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We examine the new physics sensitivity of the rare decay $\Lambda_b \to \Lambda \nu\bar\nu$ which can be accessible at future $Z$-pole machines like FCC-ee and CEPC. We find that the longitudinal polarization of $\Lambda_b$ baryons produced in $Z$ decays introduces a novel observable, the forward-backward asymmetry $A_\text{FB}^\uparrow$ in the angle between the outgoing $\Lambda$ momentum and the $\Lambda_b$ spin. We provide Standard Model predictions for the $\Lambda_b \to \Lambda \nu\bar\nu$ branching ratio and $A_\text{FB}^\uparrow$, and show that future precision measurements of these observables are complementary and probe new physics scales comparable to other $b \to s \nu\bar\nu$ and $b \to s \ell^+ \ell^-$ processes. We also show that the zero crossing of the forward-backward asymmetry offers a robust test of form factor calculations independent of new physics., Comment: 28 pages, 8 figures

This paper studies the impact of artificial intelligence on innovation, exploiting the randomized introduction of a new materials discovery technology to 1,018 scientists in the R&D lab of a large U.S. firm. AI-assisted researchers discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product innovation. These compounds possess more novel chemical structures and lead to more radical inventions. However, the technology has strikingly disparate effects across the productivity distribution: while the bottom third of scientists see little benefit, the output of top researchers nearly doubles. Investigating the mechanisms behind these results, I show that AI automates 57% of "idea-generation" tasks, reallocating researchers to the new task of evaluating model-produced candidate materials. Top scientists leverage their domain knowledge to prioritize promising AI suggestions, while others waste significant resources testing false positives. Together, these findings demonstrate the potential of AI-augmented research and highlight the complementarity between algorithms and expertise in the innovative process. Survey evidence reveals that these gains come at a cost, however, as 82% of scientists report reduced satisfaction with their work due to decreased creativity and skill underutilization.

Training neural network classifiers on datasets contaminated with noisy labels significantly increases the risk of overfitting. Thus, effectively implementing Early Stopping in noisy label environments is crucial. Under ideal circumstances, Early Stopping utilises a validation set uncorrupted by label noise to effectively monitor generalisation during training. However, obtaining a noise-free validation dataset can be costly and challenging to obtain. This study establishes that, in many typical learning environments, a noise-free validation set is not necessary for effective Early Stopping. Instead, near-optimal results can be achieved by monitoring accuracy on a noisy dataset - drawn from the same distribution as the noisy training set. Referred to as `Noisy Early Stopping' (NES), this method simplifies and reduces the cost of implementing Early Stopping. We provide theoretical insights into the conditions under which this method is effective and empirically demonstrate its robust performance across standard benchmarks using common loss functions.

This paper describes in detail how erasure codes are implemented in the Swarm system. First, in Section 1, we introduce erasure codes, and show how to apply them to files in Swarm (Section 2). In Section 3, we introduce security levels of data availability and derive their respective parameterisations. In Section 4, we describe a construct that enables cross-neighbourhood redundancy for singleton chunks and which completes erasure coding. Finally, in 5, we propose a number of retrieval strategies applicable to erasure-coded files., Comment: 14 pages, 7 figures, 4 tables

The identification of cells and particles based on their transport properties in microfluidic devices is crucial for numerous applications in biology and medicine. Neutrally buoyant particles transported in microfluidic channels, migrate laterally towards stable locations. However, the effect of the particle and flow properties on these focusing positions remain largely unknown. We conduct large scale simulations with dissipative particle dynamics, demonstrating that freely moving particles exhibit significant differences in their focusing patterns from particles that are prevented from rotation. In circular pipes, we observe drastic changes in rotating versus non-rotating focusing positions. We demonstrate that rotation-induced lateral lift force is significant, unlike previously believed, and is linearly dependent on the rotation magnitude. A simple phenomenological explanation extending existing theories is presented, that agrees well with our numerical findings. For flows in square ducts, we report four face-centered stable positions for the rotating particles, in accordance with experimental studies. However, the non-rotating particles stay scattered on a concentric one-dimensional annulus, thus revealing qualitatively different behavior with respect to the free ones. Our findings suggest new designs for micro-particle and cell sorting in microfluidics devices.

During social interactions, understanding the intricacies of the context can be vital, particularly for socially anxious individuals. While previous research has found that the presence of a social interaction can be detected from ambient audio, the nuances within social contexts, which influence how anxiety provoking interactions are, remain largely unexplored. As an alternative to traditional, burdensome methods like self-report, this study presents a novel approach that harnesses ambient audio segments to detect social threat contexts. We focus on two key dimensions: number of interaction partners (dyadic vs. group) and degree of evaluative threat (explicitly evaluative vs. not explicitly evaluative). Building on data from a Zoom-based social interaction study (N=52 college students, of whom the majority N=45 are socially anxious), we employ deep learning methods to achieve strong detection performance. Under sample-wide 5-fold Cross Validation (CV), our model distinguished dyadic from group interactions with 90\% accuracy and detected evaluative threat at 83\%. Using a leave-one-group-out CV, accuracies were 82\% and 77\%, respectively. While our data are based on virtual interactions due to pandemic constraints, our method has the potential to extend to diverse real-world settings. This research underscores the potential of passive sensing and AI to differentiate intricate social contexts, and may ultimately advance the ability of context-aware digital interventions to offer personalized mental health support., Comment: 8 pages, 4 figures, 3 tables. Accepted by ACII 2024, Glasgow, UK. To appear in the Proceedings of ACII 2024

I formulate the theory of three dimensional "Malthusian flocks" -- i.e., coherently moving collections of self-propelled entities (such as living creatures) which are being "born" and "dying" during their motion -- whose constituents all have a preference for having their velocity vectors lie parallel to the same two-dimensional plane. I determine the universal scaling exponents characterizing such systems exactly, finding that the dynamical exponent $z=3/2$, the "anisotropy" exponent $\zeta=3/4$, and the "roughness" exponent $\chi=-1/2$. I also give the scaling laws implied by these exponents., Comment: 6 pages, no figures

Tidal interactions between galaxies often give rise to tidal tails, which can harbor concentrations of stars and interstellar gas resembling dwarf galaxies. Some of these tidal dwarf galaxies (TDGs) have the potential to detach from their parent galaxies and become independent entities, but their long-term survival is uncertain. In this study, we conducted a search for detached TDGs associated with a sample of 39 interacting galaxy pairs in the local Universe using infrared, ultraviolet, and optical images. We employed IR colors and UV/optical/IR spectral energy distributions to identify potential interlopers, such as foreground stars or background quasars. Through spectroscopic observations using the Boller and Chivens spectrograph at San Pedro M\'artir Observatory, we confirmed that six candidate TDGs are at the same redshift as their putative parent galaxy pairs. We identified and measured emission lines in the optical spectra and calculated nebular oxygen abundances, which range from log(O/H) = 8.10 $\pm$ 0.01 to 8.51 $\pm$ 0.02. We have serendipitously discovered an additional detached TDG candidate in Arp72 using available spectra from SDSS. Utilizing the photometric data and the CIGALE code for stellar population and dust emission fitting, we derived the stellar masses, stellar population ages, and stellar metallicities for these detached TDGs. Compared to standard mass-metallicity relations for dwarf galaxies, five of the seven candidates have higher than expected metallicities, confirming their tidal origins. One of the seven candidates remains unclear due to large uncertainties in metallicity, and another has stellar and nebular metallicities compatible with those of a preexisting dwarf galaxy. The latter object is relatively compact in the optical relative to its stellar mass, in contrast to the other candidate TDGs [abridged]., Comment: Accepted for publication in Astronomy and Astrophysics. 15 pages, 9 figures, 6 tables

Despite their simplicity, linear models perform well at time series forecasting, even when pitted against deeper and more expensive models. A number of variations to the linear model have been proposed, often including some form of feature normalisation that improves model generalisation. In this paper we analyse the sets of functions expressible using these linear model architectures. In so doing we show that several popular variants of linear models for time series forecasting are equivalent and functionally indistinguishable from standard, unconstrained linear regression. We characterise the model classes for each linear variant. We demonstrate that each model can be reinterpreted as unconstrained linear regression over a suitably augmented feature set, and therefore admit closed-form solutions when using a mean-squared loss function. We provide experimental evidence that the models under inspection learn nearly identical solutions, and finally demonstrate that the simpler closed form solutions are superior forecasters across 72% of test settings.

Robotic manipulation of deformable linear objects (DLOs) is an active area of research, though emerging applications, like automotive wire harness installation, introduce constraints that have not been considered in prior work. Confined workspaces and limited visibility complicate prior assumptions of multi-robot manipulation and direct measurement of DLO configuration (state). This work focuses on single-arm manipulation of stiff DLOs (StDLOs) connected to form a DLO network (DLON), for which the measurements (output) are the endpoint poses of the DLON, which are subject to unknown dynamics during manipulation. To demonstrate feasibility of output-based control without state estimation, direct input-output dynamics are shown to exist by training neural network models on simulated trajectories. Output dynamics are then approximated with polynomials and found to contain well-known rigid body dynamics terms. A composite model consisting of a rigid body model and an online data-driven residual is developed, which predicts output dynamics more accurately than either model alone, and without prior experience with the system. An adaptive model predictive controller is developed with the composite model for DLON manipulation, which completes DLON installation tasks, both in simulation and with a physical automotive wire harness., Comment: Accepted to IEEE International Conference on Robotics and Automation - ICRA 2024. 7 pages. 4 figures

We show that phase separation in ordered polar active fluids belongs to a new universality class. This describes large collections of self-propelled entities (``flocks"), all spontaneously moving in the same direction, in which attractive interactions (which can be caused by, e.g., autochemotaxis) cause phase separation: the system spontaneously separates into a high density band and a low density band, moving parallel to each other, and to the direction of mean flock motion, at different speeds. The upper critical dimension for this transition is $d_c=5$, in contrast to the well-known $d_c=4$ of equilibrium phase separation. We obtain the large-distance, long-time scaling laws of the velocity and density fluctuations, which are characterized by universal critical correlation length and order parameter exponents $\nu_\perp$, $\nu_\parallel$ and $\beta$ respectively. We calculate these to $\mathcal{O} (\epsilon)$ in a $d=5-\epsilon$ expansion., Comment: 21 pages, 9 figures

Motivated by a number of realizations of long-range interacting systems, including ultra-cold atomic and molecular gases, we study a neutral plasma with power-law interactions longer-ranged than Coulombic. We find that beyond a crossover length, such interactions are universally screened down to a standard Coulomb form in all spatial dimensions. This implies, counter-intuitively, that in two dimensions and below, such a "super-Coulombic" gas is asymptotically Coulombically confining at low temperatures. At higher temperatures, the plasma undergoes a deconfining transition that in two dimensions is the same Kosterlitz-Thouless transition that occurs in a conventional Coulomb gas, but at an elevated temperature that we calculate. We also predict that in contrast, above two dimensions, even when naively the bare potential is confining, there is no confined phase of the plasma at any nonzero temperature. In addition, the super-Coulomb to Coulomb crossover is followed at longer length scales by an unconventional "Debye-Huckel" screening, which leads to faster-than-Coulombic, power-law decay of the screened potential, in contrast to the usual exponentially decaying Yukawa potential. Furthermore, we show that power-law potentials, that fall off more rapidly than Coulomb, are screened down to a shorter-ranged power-law, rather than an exponential Debye-Huckel Yukawa form. We expect these prediction to be testable in simulations, and hope they will inspire experimental studies in various platforms., Comment: 17 pages, 2 figures

We develop the hydrodynamic theory of dry, polar ordered, active matter (``flocking") with autochemotaxis; i.e., self-propelled entities moving in the same direction, each emitting a substance which attracts the others (e.g., ants). We find that sufficiently strong autochemotaxis leads to an instability to phase separation into one high and one low density band. This is very analogous to both equilibrium phase separation, and ``motility induced phase separation" (``MIPS") and can occur in flocks due to any microscopic mechanism (e.g., sufficiently strong attractive interactions) that makes the entities cohere., Comment: 6 pages, 5 figures. arXiv admin note: substantial text overlap with arXiv:2401.09461

Multimodal deep learning, especially vision-language models, have gained significant traction in recent years, greatly improving performance on many downstream tasks, including content moderation and violence detection. However, standard multimodal approaches often assume consistent modalities between training and inference, limiting applications in many real-world use cases, as some modalities may not be available during inference. While existing research mitigates this problem through reconstructing the missing modalities, they unavoidably increase unnecessary computational cost, which could be just as critical, especially for large, deployed infrastructures in industry. To this end, we propose a novel guidance network that promotes knowledge sharing during training, taking advantage of the multimodal representations to train better single-modality models to be used for inference. Real-world experiments in violence detection shows that our proposed framework trains single-modality models that significantly outperform traditionally trained counterparts, while avoiding increases in computational cost for inference., Comment: ICME 2024 Camera Ready. Code is available at https://github.com/zhuokaizhao/multimodal-guidance-network

Training neural network classifiers on datasets with label noise poses a risk of overfitting them to the noisy labels. To address this issue, researchers have explored alternative loss functions that aim to be more robust. The `forward-correction' is a popular approach wherein the model outputs are noised before being evaluated against noisy data. When the true noise model is known, applying the forward-correction guarantees consistency of the learning algorithm. While providing some benefit, the correction is insufficient to prevent overfitting to finite noisy datasets. In this work, we propose an approach to tackling overfitting caused by label noise. We observe that the presence of label noise implies a lower bound on the noisy generalised risk. Motivated by this observation, we propose imposing a lower bound on the training loss to mitigate overfitting. Our main contribution is providing theoretical insights that allow us to approximate the lower bound given only an estimate of the average noise rate. We empirically demonstrate that using this bound significantly enhances robustness in various settings, with virtually no additional computational cost.

Individuals high in social anxiety symptoms often exhibit elevated state anxiety in social situations. Research has shown it is possible to detect state anxiety by leveraging digital biomarkers and machine learning techniques. However, most existing work trains models on an entire group of participants, failing to capture individual differences in their psychological and behavioral responses to social contexts. To address this concern, in Study 1, we collected linguistic data from N=35 high socially anxious participants in a variety of social contexts, finding that digital linguistic biomarkers significantly differ between evaluative vs. non-evaluative social contexts and between individuals having different trait psychological symptoms, suggesting the likely importance of personalized approaches to detect state anxiety. In Study 2, we used the same data and results from Study 1 to model a multilayer personalized machine learning pipeline to detect state anxiety that considers contextual and individual differences. This personalized model outperformed the baseline F1-score by 28.0%. Results suggest that state anxiety can be more accurately detected with personalized machine learning approaches, and that linguistic biomarkers hold promise for identifying periods of state anxiety in an unobtrusive way., Comment: IEEE EMBC 2023

We analytically calculate the scaling exponents of a two-dimensional KPZ-like system: coherently moving incompressible polar active fluids. Using three different renormalization group approximation schemes, we obtain values for the ``roughness" exponent $\chi$ and anisotropy exponent $\zeta$ that are extremely near the known exact results. This implies our prediction for the previously completely unknown dynamic exponent $z$ is quantitatively accurate.

Correctly identifying an individual's social context from passively worn sensors holds promise for delivering just-in-time adaptive interventions (JITAIs) to treat social anxiety disorder. In this study, we present results using passively collected data from a within-subject experiment that assessed physiological response across different social contexts (i.e, alone vs. with others), social phases (i.e., pre- and post-interaction vs. during an interaction), social interaction sizes (i.e., dyadic vs. group interactions), and levels of social threat (i.e., implicit vs. explicit social evaluation). Participants in the study ($N=46$) reported moderate to severe social anxiety symptoms as assessed by the Social Interaction Anxiety Scale ($\geq$34 out of 80). Univariate paired difference tests, multivariate random forest models, and follow-up cluster analyses were used to explore physiological response patterns across different social and non-social contexts. Our results suggest that social context is more reliably distinguishable than social phase, group size, or level of social threat, but that there is considerable variability in physiological response patterns even among these distinguishable contexts. Implications for real-world context detection and deployment of JITAIs are discussed.

I show that non-equilibrium two-dimensional interfaces between three dimensional phase separated fluids exhibit a peculiar "sub-logarithmic" roughness. Specifically, an interface of lateral extent $L$ will fluctuate vertically (i.e., normal to the mean surface orientation) a typical RMS distance $w\equiv\sqrt{\langle |h(\br,t)|^2\rangle} \propto [\ln{(L/a)}]^{1/3}$ (where $a$ is a microscopic length, and $ h(\br,t)$ is the height of the interface at two dimensional position $\br$ at time $t$). In contrast, the roughness of equilibrium two-dimensional interfaces between three dimensional fluids, obeys $w \propto [\ln{(L/a)}]^{1/2}$. The exponent $1/3$ for the active case is exact. In addition, the characteristic time scales $\tau(L)$ in the active case scale according to $\tau(L)\propto L^3 [\ln{(L/a)}]^{1/3}$, in contrast to the simple $\tau(L)\propto L^3$ scaling found in equilibrium systems with conserved densities and no fluid flow., Comment: 4 pages, no figures

We show that dislocations in active 2d smectics with underlying rotational symmetry are always unbound in the presence of noise, meaning the active smectic phase does not exist for non-zero noise in $d=2$. The active smectic phase can, like equilibrium smectics in 2d, be stabilized by applying rotational symmetry breaking fields; however, even in the presence of such fields, active smectics are still much less stable against noise than equilibrium ones, when the symmetry breaking field(s) are weak., Comment: 23 pages, 3 figures

We study the moving phase of two-dimensional (2D) incompressible polar active fluids in the presence of both quenched and annealed disorder. We show that long-range polar order persists even in this defect-ridden two-dimensional system. We obtain the large-distance, long-time scaling laws of the velocity fluctuations using three distinct dynamic renormalization group schemes. These are an uncontrolled one-loop calculation in exactly two dimensions, and two $d=(d_c-\epsilon)$-expansions to $O(\epsilon)$, obtained by two different analytic continuations of our 2D model to higher spatial dimensions: a ``hard" continuation which has $d_c={7\over 3}$, and a ``soft" continuation with $d_c={5\over 2}$. Surprisingly, the quenched and annealed parts of the velocity correlation function have the same anisotropy exponent and the relaxational and propagating parts of the dispersion relation have the same dynamic exponent in the nonlinear theory even though they are distinct in the linearized theory. This is due to anomalous hydrodynamics. Furthermore, all three renormalization schemes yield very similar values for the universal exponents, and, therefore, we expect the numerical values we predict for them to be highly accurate., Comment: 39 pages, 11 figures

We present a hydrodynamic theory of incompressible polar active fluids with quenched disorder. This theory shows that such fluids can overcome the disruption caused by the quenched disorder and move coherently, in the sense of having a non-zero mean velocity in the hydrodynamic limit. However, the scaling behavior of this class of active systems cannot be described by linearized hydrodynamics in spatial dimensions between 2 and 5. Nonetheless, we obtain the exact dimension-dependent scaling exponents in these dimensions., Comment: 10 pages, 1 figure

Throughout 2020, America's Promise Alliance worked with five communities across the country that wanted to extend and deepen their efforts to support young people's social, emotional, and cognitive development. Each community planned cross-sector convenings to inspire action that would result in the approaches to learning that prioritize young people's growth and development. Just as their work was getting underway, the communities' efforts were unexpectedly and indelibly shaped by the upheaval of the past year--including the global pandemic and national reckoning with racism. The five communities in the How Learning Happens Community Convening Cohort were: (1) Nashville After Zone Alliance (Nashville, TN); (2) Parents for Public Schools -- San Francisco (San Francisco, CA); (3) Rennie Center for Education Research & Policy and Transforming Education (Boston, MA); (4) Spartanburg Academic Movement (Spartanburg, SC); and (5) The Whole Child Connection at the Children's Institute (Rochester, NY). As a group, the community organizations' efforts were fueled by a reawakened appreciation for holistic and coordinated approaches to learning and development. The organizations and their communities also became more squarely focused on the themes of anti-racism and belonging and more intentionally centered on the perspectives of young people. The brief shares three major lessons learned across the communities: (1) Equity must be an explicit desired outcome of all social, emotional, and cognitive programming; (2) Shifting Adult Practice Requires Time and Space for Adult Self-Care; and (3) Youth Voice is a Crucial Influence in Transforming Learning Environments. This brief also explores unique strategies each community took to support young people over the last year and offers insights into cross-sector partnerships allowed these convenings to have a longer impact.

We show that incompressible polar active fluids can exhibit an ordered, coherently moving phase even in the presence of quenched disorder in two dimensions. Unlike such active fluids with annealed (i.e., time-dependent) disorder only, which behave like equilibrium ferromagnets with long-range interactions, this robustness against quenched disorder is a fundamentally non-equilibrium phenomenon. The ordered state belongs to a new universality class, whose scaling laws we calculate using three different renormalization group schemes, which all give scaling exponents within 0.02 of each other, indicating that our results are quite accurate. Our predictions can be quantitatively tested in readily available artificial active systems, and imply that biological systems such as cell layers can move coherently in vivo, where disorder is inevitable., Comment: 6 pages

We consider the effect of spatial anisotropy on polar flocks by investigating active $q$-state clock models in two dimensions. In contrast to what happens in equilibrium, we find that, in the large-size limit, any amount of anisotropy changes drastically the phenomenology of the rotationally-invariant case, destroying long-range correlations, pinning the direction of global order, and transforming the traveling bands of the coexistence phase into a single moving domain. All this happens beyond a lengthscale that diverges in the $q\to\infty$ limit. A phenomenology akin to that of the Vicsek model can thus be observed in a finite system for large enough values of $q$. We provide a scaling argument which rationalizes why anisotropy has so different effects in the passive and active cases., Comment: 6 pages with 4 figures. 4 pages of supplementary information (as ancillary file)

We introduce a self-attending task generative adversarial network (SATGAN) and apply it to the problem of augmenting synthetic high contrast scientific imagery of resident space objects with realistic noise patterns and sensor characteristics learned from collected data. Augmenting these synthetic data is challenging due to the highly localized nature of semantic content in the data that must be preserved. Real collected images are used to train a network what a given class of sensor's images should look like. The trained network then acts as a filter on noiseless context images and outputs realistic-looking fakes with semantic content unaltered. The architecture is inspired by conditional GANs but is modified to include a task network that preserves semantic information through augmentation. Additionally, the architecture is shown to reduce instances of hallucinatory objects or obfuscation of semantic content in context images representing space observation scenes., Comment: 9 pages, 11 figures, 1 table, to be published in IEEE Aerospace 2022

We formulate a hydrodynamic theory of $p-$atic liquid crystals, namely two-dimensional anisotropic fluids endowed with generic $p-$fold rotational symmetry. Our approach, based on an order parameter tensor that directly embodies the discrete rotational symmetry of $p-$atic phases, allows us to unveil several unknown aspects of flowing $p-$atics, that previous theories, characterized by ${\rm O(2)}$ rotational symmetry, could not account for. This includes the onset of long-ranged orientational order in the presence of a simple shear flow of arbitrary shear rate, as opposed to the standard quasi-long-ranged order of two-dimensional liquid crystals, and the possibility of flow alignment at large shear rates., Comment: 6 pages, 5 figures. arXiv admin note: text overlap with arXiv:2106.11957

We formulate a comprehensive hydrodynamic theory of two-dimensional liquid crystals with generic $p-$fold rotational symmetry, also known as $p-$atics, of which mematics $(p=2)$ and hexatics $(p=6)$ are the two best known examples. Previous hydrodynamic theories of $p-$atics are characrerized by continuous ${\rm O}(2)$ rotational symmetry, which is higher than the discrete rotational symmetry of $p-$atic phases. By contrast, here we demonstrate that the discrete rotational symmetry allows the inclusion of additional terms in the hydrodynamic equations, which, in turn, lead to novel phenomena, such as the possibility of flow alignment at high shear rates, even for $p>2$. Furthermore, we show that any finite imposed shear will induce long-ranged orientational order in any $p-$atic liquid crystal, in contrast to the quasi-long-ranged order that occurs in the absence of shear. The induced order parameter scales like a non-universal power of the applied shear rate at small shear rates., Comment: 25 pages, 7 figures

Every adolescent faces challenges as they navigate their teenage years, but for some these challenges imperil their ability to complete their education. While these four students' experiences are dramatically different, they have one thing in common: high school didn't work for them, meaning that each faced long odds of ever attaining a diploma, much less a postsecondary credential with value in the workplace. And while each recognized the challenges they would face without finishing high school, they also didn't see many options within their existing school settings. While "early college" initiatives and dual or concurrent enrollment programs have expanded dramatically to support low income and first-generation students, they focus primarily on young people who are enrolled and making progress in high school, not those who are struggling--or who have dropped out. Growing numbers of community colleges are recognizing the importance of serving disconnected youth. [For the other briefs in the series, see "Supporting the Success of Students Enrolled Part-Time" (ED606169); "Increasing Success for Student Mothers at Community Colleges" (ED606165); and "What We're Learning" (ED606164).]

As with other student populations community colleges serve, part-time students often face multiple challenges that present additional barriers to completion, including unfamiliarity with the college experience, parenthood, and food and housing insecurity. Community colleges have historically opened their doors to all those interested in pursuing higher education, but even as their student populations have changed, too many of their structures are still built around serving the "traditional" college student--the first-time, full-time 18-year-old who is now a distinct minority on today's campuses. Given that they represent the majority of their student populations, improving outcomes for part-time students is vital for institutions to meet their goals of improving equity in outcomes and the broader economic imperative of increasing overall postsecondary attainment. And while many institutions have made efforts to encourage more part-time students to shift to full-time status, the reality of students' lives means that this can only be a limited part of the solution, particularly since part-time enrollment growth is projected to outpace that of full-time students through much of this decade. [For the other briefs in the series, see "A Gateway to College for High School Students" (ED606168); "Increasing Success for Student Mothers at Community Colleges" (ED606165); and "What We're Learning" (ED606164).]