spheroidal which hosts one SXP variable. Assuming that the fundamental and first-overtone pulsation mode for the SXP variable in Crater II, we found distance moduli of 20.03 ± 0.23 mag and 20.37 ± 0.24 mag, respectively, using the PW relation, where the latter is in excellent agreement with independent RR Lyrae based distance to Crater II dwarf galaxy.
Modeling Continuous Motion for 3D Point Cloud Object Tracking

The task of 3D single object tracking (SOT) with LiDAR point clouds is crucial for various applications, such as autonomous driving and robotics. However, existing approaches have primarily relied on appearance matching or motion modeling within only two successive frames, thereby overlooking the long-range continuous motion property of objects in 3D space. To address this issue, this paper presents a novel approach that views each tracklet as a continuous stream: at each timestamp, only the current frame is fed into the network to interact with multi-frame historical features stored in a memory bank, enabling efficient exploitation of sequential information. To achieve effective cross-frame message passing, a hybrid attention mechanism is designed to account for both long-range relation modeling and local geometric feature extraction. Furthermore, to enhance the utilization of multi-frame features for robust tracking, a contrastive sequence enhancement strategy is proposed, which uses ground truth tracklets to augment training sequences and promote discrimination against false positives in a contrastive manner. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art method by significant margins on multiple benchmarks.
Input-length-shortening and text generation via attention values

Identifying words that impact a task's performance more than others is a challenge in natural language processing. Transformers models have recently addressed this issue by incorporating an attention mechanism that assigns greater attention (i.e., relevance) scores to some words than others. Because of the attention mechanism's high computational cost, transformer models usually have an input-length limitation caused by hardware constraints. This limitation applies to many transformers, including the well-known bidirectional encoder representations of the transformer (BERT) model. In this paper, we examined BERT's attention assignment mechanism, focusing on two questions: (1) How can attention be employed to reduce input length? (2) How can attention be used as a control mechanism for conditional text generation? We investigated these questions in the context of a text classification task. We discovered that BERT's early layers assign more critical attention scores for text classification tasks compared to later layers. We demonstrated that the first layer's attention sums could be used to filter tokens in a given sequence, considerably decreasing the input length while maintaining good test accuracy. We also applied filtering, which uses a compute-efficient semantic similarities algorithm, and discovered that retaining approximately 6\% of the original sequence is sufficient to obtain 86.5\% accuracy. Finally, we showed that we could generate data in a stable manner and indistinguishable from the original one by only using a small percentage (10\%) of the tokens with high attention scores according to BERT's first layer.
Frequency-Modulated Point Cloud Rendering with Easy Editing

We develop an effective point cloud rendering pipeline for novel view synthesis, which enables high fidelity local detail reconstruction, real-time rendering and user-friendly editing. In the heart of our pipeline is an adaptive frequency modulation module called Adaptive Frequency Net (AFNet), which utilizes a hypernetwork to learn the local texture frequency encoding that is consecutively injected into adaptive frequency activation layers to modulate the implicit radiance signal. This mechanism improves the frequency expressive ability of the network with richer frequency basis support, only at a small computational budget. To further boost performance, a preprocessing module is also proposed for point cloud geometry optimization via point opacity estimation. In contrast to implicit rendering, our pipeline supports high-fidelity interactive editing based on point cloud manipulation. Extensive experimental results on NeRF-Synthetic, ScanNet, DTU and Tanks and Temples datasets demonstrate the superior performances achieved by our method in terms of PSNR, SSIM and LPIPS, in comparison to the state-of-the-art. Code is released at https://github.com/yizhangphd/FreqPCR.
Weak Degeneracy of Planar Graphs and Locally Planar Graphs

Weak degeneracy is a variation of degeneracy which shares many nice properties of degeneracy. In particular, if a graph $G$ is weakly $d$-degenerate, then for any $(d+1)$-list assignment $L$ of $G$, one can construct an $L$ coloring of $G$ by a modified greedy coloring algorithm. It is known that planar graphs of girth 5 are 3-choosable and locally planar graphs are $5$-choosable. This paper strengthens these results and proves that planar graphs of girth 5 are weakly 2-degenerate and locally planar graphs are weakly 4-degenerate.
ISimDL: Importance Sampling-Driven Acceleration of Fault Injection Simulations for Evaluating the Robustness of Deep Learning

Deep Learning (DL) systems have proliferated in many applications, requiring specialized hardware accelerators and chips. In the nano-era, devices have become increasingly more susceptible to permanent and transient faults. Therefore, we need an efficient methodology for analyzing the resilience of advanced DL systems against such faults, and understand how the faults in neural accelerator chips manifest as errors at the DL application level, where faults can lead to undetectable and unrecoverable errors. Using fault injection, we can perform resilience investigations of the DL system by modifying neuron weights and outputs at the software-level, as if the hardware had been affected by a transient fault. Existing fault models reduce the search space, allowing faster analysis, but requiring a-priori knowledge on the model, and not allowing further analysis of the filtered-out search space. Therefore, we propose ISimDL, a novel methodology that employs neuron sensitivity to generate importance sampling-based fault-scenarios. Without any a-priori knowledge of the model-under-test, ISimDL provides an equivalent reduction of the search space as existing works, while allowing long simulations to cover all the possible faults, improving on existing model requirements. Our experiments show that the importance sampling provides up to 15x higher precision in selecting critical faults than the random uniform sampling, reaching such precision in less than 100 faults. Additionally, we showcase another practical use-case for importance sampling for reliable DNN design, namely Fault Aware Training (FAT). By using ISimDL to select the faults leading to errors, we can insert the faults during the DNN training process to harden the DNN against such faults. Using importance sampling in FAT reduces the overhead required for finding faults that lead to a predetermined drop in accuracy by more than 12x.
A Stochastic Benders Decomposition Scheme for Large-Scale Data-Driven Network Design

Network design problems involve constructing edges in a transportation or supply chain network to minimize construction and daily operational costs. We study a data-driven version of network design where operational costs are uncertain and estimated using historical data. This problem is notoriously computationally challenging, and instances with as few as fifty nodes cannot be solved to optimality by current decomposition techniques. Accordingly, we propose a stochastic variant of Benders decomposition that mitigates the high computational cost of generating each cut by sampling a subset of the data at each iteration and nonetheless generates deterministically valid cuts (as opposed to the probabilistically valid cuts frequently proposed in the stochastic optimization literature) via a dual averaging technique. We implement both single-cut and multi-cut variants of this Benders decomposition algorithm, as well as a k-cut variant that uses clustering of the historical scenarios. On instances with 100-200 nodes, our algorithm achieves 4-5% optimality gaps, compared with 13-16% for deterministic Benders schemes, and scales to instances with 700 nodes and 50 commodities within hours. Beyond network design, our strategy could be adapted to generic two-stage stochastic mixed-integer optimization problems where second-stage costs are estimated via a sample average.
Controllable Mesh Generation Through Sparse Latent Point Diffusion Models

Mesh generation is of great value in various applications involving computer graphics and virtual content, yet designing generative models for meshes is challenging due to their irregular data structure and inconsistent topology of meshes in the same category. In this work, we design a novel sparse latent point diffusion model for mesh generation. Our key insight is to regard point clouds as an intermediate representation of meshes, and model the distribution of point clouds instead. While meshes can be generated from point clouds via techniques like Shape as Points (SAP), the challenges of directly generating meshes can be effectively avoided. To boost the efficiency and controllability of our mesh generation method, we propose to further encode point clouds to a set of sparse latent points with pointwise semantic meaningful features, where two DDPMs are trained in the space of sparse latent points to respectively model the distribution of the latent point positions and features at these latent points. We find that sampling in this latent space is faster than directly sampling dense point clouds. Moreover, the sparse latent points also enable us to explicitly control both the overall structures and local details of the generated meshes. Extensive experiments are conducted on the ShapeNet dataset, where our proposed sparse latent point diffusion model achieves superior performance in terms of generation quality and controllability when compared to existing methods. Project page, code and appendix: https://slide-3d.github. io.
A spectral ansatz for the long-time homogenization of the wave equation

Consider the wave equation with heterogeneous coefficients in the homogenization regime. At large times, the wave interacts in a nontrivial way with the heterogeneities, giving rise to effective dispersive effects. The main achievement of the present work is a new ansatz for the long-time two-scale expansion inspired by spectral analysis. Based on this spectral ansatz, we extend and refine all previous results in the field, proving homogenization up to optimal timescales with optimal error estimates, and covering all the standard assumptions on heterogeneities (both periodic and stationary random settings).
ECMI resonance in AKR revisited: Hyperbolic resonance, harmonics, and wave–wave interaction

Recapitulation of the resonance condition for the fundamental and higher electron cyclotron harmonics in the electron cyclotron maser instability (ECMI) enables radiation below and confirms the possibility of radiation in a narrow band above harmonics n > 1. Near n = 1 resonance on the confined lower X-mode branch, amplification is supported by the decrease in phase and group speeds. Confined slow large-amplitude quasi-electrostatic X-modes non-linearly modulate the plasma to form cavitons until self-trapped inside them at a further increasing wavenumber. They undergo wave–wave interaction, enabling escape into free space in the second harmonic band below n = 2. At a sufficiently large parallel wavenumber (oblique propagation), the fundamental resonance n = 1 is hyperbolic, a possibility so far missed but vital for an effective ECMI in the upward current region. Here, the resonance hyperbola favorably fits the loss-cone boundary, the presumably important ECMI upward-current source-electron distribution, to stimulate ECMI growth at available auroral electron energies.
Linear independence of translations

Abstract It was shown by Edgar and Rosenblatt that f ∈ Lp (ℝn), 1 ≤ p < 2n/ (n-1), and f ≠0, then f has linearly independent translates. Using a result of Hömander, it is shown here that the same theorem holds if p = 2n / (n−1). This gives a sharp result because for n ≥2, there exists f ∈C0 (ℝn), f ≠0, which is simultaneously in all Lp (ℝn), p > 2n/(n−1), that has a linear dependence relation among its translates. References and some discussion are included.
Prioritized Planning for Target-Oriented Manipulation via Hierarchical Stacking Relationship Prediction

In scenarios involving grasping multiple targets, the learning of stacking relationships between objects is fundamental for robots to execute safely and efficiently. However, current methods lack subdivision for the hierarchy of stacking relationship types. In scenes where objects are mostly stacked in an orderly manner, they are incapable of performing human-like and high-efficient grasping decisions. This paper proposes a perception-planning method to distinguish different stacking forms between objects and generate prioritized manipulation sequences based on given target designations. We utilize a Hierarchical Stacking Relationship Network (HSRN) to discriminate the hierarchy of stacking and generate a refined Stacking Relationship Tree (SRT) for relationship description. Considering objects with high stacking stability can be processed together if necessary, we introduce an elaborate decision-making planner based on Partially Observable Markov Decision Process (POMDP), which leverages observations and generates the least grasp-consuming decision chain with robustness and is suitable for simultaneously specifying multiple targets. To verify our work, we set the scene to the dining table and augment REGRAD dataset for network training. Experiments show that our method effectively generates grasping decisions that conform to human requirements, and improves the implementation efficiency compared with existing methods on the basis of guaranteeing success rate.
Sums of Products of Bernoulli Numbers

Abstract Closed expressions are obtained for sums of products of Bernoulli numbers of the form[formula], where the summation is extended over all nonnegative integers j 1 , …, j N with j 1 + j 2 +…+ j N = n . Corresponding results are derived for Bernoulli polynomials, and for Euler numbers and polynomials. As easy corollaries we obtain formulas for sums of products of the Riemann zeta function at even integers and of other related infinite series.
Eliciting Latent Predictions from Transformers with the Tuned Lens

We analyze transformers from the perspective of iterative inference, seeking to understand how model predictions are refined layer by layer. To do so, we train an affine probe for each block in a frozen pretrained model, making it possible to decode every hidden state into a distribution over the vocabulary. Our method, the \emph{tuned lens}, is a refinement of the earlier ``logit lens'' technique, which yielded useful insights but is often brittle. We test our method on various autoregressive language models with up to 20B parameters, showing it to be more predictive, reliable and unbiased than the logit lens. With causal experiments, we show the tuned lens uses similar features to the model itself. We also find the trajectory of latent predictions can be used to detect malicious inputs with high accuracy. All code needed to reproduce our results can be found at https://github.com/AlignmentResearch/tuned-lens.
A Novel Compact Dual-band Antenna with Harmonic Suppression

A novel compact dual band microstrip-fed antenna with harmonic suppression which works at 2.45 GHz and 5.8 GHz is firstly proposed in this paper. By exploiting T-shaped microstrip structure, the 1st and 2nd harmonic frequencies are suppressed effectively. Meanwhile, two Ushaped patches fit together are used to take full advantage of the space, thus the antenna total size is reduced greatly as well. Furthermore, dual bands can be achieved. General performance of the proposed antenna is studied by current distribution and parameter sweeps. The experimental results agree well with the simulated dates.