cssi/SciSciGPT-SciSciNet huggingface dataset:
Geometric distinguishability measures limit quantum channel estimation and discrimination

Quantum channel estimation and discrimination are fundamentally related information processing tasks of interest in quantum information science. In this paper, we analyze these tasks by employing the right logarithmic derivative Fisher information and the geometric Renyi relative entropy, respectively, and we also identify connections between these distinguishability measures. A key result of our paper is that a chain-rule property holds for the right logarithmic derivative Fisher information and the geometric Renyi relative entropy for the interval $$\alpha \in (0,1) $$ of the Renyi parameter $$\alpha $$ . In channel estimation, these results imply a condition for the unattainability of Heisenberg scaling, while in channel discrimination, they lead to improved bounds on error rates in the Chernoff and Hoeffding error exponent settings. More generally, we introduce the amortized quantum Fisher information as a conceptual framework for analyzing general sequential protocols that estimate a parameter encoded in a quantum channel. We then use this framework, beyond the aforementioned application, to show that Heisenberg scaling is not possible when a parameter is encoded in a classical–quantum channel. We then identify a number of other conceptual and technical connections between the tasks of estimation and discrimination and the distinguishability measures involved in analyzing each. As part of this work, we present a detailed overview of the geometric Renyi relative entropy of quantum states and channels, as well as its properties, which may be of independent interest.
Effects of Model Misspecification on Bayesian Bandits Case Studies in UX Optimization

Bayesian bandits using Thompson Sampling have seen increasing success in recent years. Yet existing value models (of rewards) are misspecified on many real-world problem. We demonstrate this on the User Experience Optimization (UXO) problem, providing a novel formulation as a restless, sleeping bandit with unobserved confounders plus optional stopping. Our case studies show how common misspecifications can lead to sub-optimal rewards, and we provide model extensions to address these, along with a scientific model building process practitioners can adopt or adapt to solve their own unique problems. To our knowledge, this is the first study showing the effects of overdispersion on bandit explore/exploit efficacy, tying the common notions of under- and over-confidence to over- and under-exploration, respectively. We also present the first model to demonstrate that vanishing regret and fast and consistent optional stopping are achievable in restless bandits with cointegration.
Social capital and the problem of opportunistic leadership: the example of Koreans in Japan

Through webs of cross-cutting ties, groups can build "social capital"—the information and informal collective punishment by which to mitigate collective action problems and enforce on each other norms of appropriate behavior. Yet not all minorities maintain such networks. And minority groups without these cross-cutting ties sometimes find themselves hijacked by opportunistic entrepreneurs who capture private benefits for themselves while generating statistical discrimination against the group as a whole. The problem becomes acute when migration from the minority to the majority group is possible (at a cost). Inevitably, the most talented members of the minority will find the migration easiest and most rewarding. Necessarily, the resulting selective out-migration will reduce the average ability of the minority members who remain and leave the group even more vulnerable to the opportunists. Consider the Korean residents of Japan. Koreans had begun to migrate to Japan in the 1910s. They were poor, single, male, young, uneducated, and did not intend to stay long. As one might expect given those demographics, they maintained low levels of social capital, and generated substantial (statistical) discrimination toward themselves. After the Second World War, most Koreans returned to their homeland. Among those who stayed, the low levels of social capital remained. Plagued by collective action problems, the group could not prevent the communists among them from taking control and manipulating the group toward their private ends. Lacking the dense networks that would let them constrain the opportunists, the resident Koreans could not stop them. Those with the most talent, sophistication, and education simply left the group and migrated into Japanese society.
Microfinance and Missing Markets

The existing theoretical analysis of microfinance focuses on the nature of the loan contract such as group liability. We draw attention to the role of missing or imperfect labor market in understanding some of the important `second generation' debates in microfinance. Our analysis helps explain a number puzzles in microfinance such as (i) high repayment rates with high interest rates, (ii) difficulties in scaling up projects, (iii) conflicting views about interest rate elasticity of demand for microcredit. The analysis implies that while microcredit can play a vital role when labor markets are underdeveloped, demand for such loans may progressively decline as markets develop.
Automating instructional support for SBT: next-generation expeditionary warfare intelligent training (NEW-IT)

Although scenario-based training (SBT) supports optimal transfer-of-training for complex domains (such as the modern battlefield), it is often inefficient and workload intensive. To mitigate these operational challenges, adaptive, intelligent, and dynamic SBT solutions are being explored. Participants in this session will discuss their approaches and progress towards achieving automated instructional support for military SBT simulations.
Brain Endothelial Cells are Exquisite Sensors of Age-Related Circulatory Cues

Brain endothelial cells (BECs) are key elements of the blood-brain barrier (BBB), protecting the brain from pathogens and restricting access of circulatory factors. Because circulatory factors have prominent age-related effects on adult neurogenesis, microglia, neuronal activity, and learning and memory in mice the question arises how signals are relayed between the blood and brain. Using single cell RNA sequencing of hippocampal BECs we discovered that capillary BECs, compared with arterial and venous cells, exhibit the greatest transcriptional changes with aging, upregulating innate immunity, TGF-β signaling and oxidative stress response pathways. Short-term infusions of aged plasma into young mice recapitulated key aspects of this aging transcriptome, and remarkably, infusions of young plasma into aged mice had the opposite effect, essentially rejuvenating the BBB endothelial transcriptome. Interestingly, we find correlations between upregulated surface receptors on aging capillaries and the level of their ligands in aging mouse plasma. Together, these findings suggest that the transcriptional age of BECs is exquisitely sensitive to age-related circulatory cues and pinpoint the BBB itself as a promising therapeutic target to treat brain disease.
Inventorship Standards for Biotechnology Inventions Under the U.S. and Japanese Patent Acts

This article highlights differences between the United States and Japanese inventorship standards by comparing a U.S. Federal Circuit decision and a Tokyo District Court decision in which inventorship of the same invention was disputed. It further discusses how Japanese courts, by applying Japanese inventorship standards, would not find one of the U.S. researchers in this case to be inventors, despite the Court of Appeals for the Federal Circuit (CAFC) finding inventorship while applying the U.S. standard. It argues that the U.S. standard is over-inclusive by including individuals who did not contribute to any part of the inventive process, but that the Japanese standard is too difficult to meet and may exclude inventors who made an important contribution. The over-inclusive U.S. standard is influenced by the legacy of “first-to-invent”: a formality requirement that all individual inventors must contribute to every claim to file a single application and a substantive requirement that an inventor’s prior joint invention is the prior art against her later invention unless the same group of individual inventors made both inventions. The article concludes with a discussion of the advantages and disadvantages of applying the U.S. and Japanese standards for determining inventorship of biotechnology inventions and proposes a harmonization of inventorship standards through the leadership of the World Intellectual Property Organization.
Configuring test generators using bug reports: a case study of GCC compiler and Csmith

The correctness of compilers is instrumental in the safety and reliability of other software systems, as bugs in compilers can produce executables that do not reflect the intent of programmers. Such errors are difficult to identify and debug. Random test program generators are commonly used in testing compilers, and they have been effective in uncovering bugs. However, the problem of guiding these test generators to produce test programs that are more likely to find bugs remains challenging. In this paper, we use the code snippets in the bug reports to guide the test generation. The main idea of this work is to extract insights from the bug reports about the language features that are more prone to inadequate implementation and using the insights to guide the test generators. We use the GCC C compiler to evaluate the effectiveness of this approach. In particular, we first cluster the test programs in the GCC bugs reports based on their features. We then use the centroids of the clusters to compute configurations for Csmith, a popular test generator for C compilers. We evaluated this approach on eight versions of GCC and found that our approach provides higher coverage and triggers more miscompilation failures than the state-of-the-art test generation techniques for GCC.
Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns

This paper studies the decision making problem with Funnel Structure. Funnel structure, a well-known concept in the marketing field, occurs in those systems where the decision maker interacts with the environment in a layered manner receiving far fewer observations from deep layers than shallow ones. For example, in the email marketing campaign application, the layers correspond to Open, Click and Purchase events. Conversions from Click to Purchase happen very infrequently because a purchase cannot be made unless the link in an email is clicked on. We formulate this challenging decision making problem as a contextual bandit with funnel structure and develop a multi-task learning algorithm that mitigates the lack of sufficient observations from deeper layers. We analyze both the prediction error and the regret of our algorithms. We verify our theory on prediction errors through a simple simulation. Experiments on both a simulated environment and an environment based on real-world data from a major email marketing company show that our algorithms offer significant improvement over previous methods.
Affinely Representable Lattices, Stable Matchings, and Choice Functions.

Birkhoff’s representation theorem [11] defines a bijection between elements of a distributive lattice \(\mathcal{L}\) and the family of upper sets of an associated poset \(\mathcal{B}\). When elements of \(\mathcal{L}\) are the stable matchings in an instance of Gale and Shapley’s marriage model, Irving et al. [22] showed how to use \(\mathcal{B}\) to devise a combinatorial algorithm for maximizing a linear function over the set of stable matchings. In this paper, we introduce a general property of distributive lattices, which we term as affine representability, and show its role in efficiently solving linear optimization problems over the elements of a distributive lattice, as well as describing the convex hull of the characteristic vectors of lattice elements. We apply this concept to the stable matching model with path-independent quota-filling choice functions, thus giving efficient algorithms and a compact polyhedral description for this model. To the best of our knowledge, this model generalizes all models from the literature for which similar results were known, and our paper is the first that proposes efficient algorithms for stable matchings with choice functions, beyond extension of the Deferred Acceptance algorithm [31].
Glucocorticoid signaling in pancreatic islets modulates gene regulatory programs and genetic risk of type 2 diabetes

Glucocorticoids are key regulators of glucose homeostasis and pancreatic islet function, but the gene regulatory programs driving responses to glucocorticoid signaling in islets and the contribution of these programs to diabetes risk are unknown. In this study we used ATAC-seq and RNA-seq to map chromatin accessibility and gene expression from eleven primary human islet samples cultured in vitro with the glucocorticoid dexamethasone at multiple doses and durations. We identified thousands of accessible chromatin sites and genes with significant changes in activity in response to glucocorticoids. Chromatin sites up-regulated in glucocorticoid signaling were prominently enriched for glucocorticoid receptor binding sites and up-regulated genes were enriched for ion transport and lipid metabolism, whereas down-regulated chromatin sites and genes were enriched for inflammatory, stress response and proliferative processes. Genetic variants associated with glucose levels and T2D risk were enriched in glucocorticoid-responsive chromatin sites, including fine-mapped variants at 51 known signals. Among fine-mapped variants in glucocorticoid-responsive chromatin, a likely casual variant at the 2p21 locus had glucocorticoid-dependent allelic effects on beta cell enhancer activity and affected SIX2 and SIX3 expression. Our results provide a comprehensive map of islet regulatory programs in response to glucocorticoids through which we uncover a role for islet glucocorticoid signaling in mediating genetic risk of T2D.
ATRX promotes heterochromatin formation to protect cells from G-quadruplex DNA-mediated stress

ATRX is a tumor suppressor that has been associated with protection from DNA replication stress, purportedly through resolution of difficult-to-replicate G-quadruplex (G4) DNA structures. While several studies demonstrate that loss of ATRX sensitizes cells to chemical stabilizers of G4 structures, the molecular function of ATRX at G4 regions during replication remains unknown. Here, we demonstrate that ATRX associates with a number of the MCM replication complex subunits and that loss of ATRX leads to G4 structure accumulation at newly synthesized DNA. We show that both the helicase domain of ATRX and its H3.3 chaperone function are required to protect cells from G4-induced replicative stress. Furthermore, these activities are upstream of heterochromatin formation mediated by the histone methyltransferase, ESET, which is the critical molecular event that protects cells from G4-mediated stress. In support, tumors carrying mutations in either ATRX or ESET show increased mutation burden at G4-enriched DNA sequences. Overall, our study provides new insights into mechanisms by which ATRX promotes genome stability with important implications for understanding impacts of its loss on human disease.
Kotlarski with a factor loading

Abstract This note extends the Kotlarski (1967) Lemma to show exactly what is identified when we allow for an unknown factor loading on the common unobserved factor. That is, this note completely characterizes identification of the model Y = cV + U and X = V + W, where the joint distribution of Y and X is known, while the constant c and the mutually independent random variables V, U, and W are unobserved. Potential applications include measurement error models and panel data factor models.
On Linear Identifiability of Learned Representations

Identifiability is a desirable property of a statistical model: it implies that the true model parameters may be estimated to any desired precision, given sufficient computational resources and data. We study identifiability in the context of representation learning: discovering nonlinear data representations that are optimal with respect to some downstream task. When parameterized as deep neural networks, such representation functions lack identifiability in parameter space, because they are overparameterized by design. In this paper, building on recent advances in nonlinear Independent Components Analysis, we aim to rehabilitate identifiability by showing that a large family of discriminative models are in fact identifiable in function space, up to a linear indeterminacy. Many models for representation learning in a wide variety of domains have been identifiable in this sense, including text, images and audio, state-of-the-art at time of publication. We derive sufficient conditions for linear identifiability and provide empirical support for the result on both simulated and real-world data.
Fed2: Feature-Aligned Federated Learning

Federated learning learns from scattered data by fusing collaborative models from local nodes. However, conventional coordinate-based model averaging by FedAvg ignored the random information encoded per parameter and may suffer from structural feature misalignment. In this work, we propose Fed2, a feature-aligned federated learning framework to resolve this issue by establishing a firm structure-feature alignment across the collaborative models. Fed2 is composed of two major designs: First, we design a feature-oriented model structure adaptation method to ensure explicit feature allocation in different neural network structures. Applying the structure adaptation to collaborative models, matchable structures with similar feature information can be initialized at the very early training stage. During the federated learning process, we then propose a feature paired averaging scheme to guarantee aligned feature distribution and maintain no feature fusion conflicts under either IID or non-IID scenarios. Eventually, Fed2 could effectively enhance the federated learning convergence performance under extensive homo- and heterogeneous settings, providing excellent convergence speed, accuracy, and computation/communication efficiency.
Re-investigating the coughing rat model of pertussis to understand Bordetella pertussis pathogenesis

Bordetella pertussis is a highly contagious bacterium that is the causative agent of whooping cough (pertussis). Currently, acellular pertussis vaccines (aP, DTaP, and Tdap) are used to prevent pertussis disease. However, it is clear that the aP vaccine efficacy quickly wanes, resulting in the reemergence of pertussis. Furthermore, recent work performed by the CDC suggest that current circulating strains are genetically distinct from strains of the past. The emergence of genetically diverging strains, combined with waning aP vaccine efficacy, calls for reevaluation of current animal models of pertussis. In this study, we used the rat model of pertussis to compare two genetically divergent strains Tohama 1 and D420. We intranasally challenged 7-week-old Sprague-Dawley rats with 108 viable Tohama 1 and D420 and measured the hallmark signs/symptoms of B. pertussis infection such as neutrophilia, pulmonary inflammation, and paroxysmal cough using whole-body plethysmography. Onset of cough occurred between 2 and 4 days after B. pertussis challenge, averaging five coughs per 15 min, with peak coughing occurring at day 8 postinfection, averaging upward of 13 coughs per 15 min. However, we observed an increase of coughs in rats infected with clinical isolate D420 through 12 days postchallenge. The rats exhibited increased bronchial restriction following B. pertussis infection. Histology of the lung and flow cytometry confirm both cellular infiltration and pulmonary inflammation. D420 infection induced higher production of anti-B. pertussis IgM antibodies compared to Tohama 1 infection. The coughing rat model provides a way of characterizing disease manifestation differences between B. pertussis strains.
The Self-Delegation False Alarm: Analyzing Auer Deference's Effect on Agency Rules

Auer deference holds that reviewing courts should defer to agencies when the latter interpret their own preexisting regulations. This doctrine relieves pressure on agencies to undergo costly notice-and-comment rulemaking each time interpretation of existing regulations is necessary. But according to some leading scholars and jurists, the doctrine actually encourages agencies to promulgate vague rules in the first instance, augmenting agency power and violating core separation of powers norms in the process. The claim that Auer perversely encourages agencies to “self-delegate”—that is, to create vague rules that can later be informally interpreted by agencies with latitude due to judicial deference—has helped to persuade the Supreme Court to take up this term the question of whether to overturn the doctrine. Yet, surprisingly, the self-delegation thesis has never been tested. This Article scrutinizes the thesis empirically, using an original and extensive dataset of the texts of federal rules from 1982 to 2016. My linguistic analysis reveals that agencies did not measurably increase the vagueness of their writing in response to Auer. If anything, rule writing arguably became more specific over time, at least by one measure, despite Auer’s increasing prominence. These findings run against common wisdom, but they should not be at all surprising. The self-delegation thesis depends on a model of agency behavior that is inconsistent with what is known about the institutional pressures and cognitive horizons that cause agencies to pursue clarity in rule writing. By revealing the failures of theoretical predictions about Auer, this Article more generally draws attention to the need to test behavioral theories of administrative law against empirical reality before unsettling settled law.
Model Choices Influence Attributive Word Associations: A Semi-supervised Analysis of Static Word Embeddings

Static word embeddings encode word associations, extensively utilized in downstream NLP tasks. Although prior studies have discussed the nature of such word associations in terms of biases and lexical regularities captured, the variation in word associations based on the embedding training procedure remains in obscurity. This work aims to address this gap by assessing attributive word associations across five different static word embedding architectures, analyzing the impact of the choice of the model architecture, context learning flavor and training corpora. Our approach utilizes a semi-supervised clustering method to cluster annotated proper nouns and adjectives, based on their word embedding features, revealing underlying attributive word associations formed in the embedding space, without introducing any confirmation bias. Our results reveal that the choice of the context learning flavor during embedding training (CBOW vs skip-gram) impacts the word association distinguishability and word embeddings’ sensitivity to deviations in the training corpora. Moreover, it is empirically shown that even when trained over the same corpora, there is significant inter-model disparity and intra-model similarity in the encoded word associations across different word embedding models, portraying specific patterns in the way the embedding space is created for each embedding architecture.
Harmonic branched coverings and uniformization of CAT(k) spheres

Let $S$ be a surface with a metric $d$ satisfying an upper curvature bound in the sense of Alexandrov (i.e. via triangle comparison). We show that an almost conformal harmonic map from a surface into $(S,d)$ is a branched covering. As a consequence, if $(S,d)$ is homeomorphically equivalent to the 2-sphere $\mathbb S^2$, then it is conformally equivalent to $\mathbb S^2$.
Learning from eXtreme Bandit Feedback

We study the problem of batch learning from bandit feedback in the setting of extremely large action spaces. Learning from extreme bandit feedback is ubiquitous in recommendation systems, in which billions of decisions are made over sets consisting of millions of choices in a single day, yielding massive observational data. In these large-scale real-world applications, supervised learning frameworks such as eXtreme Multi-label Classification (XMC) are widely used despite the fact that they incur significant biases due to the mismatch between bandit feedback and supervised labels. Such biases can be mitigated by importance sampling techniques, but these techniques suffer from impractical variance when dealing with a large number of actions. In this paper, we introduce a selective importance sampling estimator (sIS) that operates in a significantly more favorable bias-variance regime. The sIS estimator is obtained by performing importance sampling on the conditional expectation of the reward with respect to a small subset of actions for each instance (a form of Rao-Blackwellization). We employ this estimator in a novel algorithmic procedure---named Policy Optimization for eXtreme Models (POXM)---for learning from bandit feedback on XMC tasks. In POXM, the selected actions for the sIS estimator are the top-p actions of the logging policy, where p is adjusted from the data and is significantly smaller than the size of the action space. We use a supervised-to-bandit conversion on three XMC datasets to benchmark our POXM method against three competing methods: BanditNet, a previously applied partial matching pruning strategy, and a supervised learning baseline. Whereas BanditNet sometimes improves marginally over the logging policy, our experiments show that POXM systematically and significantly improves over all baselines.
Judicial Restoration of Rights as an Auxiliary to the Pardon Power

This essay outlines the pressing need for a robust use of pardon power to address the cascading consequences of criminal convictions. It argues that a scalable auxiliary to presidential pardon power is a legislative scheme that harnesses the federal court system to conduct individualized reviews and adjudications of petitions for the restoration of rights. In the digital age, where nothing can be truly forgotten, these authoritative judgments of rehabilitation, coupled with clear prohibitions on considering criminal records, are the most effective way to address the profound collateral consequences of criminal convictions.
Stabilization of Hypoxia Inducible Factor by Cobalt Chloride Can Alter Renal Epithelial Transport

Abstract Given the importance of the transcriptional regulator hypoxia‐inducible factor‐1 (HIF‐1) for adaptive hypoxia responses, we examined the effect of stabilized HIF‐1 α on renal epithelial permeability and directed sodium transport. This study was motivated by histological analysis of cystic kidneys showing increased expression levels of HIF‐1 α and HIF‐2 α . We hypothesize that compression induced localized ischemia‐hypoxia of normal epithelia near a cyst leads to local stabilization of HIF‐1 α , leading to altered transepithelial transport that encourages cyst expansion. We found that stabilized HIF‐1 α alters both transcellular and paracellular transport through renal epithelial monolayers in a manner consistent with secretory behavior, indicating localized ischemia‐hypoxia may lead to altered salt and water transport through kidney epithelial monolayers. A quantity of 100 μ mol/L Cobalt chloride (CoCl 2 ) was used acutely to stabilize HIF‐1 α in confluent cultures of mouse renal epithelia. We measured increased transepithelial permeability and decreased transepithelial resistance (TER) when HIF‐1 α was stabilized. Most interestingly, we measured a change in the direction of sodium current, most likely corresponding to abnormal secretory function, supporting our positive‐feedback hypothesis.
The need to validate exogenous shocks: Shareholder derivative litigation, universal demand laws and firm behavior

Abstract Several recent studies argue that the adoption of universal demand (UD) laws represent an exogenous decline in litigation risk by increasing the procedural hurdles associated with shareholder derivative litigation. This study examines how UD laws affect the incidence of derivative litigation risk and related decisions. We show that the adoption of UD laws had no meaningful impact on derivative litigation from 1996 to 2015. We also find no evidence that UD laws affect aggressive accounting, voluntary disclosure, executive compensation, or corporate governance decisions. Collectively, our findings cast doubt on the validity of using UD laws as an exogenous shock to litigation risk.
Context Matters: Graph-based Self-supervised Representation Learning for Medical Images

Supervised learning method requires a large volume of annotated datasets. Collecting such datasets is time-consuming and expensive. Until now, very few annotated COVID-19 imaging datasets are available. Although self-supervised learning enables us to bootstrap the training by exploiting unlabeled data, the generic self-supervised methods for natural images do not sufficiently incorporate the context. For medical images, a desirable method should be sensitive enough to detect deviation from normal-appearing tissue of each anatomical region; here, anatomy is the context. We introduce a novel approach with two levels of self-supervised representation learning objectives: one on the regional anatomical level and another on the patient-level. We use graph neural networks to incorporate the relationship between different anatomical regions. The structure of the graph is informed by anatomical correspondences between each patient and an anatomical atlas. In addition, the graph representation has the advantage of handling any arbitrarily sized image in full resolution. Experiments on large-scale Computer Tomography (CT) datasets of lung images show that our approach compares favorably to baseline methods that do not account for the context. We use the learnt embedding to quantify the clinical progression of COVID-19 and show that our method generalizes well to COVID-19 patients from different hospitals. Qualitative results suggest that our model can identify clinically relevant regions in the images.
Self-Imitation Advantage Learning

Self-imitation learning is a Reinforcement Learning (RL) method that encourages actions whose returns were higher than expected, which helps in hard exploration and sparse reward problems. It was shown to improve the performance of on-policy actor-critic methods in several discrete control tasks. Nevertheless, applying self-imitation to the mostly action-value based off-policy RL methods is not straightforward. We propose SAIL, a novel generalization of self-imitation learning for off-policy RL, based on a modification of the Bellman optimality operator that we connect to Advantage Learning. Crucially, our method mitigates the problem of stale returns by choosing the most optimistic return estimate between the observed return and the current action-value for self-imitation. We demonstrate the empirical effectiveness of SAIL on the Arcade Learning Environment, with a focus on hard exploration games.
Efficient and Robust Distributed Matrix Computations via Convolutional Coding

Distributed matrix computations – matrix-matrix or matrix-vector multiplications – are well-recognized to suffer from the problem of stragglers (slow or failed worker nodes). Much of prior work in this area is (i) either sub-optimal in terms of its straggler resilience, or (ii) suffers from numerical problems, i.e., there is a blow-up of round-off errors in the decoded result owing to the high condition numbers of the corresponding decoding matrices. Our work presents a convolutional coding approach to this problem that removes these limitations. It is optimal in terms of its straggler resilience, and has excellent numerical robustness as long as the workers’ storage capacity is slightly higher than the fundamental lower bound. Moreover, it can be decoded using a fast peeling decoder that only involves add/subtract operations. Our second approach has marginally higher decoding complexity than the first one, but allows us to operate arbitrarily close to the storage capacity lower bound. Its numerical robustness can be theoretically quantified by deriving a computable upper bound on the worst case condition number over all possible decoding matrices by drawing connections with the properties of large block Toeplitz matrices. All above claims are backed up by extensive experiments done on the AWS cloud platform.
Components of eleven-dimensional supergravity with four off-shell supersymmetries

We derive the component structure of 11D, N = 1/8 supergravity linearized around eleven-dimensional Minkowski space. This theory represents 4 local supersymmetries closing onto 4 of the 11 spacetime translations without the use of equations of motion. It may be interpreted as adding 201 auxiliary bosons and 56 auxiliary fermions to the physical supergravity multiplet for a total of 376 + 376 components. These components and their transformations are organized into representations of SL(2; C) × G2.
Learning hidden elasticity with deep neural networks

Elastography is an imaging technique to reconstruct elasticity distributions of heterogeneous objects. Since cancerous tissues are stiffer than healthy ones, for decades, elastography has been applied to medical imaging for noninvasive cancer diagnosis. Although the conventional strain-based elastography has been deployed on ultrasound diagnostic-imaging devices, the results are prone to inaccuracies. Model-based elastography, which reconstructs elasticity distributions by solving an inverse problem in elasticity, may provide more accurate results but is often unreliable in practice due to the ill-posed nature of the inverse problem. We introduce ElastNet, a de novo elastography method combining the theory of elasticity with a deep-learning approach. With prior knowledge from the laws of physics, ElastNet can escape the performance ceiling imposed by labeled data. ElastNet uses backpropagation to learn the hidden elasticity of objects, resulting in rapid and accurate predictions. We show that ElastNet is robust when dealing with noisy or missing measurements. Moreover, it can learn probable elasticity distributions for areas even without measurements and generate elasticity images of arbitrary resolution. When both strain and elasticity distributions are given, the hidden physics in elasticity-the conditions for equilibrium-can be learned by ElastNet.
A materials-design synthesis for optimum design of spacecraft energy-dissipating systems.

Spacecraft energy dissipation system design optimization, developing semigeometric solution technique
The Data Privacy Law of Brexit: Theories of Preference Change

Upon Brexit, the United Kingdom chose to follow the path of EU data protection and remain tied to the requirements of the General Data Protection Regulation (GDPR). It even enacted the GDPR into its domestic law. This Article evaluates five models relating to preference change, demonstrating how they identify different dimensions of Brexit while providing a rich explanation of why a legal system may or may not reject an established transnational legal order. While market forces and a “Brussels Effect” played the most significant role in the decision of the UK government to accept the GDPR, important nonmarket factors were also present in this choice. This Article’s models of preference change are also useful in thinking about the likely extent of the UK’s future divergence from EU data protection.
POD: A Smartphone That Flies

We present POD, a smartphone that flies, as a new way to achieve hands-free, eyes-up mobile computing. Unlike existing drone-carried user interfaces, POD features a smartphone-sized display and the computing and sensing power of a modern smartphone. We share our experience in prototyping POD, discuss the technical challenges facing it, and describe early results toward addressing them.
Coded Computing via Binary Linear Codes: Designs and Performance Limits

We consider the problem of coded distributed computing where a large linear computational job, such as a matrix multiplication, is divided into $k$ smaller tasks, encoded using an $(n,k)$ linear code, and performed over $n$ distributed nodes. The goal is to reduce the average execution time of the computational job. We provide a connection between the problem of characterizing the average execution time of a coded distributed computing system and the problem of analyzing the error probability of codes of length $n$ used over erasure channels. Accordingly, we present closed-form expressions for the execution time using binary random linear codes and the best execution time any linear-coded distributed computing system can achieve. It is also shown that there exist good binary linear codes that not only attain (asymptotically) the best performance that any linear code (not necessarily binary) can achieve but also are numerically stable against the inevitable rounding errors in practice. We then develop a low-complexity algorithm for decoding Reed-Muller (RM) codes over erasure channels. Our decoder only involves additions, subtractions, and inversion of relatively small matrices of dimensions at most $\log n+1$ , and enables coded computation over real-valued data. Extensive numerical analysis of the fundamental results as well as RM- and polar-coded computing schemes demonstrate the excellence of the RM-coded computation in achieving close-to-optimal performance while having a low-complexity decoding and explicit construction. The proposed framework in this paper enables efficient designs of distributed computing systems given the rich literature in the channel coding theory.
A Survey on Simulators for Testing Self-Driving Cars

Rigorous and comprehensive testing plays a key role in training self-driving cars to handle a variety of situations that they are expected to see on public roads. The physical testing on public roads is unsafe, costly, and not always reproducible. This is where testing in simulation helps fill the gap. However, the problem with simulation testing is that it is only as good as the simulator used for testing and how representative the simulated scenarios are of the real environment. In this paper, we identify key requirements that a good simulator must have. Further, we provide a comparison of commonly used simulators. Our analysis shows that CARLA and LGSVL simulators are the current state-of-the-art simulators for end to end testing of self-driving cars for the reasons mentioned in this paper. Finally, we present current challenges that simulation testing continues to face as we march towards building fully autonomous cars.
The Unending Onslaught on Tribal Sovereignty: State Income Taxation of Non-Member Indians

State income taxation of non-member Indians is a legal issue that the United States Supreme Court is likely to address within the next five to ten years. This article provides a framework for resolution of this issue by carefully considering the history of taxation in Indian country and the cases that the United States Supreme Court has already decided. When it finally addresses the issue, the Court will be able to consider the usefulness of my framework. In this article, I conclude that states do not have the power tax the incomes of non-member Indians who live and work on the reservations of other tribes. Because of inter-marriage and the availability of jobs with other tribes, the presence of non-member Native Americans is common on many reservations. Arizona, New Mexico, and Wisconsin currently assert that they have the power to tax non-member Indians precisely because they are non-members of the tribe where they live and work. The courts of these states acknowledge that they cannot legally impose their income taxes on tribal members who live and work on their own reservations. But in the case of non-member Indians, these states assert and their courts agree that non-member Indians should be subject to the state income taxation precisely because they cannot vote in the tribal elections where they live. The state courts come to this conclusion by misinterpreting and erroneously applying United States Supreme Court cases. State courts have a long historical bias against Native Americans and against federally recognized Indian tribes. Therefore, it is not surprising that states have been quick to tax even when they lack the power, and their courts have been just as willing to validate the existence of an absent power.
Microbial population dynamics and evolutionary outcomes under extreme energy limitation

Microorganisms commonly inhabit energy-limited ecosystems where cellular maintenance and reproduction is highly constrained. To gain insight into how individuals persist under such conditions, we derived demographic parameters from a collection of 21 heterotrophic bacterial taxa by censusing 100 populations in an effectively closed system for 1,000 d. All but one taxon survived prolonged resource scarcity, yielding estimated times to extinction ranging over four orders of magnitude from 100 to 105 y. Our findings corroborate reports of long-lived bacteria recovered from ancient environmental samples, while providing insight into mechanisms of persistence. As death rates declined over time, lifespan was extended through the scavenging of dead cells. Although reproduction was suppressed in the absence of exogenous resources, populations continued to evolve. Hundreds of mutations were acquired, contributing to genome-wide signatures of purifying selection as well as molecular signals of adaptation. Consistent ecological and evolutionary dynamics indicate that distantly related bacteria respond to energy limitation in a similar and predictable manner, which likely contributes to the stability and robustness of microbial life.
Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data. Part II: Quantifying the Uncertainty

Uncertainty quantification of groundwater (GW) aquifer parameters is critical for efficient management and sustainable extraction of GW resources. These uncertainties are introduced by the data, model, and prior information on the parameters. Here we develop a Bayesian inversion framework that uses Interferometric Synthetic Aperture Radar (InSAR) surface deformation data to infer the laterally heterogeneous permeability of a transient linear poroelastic model of a confined GW aquifer. The Bayesian solution of this inverse problem takes the form of a posterior probability density of the permeability. Exploring this posterior using classical Markov chain Monte Carlo (MCMC) methods is computationally prohibitive due to the large dimension of the discretized permeability field and the expense of solving the poroelastic forward problem. However, in many partial differential equation (PDE)-based Bayesian inversion problems, the data are only informative in a few directions in parameter space. For the poroelasticity problem, we prove this property theoretically for a one-dimensional problem and demonstrate it numerically for a three-dimensional aquifer model. We design a generalized preconditioned Crank--Nicolson (gpCN) MCMC method that exploits this intrinsic low dimensionality by using a low-rank based Laplace approximation of the posterior as a proposal, which we build scalably. The feasibility of our approach is demonstrated through a real GW aquifer test in Nevada. The inherently two dimensional nature of InSAR surface deformation data informs a sufficient number of modes of the permeability field to allow detection of major structures within the aquifer, significantly reducing the uncertainty in the pressure and the displacement quantities of interest.
Fast-Grant Learning-Based Approach for Machine-Type Communications With NOMA

In this paper, we propose a non-orthogonal multiple access (NOMA)-based communication framework that allows machine-type devices (MTDs) to access the network while avoiding congestion. The proposed technique is a two-step mechanism that first employs fast uplink grant to schedule the devices without sending a request to the base station (BS). Secondly, NOMA pairing is employed in a distributed manner to reduce signaling overhead. Due to the limited capability of information gathering at the BS in massive scenarios, learning techniques are best fit for such problems. Thus, multi-armed bandit learning is adopted to schedule the fast grant MTDs. Then, constrained random NOMA pairing is proposed to assist in decoupling the two main challenges of fast uplink grant schemes, namely active set prediction and optimal scheduling. Using NOMA, we were able to significantly reduce the resource wastage due to prediction errors. Additionally, the results show that the proposed scheme can easily attain the impractical optimal OMA performance, in terms of the achievable rewards, at an affordable complexity.
Native structure of the RhopH complex, a key determinant of malaria parasite nutrient acquisition.

The RhopH complex is implicated in malaria parasites’ ability to invade and create new permeability pathways in host erythrocytes, but its mechanisms remain poorly understood. Here, we enrich the endogenous RhopH complex in a native soluble form, comprising RhopH2, CLAG3.1, and RhopH3, directly from parasite cell lysates and determine its atomic structure using cryo–electron microscopy (cryo-EM), mass spectrometry, and the cryoID program. CLAG3.1 is positioned between RhopH2 and RhopH3, which both share substantial binding interfaces with CLAG3.1 but make minimal contacts with each other. The forces stabilizing individual subunits include 13 intramolecular disulfide bonds. Notably, CLAG3.1 residues 1210 to 1223, previously predicted to constitute a transmembrane helix, are embedded within a helical bundle formed by residues 979 to 1289 near the C terminus of CLAG3.1. Buried in the core of the RhopH complex and largely shielded from solvent, insertion of this putative transmembrane helix into the erythrocyte membrane would likely require a large conformational rearrangement. Given the unusually high disulfide content of the complex, it is possible that such a rearrangement could be initiated by the breakage of allosteric disulfide bonds, potentially triggered by interactions at the erythrocyte membrane. This first direct observation of an exported Plasmodium falciparum transmembrane protein—in a soluble, trafficking state and with atomic details of buried putative membrane-insertion helices—offers insights into the assembly and trafficking of RhopH and other parasite-derived complexes to the erythrocyte membrane. Our study demonstrates the potential the endogenous structural proteomics approach holds for elucidating the molecular mechanisms of hard-to-isolate complexes in their native, functional forms.
How Relative Performance Information Affects Exploration-exploitation Decisions

Employees often make recurring decisions that entail deciding whether to continue using a “status quo” strategy that yields reliable results or try an alternative strategy of unknown efficacy. Via an experiment, we study how relative performance information (RPI) influences this choice and its expected outcome. We theorize and find that RPI has both a social motivational effect that increases employees’ propensity to explore alternative strategies and an informational effect that helps them determine whether exploring alternative strategies will likely help or harm their performance (i.e., it conveys decision-facilitating benefits). Likewise, as predicted, we also find that RPI’s decision-facilitating benefit occurs more strongly among low versus high-performing employees. Our study helps inform employers about the decision-facilitating implications of incorporating RPI into their performance feedback systems.