Miracle-dz/UniFAR-FTUs huggingface dataset:
The Specialists: Understanding the Work Lives of Master's Prepared Professional Staff in Community Colleges.

This study examined the work experiences of master's-prepared professional staff in community colleges. In-depth interviews with six such non-faculty staff at each of three Illinois community colleges (n=18) revealed the nature of their work, their perceptions about their duties and status, and their experiences with decision making. Findings of the study are presented in three main sections: a look at the daily work of these specialists, their views on being specialists and professionals, and key themes related to their sense of place in the community college (voice, importance, status, mobility, and professional development). Most of the interviewees saw their work as being important to the mission of their institutions. They also saw their work in their community colleges as a career and cared about their futures, and their mobility, in their institutions. The specialists interviewed for this study tended to perceive their institutions as hierarchical, with administrators and faculty occupying higher rungs on the employee ladder. Their lesser status as employees belied their equivalent educational credentials and the autonomy, professional authority, and general responsibility they exercised in their daily work. The theme of perception surfaced repeatedly in this study-in terms of the specialists' perceptions of their institutions, their coworkers, and their status, and in terms of their perceptions of other's perceptions of them. (Contains 115 references.) (EMH) Reproductions supplied by EDRS are the best that can be made from the original document. LOYOLA UNIVERSITY CHICAGO
Photonic Cyclone: spatiotemporal optical vortex with controllable transverse orbital angular momentum.

Today, it is well known that light possesses a linear momentum which is along the propagation direction. Besides, scientists also discovered that light can possess an angular momentum (AM), a spin angular momentum (SAM) associated with circular polarization and an orbital angular momentum (OAM) owing to the azimuthally dependent phase. Even though such angular momenta are longitudinal in general, a SAM transverse to the propagation has opened up a variety of key applications [1]. In contrast, investigations of the transverse OAM are quite rare due to its complex nature. Here we demonstrate a simple method to generate a three dimensional (3D) optical wave packet with a controllable purely transverse OAM. Such a wave packet is a spatiotemporal (ST) vortex, which resembles an advancing cyclone, with optical energy flowing in the spatial and temporal dimension. Contrary to the transverse SAM, the magnitude of the transverse OAM carried by the photonic cyclone is scalable to a larger value by simple adjustments. Since the ST vortex carries a controllable OAM in the unique transverse dimension, it has a strong potential for novel applications that may not be possible otherwise. The scheme reported here can be readily adapted for the other spectra regime and different wave fields, opening tremendous opportunities for the study and applications of ST vortex in much broader scopes.
A Decomposition Method for Large-scale Convex Quadratically Constrained Quadratic Programs

We consider solving a convex quadratically constrained quadratic program (QCQP), which has a wide range of applications, including machine learning, data analysis and signal processing. While small to mid-sized convex QCQPs can be solved efficiently by interior-point algorithms, large-scale problems pose significant challenges to traditional centralized algorithms, since the exploding volume of data may overwhelm a single computing unit. In this paper, we propose a decomposition method for general non-separable, large-scale convex QCQPs, using the idea of predictor-corrector proximal primal-dual update with an adaptive step size. The algorithm enables distributed storage of data as well as distributed computing. We both establish convergence of the algorithm to a global optimum and test the algorithm on a computer cluster with multiple threads. The numerical test is done on data sets of different scales using Message Passing Interface, and the results show that our algorithm exhibits favourable scalability for large-scale data even when CPLEX fails to provide a solution due to memory limits.
Defeating Opaque Predicates Statically through Machine Learning and Binary Analysis

We present a new approach that bridges binary analysis techniques with machine learning classification for the purpose of providing a static and generic evaluation technique for opaque predicates, regardless of their constructions. We use this technique as a static automated deobfuscation tool to remove the opaque predicates introduced by obfuscation mechanisms. According to our experimental results, our models have up to 98% accuracy at detecting and deobfuscating state-of-the-art opaque predicates patterns. By contrast, the leading edge deobfuscation methods based on symbolic execution show less accuracy mostly due to the SMT solvers constraints and the lack of scalability of dynamic symbolic analyses. Our approach underlines the efficiency of hybrid symbolic analysis and machine learning techniques for a static and generic deobfuscation methodology.
Analysis of Contraction Effort Level in EMG-Based Gesture Recognition Using Hyperdimensional Computing

Varying contraction levels of muscles is a big challenge in electromyography-based gesture recognition. Some use cases require the classifier to be robust against varying force changes, while others demand to distinguish between different effort levels of performing the same gesture. We use brain-inspired hyperdimensional computing paradigm to build classification models that are both robust to these variations and able to recognize multiple contraction levels. Experimental results on 5 subjects performing 9 gestures with 3 effort levels show up to 39.17% accuracy drop when training and testing across different effort levels, with up to 30.35% recovery after applying our algorithm.
Collecting and Structuring Information in the Information Collage

Knowledge workers, such as scientists, journalists, or consultants, adaptively seek, gather, and consume information. These processes are often inefficient as existing user interfaces provide limited possibilities to combine information from various sources and different formats into a common knowledge representation. In this paper, we present the concept of an information collage (IC) -- a web browser extension combining manual spatial organization of gathered information fragments and automatic text analysis for interactive content exploration and expressive visual summaries. We used IC for case studies with knowledge workers from different domains and longer-term field studies over a period of one month. We identified three different ways how users collect and structure information and provide design recommendations how to support these observed usage strategies.
Untwisting 3‐strand torus knots

We prove that the signature bound for the topological 4‐genus of 3‐strand torus knots is sharp, using McCoy's twisting method. We also show that the bound is off by at most 1 for 4‐strand and 6‐strand torus knots, and improve the upper bound on the asymptotic ratio between the topological 4‐genus and the Seifert genus of torus knots from 2/3 to 14/27.
Simultaneous Estimation of Number of Clusters and Feature Sparsity in Clustering High-Dimensional Data

Estimating the number of clusters (K) is a critical and often difficult task in cluster analysis. Many methods have been proposed to estimate K, including some top performers using resampling approach. When performing cluster analysis in high-dimensional data, simultaneous clustering and feature selection is needed for improved interpretation and performance. To our knowledge, none has investigated simultaneous estimation of K and feature selection in an exploratory cluster analysis. In this paper, we propose a resampling method to meet this gap and evaluate its performance under the sparse K-means clustering framework. The proposed target function balances between sensitivity and specificity of clustering evaluation of pairwise subjects from clustering of full and subsampled data. Through extensive simulations, the method performs among the best over classical methods in estimating K in low-dimensional data. For high-dimensional simulation data, it also shows superior performance to simultaneously estimate K and feature sparsity parameter. Finally, we evaluated the methods in four microarray, two RNA-seq, one SNP and two non-omics datasets. The proposed method achieves better clustering accuracy with fewer selected predictive genes in almost all real applications.
Gravitational baryogenesis without CPT violation

We show that in theories with nonminimal curvature-matter couplings up to first order in the curvature, baryon number (B) conservation and CP are automatically violated, provided that the curvature scalar and its gradient do not vanish. Together with the associated gravitationally induced particle creation in a cosmological framework, which is an essentially out-of-equilibrium process, this implies that all of Sakharov's conditions are satisfied in the simplest extension of General Relativity, without the need of CPT violation. Here, derivatives of the curvature can be disregarded, thus evading the potential problems associated with the Gravitational Baryogenesis (GB) model in a cosmological context. Thanks to the particle creation pressure and the trace anomaly, the curvature scalar is nonzero even in a radiation-dominated Universe. Moreover, the trace anomaly or the energy density of a massive scalar field can amplify the baryon asymmetry at very high temperatures, even when the particle creation rate is very slow. As a result, a baryon asymmetry compatible with observations and a huge CP violation can be generated in the early Universe.
Understanding the phenomenon of viscous slowing down of glass-forming liquids from the static pair correlation function

A theory which uses data of the static pair-correlation function is developed to calculate quantities associated with the viscous slowing down of supercooled liquids. We calculate value of the energy fluctuations that determine the number of stable bonds a particle forms with neighbors from data of the structural relaxation time. The number of bonds and the activation energy for relaxation are shown to increase sharply in a narrow temperature range close to the glass temperature. The configurational entropy calculated from values of the configurational fluctuations is found in good agreement with the value determined from simulations.
Harnack inequality and its application to nonlocal eigenvalue problems in unbounded domains

We prove the Harnack inequality for general nonlocal elliptic equations with zero order terms. As an application we prove the existence of the principal eigenvalue in general domains. Furthermore, we study the eigenvalue problem associated to the existence of self-similar solutions to the parabolic problem and provide estimates on the decay rate.
Learning Fair Rule Lists

As the use of black-box models becomes ubiquitous in high stake decision-making systems, demands for fair and interpretable models are increasing. While it has been shown that interpretable models can be as accurate as black-box models in several critical domains, existing fair classification techniques that are interpretable by design often display poor accuracy/fairness tradeoffs in comparison with their non-interpretable counterparts. In this paper, we propose FairCORELS, a fair classification technique interpretable by design, whose objective is to learn fair rule lists. Our solution is a multi-objective variant of CORELS, a branch-and-bound algorithm to learn rule lists, that supports several statistical notions of fairness. Examples of such measures include statistical parity, equal opportunity and equalized odds. The empirical evaluation of FairCORELS on real-world datasets demonstrates that it outperforms state-of-the-art fair classification techniques that are interpretable by design while being competitive with non-interpretable ones.
Self Learning from Large Scale Code Corpus to Infer Structure of Method Invocations

Automatically generating code from a textual description of method invocation confronts challenges. There were two current research directions for this problem. One direction focuses on considering a textual description of method invocations as a separate Natural Language query and do not consider the surrounding context of the code. Another direction takes advantage of a practical large scale code corpus for providing a Machine Translation model to generate code. However, this direction got very low accuracy. In this work, we tried to improve these drawbacks by proposing MethodInfoToCode, an approach that embeds context information and optimizes the ability of learning of original Phrase-based Statistical Machine Translation (PBMT) in NLP to infer implementation of method invocation given method name and other context information. We conduct an expression prediction models learned from 2.86 million method invocations from the practical data of high qualities corpus on Github that used 6 popular libraries: JDK, Android, GWT, Joda-Time, Hibernate, and Xstream. By the evaluation, we show that if the developers only write the method name of a method invocation in a body of a method, MethodInfoToCode can predict the generated expression correctly at 73% in F1 score.
A DIRECT HAMILTONIAN MCMC APPROACH FOR RELIABILITY ESTIMATION

Accurate and efficient estimation of rare events probabilities is of significant importance, since often the occurrences of such events have widespread impacts. The focus in this work is on precisely quantifying these probabilities, often encountered in reliability analysis of complex engineering systems, by introducing a gradient-based Hamiltonian Markov Chain Monte Carlo (HMCMC) framework, termed Approximate Sampling Target with Post-processing Adjustment (ASTPA). The basic idea is to construct a relevant target distribution by weighting the high-dimensional random variable space through a one-dimensional likelihood model, using the limit-state function. To sample from this target distribution we utilize HMCMC algorithms that produce Markov chain samples based on Hamiltonian dynamics rather than random walks. We compare the performance of typical HMCMC scheme with our newly developed Quasi-Newton based mass preconditioned HMCMC algorithm that can sample very adeptly, particularly in difficult cases with high-dimensionality and very small failure probabilities. To eventually compute the probability of interest, an original post-sampling step is devised at this stage, using an inverse importance sampling procedure based on the samples. The involved user-defined parameters of ASTPA are then discussed and general default values are suggested. Finally, the performance of the proposed methodology is examined in detail and compared against Subset Simulation in a series of static and dynamic low- and high-dimensional benchmark problems.
Optimal curtailed designs for single arm phase II clinical trials

In single-arm phase II oncology trials, the most popular choice of design is Simon's two-stage design, which allows early stopping at one interim analysis. However, the expected trial sample size can be reduced further by allowing curtailment. Curtailment is stopping when the final go or no-go decision is certain, so-called non-stochastic curtailment, or very likely, known as stochastic curtailment. In the context of single-arm phase II oncology trials, stochastic curtailment has previously been restricted to stopping in the second stage and/or stopping for a no-go decision only. We introduce two designs that incorporate stochastic curtailment and allow stopping after every observation, for either a go or no-go decision. We obtain optimal stopping boundaries by searching over a range of potential conditional powers, beyond which the trial will stop for a go or no-go decision. This search is novel: firstly, the search is undertaken over a range of values unique to each possible design realisation. Secondly, these values are evaluated taking into account the possibility of early stopping. Finally, each design realisation's operating characteristics are obtained exactly. The proposed designs are compared to existing designs in a real data example. They are also compared under three scenarios, both with respect to four single optimality criteria and using a loss function. The proposed designs are superior in almost all cases. Optimising for the expected sample size under either the null or alternative hypothesis, the saving compared to the popular Simon's design ranges from 22% to 55%.
An Empirical Study on the Characteristics of Question-Answering Process on Developer Forums

Developer forums are one of the most popular and useful Q&A websites on API usages. The analysis of API forums can be a critical step towards automated question and answer approaches. In this poster, we empirically study three API forums: Twitter, eBay, and AdWords, to investigate the characteristics of question-answering process. We observe that +60% of the posts on all forums were answered with API method names or documentation. +85% of the questions were answered by API development teams and the answers from API development teams drew fewer follow-up questions. Our results provide empirical evidence in future work to build automated solutions to answer developer questions on API forums.
A Tree-based Dictionary Learning Framework

We propose a new outline for adaptive dictionary learning methods for sparse encoding based on a hierarchical clustering of the training data. Through recursive application of a clustering method, the data is organized into a binary partition tree representing a multiscale structure. The dictionary atoms are defined adaptively based on the data clusters in the partition tree. This approach can be interpreted as a generalization of a discrete Haar wavelet transform. Furthermore, any prior knowledge on the wanted structure of the dictionary elements can be simply incorporated. The computational complexity of our proposed algorithm depends on the employed clustering method and on the chosen similarity measure between data points. Thanks to the multiscale properties of the partition tree, our dictionary is structured: when using Orthogonal Matching Pursuit to reconstruct patches from a natural image, dictionary atoms corresponding to nodes being closer to the root node in the tree have a tendency to be used with greater coefficients.
Auto-completion for Data Cells in Relational Tables

We address the task of auto-completing data cells in relational tables. Such tables describe entities (in rows) with their attributes (in columns). We present the CellAutoComplete framework to tackle several novel aspects of this problem, including: (i) enabling a cell to have multiple, possibly conflicting values, (ii) supplementing the predicted values with supporting evidence, (iii) combining evidence from multiple sources, and (iv) handling the case where a cell should be left empty. Our framework makes use of a large table corpus and a knowledge base as data sources, and consists of preprocessing, candidate value finding, and value ranking components. Using a purpose-built test collection, we show that our approach is 40% more effective than the best baseline.
Efficient lock-free durable sets

Non-volatile memory is expected to co-exist or replace DRAM in upcoming architectures. Durable concurrent data structures for non-volatile memories are essential building blocks for constructing adequate software for use with these architectures. In this paper, we propose a new approach for durable concurrent sets and use this approach to build the most efficient durable hash tables available today. Evaluation shows a performance improvement factor of up to 3.3x over existing technology.
Topological Coding and Topological Matrices Toward Network Overall Security

A mathematical topology with matrix is a natural representation of a coding relational structure that is found in many fields of the world. Matrices are very important in computation of real applications, s ce matrices are easy saved in computer and run quickly, as well as matrices are convenient to deal with communities of current networks, such as Laplacian matrices, adjacent matrices in graph theory. Motivated from convenient, useful and powerful matrices used in computation and investigation of today's networks, we have introduced Topcode-matrices, which are matrices of order $3\times q$ and differ from popular matrices applied in linear algebra and computer science. Topcode-matrices can use numbers, letters, Chinese characters, sets, graphs, algebraic groups \emph{etc.} as their elements. One important thing is that Topcode-matrices of numbers can derive easily number strings, since number strings are text-based passwords used in information security. Topcode-matrices can be used to describe topological graphic passwords (Topsnut-gpws) used in information security and graph connected properties for solving some problems coming in the investigation of Graph Networks and Graph Neural Networks proposed by GoogleBrain and DeepMind. Our topics, in this article, are: Topsnut-matrices, Topcode-matrices, Hanzi-matrices, adjacency ve-value matrices and pan-Topcode-matrices, and some connections between these Topcode-matrices will be proven. We will discuss algebraic groups obtained from the above matrices, graph groups, graph networking groups and number string groups for encrypting different communities of dynamic networks. The operations and results on our matrices help us to set up our overall security mechanism to protect networks.
ShelfNet for Fast Semantic Segmentation

In this paper, we present ShelfNet, a novel architecture for accurate fast semantic segmentation. Different from the single encoder-decoder structure, ShelfNet has multiple encoder-decoder branch pairs with skip connections at each spatial level, which looks like a shelf with multiple columns. The shelf-shaped structure can be viewed as an ensemble of multiple deep and shallow paths, thus improving accuracy. We significantly reduce computation burden by reducing channel number, at the same time achieving high accuracy with this unique structure. In addition, we propose a shared-weight strategy in the residual block which reduces parameter number without sacrificing performance. Compared with popular non real-time methods such as PSPNet, our ShelfNet achieves 4× faster inference speed with similar accuracy on PASCAL VOC dataset. Compared with real-time segmentation models such as BiSeNet, our model achieves higher accuracy at comparable speed on the Cityscapes Dataset, enabling the application in speed-demanding tasks such as street-scene understanding for autonomous driving. Furthermore, our ShelfNet achieves 79.0% mIoU on Cityscapes Dataset with ResNet34 backbone, outper-forming PSPNet and BiSeNet with large backbones such as ResNet101. Through extensive experiments, we validated the superior performance of ShelfNet. We provide link to the implementation https://github.com/ juntang-zhuang/ShelfNet-lw-cityscapes.
Floer homology via Twisted Loop Spaces

Answering a question of Witten, we introduce a novel method for defining an integral version of Lagrangian Floer homology, removing the standard restriction that the Lagrangians in question must be relatively Pin. Using this technique, we derive stronger bounds on the self-intersection of certain exact Lagrangians $\mathbb{RP}^2 \times L'$ than those that follow from traditional methods. We define a integral version of Lagrangian Floer homology all oriented closed exact Lagrangians $L$ in a Liouville domain and prove a general self-intersection bound coming from the algebraic properties of the diagonal bimodule of a twist of the dg-algebra of chains on the based loop space of $L$.