Miracle-dz/UniFAR-FTUs huggingface dataset:
On Tiny Episodic Memories in Continual Learning

In continual learning (CL), an agent learns from a stream of tasks leveraging prior experience to transfer knowledge to future tasks. It is an ideal framework to decrease the amount of supervision in the existing learning algorithms. But for a successful knowledge transfer, the learner needs to remember how to perform previous tasks. One way to endow the learner the ability to perform tasks seen in the past is to store a small memory, dubbed episodic memory, that stores few examples from previous tasks and then to replay these examples when training for future tasks. In this work, we empirically analyze the effectiveness of a very small episodic memory in a CL setup where each training example is only seen once. Surprisingly, across four rather different supervised learning benchmarks adapted to CL, a very simple baseline, that jointly trains on both examples from the current task as well as examples stored in the episodic memory, significantly outperforms specifically designed CL approaches with and without episodic memory. Interestingly, we find that repetitive training on even tiny memories of past tasks does not harm generalization, on the contrary, it improves it, with gains between 7\% and 17\% when the memory is populated with a single example per class.
Lactobacillus reuteri ZJ617 Culture Supernatant Attenuates Acute Liver Injury Induced in Mice by Lipopolysaccharide.

BACKGROUND Lactobacillus rhamnosus GG culture supernatant (LGGs) promotes intestinal integrity and ameliorates acute liver injury induced by alcohol in mice. OBJECTIVES The aim of this study was to investigate the protective effects and molecular mechanisms of Lactobacillus reuteri ZJ617 culture supernatant (ZJ617s) on acute liver injury induced by lipopolysaccharide (LPS) in mice. METHODS Male C57BL/6 mice (20 ± 2 g, 8 wk old) were randomly divided into 4 groups (6 mice/group): oral inoculation with phosphate-buffered saline (control), intraperitoneal injection of LPS (10 mg/kg body weight) (LPS), oral inoculation with ZJ617s 2 wk before intraperitoneal injection of LPS (ZJ617s + LPS), or oral inoculation with LGGs 2 wk before intraperitoneal injection of LPS (LGGs + LPS). Systemic inflammation, intestinal integrity, biomarkers of hepatic function, autophagy, and apoptosis signals in the liver were determined. RESULTS Twenty-four hours after LPS injection, the activities of serum alanine transaminase and aspartate transaminase were 32.2% and 30.3% lower in the ZJ617s + LPS group compared with the LPS group, respectively (P < 0.05). The ZJ617s + LPS group exhibited higher intestinal expression of claudin 3 (62.5%), occludin (60.1%), and zonula occludens 1 (60.5%) compared with the LPS group (P < 0.05). The concentrations of hepatic interleukin-6 and tumor necrosis factor-α were 21.4% and 27.3% lower in the ZJ617s + LPS group compared with the LPS group, respectively (P < 0.05). However, the concentration of interleukin-10 was 22.2% higher in the ZJ617s + LPS group. LPS increased the expression of Toll-like receptor 4 (TLR4; by 50.5%), phosphorylation p38 mitogen-activated protein kinase (p38MAPK; by 57.1%), extracellular signal-regulated kinase (by 77.8%), c-Jun N-terminal kinase (by 42.9%), and nuclear factor-κB (NF-κB; by 36.0%) compared with the control group. Supplementation with ZJ617s or LGGs ameliorated these effects (P < 0.05). Moreover, the hepatic expression of active caspase-3 and microtubule-associated protein 1 light chain 3 II was 23.8% and 28.6% lower in the ZJ617s + LPS group compared with the LPS group, respectively (P < 0.05). CONCLUSIONS ZJ617s exerts beneficial effects on the mouse liver through suppression of hepatic TLR4/MAPK/NF-κB activation, apoptosis, and autophagy. This trial was registered at Zhejiang University (http://www.lac.zju.edu.cn) as NO.ZJU20170529.
Neuroprotective Effects of Coenzyme Q10 at Rostral Ventrolateral Medulla Against Fatality During Experimental Endotoxemia in the Rat

Coenzyme Q10 (CoQ10, ubiquinone) is a highly mobile electron carrier in the mitochondrial respiratory chain that also acts as an antioxidant. We evaluated the neuroprotective efficacy of CoQ10 against fatality in an experimental model of endotoxemia that mimics systemic inflammatory response syndrome using a novel water-soluble formulation of this quinone derivative. Experiments were conducted in adult male Sprague-Dawley rats that were maintained under propofol anesthesia. Intravenous administration of Escherichia coli lipopolysaccharide (LPS; 30 mg/kg) induced progressive hypotension, with death ensuing within 4 h. The sequence of cardiovascular events during this LPS-induced endotoxemia can be divided into a reduction (Phase I), followed by an augmentation (Phase II; “pro-life” phase) and a secondary decrease (Phase III; “pro-death” phase) in the power density of the vasomotor components (0–0.8 Hz) of systemic arterial pressure signals. Pretreatment by microinjection bilaterally of CoQ10 (1 or 2 &mgr;g) into the rostral ventrolateral medulla (RVLM), the medullary origin of sympathetic vasomotor tone, significantly diminished mortality, prolonged survival time, and reduced the slope or magnitude of the LPS-induced hypotension. CoQ10 pretreatment also significantly prolonged the duration of and augmented the total power density of the vasomotor components of systemic arterial pressure signals in Phase II endotoxemia. The increase in superoxide anion production induced by LPS at the RVLM during Phases II and III endotoxemia was also significantly blunted. We conclude that CoQ10 provides neuroprotection against fatality during experimental endotoxemia by reducing superoxide anion production at the RVLM, whose neuronal activity is intimately related to the “life-and-death” process.
Inverse seesaw mechanism and portal dark matter

We study the phenomenology of the inverse seesaw mechanism in the scalar-Higgs portal dark matter model. The model is an extension of the Standard Model including two additional neutrinos, a singlet scalar and a fermionic dark matter. We consider the inverse seesaw mechanism where the mass of 2 additional neutrinos are made dynamics by the singlet scalar. We found that the natural scale for the scalar vacuum expectation value is naturally close to the weak scale. Motivating by this fact, we focus on the possibility of the singlet scalar connecting with dark matter, i.e., the scalar is also the mediator between dark sector and the Standard Model. We perform a numerical analysis over the parameter space subject to the indirect and direct detection constraints. The feasible region of the parameter space will be discussed.
Leveraging Pretrained Word Embeddings for Part-of-Speech Tagging of Code Switching Data

Linguistic Code Switching (CS) is a phenomenon that occurs when multilingual speakers alternate between two or more languages/dialects within a single conversation. Processing CS data is especially challenging in intra-sentential data given state-of-the-art monolingual NLP technologies since such technologies are geared toward the processing of one language at a time. In this paper, we address the problem of Part-of-Speech tagging (POS) in the context of linguistic code switching (CS). We explore leveraging multiple neural network architectures to measure the impact of different pre-trained embeddings methods on POS tagging CS data. We investigate the landscape in four CS language pairs, Spanish-English, Hindi-English, Modern Standard Arabic- Egyptian Arabic dialect (MSA-EGY), and Modern Standard Arabic- Levantine Arabic dialect (MSA-LEV). Our results show that multilingual embedding (e.g., MSA-EGY and MSA-LEV) helps closely related languages (EGY/LEV) but adds noise to the languages that are distant (SPA/HIN). Finally, we show that our proposed models outperform state-of-the-art CS taggers for MSA-EGY language pair.
Recent technical and scientific highlights from the CHARA array

The CHARA Array is a six-element, optical/NIR interferometer, which currently has the largest operational baselines in the world. The Array is operated by Georgia State University and is located at the Mount Wilson Observatory in California. The Array thrives thanks to members of the CHARA consortium that includes LESIA (Observatoire de Paris), Observatoire de la Cote dAzur, University of Michigan, Sydney University, Australian National University, and University of Exeter. Here we give a brief introduction to the Array infrastructure with a focus on a developing Adaptive Optics (AO) program, the new community access program funded by the NSF, and recent science results.
A Block Diagonal Markov Model for Indoor Software-Defined Power Line Communication

A Semi-Hidden Markov Model (SHMM) for bursty error channels is defined by a state transition probability matrix A, a prior probability vector Π, and the state dependent output symbol error probability matrix B. Several processes are utilized for estimating A, Π and B from a given empirically obtained or simulated error sequence. However, despite placing some restrictions on the underlying Markov model structure, we still have a computationally intensive estimation procedure, especially given a large error sequence containing long burst of identical symbols. Thus, in this paper, we utilize under some moderate assumptions, a Markov model with random state transition matrix A equivalent to a unique Block Diagonal Markov model with state transition matrix Λ to model an indoor software-defined power line communication system. A computationally efficient modified Baum-Welch algorithm for estimation of Λ given an experimentally obtained error sequence from the indoor PLC channel is utilized. Resulting Equivalent Block Diagonal Markov models assist designers to accelerate and facilitate the procedure of novel PLC systems design and evaluation.
Semi-Unsupervised Lifelong Learning for Sentiment Classification: Less Manual Data Annotation and More Self-Studying

Lifelong machine learning is a novel machine learning paradigm which can continually accumulate knowledge during learning. The knowledge extracting and reusing abilities enable the lifelong machine learning to solve the related problems. The traditional approaches like Naïve Bayes and some neural network based approaches only aim to achieve the best performance upon a single task. Unlike them, the lifelong machine learning in this paper focus on how to accumulate knowledge during learning and leverage them for the further tasks. Meanwhile, the demand for labeled data for training also be significantly decreased with the knowledge reusing. This paper suggests that the aim of the lifelong learning is to use less labeled data and computational cost to achieve the performance as well as or even better than the supervised learning.
Interactions in a Human-Scale Immersive Environment: the CRAIVE-Lab

We describe interfaces and visualizations in the CRAIVE (Collaborative Research Augmented Immersive Virtual Environment) Lab, an interactive human scale immersive environment at Rensselaer Polytechnic Institute. We describe the physical infrastructure and software architecture of the CRAIVE-Lab, and present two immersive scenarios within it. The first is “person following”, which allows a person walking inside the immersive space to be tracked by simple objects on the screen. This was implemented as a proof of concept of the overall system, which includes visual tracking from an overhead array of cameras, communication of the tracking results, and large-scale projection and visualization. The second “smart presentation” scenario features multimedia on the screen that reacts to the position of a person walking around the environment by playing or pausing automatically, and additionally supports real-time speech-to-text transcription. Our goal is to continue research in natural human interactions in this large environment, without requiring user-worn devices for tracking or speech recording.
Tuning Water Slip Behavior in Nanochannels using Self-Assembled Monolayers.

Water slip at solid surfaces is important for a wide range of micro/nano-fluidic applications. While it is known that water slip behavior depends on surface functionalization, how it impacts the molecular level dynamics and mass transport at the interface is still not thoroughly understood. In this paper, we use nonequilibrium molecular dynamics simulations to investigate the slip behavior of water confined between gold surfaces functionalized by self-assembled monolayer (SAM) molecules with different polar functional groups. We observe a positive-to-negative slip transition from hydrophobic to hydrophilic SAM functionalizations, which is found related to the stronger interfacial interaction between water molecules and more hydrophilic SAM molecules. The stronger interaction increases the surface friction and local viscosity, making water slip more difficult. More hydrophilic functionalization also slows down the interfacial water relaxation and leads to more pronounced water trapping inside the SAM layer, which both impede water slip. The results from this work will provide useful insights to the understanding of the water slip at functionalized surfaces and design guidelines for various applications.
Age-related decline in RMR in physically active men : relation to exercise volume and energy intake

[PDF] [Full Text] [Abstract] , February 1, 2003; 94 (2): 716-723. J Appl Physiol R. K. Krishnan, W. J. Evans and J. P. Kirwan Impaired substrate oxidation in healthy elderly men after eccentric exercise [PDF] [Full Text] [Abstract] , October 1, 2003; 58 (10): M911-M916. J Gerontol A Biol Sci Med Sci T. J. Marcell Review Article: Sarcopenia: Causes, Consequences, and Preventions [PDF] [Full Text] [Abstract] , July 1, 2007; 62 (7): 752-759. J Gerontol A Biol Sci Med Sci Jazwinski, W. A. Deutsch, E. Ravussin and for the Louisiana Healthy Aging Study M. I. Frisard, A. Broussard, S. S. Davies, L. J. Roberts II, J. Rood, L. d. Jonge, X. Fang, S. M. Healthy Aging Study Aging, Resting Metabolic Rate, and Oxidative Damage: Results From the Louisiana
Study of the Mass-Ratio Distribution of Spectroscopic Binaries. II. The Boundaries of the Brown-Dwarf Desert as Seen with the APOGEE Spectroscopic Binaries

Analysis of APOGEE DR12 stellar radial-velocities by Troup et al. (2016) affirmed the existence of the well-known Brown-Dwarf Desert (BDD). They detected a dearth of spectroscopic binaries (SB) with periods shorter than $\sim 10$ - $30$ days and secondaries with masses in the range of $\sim0.01$ - $0.1\, M_{\odot}$. We reconsider here their sample of binaries, focusing on 116 systems on the main sequence of the Gaia color-magnitude diagram, with mostly K-dwarf primaries. Using our recently devised algorithm to analyze the mass-ratio distribution of a sample of SBs we confirm the BDD existence and delineate its boundaries. For the K-dwarf APOGEE $1$ - $25$ days binaries, the companion-mass range of the BDD is $\sim0.02$ - $0.2\, M_{\odot}$. The mass ratio distribution of the long-period ($25$ - $500$ days) binaries does not show any dearth at the $q$-range studied. Instead, their distribution displays a linear increase in $\log q$, implying a tendency towards low-$q$ values. The limits of the BDD do not coincide with the frequently used mass limits of the brown-dwarf population, sometimes defined as $0.013$ and $0.08\, M_{\odot}$, based on theoretically derived stellar minimum masses for burning deuterium and hydrogen in their cores. Trying to draw the boundaries of the desert, we suggest either a wedged or trapezoidal shape. We discuss briefly different scenarios that can account for the formation of the BDD, in terms of differentiating between stellar secondaries and planets in particular, and compare this desert to the Neptunian desert that can distinguish between Jovian planets and super Earths of short periods.
Quantization Loss Re-Learning Method

In order to quantize the gate parameters of the LSTM (Long Short-Term Memory) neural network model with almost no recognition performance degraded, a new quantization method named Quantization Loss Re-Learn Method is proposed in this paper. The method does lossy quantization on gate parameters during training iterations, and the weight parameters learn to offset the loss of gate parameters quantization by adjusting the gradient in back propagation during weight parameters optimization. We proved the effectiveness of this method through theoretical derivation and experiments. The gate parameters had been quantized to 0, 0.5, 1 three values, and on the Named Entity Recognition dataset, the F1 score of the model with the new quantization method on gate parameters decreased by only 0.7% compared to the baseline model.
Valproate oral loading in the treatment of acute mania.

BACKGROUND Evidence from earlier studies suggests that the antimanic activity of valproate becomes most pronounced within 1 to 4 days of achieving serum concentrations of 50 mg/L or greater. We conducted a prospective study to assess the onset of antimanic activity of oral loading dosages of valproate administered to achieve serum concentrations above 50 mg/L within 24 hours. METHOD Nineteen patients with bipolar disorder, manic phase, received divalproex sodium 20 mg/kg/day in divided dosages for 5 days, without other psychotropic agents except lorazepam up to 4 mg/day. Serum valproate concentrations were measured after 1 and 4 days of treatment. Improvement was measured daily by a blind rater using the Young Mania Rating Scale (MRS). RESULTS Serum valproate concentrations greater than 50 mg/L were reached in all 15 patients completing the study. Ten (53%) of the 19 patients who received at least one complete loading dose displayed a significant (greater than 50%) reduction in MRS scores by study termination. These responders displayed the greatest percent change in MRS scores over the first 3 days of treatment. Side effects were minimal. CONCLUSION Valproate can be safely administered by oral loading and may produce rapid onset of antimanic response in some patients.
Connecting Elasticity and Effective Interactions of Neutral Microgels: The Validity of the Hertzian Model

An important open problem in materials science is whether a direct connection exists between single-particle elastic properties and the macroscopic bulk behavior. Here we address this question by focusing on the archetype of soft colloids: thermoresponsive microgels. These colloidal-sized polymer networks are often assumed to interact through a simple Hertzian potential, a classic model in linear elasticity theory. By developing an appropriate methodology, that can be generalized to any kind of soft particle, we are able to calculate all the elastic moduli of non-ionic microgels across their volume phase transition (VPT). Remarkably, we reproduce many features seen in experiments, including the appearance of a minimum of the Poisson's ratio close to the VPT. By calculating the particle-particle effective interactions and the resulting collective behavior, we find that the Hertzian model works well up to moderate values of the packing fraction.
Do Human Rationales Improve Machine Explanations?

Work on “learning with rationales” shows that humans providing explanations to a machine learning system can improve the system’s predictive accuracy. However, this work has not been connected to work in “explainable AI” which concerns machines explaining their reasoning to humans. In this work, we show that learning with rationales can also improve the quality of the machine’s explanations as evaluated by human judges. Specifically, we present experiments showing that, for CNN-based text classification, explanations generated using “supervised attention” are judged superior to explanations generated using normal unsupervised attention.
Symbol Emergence as an Interpersonal Multimodal Categorization

This study focuses on category formation for individual agents and the dynamics of symbol emergence in a multi-agent system through semiotic communication. In this study, the semiotic communication refers to exchanging signs composed of the signifier (i.e., words) and the signified (i.e., categories). We define the generation and interpretation of signs associated with the categories formed through the agent's own sensory experience or by exchanging signs with other agents as basic functions of the semiotic communication. From the viewpoint of language evolution and symbol emergence, organization of a symbol system in a multi-agent system (i.e., agent society) is considered as a bottom-up and dynamic process, where individual agents share the meaning of signs and categorize sensory experience. A constructive computational model can explain the mutual dependency of the two processes and has mathematical support that guarantees a symbol system's emergence and sharing within the multi-agent system. In this paper, we describe a new computational model that represents symbol emergence in a two-agent system based on a probabilistic generative model for multimodal categorization. It models semiotic communication via a probabilistic rejection based on the receiver's own belief. We have found that the dynamics by which cognitively independent agents create a symbol system through their semiotic communication can be regarded as the inference process of a hidden variable in an interpersonal multimodal categorizer, i.e., the complete system can be regarded as a single agent performing multimodal categorization using the sensors of all agents, if we define the rejection probability based on the Metropolis-Hastings algorithm. The validity of the proposed model and algorithm for symbol emergence, i.e., forming and sharing signs and categories, is also verified in an experiment with two agents observing daily objects in the real-world environment. In the experiment, we compared three communication algorithms: no communication, no rejection, and the proposed algorithm. The experimental results demonstrate that our model reproduces the phenomena of symbol emergence, which does not require a teacher who would know a pre-existing symbol system. Instead, the multi-agent system can form and use a symbol system without having pre-existing categories.
Exact solution of quantum many-body collective neutrino flavor oscillations

I study the flavor evolution of a dense neutrino gas by considering vacuum contributions, matter effects and neutrino self-interactions. Assuming a system of two flavors in a uniform matter background, the time evolution of the many-body system in discretized momentum space is computed. Here neutrino-neutrino interactions are treated exactly and compared to both the single-angle approximation and mean field calculations. The mono-energetic two neutrino beam scenario is solved analytically which allows for direct comparison among these methods. I find fast collective oscillations that develop when neutrinos of each flavor are present in comparable amounts. This is not seen in the mean field treatment. The difference can be ascribed to non-negligible flavor polarization correlations being present. I proceed to solve flavor oscillations for mono-energetic cubic lattices and quadratic lattices of two energy levels. Fast collective oscillations develop even when neutrinos of different energies interact provided both flavors are present. In agreement with the two beam scenario, the mean field treatment shows no collective oscillations. In future work, I intend to include anti-neutrinos and use larger lattices.
Partial Scan Electron Microscopy with Deep Learning

We present a multi-scale conditional generative adversarial network that completes 512$\times$512 electron micrographs from partial scans. This allows electron beam exposure and scan time to be reduced by 20$\times$ with a 2.6% intensity error. Our network is trained end-to-end on partial scans created from a new dataset of 16227 scanning transmission electron micrographs. High performance is achieved with adaptive learning rate clipping of outlier losses and an auxiliary trainer network. Source code and links to our new dataset and trained network have been made publicly available at this https URL