Color-Kinematics Duality for Pure Yang-Mills and Gravity at One and Two Loops

We provide evidence in favor of the conjectured duality between color and kinematics for the case of nonsupersymmetric pure Yang-Mills amplitudes by constructing a form of the one-loop four-point amplitude of this theory that makes the duality manifest. Our construction is valid in any dimension. We also describe a duality-satisfying representation for the two-loop four-point amplitude with identical four-dimensional external helicities. We use these results to obtain corresponding gravity integrands for a theory containing a graviton, dilaton, and antisymmetric tensor, simply by replacing color factors with specified diagram numerators. Using this, we give explicit forms of ultraviolet divergences at one loop in four, six, and eight dimensions, and at two loops in four dimensions.Comment: 35 page, 10 figures, REVTex, ancillary mathematica file containing one-loop diagram numerators, latest version includes updated references, corrected two-loop numerators and various clarification

High-Resolution Labeling and Functional Manipulation of Specific Neuron Types in Mouse Brain by Cre-Activated Viral Gene Expression

We describe a method that combines Cre-recombinase knockin mice and viral-mediated gene transfer to genetically label and functionally manipulate specific neuron types in the mouse brain. We engineered adeno-associated viruses (AAVs) that express GFP, dsRedExpress, or channelrhodopsin (ChR2) upon Cre/loxP recombination-mediated removal of a transcription-translation STOP cassette. Fluorescent labeling was sufficient to visualize neuronal structures with synaptic resolution in vivo, and ChR2 expression allowed light activation of neuronal spiking. The structural dynamics of a specific class of neocortical neuron, the parvalbumin-containing (Pv) fast-spiking GABAergic interneuron, was monitored over the course of a week. We found that although the majority of Pv axonal boutons were stable in young adults, bouton additions and subtractions on axonal shafts were readily observed at a rate of 10.10% and 9.47%, respectively, over 7 days. Our results indicate that Pv inhibitory circuits maintain the potential for structural re-wiring in post-adolescent cortex. With the generation of an increasing number of Cre knockin mice and because viral transfection can be delivered to defined brain regions at defined developmental stages, this strategy represents a general method to systematically visualize the structure and manipulate the function of different cell types in the mouse brain

Qualification of single use in-line sensors for use in continuous bioprocessing

The requirements for batch versus continuous processing will be compared along the lines of the design attributes of single use sensors for pressure, temperature, conductivity, and UV absorbance and also performance over months of continuous operation. These sensors are applicable in both upstream and downsteam processing starting with pressure monitoring on single use bioreactors, sensors required for perfusion process monitoring followed by monitoring of continuous purification processes. Dissection of the materials of the sensors and their physical nature to withstand liquid exposure of up to 90 days versus (versus shorter more discrete batch processes of less than one day) will be examined on the core material basis. With single use sensors, calibration can often not be done at the time of use because of the closed nature of the bioprocess system. How the both the sensors and their corresponding monitors can meet the requirement of “no calibration required” at the point of use will be presented which is an important aspect in single use systems for continuous bioprocessing. In addition to examining impact of time and type of exposure of the sensor materials, during a continuous process of up to 90 days, the susceptibility to sensor measurement drift / change in calibration over time will be examined. Finally, during continuous processing, it is often imperative that a process can be continuously controlled and data can be logged and trended 24/7. Therefore, interface of the sensors to higher level control systems and to data historians is important and options will be examined to accomplish this for different plant architectures

Bluefish: A Relational Framework for Graphic Representations

Complex graphic representations -- such as annotated visualizations, molecular structure diagrams, or Euclidean geometry -- convey information through overlapping perceptual relations. To author such representations, users are forced to use rigid, purpose-built tools with limited flexibility and expressiveness. User interface (UI) frameworks provide only limited relief as their tree-based models are a poor fit for expressing overlaps. We present Bluefish, a diagramming framework that extends UI architectures to support overlapping perceptual relations. Bluefish graphics are instantiated as relational scenegraphs: hierarchical data structures augmented with adjacency relations. Authors specify these relations with scoped references to components found elsewhere in the scenegraph. For layout, Bluefish lazily materializes necessary coordinate transformations. We demonstrate that Bluefish enables authoring graphic representations across a diverse range of domains while preserving the compositional and abstractional affordances of traditional UI frameworks. Moreover, we show how relational scenegraphs capture previously latent semantics that can later be retargeted (e.g., for screen reader accessibility).Comment: 27 pages, 14 figure

Towards Low-Energy Adaptive Personalization for Resource-Constrained Devices

The personalization of machine learning (ML) models to address data drift is a significant challenge in the context of Internet of Things (IoT) applications. Presently, most approaches focus on fine-tuning either the full base model or its last few layers to adapt to new data, while often neglecting energy costs. However, various types of data drift exist, and fine-tuning the full base model or the last few layers may not result in optimal performance in certain scenarios. We propose Target Block Fine-Tuning (TBFT), a low-energy adaptive personalization framework designed for resource-constrained devices. We categorize data drift and personalization into three types: input-level, feature-level, and output-level. For each type, we fine-tune different blocks of the model to achieve optimal performance with reduced energy costs. Specifically, input-, feature-, and output-level correspond to fine-tuning the front, middle, and rear blocks of the model. We evaluate TBFT on a ResNet model, three datasets, three different training sizes, and a Raspberry Pi. Compared with the $Block Avg$, where each block is fine-tuned individually and their performance improvements are averaged, TBFT exhibits an improvement in model accuracy by an average of 15.30% whilst saving 41.57% energy consumption on average compared with full fine-tuning.Comment: Accepetd to The 4th Workshop on Machine Learning and Systems (EuroMLSys '24

Developmental Coordination of Gene Expression between Synaptic Partners During GABAergic Circuit Assembly in Cerebellar Cortex

The assembly of neural circuits involves multiple sequential steps such as the specification of cell-types, their migration to proper brain locations, morphological and physiological differentiation, and the formation and maturation of synaptic connections. This intricate and often prolonged process is guided by elaborate genetic mechanisms that regulate each step. Evidence from numerous systems suggests that each cell-type, once specified, is endowed with a genetic program that unfolds in response to, and is regulated by, extrinsic signals, including cell–cell and synaptic interactions. To a large extent, the execution of this intrinsic program is achieved by the expression of specific sets of genes that support distinct developmental processes. Therefore, a comprehensive analysis of the developmental progression of gene expression in synaptic partners of neurons may provide a basis for exploring the genetic mechanisms regulating circuit assembly. Here we examined the developmental gene expression profiles of well-defined cell-types in a stereotyped microcircuit of the cerebellar cortex. We found that the transcriptomes of Purkinje cell and stellate/basket cells are highly dynamic throughout postnatal development. We revealed “phasic expression” of transcription factors, ion channels, receptors, cell adhesion molecules, gap junction proteins, and identified distinct molecular pathways that might contribute to sequential steps of cerebellar inhibitory circuit formation. We further revealed a correlation between genomic clustering and developmental co-expression of hundreds of transcripts, suggesting the involvement of chromatin level gene regulation during circuit formation

Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time

Crowd-powered conversational assistants have been shown to be more robust than automated systems, but do so at the cost of higher response latency and monetary costs. A promising direction is to combine the two approaches for high quality, low latency, and low cost solutions. In this paper, we introduce Evorus, a crowd-powered conversational assistant built to automate itself over time by (i) allowing new chatbots to be easily integrated to automate more scenarios, (ii) reusing prior crowd answers, and (iii) learning to automatically approve response candidates. Our 5-month-long deployment with 80 participants and 281 conversations shows that Evorus can automate itself without compromising conversation quality. Crowd-AI architectures have long been proposed as a way to reduce cost and latency for crowd-powered systems; Evorus demonstrates how automation can be introduced successfully in a deployed system. Its architecture allows future researchers to make further innovation on the underlying automated components in the context of a deployed open domain dialog system.Comment: 10 pages. To appear in the Proceedings of the Conference on Human Factors in Computing Systems 2018 (CHI'18

Maternal experience-dependent cortical plasticity in mice is circuit- and stimulus-specific and requires MECP2

ABSTRACT The neurodevelopmental disorder Rett syndrome is caused by mutations in the gene Mecp2 . Misexpression of the protein MECP2 is thought to contribute to neuropathology by causing dysregulation of plasticity. Female heterozygous Mecp2 mutants ( Mecp2 het ) failed to acquire a learned maternal retrieval behavior when exposed to pups, an effect linked to disruption of parvalbumin-expressing inhibitory interneurons (PV+) in the auditory cortex. However, the consequences of dysregulated PV+ networks during early maternal experience for auditory cortical sensory activity are unknown. Here we show that maternal experience in wild-type adult female mice ( Mecp2 wt ) triggers suppression of PV+ auditory responses. We also observe concomitant disinhibition of auditory responses in deep-layer pyramidal neurons that is selective for behaviorally-relevant pup vocalizations. These neurons also exhibit sharpened tuning for pup vocalizations following maternal experience. All of these neuronal changes are abolished in Mecp2 het , yet a genetic manipulation of GABAergic networks that restores accurate retrieval behavior in Mecp2 het also restores maternal experience-dependent plasticity of PV+. Our data are consistent with a growing body of evidence that cortical networks are particularly vulnerable to mutations of Mecp2 in PV+ neurons