Miracle-dz/UniFAR-FTUs huggingface dataset:
Policy Choice and Best Arm Identification: Comments on "Adaptive Treatment Assignment in Experiments for Policy Choice"

Adaptive experimental design for efficient decision-making is an important problem in economics. The purpose of this paper is to connect the “policy choice” problem, proposed in Kasy and Sautmann (2021) as an instance of adaptive experimental design, to the frontiers of the bandit literature in machine learning. We discuss how the policy choice problem can be framed in a way such that it is identical to what is called the “best arm identification” (BAI) problem. By connecting the literature, we identify that the asymptotic optimality of policy choice algorithms tackled in Kasy and Sautmann (2021) is a long-standing open question in the literature. While Kasy and Sautmann (2021) presents an interesting and important empirical study, unfortunately, this connection highlights several major issues with the theoretical results. In particular, we show that Theorem 1 in Kasy and Sautmann (2021) is false. We find that the proofs of statements (1) and (2) of Theorem 1 are incorrect. Although the statements themselves may be true, they are non-trivial to fix. Statement (3), and its proof, on the other hand, is false, which we show by utilizing existing theoretical results in the bandit literature. As this question is critically important, garnering much interest in the last decade within the bandit community, we provide a review of recent developments in the BAI literature. We hope this serves to highlight the relevance to economic problems and stimulate methodological and theoretical developments in the econometric community.
Learning Sparse Graph with Minimax Concave Penalty under Gaussian Markov Random Fields

This paper presents a convex-analytic framework to learn sparse graphs from data. While our problem formulation is inspired by an extension of the graphical lasso using the so-called combinatorial graph Laplacian framework, a key difference is the use of a nonconvex alternative to the $\ell_1$ norm to attain graphs with better interpretability. Specifically, we use the weakly-convex minimax concave penalty (the difference between the $\ell_1$ norm and the Huber function) which is known to yield sparse solutions with lower estimation bias than $\ell_1$ for regression problems. In our framework, the graph Laplacian is replaced in the optimization by a linear transform of the vector corresponding to its upper triangular part. Via a reformulation relying on Moreau's decomposition, we show that overall convexity is guaranteed by introducing a quadratic function to our cost function. The problem can be solved efficiently by the primal-dual splitting method, of which the admissible conditions for provable convergence are presented. Numerical examples show that the proposed method significantly outperforms the existing graph learning methods with reasonable CPU time.
Letters for Andrei: QBism and the Unfinished Nature of Nature

Some time ago, Steven Weinberg wrote an article for the New York Review of Books with the title, “Symmetry: A ‘Key to Nature’s Secrets’. ” So too, I would like to say of quantum information: Only by identifying Hilbert space’s most stringent and hard-to-attain symmetries will we be able to unlock quantum information’s deepest secrets and greatest potential. In this talk, I introduce the “symmetric informationally complete” (SIC) sets of quantum states [54] as a candidate for that structure. By their aid, one can rewrite quantum states so that they become simply probability distributions, unitary transformations so that they become doubly stochastic matrices, and the Born rule so that it becomes a rather simple variant of the classical law of total probability. These representations hold the potential for entirely new ways of analyzing quantum communication channels and algorithms. Paradoxically though, despite the way they can be used to make quantum theory look formally close to classical information theory, there is also a sense in which the SIC states are as far from classical as possible: For instance, by some measures these states are as sensitive to quantum eavesdropping as any alphabet of quantum states can be. But there is a deep question: For all these wonderful properties, do the things actually exist? By the end of the talk, I hope you will want them to! 3 Oct. 2019 On M & W (to the Reflection Team) I send you this note because of our recent discussions on von Fraassen’s reflection principle [55, 56, 57]. In defending our 2011 take on quantum decoherence [58] to Ulrich Mohrhoff, I believe I had a thought which had not come so to the front of my mind before. Maybe Rüdiger understood it, but it strikes me only now. It’s why I’m sharing this. Ulrich wrote, Ulrichism 1 : I am well aware of you paper on decoherence. I first read it a couple of years ago, if not earlier. I completely understand your project of “Bayesianizing” all relevant arguments, but sometimes it reminds me (ever so faintly) of Bell’s remark on efforts to get rid of the shifty split: “It can be done — up to a point. But it becomes embarrassing for the spectators even before it becomes uncomfortable for the snake. ” . When I first read this paper, the statement that “Decoherence does not come conceptually before a ‘selection’ . but conceptually after the recognition of the future possibilities” caused my some headache. It seems to be an example of obscuring a point by trying to make it clearer. (I find myself doing this all the time! ) While every probability assignment comes after the recognition of all possible sequences of events, decoherence remains associated with a measurement (by the environment) that comes before the measurement to whose outcomes probabilities are being assigned.
Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

In this paper I investigate the effect of random seed selection on the accuracy when using popular deep learning architectures for computer vision. I scan a large amount of seeds (up to $10^4$) on CIFAR 10 and I also scan fewer seeds on Imagenet using pre-trained models to investigate large scale datasets. The conclusions are that even if the variance is not very large, it is surprisingly easy to find an outlier that performs much better or much worse than the average.
Hunting for intermediate-mass black holes with LISA binary radial velocity measurements

Despite their potential role as massive seeds for quasars, in dwarf galaxy feedback, and in tidal disruption events, the observational evidence for intermediate-mass black holes (IMBHs) is scarce. LISA may observe stellar-mass black hole binaries orbiting Galactic IMBHs, and reveal the presence of the IMBH by measuring the Doppler shift in the gravitational waveform induced by the binary’s radial velocity. We estimate the number of detectable Doppler shift events from the Milky Way globular clusters (assuming they host IMBHs) and we ﬁnd that it decreases with the IMBH mass. A few Galactic globular clusters (including M22 and ω Centauri) may produce at least one event detectable by LISA. Even in more pessimistic scenarios, one could still expect ∼ 1 event overall in the Milky Way. We also estimate the number of Doppler shift events for IMBHs wandering in the Milky Way as a result of the disruption of their parent clusters. If there is at least one binary black hole orbiting around each wandering IMBH, LISA may detect up to a few tens of Doppler shift events from this elusive IMBH population. Under more pessimistic assumptions, LISA may still detect ∼ 1 wandering IMBH that would hardly be observable otherwise.
Grounding Natural Language Instructions: Can Large Language Models Capture Spatial Information?

Models designed for intelligent process automation are required to be capable of grounding user interface elements. This task of interface element grounding is centred on linking instructions in natural language to their target referents. Even though BERT and similar pre-trained language models have excelled in several NLP tasks, their use has not been widely explored for the UI grounding domain. This work concentrates on testing and probing the grounding abilities of three different transformer-based models: BERT, RoBERTa and LayoutLM. Our primary focus is on these models' spatial reasoning skills, given their importance in this domain. We observe that LayoutLM has a promising advantage for applications in this domain, even though it was created for a different original purpose (representing scanned documents): the learned spatial features appear to be transferable to the UI grounding setting, especially as they demonstrate the ability to discriminate between target directions in natural language instructions.
The critical role of hydrogen on the stability of oxy-hydroxyl defect clusters in uranium oxide

Despite considerable work applying ab initio techniques to model the role of defects on mechanical, structural and electronic properties of oxides, there has been little on the role of trapped hydrogen, despite it being virtually always present. We propose a framework for identifying reversible and irreversible hydrogen traps. We demonstrate that the thermodynamic stability of oxy-hydroxyl defects is defined by an interplay of formation and binding energies. This framework is applicable to all oxides and is essential for describing the solubility and diffusivity of hydrogen at the macroscopic level. For the most important actinide oxide in nuclear energy, uranium oxide, hydrogen significantly impacts the stability of oxygen defect clusters, and with increased local hydrogen concentration it forms irreversible traps. Crucially, hydrogen stabilises isolated Willis clusters, named after their discoverer and originally reported in 1963, which all subsequent ab initio calculations have predicted to be unstable, but of course, none considered hydrogen.
Challenges of Driver Drowsiness Prediction: The Remaining Steps to Implementation

Driver drowsiness has caused a large number of serious injuries and deaths on public roads and incurred billions of taxpayer dollars in costs. Hence, monitoring of drowsiness is critical to reduce this burden on society. This paper surveys the broad range of solutions proposed to address the challenges of driver drowsiness, and identifies the key steps required for successful implementation. Although some commercial products already exist, with vehicle-based methods most commonly implemented by automotive manufacturers, these systems may not have the level of accuracy required to properly predict and monitor drowsiness. State-of-the-art models use physiological, behavioural and vehicle-based methods to detect drowsiness, with hybrid methods emerging as a superior approach. Current setbacks to implementing these methods include late detection, intrusiveness and subject diversity. In particular, physiological monitoring methods such as Electroencephalography (EEG) are intrusive to drivers; while behavioural monitoring is least robust, affected by external factors such as lighting, as well as being subject to privacy concerns. Drowsiness detection models are often developed and validated based on subjective measures, with the Karolinska Sleepiness Scale being the most popular. Subjective and incoherent labelling of drowsiness, lack of on-road data and inconsistent protocols for data collection are among other challenges to be addressed to progress drowsiness detection for reliable on-road use.
Elongated Gravity Sources as an Analytical Limit for Flat Galaxy Rotation Curves

The flattening of spiral-galaxy rotation curves is unnatural in view of the expectations from Kepler’s third law and a central mass. It is interesting, however, that the radius-independence velocity is what one expects in one less dimension. In our three-dimensional space, the rotation curve is natural if, outside the galaxy’s center, the gravitational potential corresponds to that of a very prolate ellipsoid, filament, string, or otherwise cylindrical structure perpendicular to the galactic plane. While there is observational evidence (and numerical simulations) for filamentary structure at large scales, this has not been discussed at scales commensurable with galactic sizes. If, nevertheless, the hypothesis is tentatively adopted, the scaling exponent of the baryonic Tully–Fisher relation due to accretion of visible matter by the halo comes out to reasonably be 4. At a minimum, this analytical limit would suggest that simulations yielding prolate haloes would provide a better overall fit to small-scale galaxy data.
Reproducing "ner and Pos When Nothing Is Capitalized"

Capitalization is an important feature in many NLP tasks such as Named Entity Recognition (NER) or Part of Speech Tagging (POS). We are trying to reproduce results of paper which shows how to mitigate a significant performance drop when casing is mismatched between training and testing data. In particular we show that lowercasing 50% of the dataset provides the best performance, matching the claims of the original paper. We also show that we got slightly lower performance in almost all experiments we have tried to reproduce, suggesting that there might be some hidden factors impacting our performance. Lastly, we make all of our work available in a public github repository.
Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learning

Autonomous driving in urban crowds at unregulated intersections is challenging, where dynamic occlusions and uncertain behaviors of other vehicles should be carefully considered. Traditional methods are heuristic and based on hand-engineered rules and parameters, but scale poorly in new situations. Therefore, they require high labor cost to design and maintain rules in all foreseeable scenarios. Recently, deep reinforcement learning (DRL) has shown promising results in urban driving scenarios. However, DRL is known to be sample inefficient, and most previous works assume perfect observations such as ground-truth locations and motions of vehicles without considering noises and occlusions, which might be a too strong assumption for policy deployment. In this work, we use DRL to train lidar-based end-to-end driving policies that naturally consider imperfect partial observations. We further use unsupervised contrastive representation learning as an auxiliary task to improve the sample efficiency. The comparative evaluation results reveal that our method achieves higher success rates than the state-of-the-art (SOTA) lidar-based end-to-end driving network, better trades off safety and efficiency than the carefully tuned rule-based method, and generalizes better to new scenarios than the baselines. Demo videos are available at https://caipeide.github. io/carl-lead/.
Uncovering Quasi-periodic Nature of Physical Systems: A Case Study of Signalized Intersections

This paper presents a novel approach to analyze quasiperiodically driven dynamical systems. It aims to develop a complete data-driven framework for modeling such unknown dynamics. To achieve this, we characterize Koopman eigenfrequencies as generating frequencies of the quasiperiodic driver of the system. We compute true eigenfrequencies of Koopman operators by applying the theory of Reproducing Kernel Hibert Space (RKHS) and results from ergodic theory. We also demonstrate the decomposition of quasiperiodically driven dynamics into two components, i) the quasiperiodic driving source with generating frequencies and ii) the driven nonlinear dynamics. A unique aspect of the proposed framework is that it applies to the analysis of systems where the periodic component is either non-dominant or even absent. As a case study, we analyze a system of nine traffic signalized intersections. The proposed framework accurately reconstructs the measured queue lengths of the signalized intersections and makes stable long-term predictions.
Aggregate Semantics for Propositional Answer Set Programs

Answer set programming (ASP) emerged in the late 1990s as a paradigm for knowledge representation and reasoning. The attractiveness of ASP builds on an expressive high-level modeling language along with the availability of powerful off-the-shelf solving systems. While the utility of incorporating aggregate expressions in the modeling language has been realized almost simultaneously with the inception of the first ASP solving systems, a general semantics of aggregates and its efficient implementation have been long-standing challenges. Aggregates have been proposed and widely used in database systems, and also in the deductive database language Datalog, which is one of the main precursors of ASP. The use of aggregates was, however, still restricted in Datalog (by either disallowing recursion or only allowing monotone aggregates), while several ways to integrate unrestricted aggregates evolved in the context of ASP. In this survey, we pick up at this point of development by presenting and comparing the main aggregate semantics that have been proposed for propositional ASP programs. We highlight crucial properties such as computational complexity and expressive power, and outline the capabilities and limitations of different approaches by illustrative examples.
Primer: Searching for Efficient Transformers for Language Modeling

Large Transformer models have been central to recent advances in natural language processing. The training and inference costs of these models, however, have grown rapidly and become prohibitively expensive. Here we aim to reduce the costs of Transformers by searching for a more efficient variant. Compared to previous approaches, our search is performed at a lower level, over the primitives that define a Transformer TensorFlow program. We identify an architecture, named Primer, that has a smaller training cost than the original Transformer and other variants for auto-regressive language modeling. Primer's improvements can be mostly attributed to two simple modifications: squaring ReLU activations and adding a depthwise convolution layer after each Q, K, and V projection in self-attention. Experiments show Primer's gains over Transformer increase as compute scale grows and follow a power law with respect to quality at optimal model sizes. We also verify empirically that Primer can be dropped into different codebases to significantly speed up training without additional tuning. For example, at a 500M parameter size, Primer improves the original T5 architecture on C4 auto-regressive language modeling, reducing the training cost by 4X. Furthermore, the reduced training cost means Primer needs much less compute to reach a target one-shot performance. For instance, in a 1.9B parameter configuration similar to GPT-3 XL, Primer uses 1/3 of the training compute to achieve the same one-shot performance as Transformer. We open source our models and several comparisons in T5 to help with reproducibility.
Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI

We present the Habitat-Matterport 3D (HM3D) dataset. HM3D is a large-scale dataset of 1,000 building-scale 3D reconstructions from a diverse set of real-world locations. Each scene in the dataset consists of a textured 3D mesh reconstruction of interiors such as multi-floor residences, stores, and other private indoor spaces. HM3D surpasses existing datasets available for academic research in terms of physical scale, completeness of the reconstruction, and visual fidelity. HM3D contains 112.5k m^2 of navigable space, which is 1.4 - 3.7x larger than other building-scale datasets such as MP3D and Gibson. When compared to existing photorealistic 3D datasets such as Replica, MP3D, Gibson, and ScanNet, images rendered from HM3D have 20 - 85% higher visual fidelity w.r. t. counterpart images captured with real cameras, and HM3D meshes have 34 - 91% fewer artifacts due to incomplete surface reconstruction. The increased scale, fidelity, and diversity of HM3D directly impacts the performance of embodied AI agents trained using it. In fact, we find that HM3D is `pareto optimal' in the following sense -- agents trained to perform PointGoal navigation on HM3D achieve the highest performance regardless of whether they are evaluated on HM3D, Gibson, or MP3D. No similar claim can be made about training on other datasets. HM3D-trained PointNav agents achieve 100% performance on Gibson-test dataset, suggesting that it might be time to retire that episode dataset.
Star formation in the nearby dwarf galaxy DDO 53: interplay between gas accretion and stellar feedback

We present the results of a multiwavelength study of the nearby dwarf galaxy DDO 53 – a relatively isolated member of the M 81 group. We analyse the atomic and ionized gas kinematics (based on the observations with Fabry–Perot interferometer in H α line and archival data in H i 21 cm line), distribution, excitation, and oxygen abundance of the ionized gas (based on the long-slit and integral-field spectroscopy and on imaging with narrow-band filters), and their relation with the young massive stars (based on archival HST data). We detect a faint 2-kpc sized supershell of ionized gas surrounding the galaxy. Most probably, this structure represents a large-scale gas outflow, however, it could be also created by the ionizing quanta leaking from star-forming regions to the marginally detected atomic hydrogen surrounding the galactic disc. We analyse the properties of the anomalous H i in the north part of the galaxy and find that its peculiar kinematics is also traced by ionized gas. We argue that this H i feature is related to the accreting gas cloud captured from the intergalactic medium or remaining after the merger event occurred >1 Gyr ago. The infalling gas produces shocks in the interstellar medium and could support the star formation activity in the brightest region in DDO 53.
Slot Filling for Biomedical Information Extraction

Information Extraction (IE) from text refers to the task of extracting structured knowledge from unstructured text. The task typically consists of a series of sub-tasks such as Named Entity Recognition and Relation Extraction. Sourcing entity and relation type specific training data is a major bottleneck in domains with limited resources such as biomedicine. In this work we present a slot filling approach to the task of biomedical IE, effectively replacing the need for entity and relation-specific training data, allowing us to deal with zero-shot settings. We follow the recently proposed paradigm of coupling a Tranformer-based bi-encoder, Dense Passage Retrieval, with a Transformer-based reading comprehension model to extract relations from biomedical text. We assemble a biomedical slot filling dataset for both retrieval and reading comprehension and conduct a series of experiments demonstrating that our approach outperforms a number of simpler baselines. We also evaluate our approach end-to-end for standard as well as zero-shot settings. Our work provides a fresh perspective on how to solve biomedical IE tasks, in the absence of relevant training data. Our code, models and datasets are available at https://github.com/tba.