cssi/SciSciGPT-SciSciNet huggingface dataset:
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance. We find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively. Based on these results, we articulate the "lottery ticket hypothesis:" dense, randomly-initialized, feed-forward networks contain subnetworks ("winning tickets") that - when trained in isolation - reach test accuracy comparable to the original network in a similar number of iterations. The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective. We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis and the importance of these fortuitous initializations. We consistently find winning tickets that are less than 10-20% of the size of several fully-connected and convolutional feed-forward architectures for MNIST and CIFAR10. Above this size, the winning tickets that we find learn faster than the original network and reach higher test accuracy.
Hyperband: a novel bandit-based approach to hyperparameter optimization

Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select configurations, we focus on speeding up random search through adaptive resource allocation and early-stopping. We formulate hyperparameter optimization as a pure-exploration nonstochastic infinite-armed bandit problem where a predefined resource like iterations, data samples, or features is allocated to randomly sampled configurations. We introduce a novel algorithm, Hyperband, for this framework and analyze its theoretical properties, providing several desirable guarantees. Furthermore, we compare Hyperband with popular Bayesian optimization methods on a suite of hyperparameter optimization problems. We observe that Hyperband can provide over an order-of-magnitude speedup over our competitor set on a variety of deep-learning and kernel-based learning problems.
The Euler top and canonical lifts

Abstract In this note, we prove a finiteness result for fibers that are canonical lifts in a given elliptic fibration. The question was motivated by the authors' construction of an arithmetic Euler top, and it highlights an interesting discrepancy between the arithmetic and the classical case: in the former, it is impossible to extend the flows to a compactification of the phase space, viewed as an elliptic fibration over the space of action variables.
BiLipschitz Decomposition of Lipschitz Maps between Carnot Groups

Let $f : G \to H$ be a Lipschitz map between two Carnot groups. We show that if $B$ is ball of $G$, then there exists a subset $Z \subset B$, whose image in $H$ under $f$ has small Hausdorff content, such that $B \backslash Z$ can be decomposed into a controlled number of pieces, the restriction of $f$ on each of which is quantitatively biLipschitz. This extends a result of \cite{meyerson}, which proved the same result, but with the restriction that $G$ has an appropriate discretization. We provide an example of a Carnot group not admitting such a discretization.
Learning Category-Specific Mesh Reconstruction from Image Collections

We present a learning framework for recovering the 3D shape, camera, and texture of an object from a single image. The shape is represented as a deformable 3D mesh model of an object category where a shape is parameterized by a learned mean shape and per-instance predicted deformation. Our approach allows leveraging an annotated image collection for training, where the deformable model and the 3D prediction mechanism are learned without relying on ground-truth 3D or multi-view supervision. Our representation enables us to go beyond existing 3D prediction approaches by incorporating texture inference as prediction of an image in a canonical appearance space. Additionally, we show that semantic keypoints can be easily associated with the predicted shapes. We present qualitative and quantitative results of our approach on CUB and PASCAL3D datasets and show that we can learn to predict diverse shapes and textures across objects using only annotated image collections. The project website can be found at https://akanazawa.github.io/cmr/.
Tempered representations and nilpotent orbits

Given a nilpotent orbit O of a real, reductive algebraic group, a necessary condition is given for the existence of a tempered representation pi such that O occurs in the wave front cycle of pi. The coefficients of the wave front cycle of a tempered representation are expressed in terms of volumes of precompact submanifolds of an affine space.
MEC: Memory-efficient Convolution for Deep Neural Network.

Convolution is a critical component in modern deep neural networks, thus several algorithms for convolution have been developed. Direct convolution is simple but suffers from poor performance. As an alternative, multiple indirect methods have been proposed including im2col-based convolution, FFT-based convolution, or Winograd-based algorithm. However, all these indirect methods have high memory-overhead, which creates performance degradation and offers a poor trade-off between performance and memory consumption. In this work, we propose a memory-efficient convolution or MEC with compact lowering, which reduces memory-overhead substantially and accelerates convolution process. MEC lowers the input matrix in a simple yet efficient/compact way (i.e., much less memory-overhead), and then executes multiple small matrix multiplications in parallel to get convolution completed. Additionally, the reduced memory footprint improves memory sub-system efficiency, improving performance. Our experimental results show that MEC reduces memory consumption significantly with good speedup on both mobile and server platforms, compared with other indirect convolution algorithms.
On the meaning of the h-index

The h-index—defined as the value such that an individual has published at least h papers with at least h citations—has become a popular metric for assessing the citation impact of scientists. As already noted in the original work of Hirsch and as evidenced from data for a representative sample of 255 physicists, scales as h, where c is the total number of citations for an individual. Thus appears to be equivalent to the h-index. As a further check of this equivalence, the distribution of the ratio for this sample is sharply peaked about 1. The outliers in this distribution reveal fundamentally different kinds of individual publication records.
Practical Bounds on Optimal Caching with Variable Object Sizes

Many recent caching systems aim to improve miss ratios, but there is no good sense among practitioners of how much further miss ratios can be improved. In other words, should the systems community continue working on this problem? Currently, there is no principled answer to this question. In practice, object sizes often vary by several orders of magnitude, where computing the optimal miss ratio (OPT) is known to be NP-hard. The few known results on caching with variable object sizes provide very weak bounds and are impractical to compute on traces of realistic length. We propose a new method to compute upper and lower bounds on OPT. Our key insight is to represent caching as a min-cost flow problem, hence we call our method the flow-based offline optimal (FOO). We prove that, under simple independence assumptions, FOO's bounds become tight as the number of objects goes to infinity. Indeed, FOO's error over 10M requests of production CDN and storage traces is negligible: at most 0.3%. FOO thus reveals, for the first time, the limits of caching with variable object sizes. While FOO is very accurate, it is computationally impractical on traces with hundreds of millions of requests. We therefore extend FOO to obtain more efficient bounds on OPT, which we call practical flow-based offline optimal (PFOO). We evaluate PFOO on several full production traces and use it to compare OPT to prior online policies. This analysis shows that current caching systems are in fact still far from optimal, suffering 11-43% more cache misses than OPT, whereas the best prior offline bounds suggest that there is essentially no room for improvement.
Optimal testing for properties of distributions

Given samples from an unknown discrete distribution p, is it possible to distinguish whether p belongs to some class of distributions C versus p being far from every distribution in C? This fundamental question has received tremendous attention in statistics, focusing primarily on asymptotic analysis, as well as in information theory and theoretical computer science, where the emphasis has been on small sample size and computational complexity. Nevertheless, even for basic properties of discrete distributions such as monotonicity, independence, log-concavity, unimodality, and monotone-hazard rate, the optimal sample complexity is unknown. We provide a general approach via which we obtain sample-optimal and computationally efficient testers for all these distribution families. At the core of our approach is an algorithm which solves the following problem: Given samples from an unknown distribution p, and a known distribution q, are p and q close in X2 -distance, or far in total variation distance? The optimality of our testers is established by providing matching lower bounds, up to constant factors. Finally, a necessary building block for our testers and an important byproduct of our work are the first known computationally efficient proper learners for discrete log-concave, monotone hazard rate distributions.
A search-free DOA estimation algorithm for coprime arrays

Recently, coprime arrays have been in the focus of research because of their potential in exploiting redundancy in spanning large apertures with fewer elements than suggested by theory. A coprime array consists of two uniform linear subarrays with inter-element spacings [email protected]/2 and [email protected]/2, where M and N are coprime integers and @l is the wavelength of the signal. In this paper, we propose a fast search-free method for direction-of-arrival (DOA) estimation with coprime arrays. It is based on the use of methods that operate on the uniform linear subarrays of the coprime array and that enjoy many processing advantages. We first estimate the DOAs for each uniform linear subarray separately and then combine the estimates from the subarrays. For combining the estimates, we propose a method that projects the estimated point in the two-dimensional plane onto one-dimensional line segments that correspond to the entire angular domain. By doing so, we avoid the search step and consequently, we greatly reduce the computational complexity of the method. We demonstrate the performance of the method with computer simulations and compare it with that of the FD-root MUSIC method.
Reconstructing parameters of spreading models from partial observations

Spreading processes are often modelled as a stochastic dynamics occurring on top of a given network with edge weights corresponding to the transmission probabilities. Knowledge of veracious transmission probabilities is essential for prediction, optimization, and control of diffusion dynamics. Unfortunately, in most cases the transmission rates are unknown and need to be reconstructed from the spreading data. Moreover, in realistic settings it is impossible to monitor the state of each node at every time, and thus the data is highly incomplete. We introduce an efficient dynamic message-passing algorithm, which is able to reconstruct parameters of the spreading model given only partial information on the activation times of nodes in the network. The method is generalizable to a large class of dynamic models, as well to the case of temporal graphs.
The method of polarized traces for the 3D Helmholtz equation

ABSTRACTWe have developed a fast solver for the 3D Helmholtz equation, in heterogeneous, constant density, acoustic media, in the high-frequency regime. The solver is based on the method of polariz...
An optimal treatment assignment strategy to evaluate demand response effect

Demand response is designed to motivate electricity customers to modify their loads at critical time periods. The accurate estimation of impact of demand response signals to customers' consumption is central to any successful program. In practice, learning these response is nontrivial because operators can only send a limited number of signals. In addition, customer behavior also depends on a large number of exogenous covariates. These two features lead to a high dimensional inference problem with limited number of observations. In this paper, we formulate this problem by using a multivariate linear model and adopt an experimental design approach to estimate the impact of demand response signals. We show that randomized assignment, which is widely used to estimate the average treatment effect, is not efficient in reducing the variance of the estimator when a large number of covariates is present. In contrast, we present a tractable algorithm that strategically assigns demand response signals to customers. This algorithm achieves the optimal reduction in estimation variance, independent of the number of covariates. The results are validated from simulations on synthetic data.
The rational hull of Rudin’s Klein bottle

In this note, a general result for determining the rational hulls of fibered sets in $\mathbb{C}^2$ is established. We use this to compute the rational hull of Rudin's Klein bottle, the first explicit example of a totally real nonorientable surface in $\mathbb{C}^2$. In contrast to its polynomial hull, which was shown to contain an open set by the first author in 2012, its rational hull is shown to be two-dimensional. Using the same method, we also compute the rational hulls of some other surfaces in $\mathbb{C}^2$.
Tortoise: interactive system configuration repair

System configuration languages provide powerful abstractions that simplify managing large-scale, networked systems. Thousands of organizations now use configuration languages, such as Puppet. However, specifications written in configuration languages can have bugs and the shell remains the simplest way to debug a misconfigured system. Unfortunately, it is unsafe to use the shell to fix problems when a system configuration language is in use: a fix applied from the shell may cause the system to drift from the state specified by the configuration language. Thus, despite their advantages, configuration languages force system administrators to give up the simplicity and familiarity of the shell. This paper presents a synthesis-based technique that allows administrators to use configuration languages and the shell in harmony. Administrators can fix errors using the shell and the technique automatically repairs the higher-level specification written in the configuration language. The approach (1) produces repairs that are consistent with the fix made using the shell; (2) produces repairs that are maintainable by minimizing edits made to the original specification; (3) ranks and presents multiple repairs when relevant; and (4) supports all shells the administrator may wish to use. We implement our technique for Puppet, a widely used system configuration language, and evaluate it on a suite of benchmarks under 42 repair scenarios. The top-ranked repair is selected by humans 76% of the time and the human-equivalent repair is ranked 1.31 on average.
Delay optimal secrecy in two-relay network

We consider a two-relay network in which a source aims to communicate a confidential message to a destination while keeping the message secret from the relay nodes. In the first hop, the channels from the source to the relays are assumed to be block-fading and the channel states change arbitrarily-possibly non-stationary and non-ergodic-across blocks. When the relay feedback on the states of the source-to-relay channels is available on the source with no delay, we provide an encoding strategy to achieve the optimal delay. We next consider the case in which there is one-block delayed relay feedback on the states of the source-to-relay channels. We show that for a set of channel state sequences, the optimal delay with one-block delayed feedback differs from the optimal delay with no-delayed feedback at most one block.
Conflict and convention in dynamic networks.

An important way to resolve games of conflict (snowdrift, hawk–dove, chicken) involves adopting a convention: a correlated equilibrium that avoids any conflict between aggressive strategies. Dynamic networks allow individuals to resolve conflict via their network connections rather than changing their strategy. Exploring how behavioural strategies coevolve with social networks reveals new dynamics that can help explain the origins and robustness of conventions. Here, we model the emergence of conventions as correlated equilibria in dynamic networks. Our results show that networks have the tendency to break the symmetry between the two conventional solutions in a strongly biased way. Rather than the correlated equilibrium associated with ownership norms (play aggressive at home, not away), we usually see the opposite host–guest norm (play aggressive away, not at home) evolve on dynamic networks, a phenomenon common to human interaction. We also show that learning to avoid conflict can produce realistic network structures in a way different than preferential attachment models.
Pointwise convergence for subsequences of weighted averages

We prove that if $\mu_n$ are probability measures on $Z$ such that $\hat \mu_n$ converges to 0 uniformly on every compact subset of $(0,1)$, then there exists a subsequence $\{n_k\}$ such that the weighted ergodic averages corresponding to $\mu_{n_k}$ satisfy a pointwise ergodic theorem in $L^1$. We further discuss the relationship between Fourier decay and pointwise ergodic theorems for subsequences, considering in particular the averages along $n^2+ \lfloor \rho(n)\rfloor$ for a slowly growing function $\rho$. Under some monotonicity assumptions, the rate of growth of $\rho'(x)$ determines the existence of a "good" subsequence of these averages.
Level structures on abelian varieties, Kodaira dimensions, and Lang's conjecture

Abstract Assuming Lang's conjecture, we prove that for a prime p, number field K, and positive integer g, there is an integer r such that no principally polarized abelian variety A / K has full level- p r structure. To this end, we use a result of Zuo to prove that for each closed subvariety X in the moduli space A g of principally polarized abelian varieties of dimension g, there exists a level m X such that the irreducible components of the preimage of X in A g [ m ] are of general type for m > m X .
Penalty method for reflected diffusions on the half-line

Consider a reflected diffusion on the positive half-line. We approximate it by solutions of stochastic differential equations using the penalty method: We emulate the ‘hard barrier’ of reflection by a ‘soft barrier’ of a large drift coefficient, which compells the diffusion to return to the positive half-line. The main tool of the proof is convergence of scale functions.
Routing Cryptocurrency with the Spider Network

With the growing usage of Bitcoin and other cryptocurrencies, many scalability challenges have emerged. A promising scaling solution, exemplified by the Lightning Network, uses a network of bidirectional payment channels that allows fast transactions between two parties. However, routing payments on these networks efficiently is non-trivial, since payments require finding paths with sufficient funds, and channels can become unidirectional over time blocking further transactions through them. Today's payment channel networks exacerbate these problems by attempting to deliver all payments atomically. We present the Spider network, a new packet-switched architecture for payment channel networks that addresses these challenges. Spider splits payments into transaction units and transmits them over a period of time across different paths. Spider uses congestion control, in-network scheduling, and imbalance-aware routing to optimize delivery of payments. Our results show that Spider improves the number and volume of successful payments on the network by 10-75% and 10-35% respectively compared to practical state-of-the-art approaches.
Retracting Graphs to Cycles

We initiate the algorithmic study of retracting a graph into a cycle in the graph, which seeks a mapping of the graph vertices to the cycle vertices so as to minimize the maximum stretch of any edge, subject to the constraint that the restriction of the mapping to the cycle is the identity map. This problem has its roots in the rich theory of retraction of topological spaces, and has strong ties to well-studied metric embedding problems such as minimum bandwidth and 0-extension. Our first result is an O(min{k, sqrt{n}})-approximation for retracting any graph on n nodes to a cycle with k nodes. We also show a surprising connection to Sperner's Lemma that rules out the possibility of improving this result using certain natural convex relaxations of the problem. Nevertheless, if the problem is restricted to planar graphs, we show that we can overcome these integrality gaps by giving an optimal combinatorial algorithm, which is the technical centerpiece of the paper. Building on our planar graph algorithm, we also obtain a constant-factor approximation algorithm for retraction of points in the Euclidean plane to a uniform cycle.
The decline in idiosyncratic values of US Treasury securities

Abstract Unique features and market frictions can lead to idiosyncratic pricing for some US Treasury securities. This study uses a linear programming (LP) model to measure aggregate idiosyncratic pricing of T-notes and T-bonds from 1980 to 2016. We document an average idiosyncratic pricing of $0.11 per $100 par, as compared to an average bid-ask spread of $0.08. Further, idiosyncratic pricing declined dramatically from the early 1980s to the 2010s. Empirical evidence suggests that the 1986 Tax Reform Act, increasing issue sizes and improving market liquidity contribute to the decline. At the individual security level, we identify factors contributing to and mitigating idiosyncratic pricing.
Product-Mix Auctions and Tropical Geometry

In a recent and ongoing work, Baldwin and Klemperer explore a connection between tropical geometry and economics. They give a sufficient condition for the existence of competitive equilibrium in pr...
Rickets treatment improves more than bone health in toddler with autism spectrum disorder: A brief report:

Children with autism spectrum disorder have been found to have lower levels of vitamin D than their peers. Our case report supports the hypothesis that vitamin D may be an effective treatment for developmental delay in autism. In addition, we review the literature surrounding vitamin D deficiency as a potential cause of autism spectrum disorder and the role that vitamin D may play in treatment.
Twist vertex operators for twisted modules

Abstract We introduce and study twist vertex operators for a (lower-bounded generalized) twisted modules for a grading-restricted vertex (super)algebra. We prove the duality property, weak associativity, a Jacobi identity, a generalized commutator formula, generalized weak commutativity, and convergence and commutativity for products of more than two operators involving twist vertex operators. These properties of twist vertex operators play an important role in the author's recent general, direct and explicit construction of (lower-bounded generalized) twisted modules.
Provably Efficient Q-Learning with Low Switching Cost

We take initial steps in studying PAC-MDP algorithms with limited adaptivity, that is, algorithms that change its exploration policy as infrequently as possible during regret minimization. This is motivated by the difficulty of running fully adaptive algorithms in real-world applications (such as medical domains), and we propose to quantify adaptivity using the notion of \emph{local switching cost}. Our main contribution, Q-Learning with UCB2 exploration, is a model-free algorithm for $H$-step episodic MDP that achieves sublinear regret whose local switching cost in $K$ episodes is $O(H^3SA\log K)$, and we provide a lower bound of $\Omega(HSA)$ on the local switching cost for any no-regret algorithm. Our algorithm can be naturally adapted to the concurrent setting \citep{guo2015concurrent}, which yields nontrivial results that improve upon prior work in certain aspects.
Distributionally Robust Optimization and Generalization in Kernel Methods

Distributionally robust optimization (DRO) has attracted attention in machine learning due to its connections to regularization, generalization, and robustness. Existing work has considered uncertainty sets based on phi-divergences and Wasserstein distances, each of which have drawbacks. In this paper, we study DRO with uncertainty sets measured via maximum mean discrepancy (MMD). We show that MMD DRO is roughly equivalent to regularization by the Hilbert norm and, as a byproduct, reveal deep connections to classic results in statistical learning. In particular, we obtain an alternative proof of a generalization bound for Gaussian kernel ridge regression via a DRO lense. The proof also suggests a new regularizer. Our results apply beyond kernel methods: we derive a generically applicable approximation of MMD DRO, and show that it generalizes recent work on variance-based regularization.
Convolutional Analysis Operator Learning: Acceleration and Convergence

Convolutional operator learning is gaining attention in many signal processing and computer vision applications. Learning kernels has mostly relied on so-called patch-domain approaches that extract and store many overlapping patches across training signals. Due to memory demands, patch-domain methods have limitations when learning kernels from large datasets – particularly with multi-layered structures, e.g., convolutional neural networks – or when applying the learned kernels to high-dimensional signal recovery problems. The so-called convolution approach does not store many overlapping patches, and thus overcomes the memory problems particularly with careful algorithmic designs; it has been studied within the “synthesis” signal model, e.g., convolutional dictionary learning. This paper proposes a new convolutional analysis operator learning (CAOL) framework that learns an analysis sparsifying regularizer with the convolution perspective, and develops a new convergent Block Proximal Extrapolated Gradient method using a Majorizer (BPEG-M) to solve the corresponding block multi-nonconvex problems. To learn diverse filters within the CAOL framework, this paper introduces an orthogonality constraint that enforces a tight-frame filter condition, and a regularizer that promotes diversity between filters. Numerical experiments show that, with sharp majorizers, BPEG-M significantly accelerates the CAOL convergence rate compared to the state-of-the-art block proximal gradient (BPG) method. Numerical experiments for sparse-view computational tomography show that a convolutional sparsifying regularizer learned via CAOL significantly improves reconstruction quality compared to a conventional edge-preserving regularizer. Using more and wider kernels in a learned regularizer better preserves edges in reconstructed images.
Challenges to Coordination: Understanding Intergovernmental Friction During Disasters

While idealized crisis response involves smooth coordination between relevant actors, friction between levels of government and between the state and civil society in responding to catastrophe may be more common. This article builds a theory of cross-level friction during and after crisis by analyzing the conditions when discord is most likely. With a medium-N dataset (N = 18) of disaster responses from, among other countries, Chile, Haiti, Japan, North America, the Philippines, and Somalia, I carry out quantitative and qualitative analysis of cases with a variety of levels of friction to investigate the conditions that lead to misalignment. Tobit regression, qualitative comparative analysis, and case studies that take into account levels of economic development, government structure, nongovernmental organization density, and levels of damage demonstrate that low levels of development, lower levels of economic costs from the crisis, and poor planning and logistical infrastructure correlate with a higher likelihood of friction between disaster response stakeholders. Although not definitive, these findings come with theoretical and practical implications as climate change makes extreme weather events and future disasters more likely and more powerful.
Resilient Structural Stabilizability of Undirected Networks

In this paper, we consider the structural stabilizability problem of undirected networks. More specifically, we are tasked to infer the stabilizability of an undirected network from its underlying topology, where the undirected networks are modeled as continuous-time linear time-invariant (LTI) systems involving symmetric state matrices. Firstly, we derive a graph-theoretic necessary and sufficient condition for structural stabilizability of undirected networks. Then, we propose a method to determine the maximum dimension of the stabilizable subspace solely based on the network structure. Based on these results, on one hand, we study the optimal actuator-disabling attack problem, i.e., removing a limited number of actuators to minimize the maximum dimension of the stabilizable subspace. We show this problem is NP-hard. On the other hand, we study the optimal recovery problem with respect to the same kind of attacks, i.e., adding a limited number of new actuators such that the maximum dimension of the stabilizable subspace is maximized. We prove the optimal recovery problem is also NP-hard, and we develop a $(1-1/e)$ approximation algorithm to this problem.
Fine-grained robust prosody transfer for single-speaker neural text-to-speech

We present a neural text-to-speech system for fine-grained prosody transfer from one speaker to another. Conventional approaches for end-to-end prosody transfer typically use either fixed-dimensional or variable-length prosody embedding via a secondary attention to encode the reference signal. However, when trained on a single-speaker dataset, the conventional prosody transfer systems are not robust enough to speaker variability, especially in the case of a reference signal coming from an unseen speaker. Therefore, we propose decoupling of the reference signal alignment from the overall system. For this purpose, we pre-compute phoneme-level time stamps and use them to aggregate prosodic features per phoneme, injecting them into a sequence-to-sequence text-to-speech system. We incorporate a variational auto-encoder to further enhance the latent representation of prosody embeddings. We show that our proposed approach is significantly more stable and achieves reliable prosody transplantation from an unseen speaker. We also propose a solution to the use case in which the transcription of the reference signal is absent. We evaluate all our proposed methods using both objective and subjective listening tests.
Architected lattices with adaptive energy absorption

Abstract Energy absorbing materials, like foams used in protective equipment, are able to undergo large deformations under low stresses, reducing the incoming stress wave below an injury or damage threshold. They are typically effective in absorbing energy through plastic deformation or fragmentation. However, existing solutions are passive, only effective against specific threats and they are usually damaged after use. Here, we overcome these limitations designing energy absorbing materials that use architected lattices filled with granular particles. We use architected lattices to take advantage of controlled bending and buckling of members to enhance energy absorption. We actively control the negative pressure level within the lattices, to tune the jamming phase transition of the granular particles, inducing controllable energy absorption and recoverable deformations. Our system shows tunable stiffness and yield strength by over an order of magnitude, and reduces the transmitted impact stress at different levels by up to 40% compared to the passive lattice. The demonstrated adaptive energy absorbing system sees wide potential applications from personal protective equipment, vehicle safety systems to aerospace structures.
Time-dependent competition between goal-directed and habitual response preparation.

Habits are commonly conceptualized as learned associations whereby a stimulus triggers an associated response1–3. We propose that habits may be better understood as a process whereby a stimulus triggers only the preparation of a response, without necessarily triggering its initiation. Critically, this would allow a habit to exist without ever being overtly expressed, if the prepared habitual response is replaced by a goal-directed alternative before it can be initiated. Consistent with this hypothesis, we show that limiting the time available for response preparation4,5 can unmask latent habits. Participants practiced a visuomotor association for 4 days, after which the association was remapped. Participants easily learned the new association but habitually expressed the original association when forced to respond rapidly (~300–600 ms). More extensive practice reduced the latency at which habitual responses were prepared, in turn increasing the likelihood of their being expressed. The time-course of habit expression was captured by a computational model in which habitual responses are automatically prepared at short latency but subsequently replaced by goal-directed responses. Our results illustrate robust habit formation in humans and show that practice affects habitual behaviour in two distinct ways: by promoting habit formation and by modulating the likelihood of habit expression. Hardwick et al. show that habits in human behaviour consist of automatic preparation of an action in response to a trigger. Even though we can learn to control habits to perform different action responses, under time pressure, habitual responses resurface.
A Real-Time mmWave Communication Testbed with Phase Noise Cancellation

As the spectrum under 6 GHz is being depleted, pushing wireless communications onto millimeter wave (mmWave) frequencies is a trend that promises multi-Gbps data rate. mmWave is therefore considered as a key technology for 5G wireless systems and has attracted tremendous research efforts. The booming research on mmWave necessitates a reconfigurable mmWave testbed that can be used to prototype and validate new research ideas in real wireless environments. In this paper, we develop an easy-to-use mmWave testbed using commercial off-the-shelf devices (USRP and 60 GHz Tx/Rx RF frontends) and open-source software package (GNU Radio). A key component of our testbed is a phase noise cancellation (PNC) scheme, which can significantly reduce the phase noise at the receiver by leveraging the pilot signal inserted at the transmitter. We have implemented a simplified version of IEEE 802.11 PHY on this mmWave testbed. Experimental results show that, with the PNC scheme, our testbed can achieve −20 dB EVM data transmission for real-time video streaming.
Combinatorial Proofs of Two Euler-Type Identities Due to Andrews

Let a(n) be the number of partitions of n, such that the set of even parts has exactly one element, b(n) be the difference between the number of parts in all odd partitions of n and the number of parts in all distinct partitions of n, and c(n) be the number of partitions of n in which exactly one part is repeated. Beck conjectured that a(n) = b(n) and Andrews, using generating functions, proved that a(n) = b(n) = c(n). We give a combinatorial proof of Andrews’ result. Our proof relies on bijections between a set and a multiset, where the partitions in the multiset are decorated with bit strings. We prove combinatorially Beck’s second conjecture, which was also proved by Andrews using generating functions. Let c1(n) be the number of partitions of n, such that there is exactly one part occurring three times, while all other parts occur only once and let b1(n) be the difference between the total number of parts in the partitions of n into distinct parts and the total number of different parts in the partitions of n into odd parts. Then, c1(n) = b1(n).
MHC-II neoantigens shape tumour immunity and response to immunotherapy

The ability of the immune system to eliminate and shape the immunogenicity of tumours defines the process of cancer immunoediting1. Immunotherapies such as those that target immune checkpoint molecules can be used to augment immune-mediated elimination of tumours and have resulted in durable responses in patients with cancer that did not respond to previous treatments. However, only a subset of patients benefit from immunotherapy and more knowledge about what is required for successful treatment is needed2-4. Although the role of tumour neoantigen-specific CD8+ T cells in tumour rejection is well established5-9, the roles of other subsets of T cells have received less attention. Here we show that spontaneous and immunotherapy-induced anti-tumour responses require the activity of both tumour-antigen-specific CD8+ and CD4+ T cells, even in tumours that do not express major histocompatibility complex (MHC) class II molecules. In addition, the expression of MHC class II-restricted antigens by tumour cells is required at the site of successful rejection, indicating that activation of CD4+ T cells must also occur in the tumour microenvironment. These findings suggest that MHC class II-restricted neoantigens have a key function in the anti-tumour response that is nonoverlapping with that of MHC class I-restricted neoantigens and therefore needs to be considered when identifying patients who will most benefit from immunotherapy.
“The Panhandle is Different than the Peninsula”: How Rural Colleges in Florida Implemented Education Reform

When it comes to the creation of higher education policy, state legislators are challenged with addressing the diverse academic needs of college students enrolled across a spectrum of institutions. In this paper, we explore how 203 individuals at five rural-serving colleges in Florida engaged in state-wide developmental education (DE) reform using the theoretical framework of "situated cognition." Specifically, we ask whether the colleges implemented DE reform in unique ways that may have differed from their non-rural counterparts and what their rationale was for doing so. Our work indicates that institutional culture and capacity generated a combination of strengths and constraints in how reform played out in a rural context. In some ways, a rural-serving identity made colleges more flexible and better able to adjust their advising and curricular structures to comply with the mandates of SB 1720. But in other ways, rural-serving institutions lacked the resources necessary to adequately support students and campus personnel through this dramatic transition toward new ways of providing DE. In looking toward the passage and implementation of future education reform, we call upon policy-makers to consider more deeply the design of policies to ensure they support rural and non-rural colleges alike.
Unsupervised Adversarial Domain Adaptation for Implicit Discourse Relation Classification

Implicit discourse relations are not only more challenging to classify, but also to annotate, than their explicit counterparts. We tackle situations where training data for implicit relations are lacking, and exploit domain adaptation from explicit relations (Ji et al., 2015). We present an unsupervised adversarial domain adaptive network equipped with a reconstruction component. Our system outperforms prior works and other adversarial benchmarks for unsupervised domain adaptation. Additionally, we extend our system to take advantage of labeled data if some are available.
Characterization of the C-RED 2: a high-frame rate near-infrared camera

A wave of precision radial velocity (RV) instruments will open the door to exploring the populations of companions of low-mass stars. The Palomar Radial Velocity Instrument (PARVI) will be optimized to detect RV signals of cool K and M stars with an instrument precision floor of 30 cm / s. PARVI will operate in the λ = 1.2- to 1.8-μm-wavelength range with a spectral resolution of λ / Δλ ∼ 100,000. It will operate on the Palomar 5.1-m Hale telescope and use Palomar’s PALM-3000 adaptive optics system, single-mode fibers, and an H-band laser frequency comb to probe and characterize the population of planets around cool, red stars. We describe the performance of the PARVI guide camera: a C-RED 2 from First Light Advanced Imagery. The C-RED 2 will be used in a tip-tilt loop, which requires fast readout at low noise levels to eliminate any residual guide errors and ensure the target starlight stays centered on the fiber. At −40 ° C and a frame rate of 400 frames per second in nondestructive read mode, the C-RED 2 has a combined dark and background current of 493 e − / s. Using up-the-ramp sampling, we are able to reduce the read noise to 21.2 e − . With the C-RED 2, PARVI will be able to guide using targets as faint as 14.6 H magnitude.
Discrete Modulation for Interference Mitigation

This paper analyzes the performance of discrete input distributions (coded modulation) in interference channels. This approach is motivated in part by the necessity of using coded modulation in practical systems, and in part by the potential of discrete distributions for interference alignment as well as the importance demonstrated by Dytso et al. of discrete input distributions for transmission over the $2\times 2$ interference channel when treating interference as noise. The contribution of this work includes the establishment of achievable rates subject to discrete modulations. In the process, new bounds involving the minimum distance of the sum of discrete modulations have been developed that are useful for facilitating further work in this area. These bounds are then used for finding achievable rates for $3\times 3$ interference channels. It is also shown that interference alignment can be efficiently employed with discrete modulations at finite SNR even with imperfect channel state information at the transmitter.
Actions of cocommutative Hopf algebras

Abstract Let H be a cocommutative Hopf algebra acting on an algebra A. Assuming the base field to be algebraically closed and the H-action on A to be integral, that is, it is given by a coaction of some Hopf subalgebra of the finite dual H ∘ that is an integral domain, we stratify the prime spectrum Spec A in terms of the prime spectra of certain commutative algebras. For arbitrary H-actions in characteristic 0, we show that the largest H-stable ideal of A that is contained in a given semiprime ideal of A is semiprime as well.
Deep segmentation networks predict survival of non-small cell lung cancer.

Non-small-cell lung cancer (NSCLC) represents approximately 80–85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/computed tomography (PET/CT) images have predictive power for NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new method for cancer image analysis, with significantly enhanced predictive power compared to hand-crafted radiomics features. Here we show that CNNs trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on pre-treatment PET-CT images of 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET and CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-Net algorithm has not seen any other clinical information (e.g. survival, age, smoking history, etc.) than the images and the corresponding tumor contours provided by physicians. In addition, we observed the same trend by validating the U-Net features against an extramural data set provided by Stanford Cancer Institute. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of metastasis and recurrence appear to match with the regions where the U-Net features identified patterns that predicted higher likelihoods of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination. For example, the deep learned PET/CT features can not only predict survival but also visualize high-risk regions within or adjacent to the primary tumor and hence potentially impact therapeutic outcomes by optimal selection of therapeutic strategy or first-line therapy adjustment.
Integrated optimization of Power Split, Engine Thermal Management, and Cabin Heating for Hybrid Electric Vehicles

Cabin heating demand and engine efficiency degradation in cold weather lead to considerable increase in fuel consumption of hybrid electric vehicles (HEVs), especially in congested traffic conditions. This paper presents an integrated power and thermal management (i-PTM) scheme for the optimization of power split, engine thermal management, and cabin heating of HEVs. A control-oriented model of a power split HEV, including power and thermal loops, is developed and experimentally validated against data collected from a 2017 Toyota Prius HEV. Based on this model, the dynamic programming (DP) technique is adopted to derive a bench-mark for minimal fuel consumption, using 2-dimensional (power split and engine thermal management) and 3-dimensional (power split, engine thermal management, and cabin heating) formulations. Simulation results for a real-world congested driving cycle show that the engine thermal effect and the cabin heating requirement can significantly influence the optimal behavior for the power management, and substantial potential on fuel saving can be achieved by the i-PTM optimization as compared to conventional power and thermal management strategies.
Governance mechanisms enabling inter-organizational adaptation: Lessons from grand challenge R&D programs

From climate change to terrorism, the world is confronting complex, trans-national problems. As a contemporary response, governments and non-profit organizations have established grand challenge programs, consisting of multi-sector research and development partnerships, to access innovative new ideas and rapidly scale solutions. Following recent scholarly contributions, this article investigates how problems motivating program establishment were identified, how these problems and related contextual factors evolve over time, and how grand challenge programs evolve in response. It does so through a multi-year study of ten grand challenge programs that differ substantially in purpose and organization. This article finds that adaptive capabilities—inter-organizational governance mechanisms—and operational aspects such as purpose, scope, temporal factors, and partner capabilities are critical to program evolution and impact.