cssi/SciSciGPT-SciSciNet huggingface dataset:
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

Adversarial learning methods have been proposed for a wide range of applications, but the training of adversarial models can be notoriously unstable. Effectively balancing the performance of the generator and discriminator is critical, since a discriminator that achieves very high accuracy will produce relatively uninformative gradients. In this work, we propose a simple and general technique to constrain information flow in the discriminator by means of an information bottleneck. By enforcing a constraint on the mutual information between the observations and the discriminator's internal representation, we can effectively modulate the discriminator's accuracy and maintain useful and informative gradients. We demonstrate that our proposed variational discriminator bottleneck (VDB) leads to significant improvements across three distinct application areas for adversarial learning algorithms. Our primary evaluation studies the applicability of the VDB to imitation learning of dynamic continuous control skills, such as running. We show that our method can learn such skills directly from \emph{raw} video demonstrations, substantially outperforming prior adversarial imitation learning methods. The VDB can also be combined with adversarial inverse reinforcement learning to learn parsimonious reward functions that can be transferred and re-optimized in new settings. Finally, we demonstrate that VDB can train GANs more effectively for image generation, improving upon a number of prior stabilization methods.
Scaling Law for Recovering the Sparsest Element in a Subspace

We address the problem of recovering a sparse n-vector within a given subspace. This problem is a subtask of some approaches to dictionary learning and sparse principal component analysis. Hence, if we can prove scaling laws for recovery of sparse vectors, it will be easier to derive and prove recovery results in these applications. In this paper, we present a scaling law for recovering the sparse vector from a subspace that is spanned by the sparse vector and k random vectors. We prove that the sparse vector will be the output to one of n linear programs with high probability if its support size s satises s . n= p k logn. The scaling law still holds when the desired vector is approximately sparse. To get a single estimate for the sparse vector from the n linear programs, we must select which output is the sparsest. This selection process can be based on any proxy for sparsity, and the specic proxy has the potential to improve or worsen the scaling law. If sparsity is interpreted in an ‘1=‘1 sense, then the scaling law can not be better than s . n= p k. Computer simulations show that selecting the sparsest output in the ‘1=‘2 or thresholded-‘0 senses can lead to a larger parameter range for successful recovery than that given by the ‘1=‘1 sense.
Twisted index theory on orbifold symmetric products and the fractional quantum Hall effect

We extend the noncommutative geometry model of the fractional quantum Hall effect, previously developed by Mathai and the first author, to orbifold symmetric products. It retains the same properties of quantization of the Hall conductance at integer multiples of the fractional Satake orbifold Euler characteristics. We show that it also allows for interesting composite fermions and anyon representations, and possibly for Laughlin type wave functions.
Utilizing Static Analysis and Code Generation to Accelerate Neural Networks

As datasets continue to grow, neural network (NN) applications are becoming increasingly limited by both the amount of available computational power and the ease of developing high-performance applications. Researchers often must have expert systems knowledge to make their algorithms run efficiently. Although available computing power increases rapidly each year, algorithm efficiency is not able to keep pace due to the use of general purpose compilers, which are not able to fully optimize specialized application domains. Within the domain of NNs, we have the added knowledge that network architecture remains constant during training, meaning the architecture's data structure can be statically optimized by a compiler. In this paper, we present SONNC, a compiler for NNs that utilizes static analysis to generate optimized parallel code. We show that SONNC's use of static optimizations make it able to outperform hand-optimized C++ code by up to 7.8X, and MATLAB code by up to 24X. Additionally, we show that use of SONNC significantly reduces code complexity when using structurally sparse networks.
Informative planning and online learning with sparse Gaussian processes

A big challenge in environmental monitoring is the spatiotemporal variation of the phenomena to be observed. To enable persistent sensing and estimation in such a setting, it is beneficial to have a time-varying underlying environmental model. Here we present a planning and learning method that enables an autonomous marine vehicle to perform persistent ocean monitoring tasks by learning and refining an environmental model. To alleviate the computational bottleneck caused by large-scale data accumulated, we propose a framework that iterates between a planning component aimed at collecting the most information-rich data, and a sparse Gaussian Process learning component where the environmental model and hyperparameters are learned online by taking advantage of only a subset of data that provides the greatest contribution. Our simulations with ground-truth ocean data shows that the proposed method is both accurate and efficient.
Ultraviolet light degrades the mechanical and structural properties of human stratum corneum.

Abstract Prolonged exposure of human skin to sunlight causes photodamage, which induces the early onset of wrinkles and increased tissue fragility. While solar ultraviolet (UV) light is considered to have the most damaging effect, the UV range that is most harmful remains a topic of significant debate. In this study, we take a first step towards elucidating biomechanical photoageing effects by quantifying how exposure to different UV ranges and dosages impacts the mechanical and structural properties of human stratum corneum (SC), the most superficial skin layer. Mechanical testing reveals that irradiation of isolated human SC to UVA (365 nm), UVB (302 nm), or UVC (265 nm) light with dosages of up to 4000 J/cm2 notably alters the elastic modulus, fracture stress, fracture strain, and work of fracture. For equivalent incident dosages, UVC degrades SC the greatest. However, upon discounting reflected and transmitted components of the incident light, a generalized scaling law relating the photonic energy absorbed by the SC to the energy cost of tissue fracture emerges. This relationship indicates that no one UV range is more damaging than another. Rather, the magnitude of absorbed UV energy governs the degradation of tissue mechanical integrity. Subsequent structural studies are performed to elucidate the cause of this mechanical degradation. UV absorption scales with the spatial dispersion of desmoglein 1 (Dsg 1), a component of corneocyte cell-cell junctions, away from intercellular sites. Combining both scaling laws, we establish a mechanical-structural model capable of predicting UV induced tissue mechanical integrity from Dsg 1 dispersion.
Three convolution inequalities on the real line with connections to additive combinatorics

Abstract We discuss three convolution inequalities that are connected to additive combinatorics. Cloninger and the second author showed that for nonnegative f ∈ L 1 ( − 1 / 4 , 1 / 4 ) , max − 1 / 2 ≤ t ≤ 1 / 2 ⁡ ∫ R f ( t − x ) f ( x ) d x ≥ 1.28 ( ∫ − 1 / 4 1 / 4 f ( x ) d x ) 2 which is related to g−Sidon sets (1.28 cannot be replaced by 1.52). We prove a dual statement, related to difference bases, and show that for f ∈ L 1 ( R ) , min 0 ≤ t ≤ 1 ⁡ ∫ R f ( x ) f ( x + t ) d x ≤ 0.42 ‖ f ‖ L 1 2 , where the constant 1/2 is trivial, 0.42 cannot be replaced by 0.37. This suggests a natural conjecture about the asymptotic structure of g−difference bases. Finally, we show for all functions f ∈ L 1 ( R ) ∩ L 2 ( R ) , ∫ − 1 2 1 2 ∫ R f ( x ) f ( x + t ) d x d t ≤ 0.91 ‖ f ‖ L 1 ‖ f ‖ L 2 .
A note on parameter estimation for discretely sampled SPDEs

We consider a parameter estimation problem for one-dimensional stochastic heat equations, when data is sampled discretely in time or spatial component. We prove that, the real valued parameter next...
High-extinction electro-optic modulation on lithium niobate thin film

We exploit a cascaded MachZehnder interferometry design to offset inevitable fabrication errors, demonstrating up to 53 dB modulation extinction for a wide range of wavelengths between 1500 nm and 1600 nm.
Prebiotically Plausible “Patching” of RNA Backbone Cleavage through a 3′–5′ Pyrophosphate Linkage

Achieving multiple cycles of RNA replication within a model protocell would be a critical step toward demonstrating a path from prebiotic chemistry to cellular biology. Any model for early life based on an "RNA world" must account for RNA strand cleavage and hydrolysis, which would degrade primitive genetic information and lead to an accumulation of truncated, phosphate-terminated strands. We show here that cleavage of the phosphodiester backbone is not an end point for RNA replication. Instead, 3'-phosphate-terminated RNA strands can participate in template-directed copying reactions with activated ribonucleotide monomers. These reactions form a pyrophosphate linkage, the stability of which we have characterized in the context of RNA copying chemistry. The presence of free magnesium cations results in cleavage of the pyrophosphate bond within minutes. However, we found that the pyrophosphate bond is relatively stable within an RNA duplex and in the presence of chelated magnesium. We show that, under these conditions, pyrophosphate-linked RNA can act as a template for the polymerization of ribonucleotides into canonical 3'-5' phosphodiester-linked RNA. We suggest that primer extension of 3'-phosphate-terminated RNA followed by template-directed copying represents a plausible nonenzymatic pathway for the salvage and recovery of genetic information following strand cleavage.
Price of dependence: stochastic submodular maximization with dependent items

In this paper, we study the stochastic submodular maximization problem with dependent items subject to downward-closed and prefix-closed constraints. The input of our problem is a finite set of items, and each item is in a particular state from a set of possible states. After picking an item, we are able to observe its state. We assume a monotone and submodular utility function over items and states, and our objective is to select a group of items adaptively so as to maximize the expected utility. Previous studies on stochastic submodular maximization often assume that items’ states are independent, however, this assumption may not hold in general. This motivates us to study the stochastic submodular maximization problem with dependent items. We first introduce the concept of degree of independence to capture the degree to which one item’s state is dependent on others’. Then we propose a non-adaptive policy that approximates the optimal adaptive policy within a factor of \(\alpha \left( 1-e^{-\frac{\kappa }{2}+\frac{\kappa }{18m^2}}-\frac{\kappa +2}{3m\kappa }\right) \) where the value of \(\alpha \) is depending on the type of constraints, e.g., \(\alpha =1\) for matroid constraint, \(\kappa \) is the degree of independence, e.g., \(\kappa =1\) for independent items, and m is the number of items. We also analyze the adaptivity gap, i.e., the ratio of the values of best adaptive policy and best non-adaptive policy, of our problem with prefix-closed constraints.
On complexity of representations of quivers

Abstract It is shown that, given a representation of a quiver over a finite field, one can check in polynomial time whether it is absolutely indecomposable.
Faster Biclique Mining in Near-Bipartite Graphs

Identifying dense bipartite subgraphs is a common graph data mining task. Many applications focus on the enumeration of all maximal bicliques (MBs), though sometimes the stricter variant of maximal induced bicliques (MIBs) is of interest. Recent work of Kloster et al. introduced a MIB-enumeration approach designed for “near-bipartite” graphs, where the runtime is parameterized by the size k of an odd cycle transversal (OCT), a vertex set whose deletion results in a bipartite graph. Their algorithm was shown to outperform the previously best known algorithm even when k was logarithmic in |V|. In this paper, we introduce two new algorithms optimized for near-bipartite graphs - one which enumerates MIBs in time \(O(M_I |V| |E| k)\), and another based on the approach of Alexe et al. which enumerates MBs in time \(O(M_B |V| |E| k)\), where \(M_I\) and \(M_B\) denote the number of MIBs and MBs in the graph, respectively. We implement all of our algorithms in open-source C++ code and experimentally verify that the OCT-based approaches are faster in practice than the previously existing algorithms on graphs with a wide variety of sizes, densities, and OCT decompositions.
Convolutional Composer Classification

This paper investigates end-to-end learnable models for attributing composers to musical scores. We introduce several pooled, convolutional architectures for this task and draw connections between our approach and classical learning approaches based on global and n-gram features. We evaluate models on a corpus of 2,500 scores from the KernScores collection, authored by a variety of composers spanning the Renaissance era to the early 20th century. This corpus has substantial overlap with the corpora used in several previous, smaller studies; we compare our results on subsets of the corpus to these previous works.
WiDet: Wi-Fi based device-free passive person detection with deep convolutional neural networks

Abstract To achieve device-free person detection, various types of signal features, such as moving statistics and wavelet representations, have been extracted from the Wi-Fi Received Signal Strength Index (RSSI), whose value fluctuates when human subjects move near the Wi-Fi transceivers. However, these features do not work effectively under different deployments of Wi-Fi transceivers because each transceiver has a unique RSSI fluctuation pattern that depends on its specific wireless channel and hardware characteristics. To address this problem, we present WiDet, a system that uses a deep Convolutional Neural Network (CNN) approach for person detection. The CNN takes the 2-dimensional wavelet coefficients as input, and extracts effective and robust detection features automatically. With a large number of internal parameters, the CNN can record and recognize the different RSSI fluctuation patterns from different transceivers. We further apply the data augmentation method to improve the algorithm robustness to wireless interferences and pedestrian speed changes. To take advantage of the wide availability of the existing Wi-Fi devices, we design a collaborative sensing technique that can recognize the subject’s moving directions. To validate the proposed design, we implement a prototype system that consists of three Wi-Fi packet transmitters and one receiver on low-cost off-the-shelf embedded development boards. In a multi-day experiment with a total of 163 walking events, WiDet achieves 95.5% of detection accuracy in detecting pedestrians, which outperforms the moving statistics and the wavelet representation based approaches by 22% and 8%, respectively.
Understanding the Limitations of Variational Mutual Information Estimators

Variational approaches based on neural networks are showing promise for estimating mutual information (MI) between high dimensional variables. However, they can be difficult to use in practice due to poorly understood bias/variance tradeoffs. We theoretically show that, under some conditions, estimators such as MINE exhibit variance that could grow exponentially with the true amount of underlying MI. We also empirically demonstrate that existing estimators fail to satisfy basic self-consistency properties of MI, such as data processing and additivity under independence. Based on a unified perspective of variational approaches, we develop a new estimator that focuses on variance reduction. Empirical results on standard benchmark tasks demonstrate that our proposed estimator exhibits improved bias-variance trade-offs on standard benchmark tasks.
Data-Driven Load Modeling and Forecasting of Residential Appliances

The expansion of residential demand side management programs and increased deployment of controllable loads require accurate appliance-level load modeling and forecasting. This paper proposes a conditional hidden semi-Markov model to describe the probabilistic nature of residential appliance demand. Model parameters are estimated directly from power consumption data using scalable statistical learning methods. We also propose an algorithm for short-term load forecasting as a key application for appliance-level load models. Case studies performed using granular sub-metered power measurements from various types of appliances demonstrate the effectiveness of the proposed load model for short-term prediction.
Evaluation of the National Cancer Program and Proposed Reforms

A statement by 68 prominent national experts in cancer prevention, carcinogenesis, epidemiology, and public health, released at a February 4, 1992, press conference in Washington, D.C., charged that the National Cancer Institute (NCI) has misled and confused the public by repeated claims of winning the war against cancer. In fact, age-standardized incidence rates have escalated to epidemic proportions over recent decades, while the ability to treat and cure most cancers has not materially improved. Furthermore, the NCI has minimized evidence for increasing cancer rates, which are largely attributed to smoking, trivializing the importance of occupational carcinogens as non-smoking attributable causes of lung and other cancers, and to diet perse, in spite of tenuous and inconsistent evidence and ignoring the important role of carcinogenic dietary contaminants. Reflecting this near exclusionary blame-the-victim theory of cancer causation, with lockstep support from the American Cancer Society and industry, the NCI discounts the role of avoidable involuntary exposures to industrial carcinogens in air, water, food, the home, and the workplace. The NCI has also failed to provide any scientific guidance to Congress and regulatory agencies on fundamental principles of carcinogenesis and epidemiology, and on the critical needs to reduce avoidable exposures to environmental and occupational carcinogens. Analysis of the $2 billion NCI budget, in spite of fiscal and semantic manipulation, reveals minimal allocations for research on primary cancer prevention, and for occupational cancer, which receives only $19 million annually, 1 percent of NCI's total budget. Problems of professional mindsets in the NCI leadership, fixation on diagnosis, treatment, and basic research, much of questionable relevance, and the neglect of cancer prevention, are exemplified by the composition of the National Cancer Advisory Board. Contrary to the explicit mandate of the National Cancer Act, the Board is devoid of members authoritative in occupational and environmental carcinogenesis. These problems arc further compounded by institutionalized conflicts of interest reflected in the composition of past executive President's Cancer Panels,and of the current Board of Overseers of the Sloan-Kettering Memorial Cancer Center, the NCI's prototype comprehensive cancer center,with their closely interlocking financial interests with the cancer drug and other industries. Drastic reforms of NCI policies and priorities are long overdue. Implementation of such reforms is, however, unlikely in the absence of further support from industrial medicine professionals, besides action by Congress and concerned citizen groups.
Operator-valued chordal Loewner chains and non-commutative probability

Abstract We adapt the theory of chordal Loewner chains to the operator-valued matricial upper-half plane over a C ⁎ -algebra A . We define an A -valued chordal Loewner chain as a subordination chain of analytic self-maps of the A -valued upper half-plane, such that each F t is the reciprocal Cauchy transform of an A -valued law μ t , such that the mean and variance of μ t are continuous functions of t. We relate A -valued Loewner chains to processes with A -valued free or monotone independent increments just as was done in the scalar case by Bauer [8] and Schleisinger [48] . We show that the Loewner equation ∂ t F t ( z ) = D F t ( z ) [ V t ( z ) ] , when interpreted in a certain distributional sense, defines a bijection between Lipschitz mean-zero Loewner chains F t and vector fields V t ( z ) of the form V t ( z ) = − G ν t ( z ) where ν t is a generalized A -valued law. Based on the Loewner equation, we derive a combinatorial expression for the moments of μ t in terms of ν t . We also construct non-commutative random variables on an operator-valued monotone Fock space which realize the laws μ t . Finally, we prove a version of the monotone central limit theorem which describes the behavior of F t as t → + ∞ when ν t has uniformly bounded support.
Tweets can tell: activity recognition using hybrid long short-term memory model

This paper presents techniques to detect offline activities of a person when she is tweeting in order to create a dynamic profile of the user, for uses such as better targeting of advertisements. To this end, we propose a hybrid LSTM model for rich contextual learning, along with studies on the effects of applying and combining multiple LSTM based methods with different contextual features. The hybrid model outperforms a set of baselines as well as state-of-the-art methods.
The Impact of Federal Housing Policy on Housing Demand and Homeownership: Evidence from a Quasi-Experiment

Abstract Federal housing policy promotes homeownership by subsidizing mortgage debt for many households with few assets and low credit scores. In this paper, we exploit the Federal Housing Administration's (FHA's) surprise 50 basis point cut to its annual mortgage insurance premium in January 2015 to study the impact of federal housing policy and interest rates on housing demand for a population of households likely to be influenced by changes to policy. The premium cut, which reduced monthly payments the same amount as a three-quarter percentage point drop in the mortgage rate, increased the purchasing power of the typical FHA borrower by 6 percent. Our analysis shows FHA borrowers increased the value of the housing they purchased by 2.5 percentage points relative to a control group of borrowers in areas with minimal FHA presence. The rise in spending reflected an increase in constant-quality home prices, with no significant change in the quality of housing purchased by FHA buyers. We also estimate that the premium cut induced approximately 17,000 households to become first-time homebuyers in the initial year after the cut, an increase that fell far short of the FHA's projection.
The Capacity of T -Private Information Retrieval With Private Side Information

We consider the problem of $T$ -Private Information Retrieval with private side information (TPIR-PSI). In this problem, $N$ replicated databases store $K$ independent messages, and a user, equipped with a local cache that holds $M$ messages as side information, wishes to retrieve one of the other $K-M$ messages. The desired message index and the side information must remain jointly private even if any $T$ of the $N$ databases collude. We show that the capacity of TPIR-PSI is $\left ({1+\frac {T}{N}+\cdots +\left ({\frac {T}{N}}\right)^{K-M-1}}\right)^{-1}$ . As a special case obtained by setting $T=1$ , this result settles the capacity of PIR-PSI, an open problem previously noted by Kadhe et al. We also consider the problem of symmetric-TPIR with private side information (STPIR-PSI), where the answers from all $N$ databases reveal no information about any other message besides the desired message. We show that the capacity of STPIR-PSI is $1-\frac {T}{N}$ if the databases have access to common randomness (not available to the user) that is independent of the messages, in an amount that is at least $\frac {T}{N-T}$ bits per desired message bit. Otherwise, the capacity of STPIR-PSI is zero.
What do employees know? Quality perception and ‘over-satisfaction’ in firms going public

Abstract Should IPO investors pay attention to employees' views on firm quality and work satisfaction (e.g., work-life-balance)? We track employees' opinions (Glassdoor) in private firms that subsequently go public. Employees' pre-IPO views are informative: positive reviews of firm/manager quality predict stronger post-IPO stock performance, while dispersion in opinions correlates with post-IPO return volatility. A striking finding is that employees' satisfaction in excess of that predicted by firm quality opinions (over-satisfaction) is negatively related to post-IPO performance. Finally, positive initial-day stock returns enhance employees' views regarding firm quality, suggesting that IPO underpricing may secure a boost in employees' morale.
ER membranes exhibit phase behavior at sites of organelle contact.

The endoplasmic reticulum (ER) is the site of synthesis of secretory and membrane proteins and contacts every organelle of the cell, exchanging lipids and metabolites in a highly regulated manner. How the ER spatially segregates its numerous and diverse functions, including positioning nanoscopic contact sites with other organelles, is unclear. We demonstrate that hypotonic swelling of cells converts the ER and other membrane-bound organelles into micrometer-scale large intracellular vesicles (LICVs) that retain luminal protein content and maintain contact sites with each other through localized organelle tethers. Upon cooling, ER-derived LICVs phase-partition into microscopic domains having different lipid-ordering characteristics, which is reversible upon warming. Ordered ER lipid domains mark contact sites with ER and mitochondria, lipid droplets, endosomes, or plasma membrane, whereas disordered ER lipid domains mark contact sites with lysosomes or peroxisomes. Tethering proteins concentrate at ER–organelle contact sites, allowing time-dependent behavior of lipids and proteins to be studied at these sites. These findings demonstrate that LICVs provide a useful model system for studying the phase behavior and interactive properties of organelles in intact cells.
Teaching R in the undergraduate ecology classroom: approaches, lessons learned, and recommendations

Abstract Ecology requires training in data management and analysis. In this paper, we present data from the last 10 years demonstrating the increase in the use of R, an open-source programming environment, in ecology and its prevalence as a required skill in job descriptions. Because of its transparent and flexible nature, R is increasingly used for data management and analysis in the field of ecology. Consequently, job postings targeting candidates with a bachelor’s degree and a required knowledge of R have increased over the past ten years. We discuss our experiences teaching undergraduates R in two advanced ecology classes using different approaches. One approach, in a course with a field lab, focused on collecting, cleaning, and preparing data for analysis. The other approach, in a course without a field lab, focused on analyzing existing data sets and applying the results to content discussed in the lecture portion of the course. Our experiences determined that each approach had strengths and weaknesses. We recommend that above all, instructors of ecology and related subjects should be encouraged to include R in their coursework. Furthermore, instructors should be aware of the following: learning R is a separate skill from learning statistics; writing R assignments is a significant time sink for course preparation; and, there is a tradeoff between teaching R and teaching content. Determining how one’s course fits into the curriculum and identifying resources outside of the classroom for students’ continued practice will ensure that R training is successful and will extend beyond a one-semester course.
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds

This article presents a discrete-time option pricing model that is rooted in reinforcement learning (RL), and more specifically in the famous Q-Learning method of RL. We construct a risk-adjusted Markov Decision Process for a discrete-time version of the classical Black-Scholes-Merton (BSM) model, where the option price is an optimal Q-function, while the optimal hedge is a second argument of this optimal Q-function, so that both the price and hedge are parts of the same formula. Pricing is done by learning to dynamically optimize risk-adjusted returns for an option replicating portfolio, as in Markowitz portfolio theory. Using Q-Learning and related methods, once created in a parametric setting, the model can go model-free and learn to price and hedge an option directly from data, without an explicit model of the world. This suggests that RL may provide efficient data-driven and model-free methods for the optimal pricing and hedging of options. Once we depart from the academic continuous-time limit, and vice versa, option pricing methods developed in Mathematical Finance may be viewed as special cases of model-based reinforcement learning. Further, due to the simplicity and tractability of our model, which only needs basic linear algebra (plus Monte Carlo simulation, if we work with synthetic data), and its close relationship to the original BSM model, we suggest that our model could be used in the benchmarking of different RL algorithms for financial trading applications. TOPICS:Derivatives, options Key Findings • Reinforcement learning (RL) is the most natural way for pricing and hedging of options that relies directly on data and not on a specific model of asset pricing. • The discrete-time RL approach to option pricing generalizes classical continuous-time methods, enables tracking mis-hedging risk, which disappears in the formal continuous-time limit, and provides a consistent framework for using options for both hedging and speculation. • A simple quadratic reward function, which presents a minimal extension of the classical Black-Scholes framework when combined with the Q-learning method of RL, gives rise to a particularly simple computational scheme where option pricing and hedging are semi-analytical, as they amount to multiple uses of a conventional least-squares regression.
Photoelectron spectroscopy of excited molecular states

Abstract Resonance Enhanced Multiphoton Ionization (REMPI), coupled with high-resolution photoelectron spectroscopy, is becoming an important probe of the photoionization dynamics of molecular excited states at a quantum-state specific level. In this paper we will discuss some results of our studies of ionic rotational and vibrational distributions for REMPI of several small molecules such as H2, O2, NO, OH, and CH which illustrate some dynamically important features of these processes.
Strongly Stable Networks

We analyze the formation of networks among individuals. In particular, we examine the existence of networks that are stable against changes in links by any coalition of individuals. We show that to investigate the existence of such strongly stable networks one can restrict focus on a component-wise egalitarian allocation of value. We show that when such strongly stable networks exist they coincide with the set of efficient networks (those maximizing the total productive value). We show that the existence of strongly stable networks is equivalent to core existence in a derived cooperative game and use that result to characterize the class of value functions for which there exist strongly stable networks via a “top convexity” condition on the value function on networks. We also consider a variation on strong stability where players can make side payments, and examine situations where value functions may be non-anonymous—depending on player labels.
Chapter 27 – Business Premises

A community pharmacist needs to acquire premises from which to practise, and this chapter summarises the relevant legal considerations which must be taken into account when purchasing or renting business premises. Property law is complicated and important issues such as payment of rent, duties to repair premises and covenants limiting the right to assign or sublet confront a pharmacist for the first time. This chapter alerts a pharmacist to the issues which arise.
Sargent-Wallace Meets Krugman-Flood-Garber, or: Why Sovereign Debt Swaps Don't Avert Macroeconomic Crises

This paper argues that the frequent failure of the debt swaps is not an accident. Instead, it follows from fundamental forces driven by the market's assessment of the scarcity of fiscal revenue relative to the demand for fiscal outlays. It follows from the observation that arbitrage forces systematically impact prices in asset markets. Ignoring these price adjustments would lead to too optimistic an assessment of the gains from swaps or buybacks. A by-product of our paper is to highlight the perils of financial engineering that ignores the intertemporal constraints imposed by fiscal fundamentals. As a country approaches the range of partial default (either on domestic or external debt), swaps may not provide the expected breathing room and could even bring the crisis forward. Our methodology combines three independent themes: exchange rate crises as the manifestation of excessive monetary injections [Krugman-Flood-Garber], the fiscal theory of inflation [Sargent-Wallace (1981)], and sovereign debt. The integrated framework derives devaluation and external debt repudiation as part of a public-finance optimizing problem. We shows that under conditions similar to those which prevailed in Russia and Argentina prior to their meltdown, swaps are not just neutral, but could actually make the situation worse and even trigger a speculative attack. An unsettlingly clear implication of the model is that there may be very few options left once public debt reaches levels regarded as unsustainable in relation to fiscal fundamentals. Dollarization only makes matters worse, and pushes the debt write-down option to the fore.
Serpentine optical phased arrays for scalable integrated photonic LIDAR beam steering

Optical phased arrays (OPAs) implemented in integrated photonic circuits could enable a variety of 3D sensing, imaging, illumination, and ranging applications, and their convergence in new lidar technology. However, current integrated OPA approaches do not scale—in control complexity, power consumption, or optical efficiency—to the large aperture sizes needed to support medium- to long-range lidar. We present the serpentine OPA (SOPA), a new OPA concept that addresses these fundamental challenges and enables architectures that scale up to large apertures. The SOPA is based on a serially interconnected array of low-loss grating waveguides and supports fully passive, 2D wavelength-controlled beam steering. A fundamentally space-efficient design that folds the feed network into the aperture also enables scalable tiling of SOPAs into large apertures with a high fill-factor. We experimentally demonstrate, to the best of our knowledge, the first SOPA using a 1450–1650 nm wavelength sweep to produce 16,500 addressable spots in a 27×610 array. We also demonstrate, for the first time, far-field interference of beams from two separate OPAs on a single silicon photonic chip, as an initial step towards long-range computational imaging lidar based on novel active aperture synthesis schemes.
Hybrid voltage stability and security assessment using synchrophasors with consideration of generator Q-limits

Increased power demands, push for economics and limited investment in grid infrastructure have led utilities to operate power systems closer to their stability limits. Voltage instability may trigger cascade tripping, wide-area voltage collapse, and power blackouts. Real-time voltage stability monitoring possible with deployment of phasor measurement units (PMUs) is essential to take proactive control actions and minimise the impact on the system. This study presents a novel online algorithm for (a) hybrid perturbation analysis based voltage stability monitoring (HPVSM), (b) including Q-limit in voltage stability index, and (c) real-time security analysis using voltage stability index. HPVSM-based voltage stability index is computed using the data obtained from linear state estimator and PMU measurements. Typically measurement-based schemes ignore the impact of generator Q-limits and security analysis is not feasible. The proposed HPVSM-based index considers the impact of generator Q-limits violations by anticipating the critical generators using real-time PMU measurements. Contingencies are ranked using the proposed voltage stability index for security analysis. Results simulated for the 9-bus WECC, IEEE 14, 57, and 118 bus systems highlight the superiority of the proposed method in real-time voltage stability and security analysis.
Disentangling the Independent Contributions of Visual and Conceptual Features to the Spatiotemporal Dynamics of Scene Categorization.

Human scene categorization is characterized by its remarkable speed. While many visual and conceptual features have been linked to this ability, significant correlations exist between feature spaces, impeding our ability to determine their relative contributions to scene categorization. Here, we used a whitening transformation to decorrelate a variety of visual and conceptual features and assess the time course of their unique contributions to scene categorization. Participants (both sexes) viewed 2250 full-color scene images drawn from 30 different scene categories while having their brain activity measured through 256-channel EEG. We examined the variance explained at each electrode and time point of visual event-related potential (vERP) data from nine different whitened encoding models. These ranged from low-level features obtained from filter outputs to high-level conceptual features requiring human annotation. The amount of category information in the vERPs was assessed through multivariate decoding methods. Behavioral similarity measures were obtained in separate crowdsourced experiments. We found that all nine models together contributed 78% of the variance of human scene similarity assessments and were within the noise ceiling of the vERP data. Low-level models explained earlier vERP variability (88 ms after image onset), whereas high-level models explained later variance (169 ms). Critically, only high-level models shared vERP variability with behavior. Together, these results suggest that scene categorization is primarily a high-level process, but reliant on previously extracted low-level features. SIGNIFICANCE STATEMENT In a single fixation, we glean enough information to describe a general scene category. Many types of features are associated with scene categories, ranging from low-level properties, such as colors and contours, to high-level properties, such as objects and attributes. Because these properties are correlated, it is difficult to understand each property9s unique contributions to scene categorization. This work uses a whitening transformation to remove the correlations between features and examines the extent to which each feature contributes to visual event-related potentials over time. We found that low-level visual features contributed first but were not correlated with categorization behavior. High-level features followed 80 ms later, providing key insights into how the brain makes sense of a complex visual world.
Predictable accelerator design with time-sensitive affine types

Field-programmable gate arrays (FPGAs) provide an opportunity to co-design applications with hardware accelerators, yet they remain difficult to program. High-level synthesis (HLS) tools promise to raise the level of abstraction by compiling C or C++ to accelerator designs. Repurposing legacy software languages, however, requires complex heuristics to map imperative code onto hardware structures. We find that the black-box heuristics in HLS can be unpredictable: changing parameters in the program that should improve performance can counterintuitively yield slower and larger designs. This paper proposes a type system that restricts HLS to programs that can predictably compile to hardware accelerators. The key idea is to model consumable hardware resources with a time-sensitive affine type system that prevents simultaneous uses of the same hardware structure. We implement the type system in Dahlia, a language that compiles to HLS C++, and show that it can reduce the size of HLS parameter spaces while accepting Pareto-optimal designs.
On Learning Language-Invariant Representations for Universal Machine Translation

The goal of universal machine translation is to learn to translate between any pair of languages, given a corpus of paired translated documents for \emph{a small subset} of all pairs of languages. Despite impressive empirical results and an increasing interest in massively multilingual models, theoretical analysis on translation errors made by such universal machine translation models is only nascent. In this paper, we formally prove certain impossibilities of this endeavour in general, as well as prove positive results in the presence of additional (but natural) structure of data. For the former, we derive a lower bound on the translation error in the many-to-many translation setting, which shows that any algorithm aiming to learn shared sentence representations among multiple language pairs has to make a large translation error on at least one of the translation tasks, if no assumption on the structure of the languages is made. For the latter, we show that if the paired documents in the corpus follow a natural \emph{encoder-decoder} generative process, we can expect a natural notion of ``generalization'': a linear number of language pairs, rather than quadratic, suffices to learn a good representation. Our theory also explains what kinds of connection graphs between pairs of languages are better suited: ones with longer paths result in worse sample complexity in terms of the total number of documents per language pair needed. We believe our theoretical insights and implications contribute to the future algorithmic design of universal machine translation.
Mapping natural-language problems to formal-language solutions using structured neural representations

Generating formal-language programs represented by relational tuples, such as Lisp programs or mathematical operations, to solve problems stated in natural language is a challenging task because it requires explicitly capturing discrete symbolic structural information implicit in the input. However, most general neural sequence models do not explicitly capture such structural information, limiting their performance on these tasks. In this paper, we propose a new encoder-decoder model based on a structured neural representation, Tensor Product Representations (TPRs), for mapping Natural-language problems to Formal-language solutions, called TPN2F. The encoder of TP-N2F employs TPR ‘binding’ to encode natural-language symbolic structure in vector space and the decoder uses TPR ‘unbinding’ to generate, in symbolic space, a sequential program represented by relational tuples, each consisting of a relation (or operation) and a number of arguments. TP-N2F considerably outperforms LSTM-based seq2seq models on two benchmarks and creates new state-of-the-art results. Ablation studies show that improvements can be attributed to the use of structured TPRs explicitly in both the encoder and decoder. Analysis of the learned structures shows how TPRs enhance the interpretability of TP-N2F.
Characterizing the Uncertainty of Jointly Distributed Poses in the Lie Algebra

An accurate characterization of pose uncertainty is essential for safe autonomous navigation. Early pose uncertainty characterization methods proposed by Smith, Self, and Cheeseman (SCC) used coordinate-based first-order methods to propagate uncertainty through nonlinear functions such as pose composition (head-to-tail), pose inversion, and relative pose extraction (tail-to-tail). Characterizing uncertainty in the Lie algebra of the special Euclidean group results in better uncertainty estimates. However, existing Lie-group-based uncertainty propagation techniques assume that individual poses are independent. After solving a pose graph, however, the entire trajectory is jointly distributed as factors induce correlation. Hence, the independence assumption does not capture reality. In addition, prior work has focused primarily on the pose composition operation. This article develops a framework for modeling the uncertainty of jointly distributed poses and describes how to perform the equivalent of the SSC pose operations while characterizing uncertainty in the Lie algebra. Evaluation on simulated and open-source datasets shows that the proposed methods result in more accurate uncertainty estimates and thus more accurate filtering of potential loop closures. An accompanying C++ library implementation is also released.
Black Box Submodular Maximization: Discrete and Continuous Settings.

In this paper, we consider the problem of black box continuous submodular maximization where we only have access to the function values and no information about the derivatives is provided. For a monotone and continuous DR-submodular function, and subject to a bounded convex body constraint, we propose Black-box Continuous Greedy, a derivative-free algorithm that provably achieves the tight $[(1-1/e)OPT-\epsilon]$ approximation guarantee with $O(d/\epsilon^3)$ function evaluations. We then extend our result to the stochastic setting where function values are subject to stochastic zero-mean noise. It is through this stochastic generalization that we revisit the discrete submodular maximization problem and use the multi-linear extension as a bridge between discrete and continuous settings. Finally, we extensively evaluate the performance of our algorithm on continuous and discrete submodular objective functions using both synthetic and real data.
Capuchin monkey rituals: an interdisciplinary study of form and function

Many white-faced capuchin monkey dyads in Lomas Barbudal, Costa Rica, practise idiosyncratic interaction sequences that are not part of the species-typical behavioural repertoire. These interactions often include uncomfortable or risky elements. These interactions exhibit the following characteristics commonly featured in definitions of rituals in humans: (i) they involve an unusual intensity of focus on the partner, (ii) the behaviours have no immediate utilitarian purpose, (iii) they sometimes involve 'sacred objects', (iv) the distribution of these behaviours suggests that they are invented and spread via social learning, and (v) many behaviours in these rituals are repurposed from other behavioural domains (e.g. extractive foraging). However, in contrast with some definitions of ritual, capuchin rituals are not overly rigid in their form, nor do the sequences have specific opening and closing actions. In our 9260 h of observation, ritual performance rate was uncorrelated with amount of time dyads spent in proximity but (modestly) associated with higher relationship quality and rate of coalition formation across dyads. Our results suggest that capuchin rituals serve a bond-testing rather than a bond-strengthening function. Ritual interactions are exclusively dyadic, and between-dyad consistency in form is low, casting doubt on the alternative hypothesis that they enhance group-wide solidarity. This article is part of the theme issue 'Ritual renaissance: new insights into the most human of behaviours'.