cssi/SciSciGPT-SciSciNet huggingface dataset:
Consistent Recovery Threshold of Hidden Nearest Neighbor Graphs

Motivated by applications such as discovering strong ties in social networks and assembling genome subsequences in biology, we study the problem of recovering a hidden $2k$ -nearest neighbor (NN) graph in an $n$ -vertex complete graph, whose edge weights are independent and distributed according to $P_{n}$ for edges in the hidden $2k$ -NN graph and $Q_{n}$ otherwise. The special case of Bernoulli distributions corresponds to a variant of the Watts-Strogatz small-world graph. We focus on two types of asymptotic recovery guarantees as $n\to \infty $ : (1) exact recovery: all edges are classified correctly with probability tending to one; (2) almost exact recovery: the expected number of misclassified edges is $o(nk)$ . We show that the maximum likelihood estimator achieves (1) exact recovery for $2 \le k \le n^{o(1)}$ if $\liminf \frac {2\alpha _{n}}{\log n}>1$ ; (2) almost exact recovery for $1 \le k \le o\left ({\frac {\log n}{\log \log n} }\right)$ if $\liminf \frac {kD(P_{n}||Q_{n})}{\log n}>1$ , where $\alpha _{n} \triangleq -2 \log \int \sqrt {d P_{n} d Q_{n}} ^{^{^{^{}}}}$ is the Renyi divergence of order $\frac {1}{2}$ and $D(P_{n}||Q_{n})$ is the Kullback-Leibler divergence. Under mild distributional assumptions, these conditions are shown to be information-theoretically necessary for any algorithm to succeed. A key challenge in the analysis is the enumeration of $2k$ -NN graphs that differ from the hidden one by a given number of edges. We also analyze several computationally efficient algorithms and provide sufficient conditions under which they achieve exact/almost exact recovery. In particular, we develop a polynomial-time algorithm that attains the threshold for exact recovery under the small-world model.
DirectProbe: Studying Representations without Classifiers

Understanding how linguistic structure is encoded in contextualized embedding could help explain their impressive performance across NLP. Existing approaches for probing them usually call for training classifiers and use the accuracy, mutual information, or complexity as a proxy for the representation’s goodness. In this work, we argue that doing so can be unreliable because different representations may need different classifiers. We develop a heuristic, DirectProbe, that directly studies the geometry of a representation by building upon the notion of a version space for a task. Experiments with several linguistic tasks and contextualized embeddings show that, even without training classifiers, DirectProbe can shine lights on how an embedding space represents labels and also anticipate the classifier performance for the representation.
Nova-LSM: A Distributed, Component-based LSM-tree Key-value Store

The cloud infrastructure motivates disaggregation of monolithic data stores into components that are assembled together based on an application's workload. This study investigates disaggregation of an LSM-tree key-value store into components that communicate using RDMA. These components separate storage from processing, enabling processing components to share storage bandwidth and space. The processing components scatter blocks of a file (SSTable) across an arbitrary number of storage components and balance load across them using power-of-d. They construct ranges dynamically at runtime to parallelize compaction and enhance performance. Each component has configuration knobs that control its scalability. The resulting component-based system, Nova-LSM, is elastic. It outperforms its monolithic counterparts, both LevelDB and RocksDB, by several orders of magnitude with workloads that exhibit a skewed pattern of access to data.
Designing biomaterials for the modulation of allogeneic and autoimmune responses to cellular implants in Type 1 Diabetes.

Abstract The effective suppression of adaptive immune responses is essential for the success of allogeneic cell therapies. In islet transplantation for Type 1 Diabetes, pre-existing autoimmunity provides an additional hurdle, as memory autoimmune T cells mediate both an autoantigen-specific attack on the donor beta cells and an alloantigen-specific attack on the donor graft cells. Immunosuppressive agents used for islet transplantation are generally successful in suppressing alloimmune responses, but dramatically hinder the widespread adoption of this therapeutic approach and fail to control memory T cell populations, which leaves the graft vulnerable to destruction. In this review, we highlight the capacity of biomaterials to provide local and nuanced instruction to suppress or alter immune pathways activated in response to an allogeneic islet transplant. Biomaterial immunoisolation is a common approach employed to block direct antigen recognition and downstream cell-mediated graft destruction; however, immunoisolation alone still permits shed donor antigens to escape into the host environment, resulting in indirect antigen recognition, immune cell activation, and the creation of a toxic graft site. Designing materials to decrease antigen escape, improve cell viability, and increase material compatibility are all approaches that can decrease the local release of antigen and danger signals into the implant microenvironment. Implant materials can be further enhanced through the local delivery of anti-inflammatory, suppressive, chemotactic, and/or tolerogenic agents, which serve to control both the innate and adaptive immune responses to the implant with a benefit of reduced systemic effects. Lessons learned from understanding how to manipulate allogeneic and autogenic immune responses to pancreatic islets can also be applied to other cell therapies to improve their efficacy and duration. Statement of significance This review explores key immunologic concepts and critical pathways mediating graft rejection in Type 1 Diabetes, which can instruct the future purposeful design of immunomodulatory biomaterials for cell therapy. A summary of immunological pathways initiated following cellular implantation, as well as current systemic immunomodulatory agents used, is provided. We then outline the potential of biomaterials to modulate these responses. The capacity of polymeric encapsulation to block some powerful rejection pathways is covered. We also highlight the role of cellular health and biocompatibility in mitigating immune responses. Finally, we review the use of bioactive materials to proactively modulate local immune responses, focusing on key concepts of anti-inflammatory, suppressive, and tolerogenic agents.
Unified non-metric (1, 0) tensor-Einstein supergravity theories and (4, 0) supergravity in six dimensions

The ultrashort unitary (4, 0) supermultiplet of 6d superconformal algebra OSp(8∗|8) reduces to the CPT-self conjugate supermultiplet of 4d superconformal algebra SU(2, 2|8) that represents the fields of maximal N = 8 supergravity. The graviton in the (4, 0) multiplet is described by a mixed tensor gauge field which can not be identified with the standard metric in 6d. Furthermore the (4, 0) supermultiplet can be obtained as a double copy of (2, 0) conformal supermultiplet whose interacting theories are non-Lagrangian. It had been suggested that an interacting non-metric (4, 0) supergravity theory might describe the strongly coupled phase of 5d maximal supergravity. In this paper we study the implications of the existence of an interacting non-metric (4, 0) supergravity in 6d. The (4, 0) theory can be truncated to non-metric (1, 0) supergravity coupled to 5,8 and 14 self-dual tensor multiplets that reduce to three of the unified magical supergravity theories in d = 5. This implies that the three infinite families of unified N = 2, 5d Maxwell-Einstein supergravity theories (MESGTs) plus two sporadic ones must have uplifts to unified non-metric (1, 0) tensor Einstein supergravity theories (TESGT) in d = 6. These theories have non-compact global symmetry groups under which all the self-dual tensor fields including the gravitensor transform irreducibly. Four of these theories are uplifts of the magical supergravity theories whose scalar manifolds are symmetric spaces. The scalar manifolds of the other unified theories are not homogeneous spaces. We also discuss the exceptional field theoretic formulations of non-metric unified (1, 0) tensor-Einstein supergravity theories and conclude with speculations concerning the existence of higher dimensional non-metric supergravity theories that reduce to the (4, 0) theory in d = 6.
Lifelong Learning of Hate Speech Classification on Social Media

Existing work on automated hate speech classification assumes that the dataset is fixed and the classes are pre-defined. However, the amount of data in social media increases every day, and the hot topics changes rapidly, requiring the classifiers to be able to continuously adapt to new data without forgetting the previously learned knowledge. This ability, referred to as lifelong learning, is crucial for the real-word application of hate speech classifiers in social media. In this work, we propose lifelong learning of hate speech classification on social media. To alleviate catastrophic forgetting, we propose to use Variational Representation Learning (VRL) along with a memory module based on LB-SOINN (Load-Balancing Self-Organizing Incremental Neural Network). Experimentally, we show that combining variational representation learning and the LB-SOINN memory module achieves better performance than the commonly-used lifelong learning techniques.
Reactive Navigation Framework for Mobile Robots by Heuristically Evaluated Pre-sampled Trajectories

This paper describes and analyzes a reactive navigation framework for mobile robots in unknown environments. The approach does not rely on a global map and only considers the local occupancy in its robot-centered 3D grid structure. The proposed algorithm enables fast navigation by heuristic evaluations of pre-sampled trajectories on-the-fly. At each cycle, these paths are evaluated by a weighted cost function, based on heuristic features such as closeness to the goal, previously selected trajectories, and nearby obstacles. This paper introduces a systematic method to calculate a feasible pose on the selected trajectory, before sending it to the controller for the motion execution. Defining the structures in the framework and providing the implementation details, the paper also explains how to adjust its offline and online parameters. To demonstrate the versatility and adaptability of the algorithm in unknown environments, physics-based simulations on various maps are presented. Benchmark tests show the superior performance of the proposed algorithm over its previous iteration and another state-of-art method. The open-source implementation of the algorithm and the benchmark data can be found at \url{this https URL}.
Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders

Learning prerequisite chains is an important task for one to pick up knowledge efficiently in both known and unknown domains. For example, one may be an expert in the natural language processing (NLP) domain, but want to determine the best order in which to learn new concepts in an unfamiliar Computer Vision domain (CV). Both domains share some common concepts, such as machine learning basics and deep learning models. In this paper, we solve the task of unsupervised cross-domain concept prerequisite chain learning, using an optimized variational graph autoencoder. Our model learns to transfer concept prerequisite relations from an information-rich domain (source domain) to an information-poor domain (target domain), substantially surpassing other baseline models. In addition, we expand an existing dataset by introducing two new domains—-CV and Bioinformatics (BIO). The annotated data and resources as well as the code will be made publicly available.
Learning-NUM: Network Utility Maximization with Unknown Utility Functions and Queueing Delay

Network Utility Maximization (NUM) studies the problems of allocating traffic rates to network users in order to maximize the users' total utility subject to network resource constraints. In this paper, we propose a new NUM framework, Learning-NUM, where the users' utility functions are unknown apriori and the utility function values of the traffic rates can be observed only after the corresponding traffic is delivered to the destination, which means that the utility feedback experiences queueing delay. The goal is to design a policy that gradually learns the utility functions and makes rate allocation and network scheduling/routing decisions so as to maximize the total utility obtained over a finite time horizon T. In addition to unknown utility functions and stochastic constraints, a central challenge of our problem lies in the queueing delay of the observations, which may be unbounded and depends on the decisions of the policy. We first show that the expected total utility obtained by the best dynamic policy is upper bounded by the solution to a static optimization problem. Without the presence of feedback delay, we design an algorithm based on the ideas of gradient estimation and Max-Weight scheduling. To handle the feedback delay, we embed the algorithm in a parallel-instance paradigm to form a policy that achieves O(T3/4)-regret, i.e., the difference between the expected utility obtained by the best dynamic policy and our policy is in O(T3/4). Finally, to demonstrate the practical applicability of the Learning-NUM framework, we apply it to three application scenarios including database query, job scheduling and video streaming. We further conduct simulations on the job scheduling application to evaluate the empirical performance of our policy.
Managing Cloud networking costs for data-intensive applications by provisioning dedicated network links

Many scientific high-throughput applications can benefit from the elastic nature of Cloud resources, especially when there is a need to reduce time to completion. Cost considerations are usually a major issue in such endeavors, with networking often a major component; for data-intensive applications, egress networking costs can exceed the compute costs. Dedicated network links provide a way to lower the networking costs, but they do add complexity. In this paper we provide a description of a 100 fp32 PFLOPS Cloud burst in support of IceCube production compute, that used Internet2 Cloud Connect service to provision several logically-dedicated network links from the three major Cloud providers, namely Amazon Web Services, Microsoft Azure and Google Cloud Platform, that in aggregate enabled approximately 100 Gbps egress capability to on-prem storage. It provides technical details about the provisioning process, the benefits and limitations of such a setup and an analysis of the costs incurred.
Quantifying the Long-Range Coupling of Electronic Properties in Proteins with ab initio Molecular Dynamics**

A delicate interplay of covalent and noncovalent interactions gives proteins their unique ability to flexibly play numerous roles in cellular processes. This interplay is inherently quantum mechanical and highly dynamic in nature. To directly interrogate the evolving nature of the electronic structure of proteins, we carry out 100-ps-scale ab initio molecular dynamics simulations of three representative small proteins with range-separated hybrid density functional theory. We quantify the nature and length-scale of the coupling of residue-specific charge probability distributions in these proteins. While some nonpolar residues exhibit expectedly narrow charge distributions, most polar and charged residues exhibit broad, multimodal distributions. Even for nonpolar residues, we observe sequence-specific deviations corresponding to charge accumulation or depletion that would be challenging to capture in a fixed charge force field. We quantify the effect of residue–residue interactions on charge distributions first with linear cross-correlations. We then show how additional insight can be gained from evaluating the mutual information of charge distributions. We show that a significant number of residues couple most strongly with residues that are distant in both sequence and space over a range of secondary structures including a-helical, b-sheet, disulfide bridging, and lasso motifs. The mutual information analysis is necessary to capture coupling between some polar and charged residues. These analyses are expected to be broadly useful in understanding the mechanisms of long-range charge transfer in proteins and for determining what interactions require a quantum mechanical description for predictive simulation of enzyme mechanism and protein function.
The impact of strategic agents in two-sided markets

This paper introduces strategic agents into a two-sided market. We model strategic agents as advertisers who can invest in ad quality, which in turn affects readers, platforms and themselves. When platforms can choose prices on both the reader side and the advertiser side, strategic agents intensify competition, leading to lower prices and profits for the platforms. However, when prices on the reader side are restricted to be zero, equilibrium prices on the advertiser side can be higher or lower under strategic agents. We also investigate the interplay between strategic agents and platform asymmetry, and analyze platforms’ incentive to invest in content quality. Our results suggest that strategic agents increase the degree of platform asymmetry and make it harder for the disadvantaged platform to compete. However, strategic agents may raise or lower platforms’ incentive to invest in content quality.
ROLE OF CITICOLINE IN AN IN VITRO AMD MODEL

Citicoline is the exogenous form of the nootropic, Cytidine 5'-diphosphate-choline that exerts its neuroprotective effects in the brain as well as in the eye. The current study characterized the cytoprotective effects of purified Citicoline in transmitochondrial AMD (Age-related Macular Degeneration) RPE cybrid cells which carry diseased mitochondria from clinically characterized AMD patients. The effects of Citicoline were examined via flow cytometry analysis of AnnexinV/ PI-stained cells, IncuCyte live-cell imaging analysis to quantify cells undergoing caspase-3/7-mediated apoptosis, analyses of gene expression profiles of apoptosis, hypoxia, and angiogenesis markers, and measurement of ROS levels and cell viability. Our results demonstrated that Citicoline when added exogenously alleviates apoptotic effects as evidenced by diminished AnnexinV/PI and Caspase-3/7 staining, downregulation of apoptosis genes, enhanced cell viability, and reduced oxidative stress in AMD RPE cybrid cells. In conclusion, our study identified Citicoline as a protector in AMD RPE cybrid cells in vitro. However, further studies are required to establish the merit of Citicoline as a cytoprotective molecule in AMD and to decipher the molecular underpinnings of its mechanism of action in AMD.
Cohen-Macaulay test ideals over rings of finite and countable Cohen-Macaulay type

R.G. and Perez proved that under certain conditions the test ideal of a module closure agrees with the trace ideal of the module closure. We use this fact to compute the test ideals of various rings with respect to the closures coming from their indecomposable maximal Cohen–Macaulay modules. We also give an easier way to compute the test ideal of a hypersurface ring in three variables coming from a module with a particular type of matrix factorization.
SoundFence: Securing Ultrasonic Sensors in Vehicles Using Physical-Layer Defense

Autonomous vehicles (AVs), equipped with numerous sensors such as camera, LiDAR, radar, and ultrasonic sensor, are revolutionizing the transportation industry. These sensors are expected to sense reliable information from a physical environment, facilitating the critical decision-making process of the AVs. Ultrasonic sensors, which detect obstacles in a short distance, play an important role in assisted parking and blind spot detection events. However, due to their weak security level, ultrasonic sensors are particularly vulnerable to signal injection attacks, when the attackers inject malicious acoustic signals to create fake obstacles and intentionally mislead the vehicles to make wrong decisions with disastrous aftermath. In this paper, we systematically analyze the attack model of signal injection attacks toward moving vehicles. By considering the potential threats, we propose SoundFence, a physical-layer defense system which leverages the sensors’ signal processing capability without requiring any additional equipment. SoundFence verifies the benign measurement results and detects signal injection attacks by analyzing sensor readings and the physical-layer signatures of ultrasonic signals. Our experiment with commercial sensors shows that SoundFence detects most (more than 95%) of the abnormal sensor readings with very few false alarms, and it can also accurately distinguish the real echo from injected signals to identify injection attacks.
Characterization of Domain of Fixed-time Stability under Control Input Constraints

In this paper, we study the effect of control input constraints on the domain of attraction of an FxTS equilibrium point. We first present a new result on FxTS, where we allow a positive term in the time derivative of the Lyapunov function. We provide analytical expressions for the domain of attraction and the settling time to the equilibrium in terms of the coefficients of the positive and negative terms that appear in the time derivative of the Lyapunov function. We show that this result serves as a robustness characterization of FxTS equilibria in the presence of an additive, vanishing disturbances. We use the new FxTS result in formulating a provably feasible quadratic program (QP) that computes control inputs that drive the trajectories of a class of nonlinear, control-affine systems to a goal set, in the presence of control input constraints.
RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity

We present RAWLSNET, a system for altering Bayesian Network (BN) models to satisfy the Rawlsian principle of fair equality of opportunity (FEO). RAWLSNET's BN models generate aspirational data distributions: data generated to reflect an ideally fair, FEO-satisfying society. FEO states that everyone with the same talent and willingness to use it should have the same chance of achieving advantageous social positions (e.g., employment), regardless of their background circumstances (e.g., socioeconomic status). Satisfying FEO requires alterations to social structures such as school assignments. Our paper describes RAWLSNET, a method which takes as input a BN representation of an FEO application and alters the BN's parameters so as to satisfy FEO when possible, and minimize deviation from FEO otherwise. We also offer guidance for applying RAWLSNET, including on recognizing proper applications of FEO. We demonstrate the use of RAWLSNET with publicly available data sets. RAWLSNET's altered BNs offer the novel capability of generating aspirational data for FEO-relevant tasks. Aspirational data are free from biases of real-world data, and thus are useful for recognizing and detecting sources of unfairness in machine learning algorithms besides biased data.
Lone Pine at SemEval-2021 Task 5: Fine-Grained Detection of Hate Speech Using BERToxic.

This paper describes our approach to the Toxic Spans Detection problem (SemEval-2021 Task 5). We propose BERToxic, a system that fine-tunes a pre-trained BERT model to locate toxic text spans in a given text and utilizes additional post-processing steps to refine the boundaries. The post-processing steps involve (1) labeling character offsets between consecutive toxic tokens as toxic and (2) assigning a toxic label to words that have at least one token labeled as toxic. Through experiments, we show that these two post-processing steps improve the performance of our model by 4.16% on the test set. We also studied the effects of data augmentation and ensemble modeling strategies on our system. Our system significantly outperformed the provided baseline and achieved an F1-score of 0.683, placing Lone Pine in the 17th place out of 91 teams in the competition. Our code is made available at https://github.com/Yakoob-Khan/Toxic-Spans-Detection
The Female Penalty for Novelty and the Offsetting Effect of Alternate Status Characteristics

Research has shown that audiences penalize novelty in women’s work but also that such penalty may vary. This study builds on theories of uncertainty and status signaling to identify contingencies t...
Benefit of COVID-19 vaccination accounting for potential risk compensation

People receiving COVID-19 vaccines may subsequently markedly increase their previously suppressed exposure risk. A simple model can evaluate the benefit of vaccination to the vaccinated (index) person and others exposed to that person; and calculate the amount of risk compensation required to eliminate all the benefits or to halve the benefit. As shown, 2.5-fold increase in exposure will eliminate the benefit of a vaccine of moderate efficacy (E = 0.6) unless the probability of infection in the population of interest is very high. With very high vaccine efficacy (E = 0.95), substantial benefit is maintained except in situations where there is a very low probability of infection in the population. If the vaccine efficacy decreases to 0.8, the benefit gets eroded easily with modest risk compensation. Risk compensation may markedly affect the benefit of COVID-19 vaccination, especially if vaccine efficacy in real-life or specific high-risk populations (e.g., nursing home residents) is not very high.
LES of a Jet Excited by the Localized Arc Filament Plasma Actuators

The fluid dynamics of a high-speed jet are governed by the instability waves that form in the free-shear boundary layer of the jet. Jet excitation manipulates the growth and saturation of particular instability waves to control the unsteady flow structures that characterize the energy cascade in the jet.The results may include jet noise mitigation or a reduction in the infrared signature of the jet. The Localized Arc Filament Plasma Actuators (LAFPA) have demonstrated the ability to excite a high-speed jets in laboratory experiments. Extending and optimizing this excitation technology, however, is a complex process that will require many tests and trials. Computational simulations can play an important role in understanding and optimizing this actuator technology for real-world applications. Previous research has focused on developing a suitable actuator model and coupling it with the appropriate computational fluid dynamics (CFD) methods using two-dimensional spatial flow approximations. This work is now extended to three-dimensions (3-D) in space. The actuator model is adapted to a series of discrete actuators and a 3-D LES simulation of an excited jet is run. The results are used to study the fluid dynamics near the actuator and in the jet plume.
Influence of solvent quality on depletion potentials in colloid–polymer mixtures

As first explained by the classic Asakura–Oosawa (AO) model, effective attractive forces between colloidal particles induced by depletion of nonadsorbing polymers can drive demixing of colloid–polymer mixtures into colloid-rich and colloid-poor phases, with practical relevance for purification of water, stability of foods and pharmaceuticals, and macromolecular crowding in biological cells. By idealizing polymer coils as effective penetrable spheres, the AO model qualitatively captures the influence of polymer depletion on thermodynamic phase behavior of colloidal suspensions. In previous work, we extended the AO model to incorporate aspherical polymer conformations and showed that fluctuating shapes of random-walk coils can significantly modify depletion potentials [W. K. Lim and A. R. Denton, Soft Matter 12, 2247 (2016); J. Chem. Phys. 144, 024904 (2016)]. We further demonstrated that the shapes of polymers in crowded environments sensitively depend on solvent quality [W. J. Davis and A. R. Denton, J. Chem. Phys. 149, 124901 (2018)]. Here, we apply Monte Carlo simulation to analyze the influence of solvent quality on depletion potentials in mixtures of hard-sphere colloids and nonadsorbing polymer coils, modeled as ellipsoids whose principal radii fluctuate according to random-walk statistics. We consider both self-avoiding and non-self-avoiding random walks, corresponding to polymers in good and theta solvents, respectively. Our simulation results demonstrate that depletion of polymers of equal molecular weight induces much stronger attraction between colloids in good solvents than in theta solvents and confirm that depletion interactions are significantly influenced by aspherical polymer conformations.
A LeVeque-type inequality on the ring of p-adic integers

We derive an inequality on the discrepancy of sequences on the ring of p-adic integers ℤp using techniques from Fourier analysis. This is a p-adic analogue of the classical LeVeque inequality on th...
Error Diffusion Halftoning Against Adversarial Examples

Adversarial examples contain carefully crafted perturbations that can fool deep neural networks (DNNs) into making wrong predictions. Enhancing the adversarial robustness of DNNs has gained considerable interest in recent years. Although image transformation-based defenses were widely considered at an earlier time, most of them have been defeated by adaptive attacks. In this paper, we propose a new image transformation defense based on error diffusion halftoning, and combine it with adversarial training to defend against adversarial examples. Error diffusion halftoning projects an image into a 1-bit space and diffuses quantization error to neighboring pixels. This process can remove adversarial perturbations from a given image while maintaining acceptable image quality in the meantime in favor of recognition. Experimental results demonstrate that the proposed method is able to improve adversarial robustness even under advanced adaptive attacks, while most of the other image transformation-based defenses do not. We show that a proper image transformation can still be an effective defense approach. Code: this https URL
Background measurement methods for opacity experiments conducted at the Z facility.

Laboratory experiments typically test opacity models by measuring spectrally resolved transmission of a sample using bright backlight radiation. A potential problem is that any unaccounted background signal contaminating the spectrum will artificially reduce the inferred opacity. Methods developed to measure background signals in opacity experiments at the Sandia Z facility are discussed. Preliminary measurements indicate that backgrounds are 9%–11% of the backlight signal at wavelengths less than 10 A. Background is thus a relatively modest correction for all Z opacity data published to date. Future work will determine how important background is at longer wavelengths.
Airbnb Price Prediction Using Machine Learning and Sentiment Analysis

Pricing a property and evaluating the proposed price for a property are challenges that, respectively, owners and customers of Airbnb rentals face on a daily basis. This paper aims to create a model for predicting the price of an Airbnb listing using property specifications, owner information, and customer reviews for the listing. Owners and customers can use the resulting model to estimate the expected value of an Airbnb listing. Linear regression, tree-based models, K-means Clustering, Support Vector Regression (SVR), and neural networks are trained and tuned on a dataset of Airbnb listings from New York city, and the resulting models are compared in terms of Mean Squared Error, Mean Absolute Error, and \(R^2\) score. Sentiment analysis is used to extract features from the customer reviews which help enhance the performance of the selected predictive models. Feature importance analysis is also used to select the most representative features for predicting the price of the listings. Experimentation shows that SVR model can achieve an \(R^2\) score of 69% and a MSE of 0.147 (defined on ln(price)) on the test set, outperforming the other models considered in the paper. [Link to the repository: github.com/PouyaREZ/AirBnbPricePrediction].
Triggered Ion-acoustic Waves in the Solar Wind

For more than 12 hours beginning on January 18, 2021, continuous narrowband electrostatic emissions were observed on Parker Solar Probe near 20 solar radii. The observed <1000 Hz frequencies were well below the local ion plasma frequency. Surprisingly, the emissions consisted of electrostatic wave packets with shock-like envelopes, appearing repetitively at a ~1.5 Hz rate. This repetitiveness correlated and was in phase with low frequency electromagnetic fluctuations. The emissions were associated with simultaneously observed ion beams and conditions favorable for ion-acoustic wave excitation, i.e. Te/Ti~5. Based on this information and on their velocity estimates of about 100 km/s, these electrostatic emissions are interpreted as ion-acoustic waves. Their observation demonstrates a new regime of instability and evolution of oblique ion-acoustic waves that have not been reported previously in theory or experiment.
On the Importance of Distractors for Few-Shot Classification

Few-shot classification aims at classifying categories of a novel task by learning from just a few (typically, 1 to 5) labelled examples. An effective approach to few-shot classification involves a prior model trained on a large-sample base domain, which is then finetuned over the novel few-shot task to yield generalizable representations. However, task-specific finetuning is prone to overfitting due to the lack of enough training examples. To alleviate this issue, we propose a new finetuning approach based on contrastive learning that reuses unlabelled examples from the base domain in the form of distractors. Unlike the nature of unlabelled data used in prior works, distractors belong to classes that do not overlap with the novel categories. We demonstrate for the first time that inclusion of such distractors can significantly boost few-shot generalization. Our technical novelty includes a stochastic pairing of examples sharing the same category in the few-shot task and a weighting term that controls the relative influence of task-specific negatives and distractors. An important aspect of our finetuning objective is that it is agnostic to distractor labels and hence applicable to various base domain settings. Compared to state-of-the-art approaches, our method shows accuracy gains of up to $12\%$ in cross-domain and up to $5\%$ in unsupervised prior-learning settings.
Ambulatory PCNL may be cost-effective compared to Standard PCNL.

Background COVID-19 changed the practice of medicine in America. During the March 2020 lockdown, elective cases were cancelled to conserve hospital beds/resources resulting in financial losses for health systems and delayed surgical care. Ambulatory percutaneous nephrolithotomy (aPCNL) has been shown to be safe and could be a strategy to: ensure patients receive care that has been delayed, conserve hospital resources, and maximize cost-effectiveness. We aimed to compare the safety and cost-effectiveness of patients undergoing ambulatory percutaneous nephrolithotomy (aPCNL) against standard PCNL (sPCNL). Materials and Methods 98 patients underwent PCNL at Indiana University Methodist hospital, a tertiary referral center, by three expert surgeons from January 2020 to September 2020. The primary outcome of the study was to compare the 30-day rates of ED-visits, readmissions, and complications between sPCNL and aPCNL. Secondary outcomes included: cost analysis and stone free rates (SFRs). Prospensity-score matching was performed to ensure the groups were balanced. Statistical analyses were performed using SAS 9.4 using independent t-tests for continuous variables and chi-square analyses for categorical variables. Results 98 patients underwent PCNL during the study period (sPCNL=75 and aPCNL=23). After propensity-score matching, 42 patients were available for comparison (sPCNL=19 and aPCNL=23). We found no difference in 30-day ED-visits, readmissions, or complications between the two groups. aPCNL resulted in cost savings of $5327±442 per case. Stone free rates were higher for aPCNL compared to sPCNL. Conclusions aPCNL appears safe to perform and does not have a higher rate of ED-visits or readmissions compare to sPCNL. aPCNL may also be cost-effective compared to sPCNL.
Model-Predictive Control of Blood Suction for Surgical Hemostasis using Differentiable Fluid Simulations

Recent developments in surgical robotics have led to new advancements in the automation of surgical sub-tasks such as suturing, soft tissue manipulation, tissue tensioning and cutting. However, integration of dynamics to optimize these control policies for the variety of scenes encountered in surgery remains unsolved. Towards this effort, we investigate the integration of differentiable fluid dynamics to optimizing a suction tool’s trajectory to clear the surgical field from blood as fast as possible. The fully differentiable fluid dynamics is integrated with a novel suction model for effective model predictive control of the tool. The differentiability of the fluid model is crucial because we utilize the gradients of the fluid states with respect to the suction tool position to optimize the trajectory. Through a series of experiments, we demonstrate how, by incorporating fluid models, the trajectories generated by our method can perform as good as or better than handcrafted human-intuitive suction policies. We also show that our method is adaptable and can work in different cavity conditions while using a single handcrafted strategy fails.
Using Thermal Vision for Extended VINS-Mono to Localize Vehicles in Large-Scale Outdoor Road Environments

A monocular VIO (Visual-Inertial Odometry) system provides a compact, low-cost, and easily-deployed configuration for relative localization. However, using thermal vision for VIO is much less studied than using a visible-spectrum camera. A thermal-vision camera works under all lighting conditions and has been used to detect pedestrians, cars and animals at nighttime to provide ADAS (Advanced Driver Assistance System) functions. Common problems in directly using thermal images in conventional VIO methods are: (1) lower signal-to-noise ratio and fewer reliable feature points in the texture-less thermal images, (2) periodic recalibration hampers thermal image capturing and feature-point tracking, and (3) the “jello” effect of the rolling shutter readout architecture is sensitive to aggressive vehicle maneuvers. Extended VINS-Mono, proposed in our previous work [1], aims at providing relative and absolute localization of a vehicle in large-scale outdoor road environments by introducing (1) absolute localization methods to enable VINS-Mono to output local and global state estimates simultaneously, (2) vehicle speed readings for fast (re-)initialization and reliable scale estimates, and (3) DNN-based object detection methods to remove nonstationary objects from the visible scene. In this paper, we show that Extended VINS-Mono can use thermal images to provide relative and absolute localization even when light conditions are very poor. We conducted several experiments on a 25 Km-trip journey through highways, tunnels, urban areas and suburban areas in Pittsburgh, USA during daytime and nighttime to evaluate the performance of Extended Thermal VINS-Mono, including (re-)initialization, accuracy, rate, and latency. Our evaluation confirms that using thermal vision for localization satisfies localization requirements in large-scale outdoor road environments when the visible-spectrum camera performs poorly.
A Case Study on Stochastic Games on Large Graphs in Mean Field and Sparse Regimes

We study a class of linear-quadratic stochastic differential games in which each player interacts directly only with its nearest neighbors in a given graph. We find a semiexplicit Markovian equilib...
Relaxing conditional independence in an endogenous binary response model

Abstract For binary response models, the literature primarily addresses endogeneity by a control function approach assuming conditional independence (CF-CI). However, as the literature also notes, CF-CI implies conditions like homoskedasticity (of the latent error with respect to the instruments) that fail in many empirical settings. I propose an alternative approach that allows for heteroskedasticity, achieving identification with a conditional mean restriction. These identification results apply to a latent Gaussian error term with flexibly parametrized heteroskedasticity. I propose a two step conditional maximum likelihood estimator and derive its asymptotic distribution. In simulations, the new estimator outperforms others when CF-CI fails and is fairly robust to distributional misspecification.
Antimonide-Based Avalanche Photodiodes on InP Substrates

We have investigated the multiplication characteristics of two multipliers on InP substrates: AlGaAsSb and AlInAsSb. We have demonstrated decreases in the excess noise factors by growing the multipliers as ternary superlattices, extremely low hole to electron impact ionization coefficient ratios (k~0.01), and low dark current densities $(^{\sim}10\mu \mathrm{A}/\text{cm}^{2}$ ) at 300 K.
Blockchains Enable Non-interactive MPC.

We propose to use blockchains to achieve MPC which does not require the participating parties to be online simultaneously or interact with each other. Parties who contribute inputs but do not wish to receive outputs can go offline after submitting a single message. In addition to our main result, we study combined communication- and state-complexity in MPC, as it has implications for the communication complexity of our main construction. Finally, we provide a variation of our main protocol which additionally provides guaranteed output delivery.
Optimal prediction of Markov chains with and without spectral gap

We study the following learning problem with dependent data: Observing a trajectory of length $n$ from a stationary Markov chain with $k$ states, the goal is to predict the next state. For $3 \leq k \leq O(\sqrt{n})$, using techniques from universal compression, the optimal prediction risk in Kullback-Leibler divergence is shown to be $\Theta(\frac{k^2}{n}\log \frac{n}{k^2})$, in contrast to the optimal rate of $\Theta(\frac{\log \log n}{n})$ for $k=2$ previously shown in Falahatgar et al., 2016. These rates, slower than the parametric rate of $O(\frac{k^2}{n})$, can be attributed to the memory in the data, as the spectral gap of the Markov chain can be arbitrarily small. To quantify the memory effect, we study irreducible reversible chains with a prescribed spectral gap. In addition to characterizing the optimal prediction risk for two states, we show that, as long as the spectral gap is not excessively small, the prediction risk in the Markov model is $O(\frac{k^2}{n})$, which coincides with that of an iid model with the same number of parameters.
Vertical Restraints and Antitrust Policy

Antitrust policy toward vertical restraints is the biggest substantive issue facing antitrust. (The biggest procedural/institutional/ administrative issue is overlapping enforcement authority-federal, state, and foreign, especially EU.) The Justice Department's suit against Microsoft put the issue on the map. I shall touch on exclusive dealing, tying, bundling, and loyalty rebates, but I warn the reader that the touch will in places be quite light. I want to begin by correcting the widespread impression that the issue of what if anything to do about vertical practices is an issue between the Chicago school of antitrust policy and the so-called postChicago school in any but an historical sense. The possibility that vertical practices, or as I would prefer to call them unilateral abuses of market power, can reduce economic welfare was recognized almost half a century ago by Aaron Director (the doyen of Chicago antitrust thinking) and Edward Levi in their discussion of the Standard Fashion
A comparison of food safety programs using the Customizable Tool for Online Training Evaluation

Abstract Food safety training for restaurant and food service employees is essential to reduce the risk of foodborne illness. With the large variety of food safety training programs available, it is critical to have an objective evaluation tool, which will allow managers, supervisors or purchasing agents to assessment strengths and weakness among multiple programs. The objective of this study was to use a newly created evaluation tool, the Customizable Tool for Online Training Evaluation (CTOTE) to assess and compare four online food safety training programs currently available to the food service industry. Through a comparison of the objectives of each training programs, a delivery method for training to front-line employees was identified. The preferred training approach was a blended method, which uses multiple delivery methods in a single training module to reinforce of the material being taught. The food safety training program containing the highest overall rating among respondents uses this blended delivery method and was taught at a middle school reading level. Results from this study show supervisors that front-line employees want clear, easy-to-follow instructions, the ability to check their progress, save their work and continue at a later time and include relevant examples and scenarios. Additionally, based on these results, retail employers and food processors can employ the most preferred food safety training methods for their employees.
An Anthropology of Structural Violence1

Any thorough understanding of the modern epidemics of AIDS and tuberculosis in Haiti or elsewhere in the postcolonial world requires a thorough knowledge of history and political economy. This essay, based on over a decade of research in rural Haiti, draws on the work of Sidney Mintz and others who have linked the interpretive project of modern anthropology to a historical understanding of the largescale social and economic structures in which affliction is embedded. The emergence and persistence of these epidemics in Haiti, where they are the leading causes of youngadult death, is rooted in the enduring effects of European expansion in the New World and in the slavery and racism with which it was associated. A syncretic and properly biosocial anthropology of these and other plagues moves us beyond noting, for example, their strong association with poverty and social inequalities to an understanding of how such inequalities are embodied as differential risk for infection and, among those already infected,...
When is a Game Only a Game?: The Taxation of Virtual Worlds

When is a game only a game? To date, articles on the taxation of activities in virtual worlds have not attempted an answer to this question. Instead, they have attempted to formulate rules applicable equally to worlds created solely for entertainment purposes and worlds structured to serve as venues for serious economic activity. This article proposes a bright-line test, at least with respect to the taxation of cash method taxpayers, for distinguishing between these two types of worlds. Transactions in worlds whose currencies are redeemable or convertible, it argues, should be subject to current taxation under standard cash method timing rules, applied in-world. Transactions in worlds whose currencies are neither redeemable nor convertible, by contrast, should not be currently taxable. Activities in such worlds should be treated, rather, as non-taxable entertainment unless and until players engage in real money trades. Although articulated in the context of virtual worlds, the proposed test works equally well in distinguishing taxable from non-taxable real-world games. Pinball points are neither redeemable nor convertible. If an accumulation of pinball points results in further game play, the proposed test suggests that the winning of such further game play should not be taxable. Casino chips, by contrast, are redeemable. The receipt of casino chips as gaming winnings should therefore be treated as an immediately taxable event; we should not have to wait until the patron cashes out to tax her winnings. In the course of its analysis, the Article offers, in addition, a new take on the concept of realization and a new formulation of the cash equivalence doctrine.