Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time Recognition of the Affective User State with Physiological Signals

This study aims at developing methods for recognising the affective user state with physiological signals in near real-time. A multimodal database has been collected in a simulated driving context. Relaxed and stressed states are elicited by giving the participant different tasks. The structured design of the experiment can be used to obtain a preliminary “ground truth”; a fine-grained manual annotation of the perceived stress level is currently being conducted. A data-driven, multi-resolution approach to feature extraction is taken. The classification module can deal with a dynamically varying number of input channels in the case of corrupted signals. For online, user-independent recognition of a relaxed or stressed state during the most clearly defined segments of the experiments, an accuracy of 88.8 % has been obtained using six physiological signals. Current work focuses on a reliable artefact detection, un-supervised user adaption and methods for evaluating the real-time properties of the classification.
Pituitary Control of BK Potassium Channel Function and Intrinsic Firing Properties of Adrenal Chromaffin Cells

The discovery that the hypothalamic–pituitary–adrenocortical (HPA) endocrine stress axis controls an alternative splicing decision in chromaffin Slo-encoded BK (big potassium) channels raised the possibility that activation of the HPA could serve as a mechanism to tune the intrinsic electrical properties of epinephrine-secreting adrenal chromaffin cells. To test this, we compared BK functional properties and cell excitability in chromaffin cells from normal and hypophysectomized (pituitary-ablated) rats. Hypophysectomy was found to alter the voltage dependence and kinetics of BK gating, making channels less accessible for activation from rest. Perforated-patch recordings revealed changes in action potential waveform and repetitive firing properties. The maximum number of spikes that could be elicited with a 2 sec depolarizing current pulse was reduced by ∼50% by hypophysectomy. The results indicate that pituitary hormones can adapt the mechanics of adrenal catecholamine release by tailoring BK channel function.
Clinical Interventions in Aging Dovepress Associations of Frailty and Psychosocial Factors with Autonomy in Daily Activities: a Cross-sectional Study in Italian Community-dwelling Older Adults

Frailty has been recognized as a risk factor for geriatric adverse events. Little is known of the role of psychosocial factors associated with frailty in explaining negative outcomes of aging. This study was aimed at 1) evaluating the differences in psychosocial factors among robust, prefrail, and frail individuals and 2) investigating whether there was any interaction effect of frailty status with empirically identified clusters of psychosocial factors on autonomy in the activities of daily living (ADLs). Two-hundred and ten older adults (age 73±6 years, 66% women) were involved in this study. Frailty was assessed using an adapted version of the frailty phenotype. The psychosocial factors investigated were depressive symptoms using the 20-item Center for Epidemiologic Studies Depression Scale, social isolation using the Friendship Scale, and loneliness feeling using the eight-item UCLA Loneliness Scale. The autonomy in ADLs was measured with the Groningen Activity Restriction Scale. Thirty-one percent of participants were robust, 55% prefrail, and 14% frail. We performed an analysis of covariance which showed differences between robust, prefrail, and frail individuals for all the controlling for age and sex. Using the same covariates, the two-way analysis of covari-ance indicated an interaction effect of frailty with psychosocial factors in determining ADLs, F(4, 199)=3.53, P=0.008. This study demonstrates the close relationship between frailty and psychosocial factors, suggesting the need to take into account simultaneously physical and psychosocial components of human functioning.
Rme-8 depletion perturbs Notch recycling and predisposes to pathogenic signaling

Notch signaling is a major regulator of cell fate, proliferation, and differentiation. Like other signaling pathways, its activity is strongly influenced by intracellular trafficking. Besides contributing to signal activation and down-regulation, differential fluxes between trafficking routes can cause aberrant Notch pathway activation. Investigating the function of the retromer-associated DNAJ protein Rme-8 in vivo, we demonstrate a critical role in regulating Notch receptor recycling. In the absence of Rme-8, Notch accumulated in enlarged tubulated Rab4-positive endosomes, and as a consequence, signaling was compromised. Strikingly, when the retromer component Vps26 was depleted at the same time, Notch no longer accumulated and instead was ectopically activated. Likewise, depletion of ESCRT-0 components Hrs or Stam in combination with Rme-8 also led to high levels of ectopic Notch activity. Together, these results highlight the importance of Rme-8 in coordinating normal endocytic recycling route and reveal that its absence predisposes toward conditions in which pathological Notch signaling can occur.
In Vivo Calcium Imaging Reveals Functional Rewiring of Single Somatosensory Neurons after Stroke

Functional mapping and microstimulation studies suggest that recovery after stroke damage can be attributed to surviving brain regions taking on the functional roles of lost tissues. Although this model is well supported by data, it is not clear how activity in single neurons is altered in relation to cortical functional maps. It is conceivable that individual surviving neurons could adopt new roles at the expense of their usual function. Alternatively, neurons that contribute to recovery may take on multiple functions and exhibit a wider repertoire of neuronal processing. In vivo two-photon calcium imaging was used in adult mice within reorganized forelimb and hindlimb somatosensory functional maps to determine how the response properties of individual neurons and glia were altered during recovery from ischemic damage over a period of 2–8 weeks. Single-cell calcium imaging revealed that the limb selectivity of individual neurons was altered during recovery from ischemia, such that neurons normally selective for a single contralateral limb processed information from multiple limbs. Altered limb selectivity was most prominent in border regions between stroke-altered forelimb and hindlimb macroscopic map representations, and peaked 1 month after the targeted insult. Two months after stroke, individual neurons near the center of reorganized functional areas became more selective for a preferred limb. These previously unreported forms of plasticity indicate that in adult animals, seemingly hardwired cortical neurons first adopt wider functional roles as they develop strategies to compensate for loss of specific sensory modalities after forms of brain damage such as stroke.
Interlaboratory Development and Validation of a HRM Method Applied to the Detection of JAK2 Exon 12 Mutations in Polycythemia Vera Patients

BACKGROUND Myeloproliferative disorders are characterized by clonal expansion of normal mature blood cells. Acquired mutations giving rise to constitutive activation of the JAK2 tyrosine kinase has been shown to be present in the majority of patients. Since the demonstration that the V617F mutation in the exon 14 of the JAK2 gene is present in about 90% of patients with Polycythemia Vera (PV), the detection of this mutation has become a key tool for the diagnosis of these patients. More recently, additional mutations in the exon 12 of the JAK2 gene have been described in 5 to 10% of the patients with erythrocytosis. According to the updated WHO criteria the presence of these mutations should be looked for in PV patients with no JAK2 V617F mutation. Reliable and accurate methods dedicated to the detection of these highly variable mutations are therefore necessary. METHODS/FINDINGS For these reasons we have defined the conditions of a High Resolution DNA Melting curve analysis (HRM) method able to detect JAK2 exon 12 mutations. After having validated that the method was able to detect mutated patients, we have verified that it gave reproducible results in repeated experiments, on DNA extracted from either total blood or purified granulocytes. This HRM assay was further validated using 8 samples bearing different mutant sequences in 4 different laboratories, on 3 different instruments. CONCLUSION The assay we have developed is thus a valid method, adapted to routine detection of JAK2 exon 12 mutations with highly reproducible results.
Ownership Types and Safe Lazy Upgrades in Object-Oriented Databases

This paper describes a novel mechanism for upgrading objects in an object-oriented database. Unlike earlier systems, our mechanism is expressive, supporting a rich set of upgrades; it is efficient and does not stop application access to run an upgrade; it avoids making copies of the database; yet it provides good semantics. Expressive efficient upgrades can lead to problems for the code that upgrades objects. For example, the code might observe broken invariants or interfaces unknown at the time it was written. The paper shows how to use a variant of ownership types to avoid such problems and enable programmers to reason about the correctness of their upgrades. Our approach to correctness is novel, and is a significant contribution of this paper. This paper also presents a new ownership type system. Previous ownership type systems only supported a weak encapsulation property. Enforcing object encapsulation, while supporting subtyping and paradigms like iterators, has been an open problem. Our type system provides a satisfactory solution to this open problem. The novel idea is that we handle inner classes specially—our type system allows objects of inner classes to have privileged access to the representations of the corresponding objects of the outer classes. This principled violation of encapsulation allows programmers to express paradigms like iterators, yet they can reason locally about program correctness.
The Real Stimuli In Popular Stores

The popular store has a wide and shallow assortment of items with high turnover and low margins rate. This kind of point of sale is designed for poor consumers. It was in 1873 the first trial of implantation of a popular magazine by Frank Winfield Woolworth in Watertown, in the American state of Arial. After six years, this idea has had success in Lancaster, Pennsylvania. However, this huge potential has not elapsed as much ink as it deserves. In addition, the marketers claim that the poor are affected only by the price, while atmospheric factors will not have a strong interest in the eyes of this target. This paper aims to identify closely the impact factors on behavioral intention in the popular store.
Some Implications of Increased Cooperation in World Oil Conservation Senior Economist and Assistant vice President Federal Reserve Bank of Dallas

The classic problem of free riding arises when nations act to curtail emissions of carbon dioxide (CO 2) and other potential greenhouse gases. When damages from emissions are global rather than local, countries that do not participate in policies directed at reducing global climate change receive the benefits of other countries' actions without incurring the costs. Past research and game-theoretic analyses have emphasized the gains from eliciting the cooperation of developing countries in an effort Broader participation reduces the costs of achieving any given target of emissions reductions among those nations engaged in the coordinated policies. In essence, the cost curve for countries reducing their emissions shifts downward as participation expands to include more countries. Recent estimates of possible climate-change damages allow us to examine the impact of cooperation on the optimal strategy for reducing CO 2 emissions. Because increased participation lowers the costs of coordinated policies to reduce emissions, it is likely to increase the amount of conservation that the participants would see as cost effective for any given set of estimates of the benefits of reducing emissions and avoiding environmental damage. Whether increased cooperation yields too little or too great a reduction in emissions from a world perspective depends critically upon the level of damage estimates —an empirical issue that at the moment is highly uncertain. Reduced usage of fossil fuels, through higher efficiency equipment and changing economic structures and lifestyles, is the principal vehicle for CO 2 emissions abatement. Policies that discourage the use of coal, oil, and, to a lesser extent, natural gas contribute to reduced emissions of greenhouse gases and hence lower the potential damages from climate change. Analysis of abatement policies affecting the oil market can seem complex, because actions taken by one country or group of countries are likely to influence oil consumption in other parts of the world through their effect on the world oil price. In this article, we evaluate the extent to which increasing cooperation beyond the Organization for Economic Cooperation and Development (OECD) to limit CO 2 emissions through oil conservation is desirable from a world perspective. To accomplish this task, we In this article, we evaluate the extent to which increasing cooperation beyond the Organization for Economic Cooperation and Development (OECD) to limit CO 2 emissions through oil conservation is desirable from a world perspective.
Efficacy of Tricaine Methanesulfonate (MS-222) as an Anesthetic Agent for Blocking Sensory-Motor Responses in Xenopus laevis Tadpoles

Anesthetics are drugs that reversibly relieve pain, decrease body movements and suppress neuronal activity. Most drugs only cover one of these effects; for instance, analgesics relieve pain but fail to block primary fiber responses to noxious stimuli. Alternately, paralytic drugs block synaptic transmission at neuromuscular junctions, thereby effectively paralyzing skeletal muscles. Thus, both analgesics and paralytics each accomplish one effect, but fail to singularly account for all three. Tricaine methanesulfonate (MS-222) is structurally similar to benzocaine, a typical anesthetic for anamniote vertebrates, but contains a sulfate moiety rendering this drug more hydrophilic. MS-222 is used as anesthetic in poikilothermic animals such as fish and amphibians. However, it is often argued that MS-222 is only a hypnotic drug and its ability to block neural activity has been questioned. This prompted us to evaluate the potency and dynamics of MS-222-induced effects on neuronal firing of sensory and motor nerves alongside a defined motor behavior in semi-intact in vitro preparations of Xenopus laevis tadpoles. Electrophysiological recordings of extraocular motor discharge and both spontaneous and evoked mechanosensory nerve activity were measured before, during and after administration of MS-222, then compared to benzocaine and a known paralytic, pancuronium. Both MS-222 and benzocaine, but not pancuronium caused a dose-dependent, reversible blockade of extraocular motor and sensory nerve activity. These results indicate that MS-222 as benzocaine blocks the activity of both sensory and motor nerves compatible with the mechanistic action of effective anesthetics, indicating that both caine-derivates are effective as single-drug anesthetics for surgical interventions in anamniotes.
Analysis of Mixed Natural and Symbolic Input in Mathematical Dialogs

Discourse in formal domains, such as mathematics, is characterized by a mixture of telegraphic natural language and embedded (semi-)formal symbolic mathematical expressions. We present language phenomena observed in a corpus of dialogs with a simulated tutorial system for proving theorems as evidence for the need for deep syntactic and semantic analysis. We propose an approach to input understanding in this setting. Our goal is a uniform analysis of inputs of different degree of verbalization: ranging from symbolic alone to fully worded mathematical expressions.
Pathological Brain Detection by Wavelet-Energy and Fuzzy Support Vector Machine

It is important to early detect pathological brains. Traditional methods used plain support vector machine (SVM) that is vulnerable to noises and outliers. In this study, we presented a hybrid method that combined wavelet-energy (WE) and fuzzy support vector machine (FSVM). The results over a 5x5-fold cross validation showed that the proposed "WE + FSVM" produced accuracy of 93.78%, higher than "WE + KSVM" of 91.78%, "DWT + PCA + RBF-NN" of 91.33%, "WE + BP-NN" of 86.67%, and "DWT + PCA + BP-NN" of 86.22%. Therefore, this study offered a new means to solve the problem with excellent performance.
Space-time audio-visual speech recognition with multiple multi-class probabilistic support vector machines

We extract relevant and informative audio-visual features using multiple multi-class Support Vector Machines with probabilistic outputs, and demonstrate the approach in a noisy audio-visual speech reading scenario. We first extract visual spatio-temporal features and audio cepstral coefficients from pronounced digit sequences. Two classifiers are then trained on a single modality to obtain confidence factors that are used to select the most appropriate fusion strategy. A final classifier is trained on the joint audiovisual feature space and used to recognize digits. We demonstrate the proposed approach on a standard database and compare it with alternative methods. The evaluation shows that the proposed approach outperforms the alternatives both in terms of recognition accuracy and in terms of robustness.
Robust Nonnegative Matrix Factorization via Half-Quadratic Minimization

Nonnegative matrix factorization (NMF) is a popular technique for learning parts-based representation and data clustering. It usually uses the squared residuals to quantify the quality of factorization, which is optimal specifically to zero-mean, Gaussian noise and sensitive to outliers in general cases. In this paper, we propose a robust NMF method based on the correntropy induced metric, which is much more insensitive to outliers. A half-quadratic optimization algorithm is developed to solve the proposed problem efficiently. The proposed method is further extended to handle outlier rows by incorporating structural knowledge about the outliers. Experimental results on data sets with and without apparent outliers demonstrate the effectiveness of the proposed algorithms.
On Trapping Sets and Guaranteed Error Correction Capability of LDPC Codes and GLDPC Codes

The relation between the girth and the guaranteed error correction capability of ¿ -left-regular low-density parity-check (LDPC) codes when decoded using the bit flipping (serial and parallel) algorithms is investigated. A lower bound on the size of variable node sets which expand by a factor of at least 3 ¿/4 is found based on the Moore bound. This bound, combined with the well known expander based arguments, leads to a lower bound on the guaranteed error correction capability. The decoding failures of the bit flipping algorithms are characterized using the notions of trapping sets and fixed sets. The relation between fixed sets and a class of graphs known as cage graphs is studied. Upper bounds on the guaranteed error correction capability are then established based on the order of cage graphs. The results are extended to left-regular and right-uniform generalized LDPC codes. It is shown that this class of generalized LDPC codes can correct a linear number of worst case errors (in the code length) under the parallel bit flipping algorithm when the underlying Tanner graph is a good expander. A lower bound on the size of variable node sets which have the required expansion is established.
Application Management to support Network Management and Traffic Engineering

This paper is motivated by the observation that gathering and analyzing the behavior and performance of applications leads to more informed decisions by network managers.In particular, application management can be used to improve network traﬃc engineering tasks. In this paper, we investigated the existing/potential network monitoring frameworks and showed how they can be eﬀectively used to extract application-level information from network applications, running environments and the communication links. We also identiﬁed important application-level information for network management and traﬃc engineering, which couldn’t be easily supported by the simple network-level monitoring information.
Snapshot-based data index in cloud storage systems

With the arrival of the big data era, the data in storage system becomes lager and lager. The size of data index also increasing in a high speed. It is a crucial problem that how to index data quickly in a distributed data storage system. Meanwhile, because of the increasing size of data index, the traditional way, which rebuilding the index or fully backup the index before the data is changed, cannot match the need of today's big data index. In this paper, a double-layer B+ tree based index structure that overcomes the throughput limitation triggered by single index server is proposed. Then a high efficient snapshot-based B+ tree structure is designed to adapt dynamic cloud environment. Finally, experimental results show our snapshot based solution is feasible and efficient.
Use of Internet Embedding Tools for Heterogeneous Resources Aggregation

In this paper we are interested in large scale distributed platforms like BOINC, consisting of heterogeneous resources and using the Internet as underlying communication network. In this context, we study a resource clustering problem, where the goal is to build clusters having at least a given capacity and such that any two participants to the same cluster are not too far from each other. In this context, the distance between two participants corresponds to the latency of a communication between them. Our goal is to provide algorithms with provable approximation ratios. In such large scale networks, it is not realistic to assume that the whole latency matrix (that gives the latency between any two participants) is known, and we need to rely on embedding tools such as Vivaldi or Sequoia. These tools enable to work on compact descriptions and well described metric spaces in which the distance between two points can be obtained directly from a small amount of information available at each node. We present the Bin Covering under Distance Constraint problem (BCDC for short), and propose dedicated algorithms for this problem for each metric space induced by each of the embedding tools. Then, we propose a comparison of these algorithms based on actual latency measures, that enables to decide which algorithm/embedding tool pair offers in practice for realistic datasets the best balancing between distance prediction and approximation ratios for the resource clustering problem.