Multi-level Memory for Task Oriented Dialogs

Recent end-to-end task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs. Current work makes simplifying assumptions about the structure of the knowledge base, such as the use of triples to represent knowledge, and combines dialog utterances (context) as well as knowledge base (KB) results as part of the same memory. This causes an explosion in the memory size, and makes the reasoning over memory harder. In addition, such a memory design forces hierarchical properties of the data to be fit into a triple structure of memory. This requires the memory reader to infer relationships across otherwise connected attributes. In this paper we relax the strong assumptions made by existing architectures and separate memories used for modeling dialog context and KB results. Instead of using triples to store KB results, we introduce a novel multi-level memory architecture consisting of cells for each query and their corresponding results. The multi-level memory first addresses queries, followed by results and finally each key-value pair within a result. We conduct detailed experiments on three publicly available task oriented dialog data sets and we find that our method conclusively outperforms current state-of-the-art models. We report a 15-25% increase in both entity F1 and BLEU scores.Comment: Accepted as full paper at NAACL 201

Mask & Focus: Conversation Modelling by Learning Concepts

Sequence to sequence models attempt to capture the correlation between all the words in the input and output sequences. While this is quite useful for machine translation where the correlation among the words is indeed quite strong, it becomes problematic for conversation modelling where the correlation is often at a much abstract level. In contrast, humans tend to focus on the essential concepts discussed in the conversation context and generate responses accordingly. In this paper, we attempt to mimic this response generating mechanism by learning the essential concepts in the context and response in an unsupervised manner. The proposed model, referred to as Mask \& Focus maps the input context to a sequence of concepts which are then used to generate the response concepts. Together, the context and the response concepts generate the final response. In order to learn context concepts from the training data automatically, we \emph{mask} words in the input and observe the effect of masking on response generation. We train our model to learn those response concepts that have high mutual information with respect to the context concepts, thereby guiding the model to \emph{focus} on the context concepts. Mask \& Focus achieves significant improvement over the existing baselines in several established metrics for dialogues.Comment: AAAI 202

Ocular drug delivery system: Approaches to improve ocular Bioavailability

There is a lots of route of drug delivery, but today one of them, the ocular drug delivery system becomes the most compulsive and attractive attempt in front of pharmaceutical scientists. Ocular drug delivery has always been a formidable task in the area of pharmaceutical research due to distinctive structure and function of the eye. Different attempt in ocular drug delivery have been made to enhance the bioavailability and to increase the contact time of topically applied drugs to the eye. In an ophthalmic dosage form the less bioavailability is due to the tear production, achieving less absorption, short term residence time, and less permeability of corneal epithelium. Immediate pre-corneal elimination is a biggest problem in ocular drug delivery. In order to solve this problem, researchers developed a new system; in-situ gel forming system. This formulation undergoes phase transition in the eye to form gel, thus prolonging the precorneal contact time which will result in enhance ocular bioavailability for prolonged therapeutic action ointment, suspensions and gelled systems are also used

Determination of Mitochondrial Membrane Potential and Reactive Oxygen Species in Live Rat Cortical Neurons

Mitochondrial membrane potential (ΔΨm) is critical for maintaining the physiological function of the respiratory chain to generate ATP. A significant loss of ΔΨm renders cells depleted of energy with subsequent death. Reactive oxygen species (ROS) are important signaling molecules, but their accumulation in pathological conditions leads to oxidative stress. The two major sources of ROS in cells are environmental toxins and the process of oxidative phosphorylation. Mitochondrial dysfunction and oxidative stress have been implicated in the pathophysiology of many diseases; therefore, the ability to determine ΔΨm and ROS can provide important clues about the physiological status of the cell and the function of the mitochondria

Depth-wise variations of soil physicochemical properties in the apple growing area of Mustang district, Nepal

Understanding the soil fertility is an important management tool in assessing the nutrient requirement of the crops. Considering this, a study was done to determine depth-wise soil parameters distribution in the apple growing areas of Gharpajhog Rural Municipality, Mustang during October 2019. The total 68 sampling points were selected randomly in the different sites, and collection was done from three depths viz. 0-20cm, 20-40cm and 40-60cm by using soil sampling auger. The soil separates, pH, organic matter, total N, available P2O5 and K2O were determined following standard methods in National Soil Science Research Centre, Khumaltar. The results of the study revealed that the effect of depth was significant in the sand and silt proportion, while non- significant in clay proportion. The highest (40.17±1.57%) sand content was in 40-60cm depth, meanwhile highest (45.64±1.07%) silt content was in surface (0-20cm) depth. In addition to this, soil pH, OM, total N, available P2O5 and K2O were also affected by the depth. The highest (8.27) pH was determined in the lower (40-60cm) depth. On the other hand, highest OM (4.93±0.2%), total N (0.24±0.01%), available P2O5 (43.47±4.35 mg/kg) and available K2O (95.91±5.8 mg/kg) in surface (0.20 cm) depth. The surface depth possessed strong content of studied soil parameters might be due to in-situ incorporation of leaf litter, residue etc. as well as applied manure in the surface. Finally, we can also conclude that the adopted current nutrient management practice should be continued for apple production in the study area

Active LC Clamp dv/dt Filter for Voltage Reflection due to Long Cable in Induction Motor Drives

This paper presents an active LC clamped dv/dt filter to mitigate the over voltages appearing across the motor terminals. The over voltages at motor terminal is due to voltage reflection effect of long motor cable connected between high frequency PWM inverter having high dv/dt switching waveforms and ac motor drives. The voltage reflection due to fast switching transients can be reduced by increasing the rise time and fall time of inverter output voltage pulses. The most commonly available mitigating technique is a passive dv/dt filter between inverter and cable.  Since, size, cost and losses of passive LC dv/dt filter is more, an active dv/dt filtering technique is used to reduce over voltage at motor terminals. Active LC clamp filtering technique used here consists of a small LC filter designed for a single motor cable length which can be used for any lengths of cable up to 1000m only by changing the active control of the PWM pulses to achieve the desired voltage slope during voltage transition period. The basic principle of active dv/dt filer used here is to charge and discharge the capacitor in the filter with modified PWM pulses to increase the rise time and fall time of output voltage pulses without any extra devices to handle the transient response of the LC filter. Detailed investigation is carried out by simulation using MATLAB-Simulink software with active control of common LC clamp dv/dt filter suitable for various cable lengths ranging from 100 m to 1000 m. Comparative analysis is done with active dv/dt filter designed with a common LC clamp filter and active LC clamp dv/dt filter designed for various cable lengths and also with diode clamped passive dv/dt filter. The results proves the effectiveness of the active common LC dv/dt filter to mitigate the over voltages at motor terminal for cable lengths up to 1000m

A hybrid approach to enhance streamflow simulation in data-constrained Himalayan basins: combining the Glacio-hydrological Degree-day Model and recurrent neural networks

The Glacio-hydrological Degree-day Model (GDM) is a distributed model, but it is prone to uncertainties due to its conceptual nature, parameter estimation, and limited data in the Himalayan basins. To enhance accuracy without sacrificing interpretability, we propose a hybrid model approach that combines GDM with recurrent neural networks (RNNs), hereafter referred to as GDM–RNN. Three RNN types – a simple RNN model, a gated recurrent unit (GRU) model, and a long short-term memory (LSTM) model – are integrated with GDM. Rather than directly predicting streamflow, RNNs forecast GDM's residual errors. We assessed performance across different data availability scenarios, with promising results. Under limited-data conditions (1 year of data), GDM–RNN models (GDM–simple RNN, GDM–LSTM, and GDM–GRU) outperformed standalone GDM and machine learning models. Compared with GDM's respective Nash–Sutcliffe efficiency (NSE), R2, and percent bias (PBIAS) values of 0.80, 0.63, and −4.78, the corresponding values for the GDM–simple RNN were 0.85, 0.82, and −6.21; for GDM–LSTM, they were 0.86, 0.79, and −6.37; and for GDM–GRU, they were 0.85, 0.8, and −5.64. Machine learning models yielded similar results, with the simple RNN at 0.81, 0.7, and −16.6; LSTM at 0.79, 0.65, and −21.42; and GRU at 0.82, 0.75, and −12.29, respectively. Our study highlights the potential of machine learning with respect to enhancing streamflow predictions in data-scarce Himalayan basins while preserving physical streamflow mechanisms.</p