Miracle-dz/UniFAR-FTUs huggingface dataset:
When Problem Size Matters: Differential Effects of Brain Stimulation on Arithmetic Problem Solving and Neural Oscillations

The problem size effect is a well-established finding in arithmetic problem solving and is characterized by worse performance in problems with larger compared to smaller operand size. Solving small and large arithmetic problems has also been shown to involve different cognitive processes and distinct electroencephalography (EEG) oscillations over the left posterior parietal cortex (LPPC). In this study, we aimed to provide further evidence for these dissociations by using transcranial direct current stimulation (tDCS). Participants underwent anodal (30min, 1.5 mA, LPPC) and sham tDCS. After the stimulation, we recorded their neural activity using EEG while the participants solved small and large arithmetic problems. We found that the tDCS effects on performance and oscillatory activity critically depended on the problem size. While anodal tDCS improved response latencies in large arithmetic problems, it decreased solution rates in small arithmetic problems. Likewise, the lower-alpha desynchronization in large problems increased, whereas the theta synchronization in small problems decreased. These findings reveal that the LPPC is differentially involved in solving small and large arithmetic problems and demonstrate that the effects of brain stimulation strikingly differ depending on the involved neuro-cognitive processes.
Simultaneous Detection of P300 and Steady-State Visually Evoked Potentials for Hybrid Brain-Computer Interface

Objective We study the feasibility of a hybrid Brain-Computer Interface (BCI) combining simultaneous visual oddball and Steady-State Visually Evoked Potential (SSVEP) paradigms, where both types of stimuli are superimposed on a computer screen. Potentially, such a combination could result in a system being able to operate faster than a purely P300-based BCI and encode more targets than a purely SSVEP-based BCI. Approach We analyse the interactions between the brain responses of the two paradigms, and assess the possibility to detect simultaneously the brain activity evoked by both paradigms, in a series of 3 experiments where EEG data are analysed offline. Main Results Despite differences in the shape of the P300 response between pure oddball and hybrid condition, we observe that the classification accuracy of this P300 response is not affected by the SSVEP stimulation. We do not observe either any effect of the oddball stimulation on the power of the SSVEP response in the frequency of stimulation. Finally results from the last experiment show the possibility of detecting both types of brain responses simultaneously and suggest not only the feasibility of such hybrid BCI but also a gain over pure oddball- and pure SSVEP-based BCIs in terms of communication rate.
Phase Retrieval via Reweighted Amplitude Flow

This paper deals with finding an <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -dimensional solution <inline-formula><tex-math notation="LaTeX">$\boldsymbol {x}$</tex-math></inline-formula> to a system of quadratic equations of the form <inline-formula><tex-math notation="LaTeX">$y_i=|\langle \boldsymbol {a}_i,\boldsymbol {x}\rangle |^2$</tex-math></inline-formula> for <inline-formula><tex-math notation="LaTeX">$1\leq i \leq m$</tex-math></inline-formula>, which is also known as the generalized phase retrieval problem. For this NP-hard problem, a novel approach is developed for minimizing the amplitude-based least-squares empirical loss, which starts with a weighted maximal correlation initialization obtainable through a few power or Lanczos iterations, followed by successive refinements based on a sequence of iteratively reweighted gradient iterations. The two stages (initialization and gradient flow) distinguish themselves from prior contributions by the inclusion of a fresh (re)weighting regularization procedure. For certain random measurement models, the novel scheme is shown to be able to recover the true solution <inline-formula><tex-math notation="LaTeX">$\boldsymbol {x}$</tex-math></inline-formula> in time proportional to reading the data <inline-formula><tex-math notation="LaTeX">$\lbrace (\boldsymbol {a}_i;y_i)\rbrace _{1\leq i \leq m}$</tex-math></inline-formula>. This holds with high probability and without extra assumption on the signal vector <inline-formula><tex-math notation="LaTeX">$\boldsymbol {x}$</tex-math> </inline-formula> to be recovered, provided that the number <inline-formula><tex-math notation="LaTeX">$m$</tex-math> </inline-formula> of equations is some constant <inline-formula><tex-math notation="LaTeX">$c>0$</tex-math> </inline-formula> times the number <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> of unknowns in the signal vector, namely <inline-formula><tex-math notation="LaTeX">$m>cn$</tex-math></inline-formula> . Empirically, the upshots of this contribution are: first, (almost) <inline-formula><tex-math notation="LaTeX"> $\text{100}{\%}$</tex-math></inline-formula> perfect signal recovery in the high-dimensional (say <inline-formula> <tex-math notation="LaTeX">$n\geq 2000$</tex-math></inline-formula>) regime given only an information-theoretic limit number of noiseless equations, namely <inline-formula><tex-math notation="LaTeX">$m=2n-1$</tex-math></inline-formula>, in the real Gaussian case; and second, (nearly) optimal statistical accuracy in the presence of additive noise of bounded support. Finally, substantial numerical tests using both synthetic data and real images corroborate markedly improved recovery performance and computational efficiency of the novel scheme relative to the state-of-the-art approaches.
Early 1900s Detection of Batrachochytrium dendrobatidis in Korean Amphibians

The pathogenic fungus Batrachochytrium dendrobatidis (Bd) is a major conservation concern because of its role in decimating amphibian populations worldwide. We used quantitative PCR to screen 244 museum specimens from the Korean Peninsula, collected between 1911 and 2004, for the presence of Bd to gain insight into its history in Asia. Three specimens of Rugosa emeljanovi (previously Rana or Glandirana rugosa), collected in 1911 from Wonsan, North Korea, tested positive for Bd. Histology of these positive specimens revealed mild hyperkeratosis – a non-specific host response commonly found in Bd-infected frogs – but no Bd zoospores or zoosporangia. Our results indicate that Bd was present in Korea more than 100 years ago, consistent with hypotheses suggesting that Korean amphibians may be infected by endemic Asian Bd strains.
Generation of macroscopic singlet states in atomic ensembles

We study squeezing of the spin uncertainties by quantum non-demolition (QND) measurement in non-polarized spin ensembles. Unlike the case of polarized ensembles, the QND measurements can be performed with negligible back-action, which allows, in principle, perfect spin squeezing as quantified by Tóth et al (2007 Phys. Rev. Lett. 99 250405). The generated spin states approach many-body singlet states and contain a macroscopic number of entangled particles even when individual spin is large. We introduce the Gaussian treatment of unpolarized spin states and use it to estimate the achievable spin squeezing for realistic experimental parameters. Our proposal might have applications for magnetometry with a high spatial resolution or quantum memories storing information in decoherence free subspaces.
BLOCK-NORMALIZED GRADIENT METHOD: AN EMPIRICAL STUDY FOR TRAINING DEEP NEURAL NETWORK

In this paper, we propose a generic and simple strategy for utilizing stochastic gradient information in optimization. The technique essentially contains two consecutive steps in each iteration: 1) computing and normalizing each block (layer) of the mini-batch stochastic gradient; 2) selecting appropriate step size to update the decision variable (parameter) towards the negative of the block-normalized gradient. We conduct extensive empirical studies on various non-convex neural network optimization problems, including multilayer perceptron, convolution neural networks and recurrent neural networks. The results indicate the block-normalized gradient can help accelerate the training of neural networks. In particular, we observe that the normalized gradient methods having constant step size with occasionally decay, such as SGD with momentum, have better performance in the deep convolution neural networks, while those with adaptive step sizes, such as Adam, perform better in recurrent neural networks. Besides, we also observe this line of methods can lead to solutions with better generalization properties, which is confirmed by the performance improvement over strong baselines.
Data Backup Optimization for Nonvolatile SRAM in Energy Harvesting Sensor Nodes

Nonvolatile static random access memory (nvSRAM) has been widely investigated as a promising on-chip memory architecture in energy harvesting sensor nodes, due to zero standby power, resilience to power failures, and fast read/write operations. However, conventional approaches back up all data from static random access memory into nonvolatile memory when power failures happen. It leads to significant energy overhead and peak inrush current, which has a negative impact on the system performance and circuit reliability. This paper proposes a holistic data backup optimization to mitigate these problems in nvSRAM, consisting of a partial backup algorithm and a run-time adaptive write policy. A statistic dead-block predictor is employed to achieve dead block identification with trivial hardware overhead. An adaptive policy is used to switch between write-back and write-through strategy to reduce the rollback induced by backup failures. Experimental results show that the proposed scheme improves the performance by 4.6% on average while the backup power consumption and the inrush current are reduced by 38.1% and 54% on average compared to the full backup scheme. What is more, the backup capacitor size for energy buffer can be reduced by 40% on average under the same performance constraint.
E-MAC: an elastic MAC layer for IEEE 802.11 networks

We present a system for real-time traffic support in infrastructure and ad hoc IEEE 802.11 networks. The proposed elastic MAC (E-MAC) protocol provides a distributed transmission schedule for stations with real-time traffic requirements, while allowing a seamless coexistence with standard IEEE 802.11 clients, protecting best-effort 802.11 traffic from starvation by means of admission control policies. Our scheduling decisions are based on an ‘elastic’ transmission opportunity (TXOP) assignment which allows for efficient wireless resource usage: whenever a real-time station does not use the assigned TXOP, the other real-time stations can take over the unused access opportunity, thus preventing the well-known inefficiencies of static time division multiple access (TDMA) schemes. Unlike other TDMA-based solutions for 802.11, E-MAC does not require a tight synchronization among the participating clients, thus allowing its implementation on commodity WLAN hardware via minor software changes at the client side, and no changes at the access points (APs). We studied the performance of our mechanism via ns-2 simulations and a mathematical model, showing that it outperforms IEEE 802.11e in terms of throughput, delay, and jitter. We finally provide a proof of concept through the results obtained in a real testbed where we implemented the E-MAC protocol.
Peptide microarray profiling identifies phospholipase C gamma 1 (PLC-γ1) as a potential target for t(8;21) AML

The t(8;21) (q22;q22) chromosomal translocation is one of the most frequent genetic alterations in acute myeloid leukemia (AML) which has a need for improved therapeutic strategies. We found PLC-γ1 as one of the highest phosphorylated peptides in t(8;21) AML samples compared to NBM or CN-AML in our previous peptide microarray. PLC-γ1 is known to play a role in cancer progression, however, the impact of PLC-γ1 in AML is currently unknown. Therefore, we aimed to study the functional role of PLC-γ1 by investigating the cellular growth, survival and its underlying mechanism in t(8;21) AML. In this study, PLC-γ1 expression was significantly higher in t(8;21) AML compared to other karyotypes. The PLC-γ1 protein expression was suppressed in AML1-ETO knock down cells indicating that it might induce kasumi-1 cell death. ShRNA-mediated PLC-γ1 knockdown in kasumi-1 cells significantly blocked cell growth, induced apoptosis and cell cycle arrest which was explained by the increased activation of apoptotic related and cell cycle regulatory protein expressions. Gene expression array analysis showed the up-regulation of apoptotic and DNA damage response genes together with the downregulation of cell growth, proliferation and differentiation genes in the PLC-γ1 suppressed kasumi-1 cells, consistent with the observed phenotypic effects. Importantly, PLC-γ1 suppressed kasumi-1 cells showed higher chemosensitivity to the chemotherapeutic drug treatments and lower cell proliferation upon hypoxic stress. Taken together, these in vitro finding strongly support an important role for PLC-γ1 in the survival of t(8;21) AML mimicking kasumi-1 cells and identify PLC-γ1 as a potential therapeutic target for t(8;21) AML treatment.
Analysis and applications of autoregressive moving average models with stochastic variance

It is known that volatility plays a central role in financial modelling problems. This paper studies, in detail, a class of discrete time stochastic volatility (SV) models driven by ARMA models with innovations having a stochastic variances. The auto-correlation function of this class of models is derived and methods of identification of such processes are described. An example is added to illustrate the development of the theory over the standard methods.
Reducing energy consumption in an industrial process by using model predictive control

In this paper, we propose to use Model Predictive Control techniques to reduce the energy consumption in an industrial process by incorporating energy consumption restrictions in the problem formulation. We propose to use a Model Predictive Control supervisor to calculate the optimum references for simple control loops regulated by individual PID controllers. The results obtained are compared against those obtained when a traditional control strategy exclusively based on PID controllers was used. By using this supervisory control strategy a reduction on energy consumption of about 10% was achieved.
Extracting the Population, Intervention, Comparison and Sentiment from Randomized Controlled Trials

In this paper, an identification approach for the Population (e.g. patients with headache), the Intervention (e.g. aspirin) and the Comparison (e.g. vitamin C) in Randomized Controlled Trials (RCTs) is proposed. Contrary to previous approaches, the identification is done on a word level, rather than on a sentence level. Additionally, we classify the sentiment of RCTs to determine whether an Intervention is more effective than its Comparison. Two new corpora were created to evaluate both approaches. In the experiments, an average F1 score of 0.85 for the PIC identification and 0.72 for the sentiment classification was achieved.
Scaling of the fracture toughness of freestanding metallic thin films with the yield strength

ABSTRACT The influence of the yield strength on the fracture toughness of freestanding metallic films with a thickness of ∼150 nm was investigated by bulge testing. For this purpose, gold films prepared by thermal evaporation were tested at room temperature and at 100°C. Additionally, different Au-Ag solid-solution strengthened films were studied. The fracture toughness of the films is observed to increase with increasing yield stress. This is at first sight counterintuitive but can be explained by previously observed severe necking leading to a chisel-point type of fracture in freestanding metallic thin films. GRAPHICAL ABSTRACT IMPACT STATEMENT Freestanding metallic thin films exhibit an extremely low fracture toughness. It is shown in this publication that KIC can be significantly improved by increasing the yield strength of the films.
Quercetin Potentiates the NGF-Induced Effects in Cultured PC 12 Cells: Identification by HerboChips Showing a Binding with NGF

Dementia is a persistent disorder of the mental processes and is strongly related to depression. However, the performance of current antidepression medicine is far from satisfactory. Herbal extract provides an excellent source to identify compounds for possible drug development against depression. Here, HerboChips were employed to search herbal compounds that could bind nerve growth factor (NGF). By screening over 500 types of herbal extracts, the water extract of Ginkgo Folium, the leaf of Ginkgo biloba, showed a strong binding to NGF. The herbal fractions showing NGF binding were further isolated and enriched. By using LC-MS/MS analysis, one of the NGF binding fractions was enriched, which was further identified as quercetin, a major flavonoid in Ginkgo Folium. Quercetin, similar to Ginkgo Folium extract, could enhance the effect of NGF in cultured PC 12 cells, including potentiation of neurite outgrowth and phosphorylation of Erk-1/2. This is the first report of discovering an NGF binding compound by using HerboChips from herbal extracts, which could be further developed for antidepression application.
Water transport in sunflower root systems: effects of ABA, Ca2+ status and HgCl2

Peterson, 1998). When osmotic gradients are present, the water flow is negligible in the apoplast (Quintero et al., Excised 20-d-old sunflower roots (Helianthus annuus 1998; Steudle and Peterson, 1998) and transport occurs L. cv. Sun-Gro 380) with different Ca2+ status were mainly from cell to cell (Steudle and Frensch, 1989; used to study the effects of root Ca2+ status and Steudle, 1994). In recent years, it has been shown that abscisic acid (ABA) on the exudation rate (Jv), the membrane-integral proteins, or aquaporins, act as waterhydraulic conductivity of the root (Lp r ), the flux of specific channels (Chrispeels and Maurel, 1994; Maggio exuded Ca2+ (J Ca ), and the gradient of osmotic pres- and Joly, 1995). The presence of aquaporins in roots sure between the xylem and the external medium. Jv ( Yamamoto et al., 1991; Niemietz and Tyerman, 1997) and Lp r increased in direct proportion to the Ca2+ suggests that radial flow of water could be mainly transstatus of the root. Addition of ABA (4 mM) at the onset cellular (Chrispeels and Maurel, 1994; Maggio and Joly, of exudation in the external medium made Jv and Lp r 1995) and may be controlled by the opening or closing rise, and this effect also increased with the Ca2+ of these water channels (Chrispeels and Maurel, 1994; status. The effects of HgCl 2 and its interaction with Steudle and Henzler, 1995). ABA on water transport in the root were also studied. The existence of a regulatory mechanism which controls Addition of HgCl 2 (1 mM) 2 h after the onset of exuda- radial transport of water in the root has yet to be tion in the external medium quickly inhibited Jv, inde- determined (Quintero et al., 1998). It is known that roots pendently of the presence of ABA in the root medium. oVer the greatest resistance to water flow ( Weatherley, The results recorded here point to the involvement of 1982), and that hydraulic conductivity of the root (Lp r ) ABA and Ca2+ in the regulation of root water flow, may be aVected by diverse forms of abiotic stress, such as well as the existence of aquaporins in the cell as salinity (Munns and Passioura, 1984; Joly, 1989), membranes of sunflower roots. anaerobiosis (Zhang and Tyerman, 1991), drought (North and Nobel, 1991) or nutritional stress (Radin and
The Protective Role of Sulfated Polysaccharides from Green Seaweed Udotea flabellum in Cells Exposed to Oxidative Damage

Seaweed is a rich source of bioactive sulfated polysaccharides. We obtained six sulfated polysaccharide-rich fractions (UF-0.3, UF-0.5, UF-0.6, UF-0.7, UF-1.0, and UF-2.0) from the green seaweed Udotea flabellum (UF) by proteolytic digestion followed by sequential acetone precipitation. Biochemical analysis of these fractions showed that they were enriched with sulfated galactans. The viability and proliferative capacity of 3T3 fibroblasts exposed to FeSO4 (2 µM), CuSO4 (1 µM) or ascorbate (2 mM) was not affected. However, these cells were exposed to oxidative stress in the presence of FeSO4 or CuSO4 and ascorbate, which caused the activation of caspase-3 and caspase-9, resulting in apoptosis of the cells. We also observed increased lipid peroxidation, evaluated by the detection of malondialdehyde and decreased glutathione and superoxide dismutase levels. Treating the cells with the ultrafiltrate fractions (UF) fractions protected the cells from the oxidative damage caused by the two salts and ascorbate. The most effective protection against the oxidative damage caused by iron was provided by UF-0.7 (1.0 mg/mL); on treatment with UF-0.7, cell viability was 55%. In the case of copper, cell viability on treatment with UF-0.7 was ~80%, but the most effective fraction in this model was UF-2.0, with cell viability of more than 90%. The fractions, mainly UF-0.7 and UF-2.0, showed low iron chelating activity, but high copper chelating activity and total antioxidant capacity (TAC). These results suggested that some of their protective mechanisms stem from these properties.
Neuroinflammation and Neurodegeneration in Adult Rat Brain from Binge Ethanol Exposure: Abrogation by Docosahexaenoic Acid

Evidence that brain edema and aquaporin-4 (AQP4) water channels have roles in experimental binge ethanol-induced neurodegeneration has stimulated interest in swelling/edema-linked neuroinflammatory pathways leading to oxidative stress. We report here that neurotoxic binge ethanol exposure produces comparable significant effects in vivo and in vitro on adult rat brain levels of AQP4 as well as neuroinflammation-linked enzymes: key phospholipase A2 (PLA2) family members and poly (ADP-ribose) polymerase-1 (PARP-1). In adult male rats, repetitive ethanol intoxication (3 gavages/d for 4 d, ∼9 g/kg/d, achieving blood ethanol levels ∼375 mg/dl; “Majchrowicz” model) significantly increased AQP4, Ca+2-dependent PLA2 GIVA (cPLA2), phospho-cPLA2 GIVA (p-cPLA2), secretory PLA2 GIIA (sPLA2) and PARP-1 in regions incurring extensive neurodegeneration in this model—hippocampus, entorhinal cortex, and olfactory bulb—but not in two regions typically lacking neurodamage, frontal cortex and cerebellum. Also, ethanol reduced hippocampal Ca+2-independent PLA2 GVIA (iPLA2) levels and increased brain “oxidative stress footprints” (4-hydroxynonenal-adducted proteins). For in vitro studies, organotypic cultures of rat hippocampal-entorhinocortical slices of adult age (∼60 d) were ethanol-binged (100 mM or ∼450 mg/dl) for 4 d, which augments AQP4 and causes neurodegeneration (Collins et al. 2013). Reproducing the in vivo results, cPLA2, p-cPLA2, sPLA2 and PARP-1 were significantly elevated while iPLA2 was decreased. Furthermore, supplementation with docosahexaenoic acid (DHA; 22:6n-3), known to quell AQP4 and neurodegeneration in ethanol-treated slices, blocked PARP-1 and PLA2 changes while counteracting endogenous DHA reduction and increases in oxidative stress footprints (3-nitrotyrosinated proteins). Notably, the PARP-1 inhibitor PJ-34 suppressed binge ethanol-dependent neurodegeneration, indicating PARP upstream involvement. The results with corresponding models support involvement of AQP4- and PLA2-associated neuroinflammatory pro-oxidative pathways in the neurodamage, with potential regulation by PARP-1 as well. Furthermore, DHA emerges as an effective inhibitor of these binge ethanol-dependent neuroinflammatory pathways as well as associated neurodegeneration in adult-age brain.