Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization

When approaching a novel visual recognition problem in a specialized image domain, a common strategy is to start with a pre-trained deep neural network and fine-tune it to the specialized domain. If the target domain covers a smaller visual space than the source domain used for pre-training (e.g. ImageNet), the fine-tuned network is likely to be over-parameterized. However, applying network pruning as a post-processing step to reduce the memory requirements has drawbacks: fine-tuning and pruning are performed independently; pruning parameters are set once and cannot adapt over time; and the highly parameterized nature of state-of-the-art pruning methods make it prohibitive to manually search the pruning parameter space for deep networks, leading to coarse approximations. We propose a principled method for jointly fine-tuning and compressing a pre-trained convolutional network that overcomes these limitations. Experiments on two specialized image domains (remote sensing images and describable textures) demonstrate the validity of the proposed approach.Comment: BMVC 2017 ora

Neuronal Glud1 (Glutamate Dehydrogenase 1) Over-Expressing Mice: Increased Glutamate Formation and Synaptic Release, Loss of Synaptic Activity, and Adaptive Changes in Genomic Expression

Glutamate dehydrogenase 1 (GLUD1) is a mitochondrial enzyme expressed in all tissues, including brain. Although this enzyme is expressed in glutamatergic pathways, its function as a regulator of glutamate neurotransmitter levels is still not well defined. In order to gain an understanding of the role of GLUD1 in the control of glutamate levels and synaptic release in mammalian brain, we generated transgenic (Tg) mice that over-express this enzyme in neurons of the central nervous system. The Tg mice have increased activity of GLUD, as well as elevated levels and increased synaptic and depolarization-induced release of glutamate. These mice suffer age-associated losses of dendritic spines, nerve terminals, and neurons. The neuronal losses and dendrite structural changes occur in select regions of the brain. At the transcriptional level in the hippocampus, cells respond by increasing the expression of genes related to neurite growth and synapse formation, indications of adaptive or compensatory responses to the effects of increases in the release and action of glutamate at synapses. Because these Tg mice live to a relatively old age they are a good model of the effects of a “hyperglutamatergic” state on the aging process in the nervous system. The mice are also useful in defining the molecular pathways affected by the over-activation of GLUD in glutamatergic neurons of the brain and spinal cord

Transgenic Expression of Glud1 (Glutamate Dehydrogenase 1) in Neurons: In Vivo Model of Enhanced Glutamate Release, Altered Synaptic Plasticity, and Selective Neuronal Vulnerability

This is the published version. Copyright 2009 Society for Neuroscience.The effects of lifelong, moderate excess release of glutamate (Glu) in the CNS have not been previously characterized. We created a transgenic (Tg) mouse model of lifelong excess synaptic Glu release in the CNS by introducing the gene for glutamate dehydrogenase 1 (Glud1) under the control of the neuron-specific enolase promoter. Glud1 is, potentially, an important enzyme in the pathway of Glu synthesis in nerve terminals. Increased levels of GLUD protein and activity in CNS neurons of hemizygous Tg mice were associated with increases in the in vivo release of Glu after neuronal depolarization in striatum and in the frequency and amplitude of miniature EPSCs in the CA1 region of the hippocampus. Despite overexpression of Glud1 in all neurons of the CNS, the Tg mice suffered neuronal losses in select brain regions (e.g., the CA1 but not the CA3 region). In vulnerable regions, Tg mice had decreases in MAP2A labeling of dendrites and in synaptophysin labeling of presynaptic terminals; the decreases in neuronal numbers and dendrite and presynaptic terminal labeling increased with advancing age. In addition, the Tg mice exhibited decreases in long-term potentiation of synaptic activity and in spine density in dendrites of CA1 neurons. Behaviorally, the Tg mice were significantly more resistant than wild-type mice to induction and duration of anesthesia produced by anesthetics that suppress Glu neurotransmission. The Glud1 mouse might be a useful model for the effects of lifelong excess synaptic Glu release on CNS neurons and for age-associated neurodegenerative processes

Toxin-Specific Antibodies for the Treatment of Clostridium difficile: Current Status and Future Perspectives †

Therapeutic agents targeting bacterial virulence factors are gaining interest as non-antibiotic alternatives for the treatment of infectious diseases. Clostridium difficile is a Gram-positive pathogen that produces two primary virulence factors, enterotoxins A and B (TcdA and TcdB), which are responsible for Clostridium difficile-associated disease (CDAD) and are targets for CDAD therapy. Antibodies specific for TcdA and TcdB have been shown to effectively treat CDAD and prevent disease relapse in animal models and in humans. This review summarizes the various toxin-specific antibody formats and strategies under development, and discusses future directions for CDAD immunotherapy, including the use of engineered antibody fragments with robust biophysical properties for systemic and oral delivery

Web-Based, Participant-Driven Studies Yield Novel Genetic Associations for Common Traits

Despite the recent rapid growth in genome-wide data, much of human variation remains entirely unexplained. A significant challenge in the pursuit of the genetic basis for variation in common human traits is the efficient, coordinated collection of genotype and phenotype data. We have developed a novel research framework that facilitates the parallel study of a wide assortment of traits within a single cohort. The approach takes advantage of the interactivity of the Web both to gather data and to present genetic information to research participants, while taking care to correct for the population structure inherent to this study design. Here we report initial results from a participant-driven study of 22 traits. Replications of associations (in the genes OCA2, HERC2, SLC45A2, SLC24A4, IRF4, TYR, TYRP1, ASIP, and MC1R) for hair color, eye color, and freckling validate the Web-based, self-reporting paradigm. The identification of novel associations for hair morphology (rs17646946, near TCHH; rs7349332, near WNT10A; and rs1556547, near OFCC1), freckling (rs2153271, in BNC2), the ability to smell the methanethiol produced after eating asparagus (rs4481887, near OR2M7), and photic sneeze reflex (rs10427255, near ZEB2, and rs11856995, near NR2F2) illustrates the power of the approach

Human resource management in the age of generative artificial intelligence::Perspectives and research directions on ChatGPT

ChatGPT and its variants that use generative artificial intelligence (AI) models have rapidly become a focal point in academic and media discussions about their potential benefits and drawbacks across various sectors of the economy, democracy, society, and environment. It remains unclear whether these technologies result in job displacement or creation, or if they merely shift human labour by generating new, potentially trivial or practically irrelevant, information and decisions. According to the CEO of ChatGPT, the potential impact of this new family of AI technology could be as big as “the printing press”, with significant implications for employment, stakeholder relationships, business models, and academic research, and its full consequences are largely undiscovered and uncertain. The introduction of more advanced and potent generative AI tools in the AI market, following the launch of ChatGPT, has ramped up the “AI arms race”, creating continuing uncertainty for workers, expanding their business applications, while heightening risks related to well‐being, bias, misinformation, context insensitivity, privacy issues, ethical dilemmas, and security. Given these developments, this perspectives editorial offers a collection of perspectives and research pathways to extend HRM scholarship in the realm of generative AI. In doing so, the discussion synthesizes the literature on AI and generative AI, connecting it to various aspects of HRM processes, practices, relationships, and outcomes, thereby contributing to shaping the future of HRM research

In-situ estimation of ice crystal properties at the South Pole using LED calibration data from the IceCube Neutrino Observatory

The IceCube Neutrino Observatory instruments about 1 km3 of deep, glacial ice at the geographic South Pole using 5160 photomultipliers to detect Cherenkov light emitted by charged relativistic particles. A unexpected light propagation effect observed by the experiment is an anisotropic attenuation, which is aligned with the local flow direction of the ice. Birefringent light propagation has been examined as a possible explanation for this effect. The predictions of a first-principles birefringence model developed for this purpose, in particular curved light trajectories resulting from asymmetric diffusion, provide a qualitatively good match to the main features of the data. This in turn allows us to deduce ice crystal properties. Since the wavelength of the detected light is short compared to the crystal size, these crystal properties do not only include the crystal orientation fabric, but also the average crystal size and shape, as a function of depth. By adding small empirical corrections to this first-principles model, a quantitatively accurate description of the optical properties of the IceCube glacial ice is obtained. In this paper, we present the experimental signature of ice optical anisotropy observed in IceCube LED calibration data, the theory and parametrization of the birefringence effect, the fitting procedures of these parameterizations to experimental data as well as the inferred crystal properties.</p

TXS 0506+056 with Updated IceCube Data

Past results from the IceCube Collaboration have suggested that the blazar TXS 0506+056 is a potential source of astrophysical neutrinos. However, in the years since there have been numerous updates to event processing and reconstruction, as well as improvements to the statistical methods used to search for astrophysical neutrino sources. These improvements in combination with additional years of data have resulted in the identification of NGC 1068 as a second neutrino source candidate. This talk will re-examine time-dependent neutrino emission from TXS 0506+056 using the most recent northern-sky data sample that was used in the analysis of NGC 1068. The results of using this updated data sample to obtain a significance and flux fit for the 2014 TXS 0506+056 "untriggered" neutrino flare are reported