Miracle-dz/UniFAR-FTUs huggingface dataset:
Balanced Allocations: Caching and Packing, Twinning and Thinning

We consider the sequential allocation of $m$ balls (jobs) into $n$ bins (servers) by allowing each ball to choose from some bins sampled uniformly at random. The goal is to maintain a small gap between the maximum load and the average load. In this paper, we present a general framework that allows us to analyze various allocation processes that slightly prefer allocating into underloaded, as opposed to overloaded bins. Our analysis covers several natural instances of processes, including: The Caching process (a.k.a. memory protocol) as studied by Mitzenmacher, Prabhakar and Shah (2002): At each round we only take one bin sample, but we also have access to a cache in which the most recently used bin is stored. We place the ball into the least loaded of the two. The Packing process: At each round we only take one bin sample. If the load is below some threshold (e.g., the average load), then we place as many balls until the threshold is reached; otherwise, we place only one ball. The Twinning process: At each round, we only take one bin sample. If the load is below some threshold, then we place two balls; otherwise, we place only one ball. The Thinning process as recently studied by Feldheim and Gurel-Gurevich (2021): At each round, we first take one bin sample. If its load is below some threshold, we place one ball; otherwise, we place one ball into a $\textit{second}$ bin sample. As we demonstrate, our general framework implies for all these processes a gap of $\mathcal{O}(\log n)$ between the maximum load and average load, even when an arbitrary number of balls $m \geq n$ are allocated (heavily loaded case). Our analysis is inspired by a previous work of Peres, Talwar and Wieder (2010) for the $(1+\beta)$-process, however here we rely on the interplay between different potential functions to prove stabilization.
On Coordinate Decoding for Keypoint Estimation Tasks

A series of 2D (and 3D) keypoint estimation tasks are built upon heatmap coordinate representation, i.e. a probability map that allows for learnable and spatially aware encoding and decoding of keypoint coordinates on grids, even allowing for sub-pixel coordinate accuracy. In this report, we aim to reproduce the findings of DARK that investigated the 2D heatmap representation by highlighting the importance of the encoding of the ground truth heatmap and the decoding of the predicted heatmap to keypoint coordinates. The authors claim that a) a more principled distribution-aware coordinate decoding method overcomes the limitations of the standard techniques widely used in the literature, and b), that the reconstruction of heatmaps from ground-truth coordinates by generating accurate and continuous heatmap distributions lead to unbiased model training, contrary to the standard coordinate encoding process that quantizes the keypoint coordinates on the resolution of the input image grid.
Hierarchical Skills for Efficient Exploration

In reinforcement learning, pre-trained low-level skills have the potential to greatly facilitate exploration. However, prior knowledge of the downstream task is required to strike the right balance between generality (fine-grained control) and specificity (faster learning) in skill design. In previous work on continuous control, the sensitivity of methods to this trade-off has not been addressed explicitly, as locomotion provides a suitable prior for navigation tasks, which have been of foremost interest. In this work, we analyze this trade-off for low-level policy pre-training with a new benchmark suite of diverse, sparse-reward tasks for bipedal robots. We alleviate the need for prior knowledge by proposing a hierarchical skill learning framework that acquires skills of varying complexity in an unsupervised manner. For utilization on downstream tasks, we present a three-layered hierarchical learning algorithm to automatically trade off between general and specific skills as required by the respective task. In our experiments, we show that our approach performs this trade-off effectively and achieves better results than current state-of-the-art methods for end- to-end hierarchical reinforcement learning and unsupervised skill discovery. Code and videos are available at https://facebookresearch.github. io/hsd3 .
Reinforcement Learning Based Optimal Camera Placement for Depth Observation of Indoor Scenes

Exploring the most task-friendly camera setting—optimal camera placement (OCP) problem—in tasks that use multiple cameras is of great importance. However, few existing OCP solutions specialize in depth observation of indoor scenes, and most versatile solutions work offline. To this problem, an OCP online solution to depth observation of indoor scenes based on reinforcement learning is proposed in this paper. The proposed solution comprises a simulation environment that implements scene observation and reward estimation using shadow maps and an agent network containing a soft actor-critic (SAC)-based reinforcement learning backbone and a feature extractor to extract features from the observed point cloud layer-by-layer. Comparative experiments with two state-of-the-art optimization-based offline methods are conducted. The experimental results indicate that the proposed system outperforms seven out of ten test scenes in obtaining lower depth observation error. The total error in all test scenes is also less than 90% of the baseline ones. Therefore, the proposed system is more competent for depth camera placement in scenarios where there is no prior knowledge of the scenes or where a lower depth observation error is the main objective.
An Aligned Orbit for the Young Planet V1298 Tau b

The alignment of planetary orbits with respect to the stellar rotation preserves information on their dynamical histories. Measuring this angle for young planets helps illuminate the mechanisms that create misaligned orbits for older planets, as different processes could operate over timescales ranging from a few megayears to a gigayear. We present spectroscopic transit observations of the young exoplanet V1298 Tau b; we update the age of V1298 Tau to be 28 ± 4 Myr based on Gaia EDR3 measurements. We observed a partial transit with Keck/HIRES and LBT/PEPSI, and detected the radial velocity anomaly due to the Rossiter–McLaughlin effect. V1298 Tau b has a prograde, well-aligned orbit, with λ=4−10+7 deg. By combining the spectroscopically measured vsini⋆ and the photometrically measured rotation period of the host star we also find that the orbit is aligned in 3D, ψ=8−7+4 deg. Finally, we combine our obliquity constraints with a previous measurement for the interior planet V1298 Tau c to constrain the mutual inclination between the two planets to be i mut = 0° ± 19°. This measurements adds to the growing number of well-aligned planets at young ages, hinting that misalignments may be generated over timescales of longer than tens of megayears. The number of measurements, however, is still small, and this population may not be representative of the older planets that have been observed to date. We also present the derivation of the relationship between i mut, λ, and i for the two planets.
Moiré Attack (MA): A New Potential Risk of Screen Photos

Images, captured by a camera, play a critical role in training Deep Neural Networks (DNNs). Usually, we assume the images acquired by cameras are consistent with the ones perceived by human eyes. However, due to the different physical mechanisms between human-vision and computer-vision systems, the final perceived images could be very different in some cases, for example shooting on digital monitors. In this paper, we find a special phenomenon in digital image processing, the moir\'e effect, that could cause unnoticed security threats to DNNs. Based on it, we propose a Moir\'e Attack (MA) that generates the physical-world moir\'e pattern adding to the images by mimicking the shooting process of digital devices. Extensive experiments demonstrate that our proposed digital Moir\'e Attack (MA) is a perfect camouflage for attackers to tamper with DNNs with a high success rate ($100.0\%$ for untargeted and $97.0\%$ for targeted attack with the noise budget $\epsilon=4$), high transferability rate across different models, and high robustness under various defenses. Furthermore, MA owns great stealthiness because the moir\'e effect is unavoidable due to the camera's inner physical structure, which therefore hardly attracts the awareness of humans. Our code is available at https://github.com/Dantong88/Moire_Attack.
Safety of magnetic resonance imaging in patients with retained cardiac leads

Purpose: To evaluate safety of MRI in patients with fragmented retained leads (FRLs) through numerical simulation and phantom experiments. Methods: Electromagnetic and thermal simulations were performed to determine the worst-case RF heating of 10 patient-derived FRL models during MRI at 1.5 T and 3 T and at imaging landmarks corresponding to head, chest and abdomen. RF heating measurements were performed in phantoms implanted with reconstructed FRL models that produced highest heating in numerical simulations. The potential for unintended tissue stimulation was assessed through a conservative estimation of the electric field induced in the tissue due to gradient-induced voltages developed along the length of FRLs. Results: In simulations under conservative approach, RF exposure at B1+<2 uT generated CEM43<40 at all imaging landmarks at both 1.5 T and 3 T indicating no thermal damage for acquisition times (TA)<10 minutes. In experiments, the maximum temperature rise when FRLs were positioned at the location of maximum electric field exposure was measured to be 2.4 degrees Celsius at 3 T and 2.1 degrees Celsius at 1.5 T. Electric fields induced in the tissue due to gradient-induced voltages remained below the threshold for cardiac tissue stimulation in all cases. Conclusions: Simulation and experimental results indicate that patients with FRLs can be scanned safely at both 1.5 T and 3 T with majority of clinical pulse sequences.
Tie-breaker designs provide more efficient kernel estimates than regression discontinuity designs

Tie-breaker experimental designs are hybrids of Randomized Controlled Trials (RCTs) and Regression Discontinuity Designs (RDDs) in which subjects with moderate scores are placed in an RCT while subjects with extreme scores are deterministically assigned to the treatment or control group. The tie-breaker design (TBD) has practical advantages over the RCT in settings where it is unfair or uneconomical to deny the treatment to the most deserving recipients. Meanwhile, the TBD has statistical benefits due to randomization over the RDD. In this paper we discuss and quantify the statistical benefits of the TBD compared to the RDD. If the goal is estimation of the average treatment effect or the treatment at more than one score value, the statistical benefits of using a TBD over an RDD are apparent. If the goal is estimation of the average treatment effect at merely one score value, which is typically done by fitting local linear regressions, we prove that about 2.8 times more subjects are needed for an RDD in order to achieve the same asymptotic mean squared error. We further demonstrate using both theoretical results and simulations from the Angrist and Lavy (1999) classroom size dataset, that larger experimental radii choices for the TBD lead to greater statistical efficiency.
LMSOC: An Approach for Socially Sensitive Pretraining

While large-scale pretrained language models have been shown to learn effective linguistic representations for many NLP tasks, there remain many real-world contextual aspects of language that current approaches do not capture. For instance, consider a cloze-test"I enjoyed the ____ game this weekend": the correct answer depends heavily on where the speaker is from, when the utterance occurred, and the speaker's broader social milieu and preferences. Although language depends heavily on the geographical, temporal, and other social contexts of the speaker, these elements have not been incorporated into modern transformer-based language models. We propose a simple but effective approach to incorporate speaker social context into the learned representations of large-scale language models. Our method first learns dense representations of social contexts using graph representation learning algorithms and then primes language model pretraining with these social context representations. We evaluate our approach on geographically-sensitive language-modeling tasks and show a substantial improvement (more than 100% relative lift on MRR) compared to baselines.
Shaking the foundations: delusions in sequence models for interaction and control

The recent phenomenal success of language models has reinvigorated machine learning research, and large sequence models such as transformers are being applied to a variety of domains. One important problem class that has remained relatively elusive however is purposeful adaptive behavior. Currently there is a common perception that sequence models"lack the understanding of the cause and effect of their actions"leading them to draw incorrect inferences due to auto-suggestive delusions. In this report we explain where this mismatch originates, and show that it can be resolved by treating actions as causal interventions. Finally, we show that in supervised learning, one can teach a system to condition or intervene on data by training with factual and counterfactual error signals respectively.
Topic-Guided Abstractive Multi-Document Summarization

A critical point of multi-document summarization (MDS) is to learn the relations among various documents. In this paper, we propose a novel abstractive MDS model, in which we represent multiple documents as a heterogeneous graph, taking semantic nodes of different granularities into account, and then apply a graph-to-sequence framework to generate summaries. Moreover, we employ a neural topic model to jointly discover latent topics that can act as cross-document semantic units to bridge different documents and provide global information to guide the summary generation. Since topic extraction can be viewed as a special type of summarization that"summarizes"texts into a more abstract format, i.e., a topic distribution, we adopt a multi-task learning strategy to jointly train the topic and summarization module, allowing the promotion of each other. Experimental results on the Multi-News dataset demonstrate that our model outperforms previous state-of-the-art MDS models on both Rouge metrics and human evaluation, meanwhile learns high-quality topics.
A data-centric optimization framework for machine learning

Rapid progress in deep learning is leading to a diverse set of quickly changing models, with a dramatically growing demand for compute. However, as frameworks specialize performance optimization to patterns in popular networks, they implicitly constrain novel and diverse models that drive progress in research. We empower deep learning researchers by defining a flexible and user-customizable pipeline for optimizing training of arbitrary deep neural networks, based on data movement minimization. The pipeline begins with standard networks in PyTorch or ONNX and transforms computation through progressive lowering. We define four levels of general-purpose transformations, from local intra-operator optimizations to global data movement reduction. These operate on a data-centric graph intermediate representation that expresses computation and data movement at all levels of abstraction, including expanding basic operators such as convolutions to their underlying computations. Central to the design is the interactive and introspectable nature of the pipeline. Every part is extensible through a Python API, and can be tuned interactively using a GUI. We demonstrate competitive performance or speedups on ten different networks, with interactive optimizations discovering new opportunities in EfficientNet.
Cancer-Associated Fibroblast Subpopulations With Diverse and Dynamic Roles in the Tumor Microenvironment

Close interactions between cancer cells and cancer-associated fibroblasts (CAF) have repeatedly been reported to support tumor progression. Yet, targeting CAFs has so far failed to show a real benefit in cancer treatment, as preclinical studies have shown that such a strategy can enhance tumor growth. Accordingly, recent paradigm-shifting data suggest that certain CAF subpopulations could also show tumor-inhibitory capabilities. The present review aims to provide an in-depth description of the cellular heterogeneity of the CAF compartment in tumors. Through combining information from different cancer types, here we define 4 main CAF subpopulations that might cohabitate in any tumor microenvironment (TME). In addition, a model for the evolution of CAFs during tumor development is introduced. Moreover, the presence of tumor-inhibitory CAFs in the TME as well as their molecular characteristics are extensively discussed. Finally, the potential cellular origins of these distinct CAF subpopulations are reviewed. To our knowledge, this is the first attempt at establishing a broad but comprehensive classification of CAF subpopulations. Altogether, the present manuscript aims to provide the latest developments and innovative insights that could help refine future therapeutic targeting of CAFs for cancer treatment.
Racial Disparities in Pediatric Kidney Transplantation under the New Kidney Allocation System in the United States

Visual Abstract Background and objectives In December 2014, the Kidney Allocation System (KAS) was implemented to improve equity in access to transplantation, but preliminary studies in children show mixed results. Thus, we aimed to assess how the 2014 KAS policy change affected racial and ethnic disparities in pediatric kidney transplantation access and related outcomes. Design, setting, participants, & measurements We performed a retrospective cohort study of children <18 years of age active on the kidney transplant list from 2008 to 2019 using the Scientific Registry of Transplant Recipients. Log-logistic accelerated failure time models were used to determine the time from first activation on the transplant list and the time on dialysis to deceased donor transplant, each with KAS era or race and ethnicity as the exposure of interest. We used logistic regression to assess odds of delayed graft function. Log-rank tests assessed time to graft loss within racial and ethnic groups across KAS eras. Results All children experienced longer wait times from activation to transplantation post-KAS. In univariable analysis, Black and Hispanic children and other children of color experienced longer times from activation to transplant compared with White children in both eras; this finding was largely attenuated after multivariable analysis (time ratio, 1.16; 95% confidence interval, 1.01 to 1.32; time ratio, 1.13; 95% confidence interval, 1.00 to 1.28; and time ratio, 1.17; 95% confidence interval, 0.96 to 1.41 post-KAS, respectively). Multivariable analysis also showed that racial and ethnic disparities in time from dialysis initiation to transplantation in the pre-KAS era were mitigated in the post-KAS era. There were no disparities in odds of delayed graft function. Black and Hispanic children experienced longer times with a functioning graft in the post-KAS era. Conclusions No racial and ethnic disparities from activation to deceased donor transplantation were seen before or after implementation of the KAS in multivariable analysis, whereas time on dialysis to transplantation and odds of short-term graft loss improved in equity after the implementation of the KAS, without compromising disparities in delayed graft function. Podcast This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_12_07_CJN06740521. mp3
Theta oscillations in rat infralimbic cortex are associated with the inhibition of cocaine seeking during extinction

Infralimbic cortical (IL) manipulations indicate that this region mediates extinction learning and suppresses cocaine seeking following cocaine self‐administration. However, little work has recorded IL activity during the inhibition of cocaine seeking due to the difficulty of determining precisely when cocaine‐seeking behaviour is inhibited within a cocaine‐seeking session. The present study used in vivo electrophysiology to examine IL activity across extinction as well as during cocaine self‐administration and reinstatement. Sprague–Dawley rats underwent 6‐h access cocaine self‐administration in which the response lever was available during discrete signalled trials, a procedure which allowed for the comparison between epochs of cocaine seeking versus the inhibition thereof. Subsequently, rats underwent extinction and cocaine‐primed reinstatement using the same procedure. Results indicate that theta rhythms (4–10 Hz) dominated IL local‐field potential (LFP) activity during all experimental stages. During extinction, theta power fluctuated significantly surrounding the lever press and was lower when rats engaged in cocaine seeking versus when they withheld from doing so. These patterns of oscillatory activity differed from self‐administration and reinstatement stages. Single‐unit analyses indicate heterogeneity of IL unit responses, supporting the idea that multiple neuronal subpopulations exist within the IL and promote the expression of different and even opposing cocaine‐seeking behaviours. Together, these results are consistent with the idea that aggregate synaptic and single‐unit activity in the IL represent the engagement of the IL in action monitoring to promote adaptive behaviour in accordance with task contingencies and reveal critical insights into the relationship between IL activity and the inhibition of cocaine seeking.