cssi/SciSciGPT-SciSciNet huggingface dataset:
The categoricity spectrum of large abstract elementary classes

The categoricity spectrum of a class of structures is the collection of cardinals in which the class has a single model up to isomorphism. Assuming that cardinal exponentiation is injective (a weakening of the generalized continuum hypothesis, GCH), we give a complete list of the possible categoricity spectrums of an abstract elementary class with amalgamation and arbitrarily large models. Specifically, the categoricity spectrum is either empty, an end segment starting below the Hanf number, or a closed interval consisting of finite successors of the Lowenheim–Skolem–Tarski number (there are examples of each type). We also prove (assuming a strengthening of the GCH) that the categoricity spectrum of an abstract elementary class with no maximal models is either bounded or contains an end segment. This answers several longstanding questions around Shelah’s categoricity conjecture.
Sketchforme: Composing Sketched Scenes from Text Descriptions for Interactive Applications

Sketching is an effective communication medium that augments and enhances what can be communicated in text. We introduce Sketchforme, the first neural-network-based system that can generate complex sketches based on text descriptions specified by users. Sketchforme's key contribution is to factor complex sketch rendering into layout and rendering subtasks using neural networks. The sketches composed by Sketchforme are expressive and realistic: we show in our user study that these sketches convey descriptions better than human-generated sketches in several cases, and 36.5% of those sketches were identified as human-generated. We develop some interactive applications using these generated sketches, and show that Sketchforme can significantly improve language learning applications and support intelligent language-based sketching assistants.
Conversational AI : Open Domain Question Answering and Commonsense Reasoning.

Our research is focused on making a human-like question answering system which can answer rationally. The distinguishing characteristic of our approach is that it will use automated common sense reasoning to truly "understand" dialogues, allowing it to converse like a human. Humans often make many assumptions during conversations. We infer facts not told explicitly by using our common sense. Incorporating commonsense knowledge in a question answering system will simply make it more robust.
SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks

Anomaly detection in attributed networks (instance-to-instance dependencies and interactions are available) has various applications such as monitoring suspicious accounts in social media and financial fraud in transaction networks. However, it remains a challenging task since the definition of anomaly becomes more complicated and topological structures are heterogeneous with nodal attributes. In this paper, we propose a spectral convolution and deconvolution based framework - SpecAE, to project the attributed network into a tailored space to detect global and community anomalies. SpecAE leverages Laplacian sharpening to amplify the distances between representations of anomalies and the ones of the majority. The learned representations along with reconstruction errors are combined with a density estimation model to perform the detection. Experiments on real-world datasets demonstrate the effectiveness of the proposed SpecAE.
On the Singular Set of Free Interface in an Optimal Partition Problem

We study the singular set of free interface in an optimal partition problem for the Dirichlet eigenvalues. We prove that its upper $(n-2)$-dimensional Minkowski content, and consequently, its $(n-2)$-dimensional Hausdorff measure are locally finite. We also show that the singular set is countably $(n-2)$-rectifiable, namely it can be covered by countably many $C^1$-manifolds of dimension $(n-2)$, up to a set of $(n-2)$-dimensional Hausdorff measure zero. Our results hold for optimal partitions on Riemannian manifolds and harmonic maps into homogeneous trees as well.
On critical points of Gaussian random fields under diffeomorphic transformations

Abstract Let { X ( t ) , t ∈ M } and { Z ( t ′ ) , t ′ ∈ M ′ } be smooth Gaussian random fields parameterized on Riemannian manifolds M and M ′ , respectively, such that X ( t ) = Z ( f ( t ) ) , where f : M → M ′ is a diffeomorphic transformation. We study the expected number and height distribution of the critical points of X in connection with those of Z . As an important case, when X is an anisotropic Gaussian random field, then we show that its expected number of critical points becomes proportional to that of an isotropic field Z , while the height distribution remains the same as that of Z .
Transcriptional Profiling of Non-injured Nociceptors After Spinal Cord Injury Reveals Diverse Molecular Changes

Traumatic spinal cord injury (SCI) has devastating implications for patients, including a high predisposition for developing chronic pain distal to the site of injury. Chronic pain develops weeks to months after injury, consequently, patients are treated after irreparable changes have occurred. Nociceptors are central to chronic pain; however, the diversity of this cellular population presents challenges to understanding mechanisms and attributing pain modalities to specific cell types. To begin to address how peripheral sensory neurons below the injury level may contribute to the below-level pain reported by SCI patients, we examined SCI-induced changes in gene expression in lumbar dorsal root ganglia (DRG) below the site of injury. SCI was performed at the T10 vertebral level, with injury produced by a vessel clip with a closing pressure of 15 g for 1 min. Alterations in gene expression produce long-term sensory changes, therefore, we were interested in studying SCI-induced transcripts before the onset of chronic pain, which may trigger changes in downstream signaling pathways and ultimately facilitate the transmission of pain. To examine changes in the nociceptor subpopulation in DRG distal to the site of injury, we retrograde labeled sensory neurons projecting to the hairy hindpaw skin with fluorescent dye and collected the corresponding lumbar (L2-L6) DRG 4 days post-injury. Following dissociation, labeled neurons were purified by fluorescence-activated cell sorting (FACS). RNA was extracted from sorted sensory neurons of naive, sham, or SCI mice and sequenced. Transcript abundances validated that the desired population of nociceptors were isolated. Cross-comparisons to data sets from similar studies confirmed, we were able to isolate our cells of interest and identify a unique pattern of gene expression within a subpopulation of neurons projecting to the hairy hindpaw skin. Differential gene expression analysis showed high expression levels and significant transcript changes 4 days post-injury in SCI cell populations relevant to the onset of chronic pain. Regulatory interrelationships predicted by pathway analysis implicated changes within the synaptogenesis signaling pathway as well as networks related to inflammatory signaling mechanisms, suggesting a role for synaptic plasticity and a correlation with pro-inflammatory signaling in the transition from acute to chronic pain.
High-Level Cryptographic Abstractions

The interfaces exposed by commonly used cryptographic libraries are clumsy, complicated, and assume an understanding of cryptographic algorithms. The challenge is to design high-level abstractions that require minimum knowledge and effort to use while also allowing maximum control when needed.This paper proposes such high-level abstractions consisting of simple cryptographic primitives and full declarative configuration. These abstractions can be implemented on top of any cryptographic library in any language. We have implemented these abstractions in Python, and used them to write a wide variety of well-known security protocols, including Signal, Kerberos, and TLS.We show that programs using our abstractions are much smaller and easier to write than using low-level libraries, where size of security protocols implemented is reduced by about a third on average. We show our implementation incurs a small overhead, less than 5 microseconds for shared key operations and less than 341 microseconds (
Explosives detection with hard-wired moths

Insects, such as moths, can be trained to respond to explosives odors. A prototype system that can use trained insects such as moths to detect explosives was designed, assembled, and tested. It compares the electromyographic signals of insects trained to respond or not respond to a target explosive vapor in order to determine whether or not explosive devices, such as bombs or landmines, are present. The device was designed to be portable by making it lightweight, battery-powered, and energy efficient. The prototype performed successfully during testing. This device is a novel integration of electronics and biology to create a bioinstrument that has not been previously developed.
Families of nested graphs with compatible symmetric-group actions

For fixed positive integers n and k, the Kneser graph $$KG_{n,k}$$ has vertices labeled by k-element subsets of $$\{1,2,\dots ,n\}$$ and edges between disjoint sets. Keeping k fixed and allowing n to grow, one obtains a family of nested graphs, each of which is acted on by a symmetric group in a way which is compatible with these inclusions and the inclusions of each symmetric group into the next. In this paper, we provide a framework for studying families of this kind using the $${{\,\mathrm{FI}\,}}$$-module theory of Church et al. (Duke Math J 164(9):1833–1910, 2015), and show that this theory has a variety of asymptotic consequences for such families of graphs. These consequences span a range of topics including enumeration, concerning counting occurrences of subgraphs, topology, concerning Hom-complexes and configuration spaces of the graphs, and algebra, concerning the changing behaviors in the graph spectra.
Approximating the Noise Sensitivity of a Monotone Boolean Function.

The noise sensitivity of a Boolean function $f: \{0,1\}^n \rightarrow \{0,1\}$ is one of its fundamental properties. A function of a positive noise parameter $\delta$, it is denoted as $NS_{\delta}[f]$. Here we study the algorithmic problem of approximating it for monotone $f$, such that $NS_{\delta}[f] \geq 1/n^{C}$ for constant $C$, and where $\delta$ satisfies $1/n \leq \delta \leq 1/2$. For such $f$ and $\delta$, we give a randomized algorithm performing $O\left(\frac{\min(1,\sqrt{n} \delta \log^{1.5} n) }{NS_{\delta}[f]} \text{poly}\left(\frac{1}{\epsilon}\right)\right)$ queries and approximating $NS_{\delta}[f]$ to within a multiplicative factor of $(1\pm \epsilon)$. Given the same constraints on $f$ and $\delta$, we also prove a lower bound of $\Omega\left(\frac{\min(1,\sqrt{n} \delta)}{NS_{\delta}[f] \cdot n^{\xi}}\right)$ on the query complexity of any algorithm that approximates $NS_{\delta}[f]$ to within any constant factor, where $\xi$ can be any positive constant. Thus, our algorithm's query complexity is close to optimal in terms of its dependence on $n$. We introduce a novel descending-ascending view of noise sensitivity, and use it as a central tool for the analysis of our algorithm. To prove lower bounds on query complexity, we develop a technique that reduces computational questions about query complexity to combinatorial questions about the existence of "thin" functions with certain properties. The existence of such "thin" functions is proved using the probabilistic method. These techniques also yield previously unknown lower bounds on the query complexity of approximating other fundamental properties of Boolean functions: the total influence and the bias.
Accretion Kinematics in the T Tauri Binary TWA 3A: Evidence for Preferential Accretion onto the TWA 3A Primary

We present time-series, high-resolution optical spectroscopy of the eccentric T Tauri binary TWA 3A. Our analysis focuses on variability in the strength and structure of the accretion tracing emission lines H alpha and He I 5876A. We find emission line strengths to display the same orbital-phase dependent behavior found with time-series photometry, namely, bursts of accretion near periastron passages. Such bursts are in good agreement with numerical simulations of young eccentric binaries. During accretion bursts, the emission of He I 5876A consistently traces the velocity of the primary star. After removing a model for the system's chromospheric emission, we find the primary star typically emits ~70% of the He I accretion flux. We interpret this result as evidence for circumbinary accretion streams that preferentially feed the TWA 3A primary. This finding is in contrast to most numerical simulations, which predict the secondary should be the dominant accretor in a binary system. Our results may be consistent with a model in which the precession of an eccentric circumbinary disk gap alternates between preferentially supplying mass to the primary and secondary.
In vitro implementation of robust gene regulation in a synthetic biomolecular integral controller.

Feedback mechanisms play a critical role in the maintenance of cell homeostasis in the presence of disturbances and uncertainties. Motivated by the need to tune the dynamics and improve the robustness of gene circuits, biological engineers have proposed various designs that mimic natural molecular feedback control mechanisms. However, practical and predictable implementations have proved challenging because of the complexity of synthesis and analysis of complex biomolecular networks. Here, we analyze and experimentally validate a synthetic biomolecular controller executed in vitro. The controller ensures that gene expression rate tracks an externally imposed reference level, and achieves this goal even in the presence of certain kinds of disturbances. Our design relies upon an analog of the well-known principle of integral feedback in control theory. We implement the controller in an Escherichia coli cell-free transcription-translation system, which allows rapid prototyping and implementation. Modeling and theory guide experimental implementation with well-defined operational predictability. Feedback mechanisms for synthetic gene circuits are necessary to provide robustness to external perturbations. Here the authors validate a biomolecular controller based on a sigma and anti-sigma factor to achieve stable gene expression in the face of external disturbances in an in vitro synthetic gene circuit.
Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators

Encoder-decoder based neural architectures serve as the basis of state-of-the-art approaches in end-to-end open domain dialog systems. Since most of such systems are trained with a maximum likelihood (MLE) objective they suffer from issues such as lack of generalizability and the generic response problem, i.e., a system response that can be an answer to a large number of user utterances, e.g., “Maybe, I don’t know.” Having explicit feedback on the relevance and interestingness of a system response at each turn can be a useful signal for mitigating such issues and improving system quality by selecting responses from different approaches. Towards this goal, we present a system that evaluates chatbot responses at each dialog turn for coherence and engagement. Our system provides explicit turn-level dialog quality feedback, which we show to be highly correlated with human evaluation. To show that incorporating this feedback in the neural response generation models improves dialog quality, we present two different and complementary mechanisms to incorporate explicit feedback into a neural response generation model: reranking and direct modification of the loss function during training. Our studies show that a response generation model that incorporates these combined feedback mechanisms produce more engaging and coherent responses in an open-domain spoken dialog setting, significantly improving the response quality using both automatic and human evaluation.
Relating Visual Production and Recognition of Objects in Human Visual Cortex

Drawing is a powerful tool that can be used to convey rich perceptual information about objects in the world. What are the neural mechanisms that enable us to produce a recognizable drawing of an object, and how does this visual production experience influence how this object is represented in the brain? Here we evaluate the hypothesis that producing and recognizing an object recruit a shared neural representation, such that repeatedly drawing the object can enhance its perceptual discriminability in the brain. We scanned human participants (N = 31; 11 male) using fMRI across three phases of a training study: during training, participants repeatedly drew two objects in an alternating sequence on an MR-compatible tablet; before and after training, they viewed these and two other control objects, allowing us to measure the neural representation of each object in visual cortex. We found that: (1) stimulus-evoked representations of objects in visual cortex are recruited during visually cued production of drawings of these objects, even throughout the period when the object cue is no longer present; (2) the object currently being drawn is prioritized in visual cortex during drawing production, while other repeatedly drawn objects are suppressed; and (3) patterns of connectivity between regions in occipital and parietal cortex supported enhanced decoding of the currently drawn object across the training phase, suggesting a potential neural substrate for learning how to transform perceptual representations into representational actions. Together, our study provides novel insight into the functional relationship between visual production and recognition in the brain.SIGNIFICANCE STATEMENT Humans can produce simple line drawings that capture rich information about their perceptual experiences. However, the mechanisms that support this behavior are not well understood. Here we investigate how regions in visual cortex participate in the recognition of an object and the production of a drawing of it. We find that these regions carry diagnostic information about an object in a similar format both during recognition and production, and that practice drawing an object enhances transmission of information about it to downstream regions. Together, our study provides novel insight into the functional relationship between visual production and recognition in the brain.
Luminous Galaxies near Gamma-Ray Burst Positions

The isotropic, inhomogeneous distribution of gamma-ray bursts (GRBs) suggests a cosmological origin. We are performing a deep J (1.25 μm), H (1.65 μm), and K' (2.11 μm) imaging survey of the smallest GRB error boxes from the third Interplanetary Network to examine the extragalactic content of these regions. Relatively bright galaxies are seen in each of six GRB error boxes imaged to date. It is shown that the "no-host" problem does not exist in our sample. In addition, there is an apparent overabundance of brighter galaxies in these fields, which suggests a connection between galaxies and gamma-ray bursts.
A Smart Sensor Integrated Circuit for NASA's New Millennium Spacecraft

The modern space era can greatly benefit from the rapidly growing microelectronics technologies in order to enable the ambitious exploratory and commercial space endeavors of the new millennium. A smart sensor, analog/digital, integrated circuit, suitable for spacecraft avionics data acquisition and control is presented. The Remote Input/Output (RIO) device is developed by The Johns Hopkins University/Applied Physics Laboratory, for NASA's Jet Propulsion Laboratory, to support many oncoming new millennium space missions. TRIO—the first version of RIO—is designed to interface to temperature, pressure, total radiation dose sensors, and generally to voltage/current transducers. The microchip includes front-end conditioning circuitry, an ADC, memory, serial and CPU interface, and a digital port. Furthermore the device is developed to meet extreme space qualification specifications such as radiation effects. This versatile system-on-a-chip device is becoming a key enabling technology for new-generation NASA and Commercial spacecraft systems. Missions that are intended to use the TRIO device are the: Europa Orbiter, Deep Space 4, Solar Probe, Pluto Express, Mars Sample Return, Stereo, Contour, etc.
Real monodromy action

Abstract The monodromy group is an invariant for parameterized systems of polynomial equations that encodes structure of the solutions over the parameter space. Since the structure of real solutions over real parameter spaces are of interest in many applications, real monodromy action is investigated here. A naive extension of monodromy action from the complex numbers to the real numbers is shown to be very restrictive. Therefore, we introduce a real monodromy structure which need not be a group but contains tiered characteristics about the real solutions over the parameter space. An algorithm is provided to compute the real monodromy structure. In addition, this real monodromy structure is applied to an example in kinematics which summarizes all the ways performing loops parameterized by leg lengths can cause a mechanism to change poses.
Expanded Dimensionality for Image Spectroscopy via Machine Learning

The generalized solution for the radiance equation is expanded by exploiting multiple hyperspectral image scans acquired by aerial platforms at different viewing angles. A machine learning solution...
Toward Prediction of Nonradiative Decay Pathways in Organic Compounds II: Two Internal Conversion Channels in BODIPYs

Boron-dipyrromethene (BODIPY) molecules are widely used as laser dyes and have therefore become a popular research topic within recent years. Numerous studies have been reported for the rational de...
Coordinated Use of Smart Inverters with Legacy Voltage Regulating Devices in Distribution Systems with High Distributed PV Penetration—Increase CVR Energy Savings

This paper studies the coordinated use of smart inverters with legacy voltage regulating devices to help increase the energy savings from conservation voltage reduction (CVR) in distribution systems with high photovoltaic penetration. Two voltage optimization algorithms are developed to evaluate the CVR benefit by considering two types of smart inverter functions: aggregated reactive power control and autonomous volt/VAR control. In each algorithm, smart inverters are coordinately controlled with the operation of legacy voltage regulating devices including load tap changers and capacitor banks. Both algorithms are tested on representative utility distribution system models. Simulation results demonstrate that the proposed algorithms can achieve around 1.8-3.6% energy savings when only using legacy voltage regulating devices and an additional 0.3-0.9% when adding smart inverters.
Cell-Derived Plasma Membrane Vesicles Are Permeable to Hydrophilic Macromolecules

Abstract Giant plasma membrane vesicles (GPMVs) are a widely used experimental platform for biochemical and biophysical analysis of isolated mammalian plasma membranes (PMs). A core advantage of these vesicles is that they maintain the native lipid and protein diversity of the PM while affording the experimental flexibility of synthetic giant vesicles. In addition to fundamental investigations of PM structure and composition, GPMVs have been used to evaluate the binding of proteins and small molecules to cell-derived membranes and the permeation of drug-like molecules through them. An important assumption of such experiments is that GPMVs are sealed, i.e., that permeation occurs by diffusion through the hydrophobic core rather than through hydrophilic pores. Here, we demonstrate that this assumption is often incorrect. We find that most GPMVs isolated using standard preparations are passively permeable to various hydrophilic solutes as large as 40 kDa, in contrast to synthetic giant unilamellar vesicles. We attribute this leakiness to stable, relatively large, and heterogeneous pores formed by rupture of vesicles from cells. Finally, we identify preparation conditions that minimize poration and allow evaluation of sealed GPMVs. These unexpected observations of GPMV poration are important for interpreting experiments utilizing GPMVs as PM models, particularly for drug permeation and membrane asymmetry.
Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors

Quadrotor stabilizing controllers often require careful, model-specific tuning for safe operation. We use reinforcement learning to train policies in simulation that transfer remarkably well to multiple different physical quadrotors. Our policies are low-level, i.e., we map the rotorcrafts’ state directly to the motor outputs. The trained control policies are very robust to external disturbances and can withstand harsh initial conditions such as throws. We show how different training methodologies (change of the cost function, modeling of noise, use of domain randomization) might affect flight performance. To the best of our knowledge, this is the first work that demonstrates that a simple neural network can learn a robust stabilizing low-level quadrotor controller (without the use of a stabilizing PD controller) that is shown to generalize to multiple quadrotors. The video of our experiments can be found at https://sites.google.com/view/sim-to-multi-quad.
Food supplementation affects gut microbiota and immunological resistance to parasites in a wild bird species

Supplemental feeding can increase the overall health of animals but also can have varying consequences for animals dealing with parasites. Furthermore, the mechanism mediating the effect of food supplementation on host-parasite interactions remains poorly understood. The goal of the study was to determine the effect of food supplementation on host defenses against parasitic nest flies and whether host gut microbiota, which can affect immunity, potentially mediates these relationships. In a fully crossed design, I experimentally manipulated the abundance of parasitic nest flies (Protocalliphora sialia) and food availability then characterized the gut microbiota, immune responses, and nest parasite abundance of nestling eastern bluebirds (Sialia sialis). Food supplemented birds had 75% fewer parasites than unsupplemented birds. Parasite abundance decreased throughout the breeding season for unsupplemented birds, but abundance did not change throughout the season for supplemented birds. Food supplementation increased overall fledging success. Parasitism had a sublethal effects on blood loss, but food supplementation mitigated these effects by increasing parasite resistance via the nestling IgY antibody response. Food supplementation increased the gut bacterial diversity in nestlings, which was negatively related to parasite abundance. Food supplementation also increased the relative abundance of Clostridium spp. in nestlings, which was positively related to their antibody response and negatively related to parasite abundance. Synthesis and applications. Overall, these results suggest that food supplementation, especially early in the breeding season, increases resistance to parasitism during the early life stage of the host, which could be mediated by the effect of supplementation on the gut microbiota. Wildlife food supplementation is a common pastime for humans worldwide and therefore it is important to determine the consequences of this activity on animal health. Furthermore, supplemental feeding could induce resistance to detrimental parasites (e.g. invasive parasites) in hosts when management of the parasite is not immediately possible.
WHAT'S IN A NAME? A CAUTIONARY TALE OF PROFITABILITY ANOMALIES AND LIMITS TO ARBITRAGE

The recent literature investigating profitability anomalies defines profitability in various ways (i.e., gross, operating, and cash based). We show that limits to arbitrage are associated with returns of gross and cash‐based operating profitability anomalies, suggesting mispricing. In contrast, returns from the operating profitability strategy have no relation with barriers to arbitrage and exhibit no evidence of mispricing. Additionally, we show that the differential effects of limited arbitrage‐related mispricing of gross and cash‐based operating profitability anomalies are attributable to their respective correlations with selling, general, and administrative (SG&A) expense and accruals anomalies. We find that SG&A return predictability, like that of accruals, is related to limits to arbitrage. These findings suggest that investors and researchers should proceed with caution when searching for return predictability by redefining profitability measures.
Turbulence Adjustment and Scaling in an Offshore Convective Internal Boundary Layer: A CASPER Case Study

AbstractThe characteristics of a convective internal boundary layer (CIBL) documented offshore during the East Coast phase of the Coupled Air–Sea Processes and Electromagnetic Ducting Research (CAS...
Germline Features Associated with Immune Infiltration in Solid Tumors

Summary The immune composition of the tumor microenvironment influences response and resistance to immunotherapies. While numerous studies have identified somatic correlates of immune infiltration, germline features that associate with immune infiltrates in cancers remain incompletely characterized. We analyze seven million autosomal germline variants in the TCGA cohort and test for association with established immune-related phenotypes that describe the tumor immune microenvironment. We identify one SNP associated with the amount of infiltrating follicular helper T cells; 23 candidate genes, some of which are involved in cytokine-mediated signaling and others containing cancer-risk SNPs; and networks with genes that are part of the DNA repair and transcription elongation pathways. In addition, we find a positive association between polygenic risk for rheumatoid arthritis and amount of infiltrating CD8+ T cells. Overall, we identify multiple germline genetic features associated with tumor-immune phenotypes and develop a framework for probing inherited features that contribute to differences in immune infiltration.
Applicability of the Calcaneal Apophysis Ossification Staging System to the Modern Pediatric Population

Purpose: Determining skeletal age is important for evaluating and managing growth disorders such as limb length discrepancy and scoliosis. The most common method to assess bone age is with the Greulich and Pyle atlas. However, studies have shown that the original atlas needs modification to apply to the modern population. A recent bone age system has been developed for the calcaneus using the same historic collection used …
The Effect of Associative Racial Cues in Elections

How do racial signals associating a candidate with minority supporters change voters’ perceptions about a candidate and their support for a candidate? Given the presence of competing information in...
Mitigation of Coincident Peak Charges via Approximate Dynamic Programming

A significant portion of a consumer’s annual electrical costs can be made up of coincident peak charges: a transmission surcharge for power consumed when the entire system is at peak demand. This charge occurs only a few times annually, but with per-MW prices orders of magnitudes higher than non-peak times. While predicting the moment of peak demand charges over the course of the entire billing period is possible, optimal cost mitigation strategies based on these predictions have not been explored. In this paper we cast coincident peak cost mitigation as an optimization problem and analyze conditions for optimal and near-optimal policies for mitigation. For small consumers we use approximate dynamic programming to first show the existence of a near-optimal policy and second train a neural policy for curtailing coincident peak charges when subject to ramping constraints.
Zero-sum Stochastic Games with Asymmetric Information

A general model for zero-sum stochastic games with asymmetric information is considered. For this model, a dynamic programming characterization of the value is presented under some assumptions on its existence. This dynamic program is then used for a class of zero-sum stochastic games with complete information on one side and partial information on the other, that is, games where one player has complete information about state, actions and observation history while the other player may only have partial information about the state and action history. For such games, the value is characterized using dynamic programming without making any existence assumptions. It is further shown that for this class of games, there exists a Nash equilibrium where the more informed player plays a common information belief based strategy. A dynamic programming approach is presented for computing this strategy.
Detecting and Simulating Artifacts in GAN Fake Images

To detect GAN generated images, conventional supervised machine learning algorithms require collecting a large number of real images as well as fake images generated by the targeted GAN model. However, the specific model used by the attacker is often unavailable. To address this, we propose a GAN simulator, AutoGAN, which can simulate the artifacts produced by the common pipeline shared by several popular GAN models. Additionally, we identify a unique artifact caused by the up-sampling component included in the common GAN pipelines. We show theoretically such artifacts are manifested as replications of spectra in the frequency domain and thus propose a classifier model based on the spectrum input, rather than the pixel input. By using the simulated images to train a spectrum based classifier, even without seeing the fake images produced by the targeted GAN model during training, our approach achieves state-of-the-art performances on detecting fake images generated by popular GAN models such as CycleGAN.
TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network

Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications. For example, online retailers (e.g., Amazon and eBay) use taxonomies for product recommendation, and web search engines (e.g., Google and Bing) leverage taxonomies to enhance query understanding. Enormous efforts have been made on constructing taxonomies either manually or semi-automatically. However, with the fast-growing volume of web content, existing taxonomies will become outdated and fail to capture emerging knowledge. Therefore, in many applications, dynamic expansions of an existing taxonomy are in great demand. In this paper, we study how to expand an existing taxonomy by adding a set of new concepts. We propose a novel self-supervised framework, named TaxoExpan, which automatically generates a set of ⟨query concept, anchor concept⟩ pairs from the existing taxonomy as training data. Using such self-supervision data, TaxoExpan learns a model to predict whether a query concept is the direct hyponym of an anchor concept. We develop two innovative techniques in TaxoExpan: (1) a position-enhanced graph neural network that encodes the local structure of an anchor concept in the existing taxonomy, and (2) a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self-supervision data. Extensive experiments on three large-scale datasets from different domains demonstrate both the effectiveness and the efficiency of TaxoExpan for taxonomy expansion.
MD&A Textual Similarity and Auditors

This study examines the relation between auditors and the unaudited Management Discussion and Analysis (MD&A). We show that MD&As are more textually similar when firms share the same auditor. This ...
Deep Learning of Activation Energies

Quantitative predictions of reaction properties, such as activation energy, have been limited due to a lack of available training data. Such predictions would be useful for computer-assisted reacti...
High Accuracy and High Fidelity Extraction of Neural Networks

In a model extraction attack, an adversary steals a copy of a remotely deployed machine learning model, given oracle prediction access. We taxonomize model extraction attacks around two objectives: *accuracy*, i.e., performing well on the underlying learning task, and *fidelity*, i.e., matching the predictions of the remote victim classifier on any input. To extract a high-accuracy model, we develop a learning-based attack exploiting the victim to supervise the training of an extracted model. Through analytical and empirical arguments, we then explain the inherent limitations that prevent any learning-based strategy from extracting a truly high-fidelity model---i.e., extracting a functionally-equivalent model whose predictions are identical to those of the victim model on all possible inputs. Addressing these limitations, we expand on prior work to develop the first practical functionally-equivalent extraction attack for direct extraction (i.e., without training) of a model's weights. We perform experiments both on academic datasets and a state-of-the-art image classifier trained with 1 billion proprietary images. In addition to broadening the scope of model extraction research, our work demonstrates the practicality of model extraction attacks against production-grade systems.
Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics

Mass spectrometry coupled with chromatography separation techniques provides a powerful platform for untargeted metabolomics. Determining the chemical identities of detected compounds however remains a major challenge. Here, we present a novel computational workflow, termed extended metabolic model filtering (EMMF), that aims to engineer a candidate set, a listing of putative chemical identities to be used during annotation, through an extended metabolic model (EMM). An EMM includes not only canonical substrates and products of enzymes already cataloged in a database through a reference metabolic model, but also metabolites that can form due to substrate promiscuity. EMMF aims to strike a balance between discovering previously uncharacterized metabolites and the computational burden of annotation. EMMF was applied to untargeted LC-MS data collected from cultures of Chinese hamster ovary (CHO) cells and murine cecal microbiota. EMM metabolites matched, on average, to 23.92% of measured masses, providing a > 7-fold increase in the candidate set size when compared to a reference metabolic model. Many metabolites suggested by EMMF are not catalogued in PubChem. For the CHO cell, we experimentally confirmed the presence of 4-hydroxyphenyllactate, a metabolite predicted by EMMF that has not been previously documented as part of the CHO cell metabolic model.
Inequalities that sharpen the triangle inequality for sums of $N$ functions in $L^p$

We study $L^p$ inequalities that sharpen the triangle inequality for sums of $N$ functions in $L^p$.
Presentations for the Euclidean Picard modular groups

Mark and Paupert devised a general method for obtaining presentations for arithmetic non-cocompact lattices, $$\Gamma $$ , in isometry groups of negatively curved symmetric spaces. The method involves a classical theorem of Macbeath applied to a $$\Gamma $$ -invariant covering by horoballs of the negatively curved symmetric space upon which $$\Gamma $$ acts. In this paper, we will discuss the application of their method to the Picard modular groups, PU $$(2,1;{\mathcal {O}}_{d})$$ , when $$d=2,11$$ , and obtain presentations for these groups, which completes the list of presentations for Picard modular groups whose entries lie in Euclidean domains, namely those with $$d=1,2,3,7,11$$ .
Secondary Attack Rate

The secondary attack rate is defined as the probability that infection occurs among susceptible persons within a reasonable incubation period following known contact with an infectious person or an infectious source. It is a key epidemiologic parameter in infectious diseases that are transmitted by contact. It can be estimated using a variety of epidemiologic study designs, models, and methods of estimation. Inference needs to take into account the correlation of susceptibles exposed to the same infectious source. Keywords: incubation period; study design; vaccine efficacy; household secondary attack rate
Nonergodic economic growth

This paper explores the role of complementarities and incomplete markets in economic growth. We analyze the evolution of an economy composed of a countable set of industries. Individual industries exhibit non-convexities in production and are linked by localized technological complementarities. These complementarities, when strong enough, produce multiple equilibria in long-run economic activity. The equilibria have a simple probabilistic structure that demonstrates how local interactions can affect the aggregate equilibrium. The model generates interesting cross-sectional and intertemporal dynamics as coordination problems become the source of aggregate and individual industry volatility. The model also illustrates how the growth of leading sectors can cause a takeoff to a high aggregate production equilibrium.
Electromagnetic Ferrite Tile Absorber

In this chapter, we discuss the ferrite tile, ferrite grids, and hybrid combinations of urethane and ferrite absorbers typically used in electromagnetic test chambers for the 30MHz to 1000MHz frequency range. We present formulas and calculated reflectivities (or reflection coefficients). We also discuss how material properties and reflectivities are measured for the ferrite-tile absorbers. Keywords: electromagnetic; ferrite tile; anechoic chambers; ferrite grid; absorber
A 4.0 GHz 291 Mb Voltage-Scalable SRAM Design in a 32 nm High-k + Metal-Gate CMOS Technology With Integrated Power Management

This paper introduces a high-performance voltage-scalable SRAM design in a 32 nm strain-enhanced high-k + metal-gate logic CMOS technology. The 291 Mb SRAM design features a 0.171 μm 2 six-transistor bitcell that supports a broad range of operating voltages for low-power and high-frequency embedded applications. The tileable 128 kb SRAM subarray achieves 72 % array efficiency with 4.2 Mb/mm 2 bit density, and consumes 5 mW of leakage power at the supply voltage of 1 V. The design provides 4 GHz and 2 GHz of operating frequencies at the supply voltages of 1.0 V and 0.8 V, respectively. The integrated power management scheme features close-loop memory array leakage control, floating bitline, and wordline driver sleep transistor, resulting in a 58% reduction in subarray leakage power consumption.
Universal Lossless Compression of Graphical Data

Graphical data is comprised of a graph with marks on its edges and vertices. The mark indicates the value of some attribute associated to the respective edge or vertex. Examples of such data arise in social networks, molecular and systems biology, and web graphs, as well as in several other application areas. Our goal is to design schemes that can efficiently compress such graphical data without making assumptions about its stochastic properties. Namely, we wish to develop a universal compression algorithm for graphical data sources. To formalize this goal, we employ the framework of local weak convergence, also called the objective method, which provides a technique to think of a marked graph as a kind of stationary stochastic processes, stationary with respect to movement between vertices of the graph. In recent work, we have generalized a notion of entropy for unmarked graphs in this framework, due to Bordenave and Caputo, to the case of marked graphs. We use this notion to evaluate the efficiency of a compression scheme. The lossless compression scheme we propose in this paper is then proved to be universally optimal in a precise technical sense. It is also capable of performing local data queries in the compressed form.
Commerce, markets, and peace: Richard Cobden's enduring lessons

Do capitalism and conflicts go hand in hand? Are the military and markets complements? Indeed, many conservative advocates of markets also passionately support the military, and many people who oppose war also oppose markets. Nineteenth-century writer Richard Cobden, however, maintained that the military and markets were substitutes: more military entails less market. Although the ideas in The Political Writings of Richard Cobden (1903) are a century and a half old, Cobden considered many arguments for military intervention still made today. He discussed whether military spending was beneficial to the economy, to commerce, and to peace, and in all three cases he answered no. Both conservatives and left liberals can learn much from Cobden’s discussion of commerce, markets, and peace. As he demonstrated, the advocate of markets must be an advocate of peace.
Linear Extension Numbers of n-Element Posets

We address the following natural but hitherto unstudied question: what are the possible linear extension numbers of an n-element poset? Let LE(n) denote the set of all positive integers that arise as the number of linear extensions of some n-element poset. We show that LE(n) skews towards the “small” end of the interval [1, n!]. More specifically, LE(n) contains all of the positive integers up to $\exp \left (c\frac {n}{\log n}\right )$ for some absolute constant c, and |LE(n) ∩ ((n − 1)!, n!]| < (n − 3)!. The proof of the former statement involves some intermediate number-theoretic results about the Stern-Brocot tree that are of independent interest.
Mad families of vector subspaces and the smallest nonmeager set of reals

We show that a parametrized $\diamondsuit$ principle, corresponding to the uniformity of the meager ideal, implies that the minimum cardinality of an infinite maximal almost disjoint family of block subspaces in a countable vector space is $\aleph_1$. Consequently, this cardinal invariant is $\aleph_1$ in the Miller model.
StegaStamp: Invisible Hyperlinks in Physical Photographs

Printed and digitally displayed photos have the ability to hide imperceptible digital data that can be accessed through internet-connected imaging systems. Another way to think about this is physical photographs that have unique QR codes invisibly embedded within them. This paper presents an architecture, algorithms, and a prototype implementation addressing this vision. Our key technical contribution is StegaStamp, a learned steganographic algorithm to enable robust encoding and decoding of arbitrary hyperlink bitstrings into photos in a manner that approaches perceptual invisibility. StegaStamp comprises a deep neural network that learns an encoding/decoding algorithm robust to image perturbations approximating the space of distortions resulting from real printing and photography. We demonstrates real-time decoding of hyperlinks in photos from in-the-wild videos that contain variation in lighting, shadows, perspective, occlusion and viewing distance. Our prototype system robustly retrieves 56 bit hyperlinks after error correction -- sufficient to embed a unique code within every photo on the internet.
Maximizing Welfare with Incentive-Aware Evaluation Mechanisms.

Motivated by applications such as college admission and insurance rate determination, we propose an evaluation problem where the inputs are controlled by strategic individuals who can modify their features at a cost. A learner can only partially observe the features, and aims to classify individuals with respect to a quality score. The goal is to design an evaluation mechanism that maximizes the overall quality score, i.e., welfare, in the population, taking any strategic updating into account. We further study the algorithmic aspect of finding the welfare maximizing evaluation mechanism under two specific settings in our model. When scores are linear and mechanisms use linear scoring rules on the observable features, we show that the optimal evaluation mechanism is an appropriate projection of the quality score. When mechanisms must use linear thresholds, we design a polynomial time algorithm with a (1/4)-approximation guarantee when the underlying feature distribution is sufficiently smooth and admits an oracle for finding dense regions. We extend our results to settings where the prior distribution is unknown and must be learned from samples.