cssi/SciSciGPT-SciSciNet huggingface dataset:
Improving the distribution of FDI benefits: The need for policy-oriented research, advice, and advocacy

This article explores the different ways in which governments can increase the benefits of FDI and manage the relationship between governments and international investors in a mutually beneficial manner. It focusses on three concrete policy fields in which discussions are afoot that bear on this issue, and where opportunities to exercise influence exist: encouraging more sustainable FDI; enhancing the distribution of benefits associated with FDI; and anchoring sustainable FDI, corporate social responsibility, and responsible business conduct in the international investment law and policy regime and, specifically, in the context of the negotiations of an Investment Facilitation Framework for Development at the WTO. These are examples of policy areas that warrant attention, with a view toward scholars seeking to influence the way in which they are tackled, through applied multidisciplinary policy-oriented research, concrete recommendations, and targeted advocacy.
Avidity and surface mobility in multivalent ligand–receptor binding

Targeted drug delivery relies on two physical processes: the selective binding of a therapeutic particle to receptors on a specific cell membrane, followed by transport of the particle across the membrane. In this article, we address some of the challenges in controlling the thermodynamics and dynamics of these two processes by combining a simple experimental system with a statistical mechanical model. Specifically, we characterize and model multivalent ligand–receptor binding between colloidal particles and fluid lipid bilayers, as well as the surface mobility of membrane-bound particles. We show that the mobility of the receptors within the fluid membrane is key to both the thermodynamics and dynamics of binding. First, we find that the particle-membrane binding free energy—or avidity—is a strongly nonlinear function of the ligand–receptor affinity. We attribute the nonlinearity to a combination of multivalency and recruitment of fluid receptors to the binding site. Our results also suggest that partial wrapping of the bound particles by the membrane enhances avidity further. Second, we demonstrate that the lateral mobility of membrane-bound particles is also strongly influenced by the recruitment of receptors. Specifically, we find that the lateral diffusion coefficient of a membrane-bound particle is dominated by the hydrodynamic drag against the aggregate of receptors within the membrane. These results provide one of the first direct validations of the working theoretical framework for multivalent interactions. They also highlight that the fluidity and elasticity of the membrane are as important as the ligand–receptor affinity in determining the binding and transport of small particles attached to membranes.
Revelation gap for pricing from samples

This paper considers prior-independent mechanism design, in which a single mechanism is designed to achieve approximately optimal performance on every prior distribution from a given class. Most results in this literature focus on mechanisms with truthtelling equilibria, a.k.a., truthful mechanisms. Feng and Hartline [FOCS 2018] introduce the revelation gap to quantify the loss of the restriction to truthful mechanisms. We solve a main open question left in Feng and Hartline [FOCS 2018]; namely, we identify a non-trivial revelation gap for revenue maximization. Our analysis focuses on the canonical problem of selling a single item to a single agent with only access to a single sample from the agent's valuation distribution. We identify the sample-bid mechanism (a simple non-truthful mechanism) and upper-bound its prior-independent approximation ratio by 1.835 (resp. 1.296) for regular (resp. MHR) distributions. We further prove that no truthful mechanism can achieve prior-independent approximation ratio better than 1.957 (resp. 1.543) for regular (resp. MHR) distributions. Thus, a non-trivial revelation gap is shown as the sample-bid mechanism outperforms the optimal prior-independent truthful mechanism. On the hardness side, we prove that no (possibly non-truthful) mechanism can achieve prior-independent approximation ratio better than 1.073 even for uniform distributions.
An observable effect of spin inertia in slow magneto-dynamics: Increase of the switching error rates in nanoscale ferromagnets.

The Landau-Lifshitz-Gilbert (LLG) equation, used to model magneto-dynamics in ferromagnets, tacitly assumes that the angular momentum associated with spin precession can relax instantaneously when the real or effective magnetic field causing the precession is turned off. This neglect of 'spin inertia' is unphysical and would violate energy conservation. Recently, the LLG equation was modified to account for inertia effects. The consensus, however, seems to be that such effects would be unimportant inslowmagneto-dynamics that take place over time scales much longer that the relaxation time of the angular momentum, which is typically few fs to perhaps ∼100 ps in ferromagnets. Here, we show that there is at least one very serious and observable effect of spin inertia even in slow magneto-dynamics. It involves the switching error probability associated with flipping the magnetization of a nanoscale ferromagnet with an external agent, such as a magnetic field. The switching may take ∼ns to complete when the field strength is close to the threshold value for switching, which is much longer than the angular momentum relaxation time, and yet the effect of spin inertia is felt in the switching error probability. This is because the ultimate fate of a switching trajectory, i.e. whether it results in success or failure, is influenced by what happens in the first few ps of the switching action when nutational dynamics due to spin inertia hold sway. Spin inertiaincreasesthe error probability, which makes the switching more error-prone. This has vital technological significance because it relates to the reliability of magnetic logic and memory.
What Makes a Good Local Leader? Evidence from U.S. Mayors and City Managers

What are the traits of a good local leader? While most studies of local officials focus on the mayors of large cities, 85% of municipalities in the U.S. have a population of less than 20,000. We conduct in-depth phone interviews with nearly 300 mayors and city managers from predominantly small and mid-sized cities in the U.S. to learn about their backgrounds. We focus on two standard ability measures (education and prior occupation) and draw from research in public administration and economics to introduce two new dimensions of quality: public service motivation and managerial skill. We paint a comprehensive descriptive portrait of the respondents in our sample and the cities they represent, and we then examine whether these traits matter for the policy goals that local leaders choose to focus on during their time in office. These results offer a promising new approach for researchers studying political leadership and its consequences, both in the local context and beyond.
The Little Heat Engine: Heat Transfer in Solids, Liquids and Gases

In this work, an introductory exposition of the laws of thermodynamics and radiative heat transfer is presented while exploring the concepts of the ideal solid, the lattice, and the vibrational, translational, and rotational degrees of freedom. Analysis of heat transfer in this manner helps scientists to recognize that the laws of thermal radiation are strictly applicable only to the ideal solid. On the Earth, such a solid is best represented by either graphite or soot. Indeed, certain forms of graphite can approach perfect absorption over a relatively large frequency range. Nonetheless, in dealing with heat, solids will eventually sublime or melt. Similarly, liquids will give way to the gas phase. That thermal conductivity eventually decreases in the solid signals an inability to further dissipate heat and the coming breakdown of Planck’s law. Ultimately, this breakdown is reflected in the thermal emission of gases. Interestingly, total gaseous emissivity can decrease with increasing temperature. Consequently, neither solids, liquids, or gases can maintain the behavior predicted by the laws of thermal emission. Since the laws of thermal emission are, in fact, not universal, the extension of these principles to non-solids constitutes a serious overextension of the work of Kirchho , Wien, Stefan and Planck.
On the relative Minimal Model Program for threefolds in low characteristics

We show the validity of the relative dlt MMP over $${\mathbb{Q}}$$ -factorial threefolds in all characteristics $$p>0$$ . As a corollary, we generalise many recent results to low characteristics including: $$W{\mathcal{O}}$$ -rationality of klt singularities, inversion of adjunction, and normality of divisorial centres up to a universal homeomorphism.
A Permutation-Equivariant Neural Network Architecture For Auction Design

Designing an incentive compatible auction that maximizes expected revenue is a central problem in Auction Design. Theoretical approaches to the problem have hit some limits in the past decades and analytical solutions are known for only a few simple settings. Computational approaches to the problem through the use of LPs have their own set of limitations. Building on the success of deep learning, a new approach was recently proposed by Duetting et al. (2019) in which the auction is modeled by a feed-forward neural network and the design problem is framed as a learning problem. The neural architectures used in that work are general purpose and do not take advantage of any of the symmetries the problem could present, such as permutation equivariance. In this work, we consider auction design problems that have permutation-equivariant symmetry and construct a neural architecture that is capable of perfectly recovering the permutation-equivariant optimal mechanism, which we show is not possible with the previous architecture. We demonstrate that permutation-equivariant architectures are not only capable of recovering previous results, they also have better generalization properties.
Investigating Memorization of Conspiracy Theories in Text Generation

The adoption of natural language generation (NLG) models can leave individuals vulnerable to the generation of harmful information memorized by the models, such as conspiracy theories. While previous studies examine conspiracy theories in the context of social media, they have not evaluated their presence in the new space of generative language models. In this work, we investigate the capability of language models to generate conspiracy theory text. Specifically, we aim to answer: can we test pretrained generative language models for the memorization and elicitation of conspiracy theories without access to the model's training data? We highlight the difficulties of this task and discuss it in the context of memorization, generalization, and hallucination. Utilizing a new dataset consisting of conspiracy theory topics and machine-generated conspiracy theories helps us discover that many conspiracy theories are deeply rooted in the pretrained language models. Our experiments demonstrate a relationship between model parameters such as size and temperature and their propensity to generate conspiracy theory text. These results indicate the need for a more thorough review of NLG applications before release and an in-depth discussion of the drawbacks of memorization in generative language models.
Adaptive Verifiable Training Using Pairwise Class Similarity.

Verifiable training has shown success in creating neural networks that are provably robust to a given amount of noise. However, despite only enforcing a single robustness criterion, its performance scales poorly with dataset complexity. On CIFAR10, a non-robust LeNet model has a 21.63% error rate, while a model created using verifiable training and a L-infinity robustness criterion of 8/255, has an error rate of 57.10%. Upon examination, we find that when labeling visually similar classes, the model's error rate is as high as 61.65%. We attribute the loss in performance to inter-class similarity. Similar classes (i.e., close in the feature space) increase the difficulty of learning a robust model. While it's desirable to train a robust model for a large robustness region, pairwise class similarities limit the potential gains. Also, consideration must be made regarding the relative cost of mistaking similar classes. In security or safety critical tasks, similar classes are likely to belong to the same group, and thus are equally sensitive. In this work, we propose a new approach that utilizes inter-class similarity to improve the performance of verifiable training and create robust models with respect to multiple adversarial criteria. First, we use agglomerate clustering to group similar classes and assign robustness criteria based on the similarity between clusters. Next, we propose two methods to apply our approach: (1) Inter-Group Robustness Prioritization, which uses a custom loss term to create a single model with multiple robustness guarantees and (2) neural decision trees, which trains multiple sub-classifiers with different robustness guarantees and combines them in a decision tree architecture. On Fashion-MNIST and CIFAR10, our approach improves clean performance by 9.63% and 30.89% respectively. On CIFAR100, our approach improves clean performance by 26.32%.
The Nirenberg problem of prescribed Gauss curvature on $S^2$

We introduce a new perspective on the classical Nirenberg problem of understanding the possible Gauss curvatures of metrics on $S^{2}$ conformal to the round metric. A key tool is to employ the smooth Cheeger-Gromov compactness theorem to obtain general and essentially sharp a priori estimates for Gauss curvatures $K$ contained in naturally defined stable regions. We prove that in such stable regions, the map $u \rightarrow K_{g}$, $g = e^{2u}g_{+1}$ is a proper Fredholm map with well-defined degree on each component. This leads to a number of new existence and non-existence results. We also present a new proof and generalization of the Moser theorem on Gauss curvatures of even conformal metrics on $S^{2}$. In contrast to previous work, the work here does not use any of the Sobolev-type inequalities of Trudinger-Moser-Aubin-Onofri.
Highly specific and rapid glycan based amperometric detection of influenza viruses

Rapid and precise detection of influenza viruses in a point of care setting is critical for applying appropriate countermeasures. Current methods such as nucleic acid or antibody based techniques are expensive or suffer from low sensitivity, respectively. We have developed an assay that uses glucose test strips and a handheld potentiostat to detect the influenza virus with high specificity. Influenza surface glycoprotein neuraminidase (NA), but not bacterial NA, cleaved galactose bearing substrates, 4,7di-OMe N-acetylneuraminic acid attached to the 3 or 6 position of galactose, to release galactose. In contrast, viral and bacterial NA cleaved the natural substrate, N-acetylneuraminic acid attached to the 3 or 6 position of galactose. The released galactose was detected amperometrically using a handheld potentiostat and dehydrogenase bearing glucose test strips. The specificity for influenza was confirmed using influenza strains and different respiratory pathogens that include Streptococcus pneumoniae and Haemophilus influenzae; bacteria do not cleave these molecules. The assay was also used to detect co-infections caused by influenza and bacterial NA. Viral drug susceptibility and testing with human clinical samples was successful in 15 minutes, indicating that this assay could be used to rapidly detect influenza viruses at primary care or resource poor settings using ubiquitous glucose meters.
CHECKED: Chinese COVID-19 fake news dataset

COVID-19 has impacted all lives. To maintain social distancing and avoiding exposure, works and lives have gradually moved online. Under this trend, social media usage to obtain COVID-19 news has increased. Also, misinformation on COVID-19 is frequently spread on social media. In this work, we develop CHECKED, the first Chinese dataset on COVID-19 misinformation. CHECKED provides a total 2,104 verified microblogs related to COVID-19 from December 2019 to August 2020, identified by using a specific list of keywords. Correspondingly, CHECKED includes 1,868,175 reposts, 1,185,702 comments, and 56,852,736 likes that reveal how these verified microblogs are spread and reacted on Weibo. The dataset contains a rich set of multimedia information for each microblog including ground-truth label, textual, visual, temporal, and network information. Extensive experiments have been conducted to analyze CHECKED data and to provide benchmark results for well-established methods when predicting fake news using CHECKED. We hope that CHECKED can facilitate studies that target misinformation on coronavirus. The dataset is available at https://github.com/cyang03/CHECKED.
Facile ab initio approach for self-localized polarons from canonical transformations

Electronic states in a crystal can localize due to strong electron-phonon ($e$-ph) interactions, forming so-called small polarons. Methods to predict the formation and energetics of small polarons are either computationally costly or not geared toward quantitative predictions. Here we show a formalism based on canonical transformations to compute the polaron formation energy and wave function using ab initio $e$-ph interactions. Comparison of the calculated polaron and band-edge energies allows us to determine whether charge carriers in a material favor a localized small polaron over a delocalized Bloch state. Due to its low computational cost, our approach enables efficient studies of the formation and energetics of small polarons, as we demonstrate by investigating electron and hole polaron formation in alkali halides and metal oxides and peroxides. We outline refinements of our scheme and extensions to compute transport in the polaron hopping regime.
Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requests.

Modeling persuasive language has the potential to better facilitate our decision-making processes. Despite its importance, computational modeling of persuasion is still in its infancy, largely due to the lack of benchmark datasets that can provide quantitative labels of persuasive strategies to expedite this line of research. To this end, we introduce a large-scale multi-domain text corpus for modeling persuasive strategies in good-faith text requests. Moreover, we design a hierarchical weakly-supervised latent variable model that can leverage partially labeled data to predict such associated persuasive strategies for each sentence, where the supervision comes from both the overall document-level labels and very limited sentence-level labels. Experimental results showed that our proposed method outperformed existing semi-supervised baselines significantly. We have publicly released our code at https://github.com/GT-SALT/Persuasion_Strategy_WVAE.
Active Safety Envelopes using Light Curtains with Probabilistic Guarantees

To safely navigate unknown environments, robots must accurately perceive dynamic obstacles. Instead of directly measuring the scene depth with a LiDAR sensor, we explore the use of a much cheaper and higher resolution sensor: programmable light curtains. Light curtains are controllable depth sensors that sense only along a surface that a user selects. We use light curtains to estimate the safety envelope of a scene: a hypothetical surface that separates the robot from all obstacles. We show that generating light curtains that sense random locations (from a particular distribution) can quickly discover the safety envelope for scenes with unknown objects. Importantly, we produce theoretical safety guarantees on the probability of detecting an obstacle using random curtains. We combine random curtains with a machine learning based model that forecasts and tracks the motion of the safety envelope efficiently. Our method accurately estimates safety envelopes while providing probabilistic safety guarantees that can be used to certify the efficacy of a robot perception system to detect and avoid dynamic obstacles. We evaluate our approach in a simulated urban driving environment and a real-world environment with moving pedestrians using a light curtain device and show that we can estimate safety envelopes efficiently and effectively.
Thermomechanical buckling of multilayered composite panels with cutouts

A study is made of the thermomechanical buckling of flat unstiffened composite panels with central circular cutouts. The panels are subjected to combined temperature changes and applied edge loading (or edge displacements). The analysis is based on a first-order shear deformation plate theory. A mixed formulation is used with the fundamental unknowns consisting of the generalized displacements and the stress resultants of the plate. Both the stability boundary and the sensitivity coefficients are evaluated. The sensitivity coefficients measure the sensitivity of the buckling response to variations in the different lamination and material parameters of the panel. Numerical results are presented showing the effects of the variations in the hole diameter, laminate stacking sequence, fiber orientation, and aspect ratio of the panel on the thermomechanical buckling response and its sensitivity coefficients. 19 refs.
Formation of oxidized gases and secondary organic aerosol from a commercial oxidant-generating electronic air cleaner

The COVID-19 pandemic increased the demand for indoor air cleaners. While some commercial electronic air cleaners can be effective in reducing primary pollutants and inactivating bioaerosol, studies on the formation of secondary products from oxidation chemistry during their use are limited. Here, we measured oxygenated volatile organic compounds (OVOCs) and the chemical composition of particles generated from a hydroxyl radical generator in an office. During operation, enhancements in OVOCs, especially low-molecular-weight organic acids, were detected. Rapid increases in particle number and mass concentrations were observed, corresponding to the formation of highly oxidized secondary organic aerosol (SOA) (O:C ∼1.3), with an enhanced signal at m/z 44 (CO2+) in the organic mass spectra. These results suggest that organic acids generated during VOC oxidation contributed to particle nucleation and SOA formation. Nitrate, sulfate, and chloride also increased during the oxidation without a corresponding increase in ammonium, suggesting organic nitrate, organic sulfate, and organic chloride formation. As secondary species are reported to have detrimental health effects, further studies should not be limited to the inactivation of bioaerosol or reduction of particular VOCs, but should also evaluate potential OVOCs and SOA formation from electronic air cleaners in different indoor environments. © 2021 American Chemical Society.
Neighbor Oblivious Learning (NObLe) for Device Localization and Tracking

On-device localization and tracking are increasingly crucial for various applications. Machine learning (ML) techniques are widely adopted along with the rapidly growing amount of data. However, during training, almost none of ML techniques incorporate the known structural information such as floor plan, which can be especially useful in indoor or other structured environments. The problem is incredibly hard because the structural properties are not explicitly available, making most structural learning approaches inapplicable. We study our method through the intuitions from manifold learning. Whereas existing manifold methods utilizes neighborhood information such as Euclidean distances, we quantize the output space to measure closeness on the structure. We propose Neighbor Oblivious Learning (NObLe) and demonstrate our approach's effectiveness on two applications, Wi-Fi-based fingerprint localization and inertial measurement unit(IMU) based device tracking. We show that NObLe gives significant improvement over state-of-art prediction accuracy.
Intermittent Status Updating Through Joint Scheduling of Sensing and Retransmissions

Consider an energy harvesting node where generation of a status update message takes non-negligible time due to sensing, computing and analytics operations performed before making update transmissions. The node has to harmonize its (re)transmission strategy with the sensing/computing. We call this general set of problems intermittent status updating. In this paper, we consider intermittent status updating through non-preemptive sensing/computing (S/C) and transmission (Tx) operations, each costing a single energy recharge of the node, through an erasure channel with perfect channel feedback and no channel feedback. The S/C time for each update is independent with a general distribution. The Tx queue has a single data buffer to save the latest packet generated after the S/C operation and a single transmitter where transmission time is deterministic. Once energy is harvested, the node has to decide whether to activate S/C to generate a new update or to (re)send the existing update (if any) to the receiver. We prove that when feedback is available average peak age of information (AoI) at the receiver is minimized by a threshold-based policy that allows only young packets to be (re)sent or else generates a new update. We additionally propose window based and probabilistic retransmission schemes and obtain closed form average peak AoI expressions. Our numerical results show average peak AoI performance comparisons and improvements.
Reducing sedated lumbar punctures in pediatric patients with acute lymphoblastic leukemia.

Background Sedation for lumbar punctures (LPs) in pediatric acute lymphoblastic leukemia (ALL) patients has been the standard for decades to reduce pain and anxiety. Recent studies on the potential long-term neurocognitive effects of cumulative propofol exposure have raised concerns about this practice. The recent pandemic introduced additional burdens to patients, with the requirement of a negative COVID-19 test prior to each sedated procedure. Procedure These factors prompted a quality improvement intervention at our institution where we aimed to reduce postinduction sedated LPs by 50%. Our intervention included patient and family education, followed by a simulation of the procedure for selected patients. Those converted to unsedated LPs were queried for their preference. Comparative cost, clinical time, and LP success rates were collected for sedated and unsedated LPs. Results Following the intervention, the percentage of LPs performed with sedation dropped from 100% to 48%. All LPs were successful using both techniques. Most patients who experienced the unsedated LP technique, and their guardians, strongly preferred this approach. Unsedated LPs significantly reduced clinical time (169 vs. 83 minutes) for families, decreased expenditures ($5736 reduction per procedure), and improved institutional opportunity cost due to a decrease in last-minute cancelations. Conclusion We have shown that it is feasible to significantly reduce the use of sedation for LPs in patients with ALL, which has the potential to improve health and patient experience at a lower cost.
Towers and elementary embeddings in toral relatively hyperbolic groups

In a remarkable series of papers, Zlil Sela classified the first-order theories of free groups and torsion-free hyperbolic groups using geometric structures he called towers. It was later proved by...
Yeast-expressed recombinant SARS-CoV-2 receptor binding domain RBD203-N1 as a COVID-19 protein vaccine candidate

SARS-CoV-2 protein subunit vaccines are currently being evaluated by multiple manufacturers to address the global vaccine equity gap, and need for low-cost, easy to scale, safe, and effective COVID-19 vaccines. In this paper, we report on the generation of the receptor-binding domain RBD203-N1 yeast expression construct, which produces a recombinant protein capable of eliciting a robust immune response and protection in mice against SARS-CoV-2 challenge infections. The RBD203-N1 antigen was expressed in the yeast Pichia pastoris X33. After fermentation at the 5 L scale, the protein was purified by hydrophobic interaction chromatography followed by anion exchange chromatography. The purified protein was characterized biophysically and biochemically, and after its formulation, the immunogenicity was evaluated in mice. Sera were evaluated for their efficacy using a SARS-CoV-2 pseudovirus assay. The RBD203-N1 protein was expressed with a yield of 492.9 ± 3.0 mg/L of fermentation supernatant. A two-step purification process produced a >96% pure protein with a recovery rate of 55 ± 3% (total yield of purified protein: 270.5 ± 13.2 mg/L fermentation supernatant). The protein was characterized to be a homogeneous monomer that showed a well-defined secondary structure, was thermally stable, antigenic, and when adjuvanted on Alhydrogel in the presence of CpG it was immunogenic and induced high levels of neutralizing antibodies against SARS-CoV-2 pseudovirus. The characteristics of the RBD203-N1 protein-based vaccine show that this candidate is another well suited RBD-based construct for technology transfer to manufacturing entities and feasibility of transition into the clinic to evaluate its immunogenicity and safety in humans.
Mechanosensory input during circuit formation shapes Drosophila motor behavior through patterned spontaneous network activity

Summary Neural activity sculpts circuit wiring in many animals. In vertebrates, patterned spontaneous network activity (PaSNA) generates sensory maps and establishes local circuits. 1 , 2 , 3 However, it remains unclear how PaSNA might shape neuronal circuits and behavior in invertebrates. Previous work in the developing Drosophila embryo discovered intrinsic muscle activity that did not require synaptic transmission, and hence was myogenic, preceding PaSNA. 4 , 5 , 6 These studies, however, monitored muscle movement, not neural activity, and were therefore unable to observe how myogenic activity might relate to subsequent neural network engagement. Here we use calcium imaging to directly record neural activity and characterize the emergence of PaSNA. We demonstrate that the spatiotemporal properties of PaSNA are highly stereotyped across embryos, arguing for genetic programming. Neural activity begins well before it becomes patterned, emerging during the myogenic stage. Remarkably, inhibition of mechanosensory input, as well as inhibition of muscle contractions, results in premature and excessive PaSNA, demonstrating that muscle movement serves as a brake on this process. Finally, transient mechanosensory inhibition during PaSNA, followed by quantitative modeling of larval behavior, shows that mechanosensory modulation during development is required for proper larval foraging. This work provides a foundation for using the Drosophila embryo to study the role of PaSNA in circuit formation, provides mechanistic insight into how PaSNA is entrained by motor activity, and demonstrates that spontaneous network activity is essential for locomotor behavior. These studies argue that sensory feedback during the earliest stages of circuit formation can sculpt locomotor behaviors through innate motor learning.
A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization

Abstract The closed-loop performance of model predictive controllers (MPCs) is highly dependent on the choice of prediction models, controller formulation, and tuning parameters. However, prediction models are typically optimized for prediction accuracy, instead of performance, and MPC tuning is typically done manually to satisfy (probabilistic) constraints. In this work, we demonstrate a general approach for automating the tuning of MPC under uncertainty. In particular, we formulate the automated tuning problem as a constrained black-box optimization problem that can be tackled with derivative-free optimization. We rely on a constrained variant of Bayesian optimization to solve the MPC tuning problem that can directly handle noisy and expensive-to-evaluate functions. The benefits of the proposed automated tuning approach are demonstrated on a benchmark continuously stirred tank reactor case study.
Stock Market Volatility and Recent Crises

We study the volatility of the US stock market and its sectors as defined by S&P before and after four recent crises: the Mexican crises, the Asian crises, the Dotcom crises and the Great Recession. We compare the increase in daily volatility with the increase in the implied daily volatility (derived from the monthly volatility) to determine if there was a lasting economic effect of each crisis or the increase in volatility was due to financial transitory components. We find that for each crisis the effect was different even though the increase in volatility was present for most of the crises in the post crises period. The paper helps investors and economic policy makers understand what the response to each crisis should be to stabilize the economy.
Spontaneously broken boosts in CFTs

Conformal Field Theories (CFTs) have rich dynamics in heavy states. We describe the constraints due to spontaneously broken boost and dilatation symmetries in such states. The spontaneously broken boost symmetries require the existence of new low-lying primaries whose scaling dimension gap, we argue, scales as O(1). We demonstrate these ideas in various states, including fluid, superfluid, mean field theory, and Fermi surface states. We end with some remarks about the large charge limit in 2d and discuss a theory of a single compact boson with an arbitrary conformal anomaly.
Memory-Sample Lower Bounds for Learning Parity with Noise

In this work, we show, for the well-studied problem of learning parity under noise, where a learner tries to learn x = (x₁,…,x_n) ∈ {0,1}ⁿ from a stream of random linear equations over 𝔽₂ that are correct with probability 1/2+e and flipped with probability 1/2-e (0 < e < 1/2), that any learning algorithm requires either a memory of size Ω(n²/e) or an exponential number of samples. In fact, we study memory-sample lower bounds for a large class of learning problems, as characterized by [Garg et al., 2018], when the samples are noisy. A matrix M: A × X → {-1,1} corresponds to the following learning problem with error parameter e: an unknown element x ∈ X is chosen uniformly at random. A learner tries to learn x from a stream of samples, (a₁, b₁), (a₂, b₂) …, where for every i, a_i ∈ A is chosen uniformly at random and b_i = M(a_i,x) with probability 1/2+e and b_i = -M(a_i,x) with probability 1/2-e (0 < e < 1/2). Assume that k,𝓁, r are such that any submatrix of M of at least 2^{-k} ⋅ |A| rows and at least 2^{-𝓁} ⋅ |X| columns, has a bias of at most 2^{-r}. We show that any learning algorithm for the learning problem corresponding to M, with error parameter e, requires either a memory of size at least Ω((k⋅𝓁)/e), or at least 2^{Ω(r)} samples. The result holds even if the learner has an exponentially small success probability (of 2^{-Ω(r)}). In particular, this shows that for a large class of learning problems, same as those in [Garg et al., 2018], any learning algorithm requires either a memory of size at least Ω(((log|X|)⋅(log|A|))/e) or an exponential number of noisy samples. Our proof is based on adapting the arguments in [Ran Raz, 2017; Garg et al., 2018] to the noisy case.
Approximation and localized polynomial frame on conic domains

Abstract Highly localized kernels constructed by orthogonal polynomials have been fundamental in the recent development of approximation and computational analysis on the unit sphere, unit ball, and several other regular domains. In this work, we first study homogeneous spaces that are assumed to contain highly localized kernels and establish a framework for approximation and localized tight frames in such spaces, which extends recent works on bounded regular domains. We then show that the framework is applicable to homogeneous spaces defined on bounded conic domains, which consists of conic surfaces and the solid domains bounded by such surfaces and hyperplanes. The highly localized kernels on conic domains require precise estimates that rely on recently discovered addition formulas for orthogonal polynomials with respect to special weight functions on each domain and an intrinsic distance that takes into account the boundary of the domain, the latter is not comparable to the Euclidean distance at around the apex of the cone. The main results provide construction of semi-discrete localized tight frame in weighted L 2 norm and characterization of best approximation by polynomials on conic domains. The latter is achieved by using a K-functional, defined via the differential operator that has orthogonal polynomials as eigenfunctions, as well as a modulus of smoothness defined via a multiplier operator that is equivalent to the K-functional. Several intermediate results are of interest in their own right, including the Marcinkiewicz-Zygmund inequalities, positive cubature rules, Christoeffel functions, and Bernstein type inequalities. Moreover, although the highly localizable kernels hold only for special families of weight functions on each domain, many intermediate results are shown to hold for doubling weights defined via the intrinsic distance on the domain.
An Efficient Closed-Form Method for Optimal Hybrid Force-Velocity Control

This paper derives a closed-form method for computing hybrid force-velocity control. The key idea is to maximize the kinematic conditioning of the mechanical system, which includes a robot, free objects, a rigid environment and contact constraints. The method is complete, in that it always produces an optimal/near optimal solution when a solution exists. It is efficient, since it is in closed form, avoiding the iterative search of our previous work. We test the method on 78,000 randomly generated test cases. The method outperforms our previous search-based technique by being from 7 to 40 times faster, while consistently producing better solutions in the sense of robustness to kinematic singularity. We also test the method in several representative manipulation experiments.
Preserving new physics while simultaneously unfolding all observables

Direct searches for new particles at colliders have traditionally been factorized into model proposals by theorists and model testing by experimentalists. With the recent advent of machine learning methods that allow for the simultaneous unfolding of all observables in a given phase space region, there is a new opportunity to blur these traditional boundaries by performing searches on unfolded data. This could facilitate a research program where data are explored in their natural high dimensionality with as little model bias as possible. We study how the information about physics beyond the Standard Model is preserved by full phase space unfolding using an important physics target at the Large Hadron Collider (LHC); exotic Higgs boson decays involving hadronic final states. We find that if the signal cross section is high enough, information about the new physics is visible in the unfolded data. We will show that in some cases, quantifiably all of the high-level information about the new physics is encoded in the unfolded data. Finally, we show that there are still many cases when the unfolding does not work fully or precisely, such as when the signal cross section is small. This study will serve as an important benchmark for enhancing unfolding methods for the LHC and beyond.
Bubblewrap: Online tiling and real-time flow prediction on neural manifolds

While most classic studies of function in experimental neuroscience have focused on the coding properties of individual neurons, recent developments in recording technologies have resulted in an increasing emphasis on the dynamics of neural populations. This has given rise to a wide variety of models for analyzing population activity in relation to experimental variables, but direct testing of many neural population hypotheses requires intervening in the system based on current neural state, necessitating models capable of inferring neural state online. Existing approaches, primarily based on dynamical systems, require strong parametric assumptions that are easily violated in the noise-dominated regime and do not scale well to the thousands of data channels in modern experiments. To address this problem, we propose a method that combines fast, stable dimensionality reduction with a soft tiling of the resulting neural manifold, allowing dynamics to be approximated as a probability flow between tiles. This method can be fit efficiently using online expectation maximization, scales to tens of thousands of tiles, and outperforms existing methods when dynamics are noise-dominated or feature multi-modal transition probabilities. The resulting model can be trained at kiloHertz data rates, produces accurate approximations of neural dynamics within minutes, and generates predictions on submillisecond time scales. It retains predictive performance throughout many time steps into the future and is fast enough to serve as a component of closed-loop causal experiments.
Bootstrapping the Error of Oja's Algorithm

We consider the problem of quantifying uncertainty for the estimation error of the leading eigenvector from Oja's algorithm for streaming principal component analysis, where the data are generated IID from some unknown distribution. By combining classical tools from the U-statistics literature with recent results on high-dimensional central limit theorems for quadratic forms of random vectors and concentration of matrix products, we establish a $\chi^2$ approximation result for the $\sin^2$ error between the population eigenvector and the output of Oja's algorithm. Since estimating the covariance matrix associated with the approximating distribution requires knowledge of unknown model parameters, we propose a multiplier bootstrap algorithm that may be updated in an online manner. We establish conditions under which the bootstrap distribution is close to the corresponding sampling distribution with high probability, thereby establishing the bootstrap as a consistent inferential method in an appropriate asymptotic regime.
Optimal Covariate Balancing Conditions in Propensity Score Estimation

Inverse probability of treatment weighting (IPTW) is a popular method for estimating the average treatment effect (ATE). However, empirical studies show that the IPTW estimators can be sensitive to the misspecification of the propensity score model. To address this problem, researchers have proposed to estimate propensity score by directly optimizing the balance of pre-treatment covariates. While these methods appear to empirically perform well, little is known about how the choice of balancing conditions affects their theoretical properties. To fill this gap, we first characterize the asymptotic bias and efficiency of the IPTW estimator based on the Covariate Balancing Propensity Score (CBPS) methodology under local model misspecification. Based on this analysis, we show how to optimally choose the covariate balancing functions and propose an optimal CBPS-based IPTW estimator. This estimator is doubly robust; it is consistent for the ATE if either the propensity score model or the outcome model is correct. In addition, the proposed estimator is locally semiparametric efficient when both models are correctly specified. To further relax the parametric assumptions, we extend our method by using a sieve estimation approach. We show that the resulting estimator is globally efficient under a set of much weaker assumptions and has a smaller asymptotic bias than the existing estimators. Finally, we evaluate the finite sample performance of the proposed estimators via simulation and empirical studies. An open-source software package is available for implementing the proposed methods.
The Degree-Dependent Threshold Model: Towards a Better Understanding of Opinion Dynamics on Online Social Networks.

With the rapid growth of online social media, people become increasingly overwhelmed by the volume and the content of the information present in the environment. The fact that people express their opinions and feelings through social media channels, influence other people, and get influenced by them has led researchers from various disciplines to focus on understanding the mechanism of information and emotion contagion. The threshold model is currently one of the most common methods to capture the effect of people on others’ opinions and emotions. Although many studies employ and try to improve upon the threshold model, the search for an appropriate threshold function for defining human behavior is an essential and yet an unattained quest. The definition of heterogeneity in thresholds of individuals is oftentimes poorly defined, which leads to the rather simplistic use of uniform and binary functions, albeit they are far from representing reality. In this study, we use Twitter data of size 30,704,025 tweets to mimic the adoption of a new opinion. Our results show that the threshold is not only correlated with the out-degree of nodes, which contradicts other studies, but also correlated with nodes’ in-degree. Therefore, we simulated two cases in which thresholds are out-degree and in-degree dependent, separately. We concluded that the system is more likely to reach a consensus when thresholds are in-degree dependent; however, the time elapsed until all nodes fix their opinions is significantly higher in this case. Additionally, we did not observe a notable effect of mean-degree on either the average opinion or the fixation time of opinions for both cases, and increasing seed size has a negative effect on reaching a consensus. Although threshold heterogeneity has a slight influence on the average opinion, the positive effect of heterogeneity on reaching a consensus is more pronounced when thresholds are in-degree dependent.
Transsphenoidal resection of pituitary adenomas in an intraoperative MRI unit.

Background: Transsphenoidal resection is a well known surgical procedure used in the treatment of sellar and parasellar lesions. In certain cases, operative identification of the lesion and achieving a complete resection can be problematic. We describe a surgical image guidance method using an intraoperative MRI which provides definitive identification of a lesion and verification of a complete resection. Material and Methods: Five patients with pituitary macroadenomas underwent transsphenoidal resection of their tumors in the intraoperative MRI unit at the Brigham and Women's Hospital. This unit was developed as a collaboration between General Electric and BWH and is an open configuration 0.5 Tesla MR imager in which surgery can be performed with real time, intraoperative MR guidance. Results: The transsphenoidal resections were performed in a standard fashion in this unit. Intraoperative imaging allowed accurate localization of the lesions, identification of pertinent surrounding structures, and the evaluation of the extent of each resection. In two patients, the resection would have been considered complete based on the surgical field of view alone but MR imaging allowed remaining tissue to be identified. Developing clot was seen in one patient after a fat graft had been placed. Conclusion: The intraoperative MRI is a surgical modality by which transsphenoidal resection of pituitary macroadenomas can be performed using real-time image guidance. It allows definitive identification of the tumor and an immediate evaluation of whether resection is complete. It also can monitor the occurrence of intraoperative complications such as hemorrhage. This revolutionary device can be an important tool for the surgical management of pituitary tumors.