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Referring expression comprehension aims to localize objects identified by natural language descriptions. This is a challenging task as it requires understanding of both visual and language domains. One nature is that each object can be described by synonymous sentences with paraphrases, and such varieties in languages have critical impact on learning a comprehension model. While prior work usually treats each sentence and attends it to an object separately, we focus on learning a referring expression comprehension model that considers the property in synonymous sentences. To this end, we develop an end-to-end trainable framework to learn contrastive features on the image and object instance levels, where features extracted from synonymous sentences to describe the same object should be closer to each other after mapping to the visual domain. We conduct extensive experiments to evaluate the proposed algorithm on several benchmark datasets, and demonstrate that our method performs favorably against the state-of-the-art approaches. Furthermore, since the varieties in expressions become larger across datasets when they describe objects in different ways, we present the cross-dataset and transfer learning settings to validate the ability of our learned transferable features.

 

Anorexia nervosa (AN) is a mental illness with the highest rates of mortality and relapse, and no approved pharmacological treatment. Using an animal model of AN, called activity‐based anorexia (ABA), we showed earlier that a single intraperitoneal injection of ketamine at a dose of 30 mg/kg (30mgKET), but not 3 mg/kg (3mgKET), has a long‐lasting effect upon adolescent females of ameliorating anorexia‐like symptoms through the following changes: enhanced food consumption and body weight; reduced running and anxiety‐like behavior. However, there were also individual differences in the drug's efficacy. We hypothesized that individual differences in ketamine's ameliorative effects involve drebrin A, an F‐actin‐binding protein known to be required for the activity‐dependent trafficking of NMDA receptors (NMDARs). We tested this hypothesis by electron microscopic quantifications of drebrin A immunoreactivity at excitatory synapses of pyramidal neurons (PN) and GABAergic interneurons (GABA‐IN) in deep layer 1 of prefrontal cortex (PFC) of these mice. Results reveal that (1) the areal density of excitatory synapses on GABA‐IN is greater for the 30mgKET group than the 3mgKET group; (2) the proportion of drebrin A+ excitatory synapses is greater for both PN and GABA‐IN of 30mgKET than 3mgKET group. Correlation analyses with behavioral measurements revealed that (3) 30mgKET's protection is associated with reduced levels of drebrin A in the cytoplasm of GABA‐IN and higher levels at extrasynaptic membranous sites of PN and GABA‐IN; (5) altogether pointing to 30mgKET‐induced homeostatic plasticity that engages drebrin A at excitatory synapses of both PN and GABA‐IN.

 

Severe voluntary food restriction is the defining symptom of anorexia nervosa (AN), but anxiety and excessive exercise are maladaptive symptoms that contribute significantly to the severity of AN and which individuals with AN have difficulty suppressing. We hypothesized that the excitability of hippocampal pyramidal neurons, known to contribute to anxiety, leads to the maladaptive behavior of excessive exercise. Conversely, since glutamate transporter GLT‐1 dampens the excitability of hippocampal pyramidal neurons through the uptake of ambient glutamate and suppression of the GluN2B‐subunit containing NMDA receptors (GluN2B‐NMDARs), GLT‐1 may contribute toward dampening excessive exercise. This hypothesis was tested using the mouse model of AN, called activity‐based anorexia (ABA), whereby food restriction evokes the maladaptive behavior of excessive wheel running (food restriction‐evoked running, FRER). We tested whether individual differences in ABA vulnerability of mice, quantified based on FRER, correlated with individual differences in the levels of GLT‐1 at excitatory synapses of the hippocampus. Electron microscopic immunocytochemistry (EM‐ICC) was used to quantify GLT‐1 levels at the excitatory synapses of the hippocampus. The FRER seen in individual mice varied more than 10‐fold, and Pearson correlation analyses revealed a strong negative correlation (p = .02) between FRER and GLT‐1 levels at the axon terminals of excitatory synapses and at the surrounding astrocytic plasma membranes. Moreover, synaptic levels of GluN2B‐NMDARs correlated strongly with GLT‐1 levels at perisynaptic astrocytic plasma membranes. There is at present no accepted pharmacotherapy for AN, and little is known about the etiology of this deadly illness. Current findings suggest that drugs increasing GLT‐1 expression may reduce AN severity through the reduction of GluN2B‐NMDAR activity.

 

This study tested the effects of ketamine on vulnerability of female adolescent mice to activity‐based anorexia (ABA).

Twenty‐four female C57Bl/6 J mice underwent ABA induction, which involved exposing wheel‐acclimated adolescent mice to two bouts of food restriction (FR)—the first ABA (P41–44, mid‐adolescence) and the second ABA (P55–59, late adolescence), with recovery in between. Ketamine (3 or 30 mg/kg) or vehicle was given once, on the second day of FR of the first ABA (P42). Food consumption, body weight and wheel running activity were measured daily. Anxiety‐like behaviors were accessed by elevated plus maze on P49 and P62, after weight restoration during the recovery phase.

Ketamine (30 mg/kg) increased food intake during the first ABA (+38%,p = .015) and facilitated weight gain during recovery (+42%,p = .003). During the second ABA, the effect was manifested as increased food intake (+38%,p = .001) and weight gain (+47%,p = .001) while attenuating FR‐induced wheel running activity (−24%,p = .09) and weight loss (−17%,p = .056). Ketamine also reduced anxiety‐like behaviors.

Thus, single injection of ketamine during mid‐adolescence effectively attenuates vulnerability of female mice to repeated ABA exposures.

 

Abstract We present the detection potential for the diffuse supernova neutrino background (DSNB) at the Jiangmen Underground Neutrino Observatory (JUNO), using the inverse-beta-decay (IBD) detection channel on free protons. We employ the latest information on the DSNB flux predictions, and investigate in detail the background and its reduction for the DSNB search at JUNO. The atmospheric neutrino induced neutral current (NC) background turns out to be the most critical background, whose uncertainty is carefully evaluated from both the spread of model predictions and an envisaged in situ measurement. We also make a careful study on the background suppression with the pulse shape discrimination (PSD) and triple coincidence (TC) cuts. With latest DSNB signal predictions, more realistic background evaluation and PSD efficiency optimization, and additional TC cut, JUNO can reach the significance of 3σ for 3 years of data taking, and achieve better than 5σ after 10 years for a reference DSNB model. In the pessimistic scenario of non-observation, JUNO would strongly improve the limits and exclude a significant region of the model parameter space. 

A bstract We study damping signatures at the Jiangmen Underground Neutrino Observatory (JUNO), a medium-baseline reactor neutrino oscillation experiment. These damping signatures are motivated by various new physics models, including quantum decoherence, ν 3 decay, neutrino absorption, and wave packet decoherence. The phenomenological effects of these models can be characterized by exponential damping factors at the probability level. We assess how well JUNO can constrain these damping parameters and how to disentangle these different damping signatures at JUNO. Compared to current experimental limits, JUNO can significantly improve the limits on τ 3 / m 3 in the ν 3 decay model, the width of the neutrino wave packet σ x , and the intrinsic relative dispersion of neutrino momentum σ rel . 

A bstract JUNO is a massive liquid scintillator detector with a primary scientific goal of determining the neutrino mass ordering by studying the oscillated anti-neutrino flux coming from two nuclear power plants at 53 km distance. The expected signal anti-neutrino interaction rate is only 60 counts per day (cpd), therefore a careful control of the background sources due to radioactivity is critical. In particular, natural radioactivity present in all materials and in the environment represents a serious issue that could impair the sensitivity of the experiment if appropriate countermeasures were not foreseen. In this paper we discuss the background reduction strategies undertaken by the JUNO collaboration to reduce at minimum the impact of natural radioactivity. We describe our efforts for an optimized experimental design, a careful material screening and accurate detector production handling, and a constant control of the expected results through a meticulous Monte Carlo simulation program. We show that all these actions should allow us to keep the background count rate safely below the target value of 10 Hz (i.e. ∼ 1 cpd accidental background) in the default fiducial volume, above an energy threshold of 0.7 MeV. 

Abstract The OSIRIS detector is a subsystem of the liquid scintillator filling chain of the JUNO reactor neutrino experiment. Its purpose is to validate the radiopurity of the scintillator to assure that all components of the JUNO scintillator system work to specifications and only neutrino-grade scintillator is filled into the JUNO Central Detector. The aspired sensitivity level of $$10^{-16}\hbox { g/g}$$ 10 - 16 g/g of $$^{238}\hbox {U}$$ 238 U and $$^{232}\hbox {Th}$$ 232 Th requires a large ( $$\sim 20\,\hbox {m}^3$$ ∼ 20 m 3 ) detection volume and ultralow background levels. The present paper reports on the design and major components of the OSIRIS detector, the detector simulation as well as the measuring strategies foreseen and the sensitivity levels to U/Th that can be reached in this setup. 

Abstract Atmospheric neutrinos are one of the most relevant natural neutrino sources that can be exploited to infer properties about cosmic rays and neutrino oscillations. The Jiangmen Underground Neutrino Observatory (JUNO) experiment, a 20 kton liquid scintillator detector with excellent energy resolution is currently under construction in China. JUNO will be able to detect several atmospheric neutrinos per day given the large volume. A study on the JUNO detection and reconstruction capabilities of atmospheric $$\nu _e$$ ν e  and $$\nu _\mu $$ ν μ  fluxes is presented in this paper. In this study, a sample of atmospheric neutrino Monte Carlo events has been generated, starting from theoretical models, and then processed by the detector simulation. The excellent timing resolution of the 3” PMT light detection system of JUNO detector and the much higher light yield for scintillation over Cherenkov allow to measure the time structure of the scintillation light with very high precision. Since $$\nu _e$$ ν e  and $$\nu _\mu $$ ν μ  interactions produce a slightly different light pattern, the different time evolution of light allows to discriminate the flavor of primary neutrinos. A probabilistic unfolding method has been used, in order to infer the primary neutrino energy spectrum from the detector experimental observables. The simulated spectrum has been reconstructed between 100 MeV and 10 GeV, showing a great potential of the detector in the atmospheric low energy region.