c.1935

1968.

We asked parents, students, and educators to share what their home learning environments look like as nearly all schools are shut down for extended periods because of the coronavirus pandemic.                          

Concerned that schools don't notice or nurture business skills, nonprofits are using the humble lemonade stand to foster entrepreneurship.

Chief technology officers are facing an unprecedented test of digital preparedness due to the coronavirus pandemic, struggling with shortfalls of available learning devices and huge Wi-Fi access challenges.

A little-known monument in Emporia, Kan., which recently received federal recognition, will add 10 new names to the list of teachers and support staff who’ve died on the job.

Leipzig : Engelmann, 1904.

London : S. Highley, 1848.

London : James Epps & Co., Limited, 1894.

Valencia : M. Alufre, 1886.

London : J. & A. Churchill, 1887.

London : Smith, Elder, 1887.

London : J. & A. Churchill, 1873.

When Idaho education leaders pitched social-emotional learning training for teachers, some state lawmakers compared the plan to dystopian behavior control. Some walked out of the meeting.

In the Green Mountain State, education leaders discuss their focus on the whole child.

What would a new Democratic administration mean for education? We're getting a clearer idea as former Vice President Joe Biden and Vermont Sen. Bernie Sanders emerge as top contenders for the nomination.

[London?] : [Endell Street Military Hospital?], [1920] (Harlesden, London N.W. 10 : Leveridge & Co.)

London : James Nisbet & Co., MDCCCLXXX [1880]

Paris, [France] : J.B. Baillière, Libraire de l'Académie Impériale de Médicine, 1855.

London : Stacy Bias, 2019.

Multi-Class Incremental Learning (MCIL) aims to learn new concepts by incrementally updating a model trained on previous concepts. However, there is an inherent trade-off to effectively learning new concepts without catastrophic forgetting of previous ones. To alleviate this issue, it has been proposed to keep around a few examples of the previous concepts but the effectiveness of this approach heavily depends on the representativeness of these examples. This paper proposes a novel and automatic framework we call mnemonics, where we parameterize exemplars and make them optimizable in an end-to-end manner. We train the framework through bilevel optimizations, i.e., model-level and exemplar-level. We conduct extensive experiments on three MCIL benchmarks, CIFAR-100, ImageNet-Subset and ImageNet, and show that using mnemonics exemplars can surpass the state-of-the-art by a large margin. Interestingly and quite intriguingly, the mnemonics exemplars tend to be on the boundaries between different classes.

We present SmartExchange, an algorithm-hardware co-design framework to trade higher-cost memory storage/access for lower-cost computation, for energy-efficient inference of deep neural networks (DNNs). We develop a novel algorithm to enforce a specially favorable DNN weight structure, where each layerwise weight matrix can be stored as the product of a small basis matrix and a large sparse coefficient matrix whose non-zero elements are all power-of-2. To our best knowledge, this algorithm is the first formulation that integrates three mainstream model compression ideas: sparsification or pruning, decomposition, and quantization, into one unified framework. The resulting sparse and readily-quantized DNN thus enjoys greatly reduced energy consumption in data movement as well as weight storage. On top of that, we further design a dedicated accelerator to fully utilize the SmartExchange-enforced weights to improve both energy efficiency and latency performance. Extensive experiments show that 1) on the algorithm level, SmartExchange outperforms state-of-the-art compression techniques, including merely sparsification or pruning, decomposition, and quantization, in various ablation studies based on nine DNN models and four datasets; and 2) on the hardware level, the proposed SmartExchange based accelerator can improve the energy efficiency by up to 6.7$ imes$ and the speedup by up to 19.2$ imes$ over four state-of-the-art DNN accelerators, when benchmarked on seven DNN models (including four standard DNNs, two compact DNN models, and one segmentation model) and three datasets.

The next seminar in the 2017–18 History of Pre-Modern Medicine seminar series takes place on Tuesday 7 November. Speaker: Dr Michael Brown (University of Roehampton), ‘Anxiety and compassion: emotions and the surgical encounter in early 19th-century Britain’ The historical study of the… Continue reading

Surveys provide secure, anonymous feedback from staff at all levels of healthcare organizations

(PRWeb May 06, 2020)

Read the full story at https://www.prweb.com/releases/health_worker_data_alliance_monitoring_emotional_physical_and_occupational_health_of_healthcare_workers_during_covid_19/prweb17101008.htm

Xi Chen, Weidong Liu, Yichen Zhang.

Source: The Annals of Statistics, Volume 47, Number 6, 3244--3273.

Abstract:
This paper studies the inference problem in quantile regression (QR) for a large sample size $n$ but under a limited memory constraint, where the memory can only store a small batch of data of size $m$. A natural method is the naive divide-and-conquer approach, which splits data into batches of size $m$, computes the local QR estimator for each batch and then aggregates the estimators via averaging. However, this method only works when $n=o(m^{2})$ and is computationally expensive. This paper proposes a computationally efficient method, which only requires an initial QR estimator on a small batch of data and then successively refines the estimator via multiple rounds of aggregations. Theoretically, as long as $n$ grows polynomially in $m$, we establish the asymptotic normality for the obtained estimator and show that our estimator with only a few rounds of aggregations achieves the same efficiency as the QR estimator computed on all the data. Moreover, our result allows the case that the dimensionality $p$ goes to infinity. The proposed method can also be applied to address the QR problem under distributed computing environment (e.g., in a large-scale sensor network) or for real-time streaming data.

Shuyang Bai, Murad S. Taqqu.

Source: Bernoulli, Volume 26, Number 2, 1473--1503.

Abstract:
We consider a long-memory stationary process, defined not through a moving average type structure, but by a flow generated by a measure-preserving transform and by a multiple Wiener–Itô integral. The flow is described using a notion of mixing for infinite-measure spaces introduced by Krickeberg (In Proc. Fifth Berkeley Sympos. Math. Statist. and Probability (Berkeley, Calif., 1965/66), Vol. II: Contributions to Probability Theory, Part 2 (1967) 431–446 Univ. California Press). Depending on the interplay between the spreading rate of the flow and the order of the multiple integral, one can recover known central or non-central limit theorems, and also obtain joint convergence of multiple integrals of different orders.

Fuhlbohm (Family)

Fuhlbohm (Family)

Kraus, Peter -- Biography.

ACT and the American Federation of Teachers are partnering to provide free resources as educators increasingly switch to distance learning amid the COVID-19 pandemic.

The post ACT and Teachers’ Union Partner to Provide Remote Learning Resources Amid Pandemic appeared first on Market Brief.

While President Donald Trump traveled to the battleground state of Arizona this week, his Democratic opponent for the White House, Joe Biden, campaigned from his basement as he has done throughout the coronavirus pandemic. The freeze on in-person campaigning during the outbreak has had an upside for Biden, giving the former vice president more time to court donors and shielding him from on-the-trail gaffes. "I personally would like to see him out more because he's in his element when he's meeting people," said Tom Sacks-Wilner, a fundraiser for Biden who is on the campaign's finance committee.

Some Democrats took "Believe Women" literally until Joe Biden was accused. Now they're relearning that guilt-by-accusation doesn't serve justice.

And they’re available in 19 fun prints.

Larry R. Squire
Oct 14, 2009; 29:12711-12716
40th Anniversary Retrospective

Irune Diaz-Aparicio
Feb 12, 2020; 40:1453-1482
Development Plasticity Repair

Earl K. Miller
Aug 15, 1996; 16:5154-5167
Articles

Traditional views of sensorimotor adaptation (i.e., adaptation of movements to perturbed sensory feedback) emphasize the role of automatic, implicit correction of sensory prediction errors. However, latent memories formed during sensorimotor adaptation, manifest as improved relearning (e.g., savings), have recently been attributed to strategic corrections of task errors (failures to achieve task goals). To dissociate contributions of task errors and sensory prediction errors to latent sensorimotor memories, we perturbed target locations to remove or enforce task errors during learning and/or test, with male/female human participants. Adaptation improved after learning in all conditions where participants were permitted to correct task errors, and did not improve whenever we prevented correction of task errors. Thus, previous correction of task errors was both necessary and sufficient to improve adaptation. In contrast, a history of sensory prediction errors was neither sufficient nor obligatory for improved adaptation. Limiting movement preparation time showed that the latent memories driven by learning to correct task errors take at least two forms: a time-consuming but flexible component, and a rapidly expressible, inflexible component. The results provide strong support for the idea that movement corrections driven by a failure to successfully achieve movement goals underpin motor memories that manifest as savings. Such persistent memories are not exclusively mediated by time-consuming strategic processes but also comprise a rapidly expressible but inflexible component. The distinct characteristics of these putative processes suggest dissociable underlying mechanisms, and imply that identification of the neural basis for adaptation and savings will require methods that allow such dissociations.

SIGNIFICANCE STATEMENT Latent motor memories formed during sensorimotor adaptation manifest as improved adaptation when sensorimotor perturbations are reencountered. Conflicting theories suggest that this "savings" is underpinned by different mechanisms, including a memory of successful actions, a memory of errors, or an aiming strategy to correct task errors. Here we show that learning to correct task errors is sufficient to show improved subsequent adaptation with respect to naive performance, even when tested in the absence of task errors. In contrast, a history of sensory prediction errors is neither sufficient nor obligatory for improved adaptation. Finally, we show that latent sensorimotor memories driven by task errors comprise at least two distinct components: a time-consuming, flexible component, and a rapidly expressible, inflexible component.

Endogenous neuropeptide Y (NPY) and corticotrophin-releasing factor (CRF) modulate the responses of the basolateral amygdala (BLA) to stress and are associated with the development of stress resilience and vulnerability, respectively. We characterized persistent effects of repeated NPY and CRF treatment on the structure and function of BLA principal neurons in a novel organotypic slice culture (OTC) model of male rat BLA, and examined the contributions of specific NPY receptor subtypes to these neural and behavioral effects. In BLA principal neurons within the OTCs, repeated NPY treatment caused persistent attenuation of excitatory input and induced dendritic hypotrophy via Y₅ receptor activation; conversely, CRF increased excitatory input and induced hypertrophy of BLA principal neurons. Repeated treatment of OTCs with NPY followed by an identical treatment with CRF, or vice versa, inhibited or reversed all structural changes in OTCs. These structural responses to NPY or CRF required calcineurin or CaMKII, respectively. Finally, repeated intra-BLA injections of NPY or a Y₅ receptor agonist increased social interaction, a validated behavior for anxiety, and recapitulated structural changes in BLA neurons seen in OTCs, while a Y₅ receptor antagonist prevented NPY's effects both on behavior and on structure. These results implicate the Y₅ receptor in the long-term, anxiolytic-like effects of NPY in the BLA, consistent with an intrinsic role in stress buffering, and highlight a remarkable mechanism by which BLA neurons may adapt to different levels of stress. Moreover, BLA OTCs offer a robust model to study mechanisms associated with resilience and vulnerability to stress in BLA.

SIGNIFICANCE STATEMENT Within the basolateral amygdala (BLA), neuropeptide Y (NPY) is associated with buffering the neural stress response induced by corticotropin releasing factor, and promoting stress resilience. We used a novel organotypic slice culture model of BLA, complemented with in vivo studies, to examine the cellular mechanisms associated with the actions of NPY. In organotypic slice cultures, repeated NPY treatment reduces the complexity of the dendritic extent of anxiogenic BLA principal neurons, making them less excitable. NPY, via activation of Y5 receptors, additionally inhibits and reverses the increases in dendritic extent and excitability induced by the stress hormone, corticotropin releasing factor. This NPY-mediated neuroplasticity indicates that resilience or vulnerability to stress may thus involve neuropeptide-mediated dendritic remodeling in BLA principal neurons.

The PFC, through its high degree of interconnectivity with cortical and subcortical brain areas, mediates cognitive and emotional processes in support of adaptive behaviors. This includes the formation of fear memories when the anticipation of threat demands learning about temporal or contextual cues, as in trace fear conditioning. In this variant of fear learning, the association of a cue and shock across an empty trace interval of several seconds requires sustained cue-elicited firing in the prelimbic cortex (PL). However, it is unknown how and when distinct PL afferents contribute to different associative components of memory. Among the prominent inputs to PL, the hippocampus shares with PL a role in both working memory and contextual processing. Here we tested the necessity of direct hippocampal input to the PL for the acquisition of trace-cued fear memory and the simultaneously acquired contextual fear association. Optogenetic silencing of ventral hippocampal (VH) terminals in the PL of adult male Long-Evans rats selectively during paired trials revealed that direct communication between the VH and PL during training is necessary for contextual fear memory, but not for trace-cued fear acquisition. The pattern of the contextual memory deficit and the disruption of local PL firing during optogenetic silencing of VH-PL suggest that the VH continuously updates the PL with the current contextual state of the animal, which, when disrupted during memory acquisition, is detrimental to the subsequent rapid retrieval of aversive contextual associations.

SIGNIFICANCE STATEMENT Learning to anticipate threat from available contextual and discrete cues is crucial for survival. The prelimbic cortex is required for forming fear memories when temporal or contextual complexity is involved, as in trace fear conditioning. However, the respective contribution of distinct prelimbic afferents to the temporal and contextual components of memory is not known. We report that direct input from the ventral hippocampus enables the formation of the contextual, but not trace-cued, fear memory necessary for the subsequent rapid expression of a fear response. This finding dissociates the contextual and working-memory contributions of prelimbic cortex to the formation of a fear memory and demonstrates the crucial role for hippocampal input in contextual fear learning.

Pattern separation and completion are fundamental hippocampal computations supporting memory encoding and retrieval. However, despite extensive exploration of these processes, it remains unclear whether and how top-down processes adaptively modulate the dynamics between these computations. Here we examine the role of expectation in shifting the hippocampus to perform pattern separation. In a behavioral task, 29 participants (7 males) learned a cue-object category contingency. Then, at encoding, one-third of the cues preceding the to-be-memorized objects, violated the studied rule. At test, participants performed a recognition task with old objects (targets) and a set of parametrically manipulated (very similar to dissimilar) foils for each object. Accuracy was found to be better for foils of high similarity to targets that were contextually unexpected at encoding compared with expected ones. Critically, there were no expectation-driven differences for targets and low similarity foils. To further explore these effects, we implemented a computational model of the hippocampus, performing the same task as the human participants. We used representational similarity analysis to examine how top-down expectation interacts with bottom-up perceptual input, in each layer. All subfields showed more dissimilar representations for unexpected items, with dentate gyrus (DG) and CA3 being more sensitive to expectation violation than CA1. Again, representational differences between expected and unexpected inputs were prominent for moderate to high levels of input similarity. This effect diminished when inputs from DG and CA3 into CA1 were lesioned. Overall, these novel findings strongly suggest that pattern separation in DG/CA3 underlies the effect that violation of expectation exerts on memory.

SIGNIFICANCE STATEMENT What makes some events more memorable than others is a key question in cognitive neuroscience. Violation of expectation often leads to better memory performance, but the neural mechanism underlying this benefit remains elusive. In a behavioral study, we found that memory accuracy is enhanced selectively for unexpected highly similar foils, suggesting expectation violation does not enhance memory indiscriminately, but specifically aids the disambiguation of overlapping inputs. This is further supported by our subsequent investigation using a hippocampal computational model, revealing increased representational dissimilarity for unexpected highly similar foils in DG and CA3. These convergent results provide the first evidence that pattern separation plays an explicit role in supporting memory for unexpected information.