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Thermal nonequilibrium (TNE) is a condition of the plasma in the solar corona in which the local rate of energy loss due to radiation increases to the point that it cannot be sustained by the various heating terms acting on the plasma, precluding the existence of a steady state. The limit cycles of precipitation and evaporation that result from TNE have been simulated in 1D models of coronal loops, as well as 2D and 3D models of the solar chromosphere and lower corona. However, a careful study of TNE in the solar wind has not been performed until now. Here we demonstrate that for suitable combinations of local and global heating rates it is possible for the plasma to exhibit a TNE condition, even in the context of a transonic solar wind with appreciable mass and energy fluxes. This implies limits on the amount of foot-point heating that can be withstood under steady-state conditions in the solar wind, and may help to explain the variability of solar wind streams that emanate from regions of highly concentrated magnetic flux on the solar surface. The implications of this finding pertain to various sources of high-density solar wind, including plumes that form above regions of mixed magnetic polarity in polar coronal holes and the slow solar wind (SSW) that emanates from coronal hole boundaries.

In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This comprehensive review examines state-of-the-art (SOTA) pre-trained models and foundation models in music, spanning from representation learning, generative learning and multimodal learning. We first contextualise the significance of music in various industries and trace the evolution of AI in music. By delineating the modalities targeted by foundation models, we discover many of the music representations are underexplored in FM development. Then, emphasis is placed on the lack of versatility of previous methods on diverse music applications, along with the potential of FMs in music understanding, generation and medical application. By comprehensively exploring the details of the model pre-training paradigm, architectural choices, tokenisation, finetuning methodologies and controllability, we emphasise the important topics that should have been well explored, like instruction tuning and in-context learning, scaling law and emergent ability, as well as long-sequence modelling etc. A dedicated section presents insights into music agents, accompanied by a thorough analysis of datasets and evaluations essential for pre-training and downstream tasks. Finally, by underscoring the vital importance of ethical considerations, we advocate that following research on FM for music should focus more on such issues as interpretability, transparency, human responsibility, and copyright issues. The paper offers insights into future challenges and trends on FMs for music, aiming to shape the trajectory of human-AI collaboration in the music realm.

We describe a novel approach for developing realistic digital models of dynamic range compressors for digital audio production by analyzing their analog prototypes. While realistic digital dynamic compressors are potentially useful for many applications, the design process is challenging because the compressors operate nonlinearly over long time scales. Our approach is based on the structured state space sequence model (S4), as implementing the state-space model (SSM) has proven to be efficient at learning long-range dependencies and is promising for modeling dynamic range compressors. We present in this paper a deep learning model with S4 layers to model the Teletronix LA-2A analog dynamic range compressor. The model is causal, executes efficiently in real time, and achieves roughly the same quality as previous deep-learning models but with fewer parameters.

An expansion of cross-sectional area directly impacts the mass flow along a coronal loop, and significantly alters the radiative and hydrodynamic evolution of that loop as a result. Previous studies have found that an area expansion from chromosphere to corona significantly lengthens the cooling time of the corona, and appears to suppress draining from the corona. In this work, we examine the fluid dynamics to understand how the mass flow rate, the energy balance, and the cooling and draining timescales are affected by a non-uniform area. We find that in loops with moderate or large expansion (cross-sectional area expansion factors of 2, 3, 10, 30, 100 from photosphere to apex), impulsive heating, for either direct thermal heating or electron beam heating, induces a steady flow into the corona, so that the coronal density continues to rise during the cooling phase, whereas a uniform loop drains during the cooling phase. The induced upflow carries energy into the corona, balancing the losses from thermal conduction, and continues until thermal conduction weakens enough so that it can no longer support the radiative losses of the transition region (TR). As a result, the plasma cools primarily radiatively until the onset of catastrophic collapse. The speed and duration of the induced upflow both increase in proportion to the rate of area expansion. We argue that observations of blue-shifted spectral lines, therefore, could place a constraint on a loop's area expansion., Comment: Resubmitted to ApJ. Comments and criticisms are welcomed!

Music motif, as a conceptual building block of composition, is crucial for music structure analysis and automatic composition. While human listeners can identify motifs easily, existing computational models fall short in representing motifs and their developments. The reason is that the nature of motifs is implicit, and the diversity of motif variations extends beyond simple repetitions and modulations. In this study, we aim to learn the implicit relationship between motifs and their variations via representation learning, using the Siamese network architecture and a pretraining and fine-tuning pipeline. A regularization-based method, VICReg, is adopted for pretraining, while contrastive learning is used for fine-tuning. Experimental results on a retrieval-based task show that these two methods complement each other, yielding an improvement of 12.6% in the area under the precision-recall curve. Lastly, we visualize the acquired motif representations, offering an intuitive comprehension of the overall structure of a music piece. As far as we know, this work marks a noteworthy step forward in computational modeling of music motifs. We believe that this work lays the foundations for future applications of motifs in automatic music composition and music information retrieval.

Structure is one of the most essential aspects of music, and music structure is commonly indicated through repetition. However, the nature of repetition and structure in music is still not well understood, especially in the context of music generation, and much remains to be explored with Music Information Retrieval (MIR) techniques. Analyses of two popular music datasets (Chinese and American) illustrate important music construction principles: (1) structure exists at multiple hierarchical levels, (2) songs use repetition and limited vocabulary so that individual songs do not follow general statistics of song collections, (3) structure interacts with rhythm, melody, harmony, and predictability, and (4) over the course of a song, repetition is not random, but follows a general trend as revealed by cross-entropy. These and other findings offer challenges as well as opportunities for deep-learning music generation and suggest new formal music criteria and evaluation methods. Music from recent music generation systems is analyzed and compared to human-composed music in our datasets, often revealing striking differences from a structural perspective., Comment: In Proceedings of the 23rd Int. Society for Music Information Retrieval (ISMIR) 2022

Interchange reconnection is thought to play an important role in determining the dynamics and material composition of the slow solar wind that originates from near coronal hole boundaries. To explore the implications of this process we simulate the dynamic evolution of a solar wind stream along a newly-opened magnetic flux tube. The initial condition is composed of a piecewise continuous dynamic equilibrium in which the regions above and below the reconnection site are extracted from steady-state solutions along open and closed field lines. The initial discontinuity at the reconnection site is highly unstable and evolves as a Riemann problem, decomposing into an outward-propagating shock and inward-propagating rarefaction that eventually develop into a classic N-wave configuration. This configuration ultimately propagates into the heliosphere as a coherent structure and the entire system eventually settles to a quasi-steady wind solution. In addition to simulating the fluid evolution we also calculate the time-dependent non-equilibrium ionization of oxygen in real time in order to construct in situ diagnostics of the conditions near the reconnection site. This idealized description of the plasma dynamics along a newly-opened magnetic field line provides a baseline for predicting and interpreting the implications of interchange reconnection for the slow solar wind. Notably, the density and velocity within the expanding N-wave are generally enhanced over the ambient wind, as is the O7+/O6+ ionization ratio, which exhibits a discontinuity across the reconnection site that is transported by the flow and arrives later than the propagating N-wave.

Personal space (PS) is the distance that people prefer to maintain between themselves and unfamiliar others. Interpersonal intrusion into a given persons PS evokes discomfort, and an urge to move further apart. We hypothesized that in parietal cortex: 1. PS processing involves a previously-described threat-sensitive brain circuit, and 2. the spatial encoding of distance is transformed accordingly, from purely sensory to PS-related. These hypotheses were tested using 7T fMRI at high spatial resolution. In response to images of different visual stimuli across a range of virtual distances, we found two categories of distance encoding in functionally corresponding columns within parietal cortex. First, PD (personal distance) columns responded selectively to moving and stationary face images presented at virtual distances nearer (but not further) than each subjects behaviorally-defined PS boundary. In the majority of PD columns, BOLD response amplitudes increased monotonically and nonlinearly with increasing virtual face proximity. In the remaining PD columns, BOLD responses decreased with increasing proximity. These fMRI response functions appear related to previously-reported variations in subjective discomfort levels, and physiologic arousal, during intrusion into but not beyond personal space. Secondly, DD (disparity distance) columns in parietal cortex responded selectively to disparity-based near or far distances, in random dot stimuli, like disparity-selective columns described previously in occipital cortex. In parietal cortex, maps of DD columns were systematically non-overlapping (interdigitated) with the PD columns. These results suggest that the transformation of spatial information, from visual to higher-order, may be computed in multiple small sites, rather than across a larger cortical gradient, in parietal cortex., Comment: 19 pages, 10 figures and 1 table. Tootell and Nasiriavanaki share joint first authorship

Recent advances in deep learning have expanded possibilities to generate music, but generating a customizable full piece of music with consistent long-term structure remains a challenge. This paper introduces MusicFrameworks, a hierarchical music structure representation and a multi-step generative process to create a full-length melody guided by long-term repetitive structure, chord, melodic contour, and rhythm constraints. We first organize the full melody with section and phrase-level structure. To generate melody in each phrase, we generate rhythm and basic melody using two separate transformer-based networks, and then generate the melody conditioned on the basic melody, rhythm and chords in an auto-regressive manner. By factoring music generation into sub-problems, our approach allows simpler models and requires less data. To customize or add variety, one can alter chords, basic melody, and rhythm structure in the music frameworks, letting our networks generate the melody accordingly. Additionally, we introduce new features to encode musical positional information, rhythm patterns, and melodic contours based on musical domain knowledge. A listening test reveals that melodies generated by our method are rated as good as or better than human-composed music in the POP909 dataset about half the time., Comment: 6 pages, 9 figures, in Proc. of the 22nd Int. Society for Music Information Retrieval Conf.,Online, 2021

Many practices have been presented in music generation recently. While stylistic music generation using deep learning techniques has became the main stream, these models still struggle to generate music with high musicality, different levels of music structure, and controllability. In addition, more application scenarios such as music therapy require imitating more specific musical styles from a few given music examples, rather than capturing the overall genre style of a large data corpus. To address requirements that challenge current deep learning methods, we propose a statistical machine learning model that is able to capture and imitate the structure, melody, chord, and bass style from a given example seed song. An evaluation using 10 pop songs shows that our new representations and methods are able to create high-quality stylistic music that is similar to a given input song. We also discuss potential uses of our approach in music evaluation and music therapy., Comment: 26 pages, 12 figures

Repetition is a basic indicator of musical structure. This study introduces new algorithms for identifying musical phrases based on repetition. Phrases combine to form sections yielding a two-level hierarchical structure. Automatically detected hierarchical repetition structures reveal significant interactions between structure and chord progressions, melody and rhythm. Different levels of hierarchy interact differently, providing evidence that structural hierarchy plays an important role in music beyond simple notions of repetition or similarity. Our work suggests new applications for music generation and music evaluation., Comment: In Proceedings of the 2020 Joint Conference on AI Music Creativity (CSMC-MuMe 2020), Stockholm, Sweden, October 21-24, 2020

In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness. In principle, one could apply image-based style transfer techniques to a time-frequency representation of an audio signal, but this depends on having a representation that allows independent manipulation of timbre as well as high-quality waveform generation. We introduce TimbreTron, a method for musical timbre transfer which applies "image" domain style transfer to a time-frequency representation of the audio signal, and then produces a high-quality waveform using a conditional WaveNet synthesizer. We show that the Constant Q Transform (CQT) representation is particularly well-suited to convolutional architectures due to its approximate pitch equivariance. Based on human perceptual evaluations, we confirmed that TimbreTron recognizably transferred the timbre while otherwise preserving the musical content, for both monophonic and polyphonic samples., Comment: 17 pages, published as a conference paper at ICLR 2019

The topology of magnetic fields near the open-closed flux boundary in the Sun's corona is an important influencing factor in the process of interchange reconnection, whereby plasma is exchanged between open and closed flux domains. Maps of the magnetic squashing factor at the radial outer boundary in coronal field models reveal the presence of the so-called `S-web', and suggest that interchange reconnection could potentially deposit closed coronal material into high-latitude regions far from the heliospheric current sheet. Here we demonstrate that certain features of the S-web reveal the underlying topological structure of the magnetic field. Specifically, in order for the arcing bands of highly squashed magnetic flux of the S-web to terminate or intersect away from the helmet streamer apex, there must be a null spine line that maps a finite segment of the photospheric open-closed boundary up to a singular point in the open flux domain. We propose that this association between null spine lines and arc termination points may be used to identify locations in the heliosphere that are preferential for the appearance of solar energetic particles or slow solar wind plasma with certain characteristics.

In this work, we show that saturating output activation functions, such as the softmax, impede learning on a number of standard classification tasks. Moreover, we present results showing that the utility of softmax does not stem from the normalization, as some have speculated. In fact, the normalization makes things worse. Rather, the advantage is in the exponentiation of error gradients. This exponential gradient boosting is shown to speed up convergence and improve generalization. To this end, we demonstrate faster convergence and better performance on diverse classification tasks: image classification using CIFAR-10 and ImageNet, and semantic segmentation using PASCAL VOC 2012. In the latter case, using the state-of-the-art neural network architecture, the model converged 33% faster with our method (roughly two days of training less) than with the standard softmax activation, and with a slightly better performance to boot.

Deep residual networks (ResNets) have significantly pushed forward the state-of-the-art on image classification, increasing in performance as networks grow both deeper and wider. However, memory consumption becomes a bottleneck, as one needs to store the activations in order to calculate gradients using backpropagation. We present the Reversible Residual Network (RevNet), a variant of ResNets where each layer's activations can be reconstructed exactly from the next layer's. Therefore, the activations for most layers need not be stored in memory during backpropagation. We demonstrate the effectiveness of RevNets on CIFAR-10, CIFAR-100, and ImageNet, establishing nearly identical classification accuracy to equally-sized ResNets, even though the activation storage requirements are independent of depth.

Markov chain Monte Carlo (MCMC) is one of the main workhorses of probabilistic inference, but it is notoriously hard to measure the quality of approximate posterior samples. This challenge is particularly salient in black box inference methods, which can hide details and obscure inference failures. In this work, we extend the recently introduced bidirectional Monte Carlo technique to evaluate MCMC-based posterior inference algorithms. By running annealed importance sampling (AIS) chains both from prior to posterior and vice versa on simulated data, we upper bound in expectation the symmetrized KL divergence between the true posterior distribution and the distribution of approximate samples. We present Bounding Divergences with REverse Annealing (BREAD), a protocol for validating the relevance of simulated data experiments to real datasets, and integrate it into two probabilistic programming languages: WebPPL and Stan. As an example of how BREAD can be used to guide the design of inference algorithms, we apply it to study the effectiveness of different model representations in both WebPPL and Stan.

Computing the marginal likelihood (ML) of a model requires marginalizing out all of the parameters and latent variables, a difficult high-dimensional summation or integration problem. To make matters worse, it is often hard to measure the accuracy of one's ML estimates. We present bidirectional Monte Carlo, a technique for obtaining accurate log-ML estimates on data simulated from a model. This method obtains stochastic lower bounds on the log-ML using annealed importance sampling or sequential Monte Carlo, and obtains stochastic upper bounds by running these same algorithms in reverse starting from an exact posterior sample. The true value can be sandwiched between these two stochastic bounds with high probability. Using the ground truth log-ML estimates obtained from our method, we quantitatively evaluate a wide variety of existing ML estimators on several latent variable models: clustering, a low rank approximation, and a binary attributes model. These experiments yield insights into how to accurately estimate marginal likelihoods.

Let $G$ be the unit distance graph in the plane. A well-known problem in combinatorial geometry is that of determining the chromatic number of $G$. It is known that $4\le \chi(G)\le 7$. The upper bound of 7 is obtained using tilings of the plane. The present paper studies two problems where we seek proper colourings of $G$, adding restrictions inspired by tilings: Let $H(\epsilon)$ be the graph whose vertices are the points of ${\mathbb R}^2$, with an edge between two points if their distance lies in the interval $[1,1+\epsilon]$. We show that for small $\epsilon$, $0<\epsilon\le \frac{3\sqrt{2}}{4}-1$, we have $6\le \chi(H(\epsilon))\le 7$. This improves the result of Exoo and Grytczuk et al. that $5\le \chi(H(\epsilon))$ for small $\epsilon$. Suppose that $G$ is properly coloured, but so that two solidly coloured regions meet along a straight line in some neighbourhood. Then at least 5 colours must be used., Comment: 11 pages, 6 figures. Reference to Grytczuk et al. added, March 22, 2016

Markov random fields (MRFs) are difficult to evaluate as generative models because computing the test log-probabilities requires the intractable partition function. Annealed importance sampling (AIS) is widely used to estimate MRF partition functions, and often yields quite accurate results. However, AIS is prone to overestimate the log-likelihood with little indication that anything is wrong. We present the Reverse AIS Estimator (RAISE), a stochastic lower bound on the log-likelihood of an approximation to the original MRF model. RAISE requires only the same MCMC transition operators as standard AIS. Experimental results indicate that RAISE agrees closely with AIS log-probability estimates for RBMs, DBMs, and DBNs, but typically errs on the side of underestimating, rather than overestimating, the log-likelihood.

Markov Chain Monte Carlo (MCMC) algorithms are a workhorse of probabilistic modeling and inference, but are difficult to debug, and are prone to silent failure if implemented naively. We outline several strategies for testing the correctness of MCMC algorithms. Specifically, we advocate writing code in a modular way, where conditional probability calculations are kept separate from the logic of the sampler. We discuss strategies for both unit testing and integration testing. As a running example, we show how a Python implementation of Gibbs sampling for a mixture of Gaussians model can be tested., Comment: Presented at the 2014 NIPS workshop on Software Engineering for Machine Learning

Measurements of temperature and density near supra-arcade current sheets suggest that plasma on unreconnected field lines may experience some degree of "pre-heating" and "pre-densification" prior to their reconnection. Models of patchy reconnection allow for heating and acceleration of plasma along reconnected field lines but do not offer a mechanism for transport of thermal energy across field lines. Here we present a model in which a reconnected flux tube retracts, deforming the surrounding layer of unreconnected field. The deformation creates constrictions that act as peristaltic pumps, driving plasma flow along affected field lines. Under certain circumstances these flows lead to shocks that can extend far out into the unreconnected field, altering the plasma properties in the affected region. These findings have direct implications for observations in the solar corona, particularly in regard to such phenomena as high temperatures near current sheets in eruptive solar flares and wakes seen in the form of descending regions of density depletion or supra-arcade downflows.

The purpose of this study was to find critical features of engineering design that can be incorporated within technology education learning activities, and develop a rubric for assessing these features. Data were collected through semi-structured interviews with three professors actively involved in engineering education. Supporting documents such as engineering design course outlines and rubrics were also examined. Using a phenomenological approach, this study identified the concept of engineering design, key features of the engineering design process, and critical elements that should be assessed in an engineering design activity in the context of technology education. A key product of the study was development of a rubric to be used in evaluating integration of engineering design as a focus for technology education. (Contains 1 table and 2 figures.)

The purpose of this study was to better understand cognitive strategies used by high school technology education students who have participated in technology education instruction with an engineering design focus. Specifically, this study evaluated the cognitive strategies of students participating in "Project Lead the Way" curriculum programs compared with students participating in technology education programs partnering with the National Center for Engineering and Technology Education (NCETE). High school students from these two groups were studied as they worked through an ill-defined problem: moving drinking water in developing countries. The data collected from these protocols was analyzed using a coding process and a list of universal technical mental processes (Halfin,1973) and OPTEMP software, (Hill, 1997) to record frequency and time of each mental process employed by the students. The study identified common cognitive strategies employed by students and identified where greatest emphasis was placed in the design process among the two groups. This study provides important insight for technology education as it seeks to implement engineering design. Appended are: (1) Activity; (2) Group 1 Student Demographic Information; (3) Demographic Information for Group 1 Teacher; (4) Demographic Information for Group 2 Teacher; and (5) The Original Cognitive Processes identified by Halfin's 1973 Study of High-level Designers. (Contains 4 tables and 5 figures.)

The purpose of this study was to describe a process of preparing technology education teachers to teach engineering design concepts in the context of technology education. This process was identified through a study of professional development activities that were organized and conducted by technology teacher education partner universities of the National Center for Engineering and Technology Education (NCETE) to prepare middle school and high school technology teachers to infuse engineering design, problem solving, content, and analytical skills into the K-12 curriculum. A collective multisite case study formed the methodology for this study. Data were collected through individual interview sessions that lasted 30-40 minutes, video footage, observations and artifacts. A total of 15 interviews were individually analyzed, and then compared through a cross-case analysis. Professional development emerged as a core theme and comprised the following sub themes: planning, communities of practice, professional development administration and learning environment, professional development for technology education teachers, professional development activities in the classroom, assessment, expertise, and meaning making. (Contains 4 figures.)

Cinema as an art and communication form is entering its second century of development. Sergei Eisenstein conceived of editing in horizontal and vertical terms. He saw vertical editing patterns primarily as the synchronization of simultaneous image and sound elements, particularly music, no create cinematic meaning by means of the relationship between them. The Eisensteinian editing theories and techniques embody the values of the dominant paradigm of the time. They were grounded in a linear, assembled, progressive, and fixed worldview of the positivist paradigm of the industrial era. In recent years, both paradigm and technology have changed. At the paradigmatic level, emergent systems theory derived from biology and ecological consciousness, posits a worldview of interdependent systems that are self-organizing, self-renewing, and self-transcending. At the technological level, the rise of cybernetic and electronic technologies, replete with computers and networks, now dominate the technological landscape. The cinematic technology revolution of the last decade along with the revisioning of the theory base has led to a reconceptualization of vertical editing. The techniques of digital technology are informed by emergent systems thinking. The spontaneous juxtaposition of layers, immediate feedback, and layering of imagery all create new meanings. Because a digital image is not fixed it can be revised at any time. Informed by a systems view and implemented by digital tools and techniques, the Eisensteinian notion of vertical editing has acquired new meaning. (AEF)

Digital electronic technology systems are composed of hardware, software, thoughtware, and their context. The four elements of the model are in a context sensitive relationship where to alter one element is to change them all. Digital Cinema is a theoretical construct for understanding moving images produced and viewed in a digital context. Contemporary global culture is evolving from a mechanistic worldview into an emergent systems model in an age of information. Emergent systems are self-organizing, self-maintaining, self-renewing, and self-transcending. A digital production system for a digital cinema emerges from this theoretical context. The Digital Cinema production system is composed of video, computers, mass storage devices, telecommunications, and audio. Digital moving image productions are the result of a three phase technology restructuring process. In the first phase, new technology enhances existing tasks and processes. It makes them better, faster, cheaper or easier. Phase two is an integrative stage; a technological integration of multiple changed tasks into an integrated whole occurs. At the third phase, technological reconceptualization not only alters the tasks and the processes, but the rational and the outcomes of the process as well. Not only the "how," but the "what" is redefined. Digital Cinema is a phase three technological development. Moving images go through a four phase process regardless of medium format, or scale. The phases are: pre-production, production, post-production, and dissemination. All four phases are profoundly affected by digital restructuring. Digital storyboards point the way to a non-linear, emergent organizational approach to the four phase production process. Specific digital hardware and software applications are designed for each of the four phases to facilitate the digital restructuring process. (AEF)

We present the results of generalized measurements of optical polarization designed to provide one of three or four distinct outcomes. This has allowed us to discriminate between nonorthogonal polarization states with an error probability that is close to the minimum allowed by quantum theory. Employing these optimal detection strategies on sets of three (trine) or four (tetrad) equiprobable symmetric nonorthogonal polarization states, we obtain a mutual information that exceeds the maximum value attainable using conventional (von Neumann) polarization measurements., Comment: RevTeX4, 14 pages, 10 figures (included as eps), submitted to Phys. Rev. A. Typos corrected, Sec II B rewritten, no changes to results or conclusions

We present the first full demonstration of unambiguous state discrimination between non-orthogonal quantum states. Using a novel free space interferometer we have realised the optimum quantum measurement scheme for two non-orthogonal states of light, known as the Ivanovic-Dieks-Peres (IDP) measurement. We have for the first time gained access to all three possible outcomes of this measurement. All aspects of this generalised measurement scheme, including its superiority over a standard von Neumann measurement, have been demonstrated within 1.5% of the IDP predictions.

Streamers and pseudostreamers structure the corona at the largest scales, as seen in both eclipse and coronagraph white-light images. Their inverted-goblet appearance encloses broad coronal loops at the Sun and tapers to a narrow radial stalk away from the star. The streamer associated with the global solar dipole magnetic field is long-lived, predominantly contains a single arcade of nested loops within it, and separates opposite-polarity interplanetary magnetic fields with the heliospheric current sheet anchored at its apex. Pseudostreamers, on the other hand, are transient, enclose double arcades of nested loops, and separate like-polarity fields with a dense plasma sheet. We use numerical magnetohydrodynamic simulations to calculate, for the first time, the formation of pseudostreamers in response to photospheric magnetic-field evolution. Convective transport of a minority-polarity flux concentration, initially positioned under one side of a streamer, through the streamer boundary into the adjacent, pre-existing coronal hole forms the pseudostreamer. Interchange magnetic reconnection at the overlying coronal null point(s) governs the development of the pseudostreamer above – and of anew, satellite coronal hole behind – the moving minority polarity. The reconnection dynamics liberate coronal-loop plasma that can escape into the heliosphere along so-called separatrix-web (“S-Web”)arcs, which reach far from the heliospheric current sheet and the solar equatorial plane, and can explain the origin of high-latitude slow solar wind. We describe the implications of our results for in-situ and remote-sensing observations of the corona and heliosphere as obtained, most recently, by Parker Solar Probe and Solar OrbiteR.

This paper reports on a study conducted to examine the status of efforts by the State of Minnesota, its institutions of higher learning, and its public school districts to recruit, prepare, and retain staff members of color (minority) for professional teaching service in public school districts. Methodology employed for the study included a review of national literature on approaches used in recruiting staff members of color for professional teaching positions. Then, through consultation with in-state expert resource personnel, some models, processes, and strategies for teacher recruitment, preparation, and retention were identified. On-site visits were made to observe promising programs and models and/or to interview key program directors or sponsors of those programs. A sketch of each program, an assessment of transportability, and ratings as "fast-track,""moderate-track," and "slow-track" were then ascribed to project the program, process, or strategy turn-around time to prepare staff members of color for professional teaching positions. A myriad of support systems were identified as essential to enhance the success of the program. It was recommended that state policy making and implementation bodies, public school districts, colleges and universities, media support coalitions, and businesses develop, implement, and support a major initiative to recruit and prepare an average of 250 teaching staff members of color annually for each of the next 20 years. Five appendixes include: the urban collaborative educator development program grant proposal; a description of the "Common Ground Consortium"; a profile of the "Multicultural Teacher Development Project"; a flyer for the Minneapolis (Minnesota) magnet schools; and a flyer for the St. Paul (Minnesota) public schools minority education program. (LL)

The staffing process in student affairs is one of the most important leadership and management functions that administrators are called upon to perform. How well the interrelated functions of recruitment, selection, orientation, supervision, performance appraisal, professional development, and departure by staff are handled determines the effectiveness of student affairs divisions. (Contains 21 references.) (Author)

A geographically stratified sample of 263 chief student affairs administrators responded to questions related to their division's professional staff recruiting and selection processes, professional development and activities, and performance appraisal procedures. This produced a descriptive overview of national staffing practice sin student affairs. (Contains 21 references and 12 tables.) (Author)

Describes the development andexplores the validity of the Synergistic Supervision Scale (SSS), which measures the extent to which staff perceive that their supervisor focuses on the advancement of the institutional mission and the personal and professional advancement of staff. Results indicate that synergistic supervision seems to be a valid construct that has significance for staff in student affairs divisions. (Contains 22 references.) (Author/GCP)

Student affairs professionals often find it difficult to become aware of opportunities to get involved in professional activities in other countries. Article addresses this situation by presenting a wide array of professional development opportunities for student affairs leaders to gain an international perspective. The author provides up-to-date contact information for international organizations in over thirty countries. (Author/JDM)

Performance appraisal is widely endorsed as an essential part of the staffing process, yet most student affairs practitioners acknowledge that it is done poorly, or not at all, in their institutions. The reasons for this neglect are outlined, and suggestions are offered for ways to make performance appraisal a more valuable staffing tool. (Contains 23 references.) (Author)

Examines student affairs professionals' areas of practice in which legal risk-management and ethical practice frequently clash. Offers a series of recommendations to maximize ethical practice while minimizing risks. Provides a framework for further discussion and reflection. (MKA)

Asserts that staff development is best conceptualized and operationalized as one facet of an integrated staffing function that is inextricably and purposefully intertwined with supervision and performance appraisal. Offers suggestions for implementing staff-development plans in the form of a scheme to analyze available methods or avenues for pursuing jointly determined goals. (Author/MKA)

Research to determine the attitudes of technology education teachers (n=149), school principals (n=105), and school counselors (n=99) about selected characteristics of the field reflected considerable agreement in perceptions of technology education, especially that it should be for all students. However, principals and counselors rated the latter more highly than did teachers. (JOW)

The Occupational Work Ethic Inventory was completed by 183 high school vocational students and 1,160 workers in the same county. Students had lower scores than workers on initiative and dependability, but not interpersonal skills. Females scored higher than males in all three areas. (SK)