Abstract

In recent years, the microblogging services like Twitter has become a major tool for sharing events, expressing opinions and communicating with friends. Several thousand of microblogs are posted each second describing the ongoing events around the world. Because of this recent rise in the popularity and size of social media, there is a growing need for system that can extract useful information from social media. The use of limited words (140 characters), special vocabulary with incomplete sentences and emoticons, heavy use of abbreviations and misspellings make traditional text analysis methods less effective on Twitter. Entity Linking is the task of extracting semantics from text by linking the entities present within the text to the knowledge base. For example, given a text “India is my country”, we need to extract important mentions like ‘India’ and link them to their corresponding Wikipedia pages. Entity Linking is an important task as it not only provides important meta-information about the linked entities but also helps us in extracting the relations among various entities present within the text. Meta-information and relations are really helpful in extracting other important information like documents’ topics etc. Since this also removes the problem of synonymy and polysemy, therefore it is more easy for computers to analyse the text. So, entity linking is really helpful in making computers understand the text usually meant for human consumption. In this thesis, we device a method for efficient entity linking. We have devised various features that helps in detecting and linking entities present within the tweet. Firstly, the mentions present within the tweets are identified. We experimented various approaches like heuristics based approach, probabilistic approach and sequence labelling based approach for detecting mentions in the tweet. Finally, we proposed a classification approach for mention detection. Along with identification, the detected mentions are also ranked based on their predicted informativeness content. The user can select the mentions by setting the threshold on the ranks based on application. All the approaches are evaluated using various datasets like NEEL Challenge Dataset, User Dataset, TagMe Dataset and IITB dataset. Classification based methods performs best on all the datasets with performance of 0.59 F-measure on NEEL Dataset. The detected mentions are then disambiguated on the basis of context. Disambiguation means selecting the best entity from the set of candidate entities for the given mention. Due to limited characters, context is usually not sufficient to disambiguate all the entities present within the tweet. So, we have also explored users’ interests and recent tweet information to improve the disambiguation on the short and noisy tweets. The information provided by users’ previous tweets is modeled to build users’ topic of interests. These interests along with the context is then used to disambiguate the entities in the users’ new tweet. We also explored trending topics information (at a particular time on Twitter) to improve entity disambiguation in the tweets. The user based approach is tested using user dataset collected from around 200 tweets of 20 users with two context modeling systems. Recent tweet information based approach is tested using the standard NEEL Dataset and tweets from recent events collected in June 2014. Experiments show that extra information based on users’ previous history and recent tweets helps in improving the performance of entity disambiguation systems. Hashtags are semantico-syntactic constructs used across various social networking and microblogging platforms to enable users to start a topic specific discussion or classify a post into a desired category. Segmenting and linking the entities present within the hashtags could therefore help in better understanding and extraction of information shared across the social media. However, due to lack in space delimiter, extracting semantics from hashtags is a non-trivial task. Most of the current state-ofthe- art social media analytics systems like Sentiment Analysis and Entity Linking tend to either ignore hashtags, or treat them as a single word. We present a context aware approach to segment and link entities in the hashtags to a knowledge base (KB) entry, based on the context within the tweet. Our approach segments and links the entities in hashtags, such that the coherence between hashtag semantics and the tweet is maximized. As the segmentation and linking is jointly modeled, the system chooses the segment that maximizes the relatedness between tweet and the hashtag. So, the best segmentation is picked based on the context, if multiple segments are possible. The approach is tested on two different datasets, one is synthetically created using NEEL Dataset and the other is manually created. We have achieved the P@1 score of 0.914 on NEEL Dataset and

Abstract

Statistical machine translation (SMT) depends heavily on parallel training data available for a language pair. This thesis addresses problems of data-sparsity and translation quality of a SMT system for English-Hindi and Indo-Aryan language pairs. We also show use of machine translation (MT) in improving speech recognition output in the scenario of a computer assisted translation (CAT) system. SMT depends heavily on parallel training data available for a language pair. We describe a MT system for English to Hindi in which we use a pre-processing module to transform English data into Hindi-Order to get better phrase alignments. Morphologically rich languages require large amounts of parallel data to adequately estimate parameters in a statistical MT system. We explore alternate strategies, given that the collection of large parallel corpora is expensive. Firstly, we explore the use of distributed representations in a language model in a Recurrent neural network based Language Model (RNNLM) framework with different linguistic features and secondly, we explore the use of lexical resources such as WordNet to overcome sparsity. The techniques used above rely on large mono-lingual data for the target language and also requires WordNet of the source language. WordNet is not available for Indo-Aryan languages except Hindi and there are not many sources available for extracting clean mono-lingual data. So we improved the translation quality of Indo-Aryan MT systems by exploring popular available techniques called ``Triangulation'' and ``System Combination'' to reduce problem of data sparsity and improve quality of the system. MT systems for other Indo-Aryan language pairs which are trained on small parallel corpora are improved using various recently developed techniques in the field of SMTs. Triangulation is a technique which has been found to be very useful in improving the translations when multilingual parallel corpora are present. Triangulation is the process of using an intermediate language as a pivot to translate a source language to a target language. We have used phrase table triangulation instead of sentence based triangulation as it gives better translations. As triangulation technique explores additional multi parallel data, it provides us with separately estimated phrase-tables which could be further smoothed using smoothing methods. The subsequent approach explores the various system combination techniques through which these triangulated systems can be combined to improve the translations. In concluding chapter of the thesis, we show the application of MT in improving the Automatic Speech Recognition (ASR) output in the scenario of ocite{*}a CAT system where a human translator translates a source language string into a target language string using different input methods such as speech and typing. In this work, we have improved the performance of speech recognition of a translator speaking in the target language, taking the advantage of source language string and synset information from WordNet. We used MT system to translate the source language string to target language. We use the semantic information we get for the words in the translated string from WordNet to bias the speech recognizer towards the gained knowledge. We perform different experiments including variation of number of hypothesis of MT and also different techniques of incorporating the semantic information. Overall we outperformed the baseline system having no semantic information by the increase in word accuracy of 1.6% for the Hindi ASR in English-Hindi system.

Abstract

Binding of gold clusters (Au3 and Au4 ) with DNA bases and extended DNA bases (x-bases) has been studied in this work. An attempt to locate the energetically low-lying minimum structures in case of Aun clusters (n=3-8) was carried out using density functional theory. Both the studies have been performed by employing high quality basis set. A number of geometrical isomers, in case of Aun clusters, was studied. The relative stabilities were then compared to determine the minima. Electronic properties such as HOMO-LUMO gaps, polarizability and dipole moments,etc., were also investigated. In gold-nucleobase complexes, all possible orientations of Au clusters with respect to the titratable sites of bases were examined. The geometric and electronic features of these complexes provided evidence for existence of non-conventional hydrogen bonds. This was further substantiated via normal mode and NBO analysis. The nucleobases, both canonical and extended forms, form stable complexes with the gold clusters considered here. Upon complexation, a marked decrease in the HOMO-LUMO gaps and ionization potentials was observed, which was more profound in case of x-bases. This study attempts to provide an insight into the stability of different-sized gold clusters and demonstrates that combining structural modifications to DNA bases and subsequent binding to gold nano particles provides an opportunity to modulate and achieve desired optico-electronic properties.

Abstract

Arithmetic and Logic Unit (ALU) is a critical component of any CPU. In ALU, adders play a major role not only in addition but also in performing many other basic arithmetic operations like subtraction, multiplication, etc. Thus realizing an efficient adder is required for better performance of an ALU and therefore the processor. Research started in late 1950s on designing efficient adder algorithms and their hardware implementation. Many designs based on serial and parallel structures have been proposed to optimize different parameters from time to time. The first contribution of this thesis is the development of an efficient adder architecture that addresses the problems for higher bit operand lengths like fan-out, wiring complexity, etc. Another important element in an ALU after adder is a multiplier. In multipliers, for reducing partial products and computing final result, multi-operand adders and fast adders are required. A special structure known as counters/compressors are typically used for designing multi-operand adders. Counters are multi-input, multi-output combinational logic circuits, which determine the number of logic 1’s in their input vectors, and generate a binary coded output vector that corresponds to this number. Large parallel counters like (15, 4), (32, 5), etc. can be constructed using this small counter and similar approach can be adopted in the case of compressors. The second contribution of the thesis is development of efficient counters and compressors for better performance of multiplier. Apart from adders and multipliers in arithmetic units, elements like, incrementer/decrementer (INC/DEC) also play a major role in an ALU and also in address generation unit. A loop algorithm, for example, often needs a increment/decrement. These operations can be realized using adders but with a cost in terms of power and area. Therefore, standalone designs or unified designs for INC/DEC are required for low power applications. The third contribution of this thesis is the design of a multi-functional INC/DEC/2's complement/Priority encoder circuit. A design for binary INC/DECs is presented that is efficient in terms of speed without compromising on power. The need to have hardware support for decimal arithmetic is increasing in recent years because of the growth in decimal data processing in commercial, financial and internet-based applications. To facilitate binary computations on the same hardware, a reconfigurable approach needs to be adopted. The fourth contribution of this thesis is the design of a new architecture for efficient Binary Coded Decimal (BCD) addition/subtraction that can be configured to perform binary addition/subtraction also. The architecture has been designed keeping in view the signed magnitude format where the adder logic itself detects the larger operand and carries out corresponding operations. Finally, novel versions of two widely used arithmetic blocks i.e., multiplier and floating point adder, are designed. Efficient and proven basic functional units described above are used to implement these blocks. Simulations of these blocks have been carried out and comparisons made with existing designs that clearly demonstrate the efficiency of proposed units. Finally, a segment of a core of a processor is designed with incorporating all the above elements resulting in an efficient architecture.

Abstract

One of the main goals of modern biotechnology and biophysics is to understand the structure and interactions of biomolecules such as nucleic acids and proteins. The complexity in understanding the living systems and the processes occurring in them primarily revolves around these biomolecules. Understanding the molecular level details of such processes is difficult and is not always possible using experimental methods. Computational methods prove to be efficient alternative tools in studying the atomistic details of molecular processes. Molecular dynamics (MD) simulation is one such method that is extremely used to study structure-function relationships of biomolecules. The present thesis has two parts: heterogeneous nucleic acids and homogeneous nucleic acids. The heterogeneous nucleic acids include DNA-RNA hybrids, locked nucleic acids and 2’-O-methyl modified duplexes that have a wide range of applications in nucleic acid-based therapies. Extensive analyses of MD trajectories suggest that the structural and energetic properties of DNA-RNA hybrids depend on the extent of mixing of base composition in their DNA strand. However, such variations were not seen in their RNA strand. The MD simulation results also showed that the decreasing deoxypyrimidine composition in DNA strand leads to transformation of the DNA-RNA hybrid conformation from A-type to A/B-type. The possible factors responsible for the inactivity of ribonuclease H (RNase H) enzyme towards hybrids with high deoxypyrimidine composition have also been proposed. Thermodynamic stabilities of the duplexes were explained by using binding free energies. Next, investigations have been carried out to understand the interactions between rnase H enzyme with the DNA-RNA hybrid and the energy cost accompanying the complex formation. The binding of enzyme to the nucleic acid induces conformational transitions towards N-type or S-type in deoxyribose sugars that are important for efficient binding. Since this enzyme exhibit close structural and functional similarity with RNase H of the HIV-I reverse transcriptase and several other nucleic acid processing enzymes, the present study is helpful in understanding the molecular level details of their dynamics and stability. A comprehensive study has been performed on DNA-RNA hybrid duplexes by systematically modifying the deoxyribose sugars to isomeric locked nucleic acid (α/β-LNA) modifications; which leads to increase in duplex stability and change in conformation to A-type. The differential effects of these two modifications were studied extensively. The results indicate that the α-LNA modification induces conformational changes in the duplex backbone in order to adjust in the duplex. However, such changes are not necessary for β-LNA modification. The structure and thermodynamic stabilities of the full LNA duplexes have also been explored. Free energy calculations suggest that hydrophobicity of 2'-O-Methyl groups is primarily responsible for the reversal thermodynamic stabilities of DNA-RNA hybrids. RNA molecules have been shown to fold to form complex three-dimensional structures that are important for catalytic cellular processes. Investigation of RNA folding is crucial for understanding its diverse functions. RNA folding dynamics has been studied by taking a 12 nucleotide GNRA RNA hairpin in water, urea and MgCl 2 solutions using replica exchange molecular dynamics simulations. Urea decreases the melting temperature of the RNA hairpin while Mg 2+ increases melting temperature that agree well with the experimental data. Urea reduces the hydrogen bonding interactions of base pairs and stacking interactions among nucleobases that results in faster unfolding of RNA hairpin. Urea prevents refolding of hairpin by forming strong hydrogen bond and stacking interactions with the nucleobases when the nucleobases separate from hairpin during unfolding process. The components of calculated interaction energies suggest that dispersion interactions play key role in RNA denaturation and unfolding processes.

Abstract

Advances in cloud computing have redefined the landscape of modern information technology as seen by the popularity of cloud service providers such as Amazon, Microsoft Azure and Google App Engine. Data storage outsourcing is a prime application in cloud computing, which allows organizations to provide reliable data services to users without concerning with the data management overheads. But, there are several practical apprehensions regarding the privacy of the outsourced data and these concerns are a major hindrance towards the uninhibited adoption of cloud storage services. Generally, the cloud service provider does not attempt to violate data privacy, there are internal factors like malicious employees who will abuse the client data at the first available opportunity. Thus, the data needs to be encrypted prior to outsourcing. However, the encryption is a major hindrance to perform regular data access operations, such as searching for documents containing specific keywords or patterns. Therefore, there is a crucial need for techniques to support a rich set of querying functionality on the encrypted data without violating the privacy of the users. While there have been many works on efficiently solving the problem of privacy preserving single keyword and multi-keyword search on outsourced data in cloud computing, both in public key and symmetric key domains, the problem of privacy preserving string pattern search has only been solved in public key domain and that too is not efficient enough for adopting to practical scenarios like cloud. We note that the solution to this problem is unexplored in symmetric key domain. In this work, we present the first ever symmetric key based approach to support privacy preserving string pattern matching on outsourced data in cloud computing. String pattern matching functionality can find occurrence of a string pattern anywhere within a keyword. We describe an efficient and accurate indexing structure, the PASStree, which can execute a string pattern query in logarithmic time complexity over a set of data items. The PASStree provides strong privacy guarantees against attacks from a semi-honest adversary. We have comprehensively evaluated our scheme over large real-life data, such as Wikipedia and Enron documents, containing up to 100000 keywords, and show that our algorithms achieve pattern search in less than a few milliseconds with 100% accuracy. We have performed prefix and sub-string pattern search on Wikipedia dataset, and performed multi-keyword phrase search of sender/receiver queries on Enron dataset. Furthermore, we also describe a relevance ranking algorithm to return the most relevant documents to the user based on the query pattern. Our ranking algorithm achieves 90%+ above precision in ranking the returned documents.

Abstract

An overlay network is a logical network built on top of some underlying network (the underlay) with an objective to provide some service to the underlying network. The internet, for instance, was initially an overlay network built on top of the Public Switched Telephone Network and has today become the underlying substrate for overlay networks of numerous types - peer-to-peer file sharing (BitTorrent), multimedia (Skype), security (VPNs), experimental test beds (PlanetLab), etc. Pioneered by Napster in 1999 and strongly purported by further advances like Gnutella, Kaaza, Freenet, shortly thereafter, peer-to-peer content sharing became quite popular across a wide range of internet audience during the early 2000s and has been growing ever since. As a result, peer-to-peer systems (not just file sharing), in general, are today some of the most popular overlay applications across the internet. Peer-to-peer overlay networks can be classified into two broad categories - structured and unstructured. In the unstructured category, any peer may host any content which means that in order to find a specific content, only a blind search can be carried out in the overlay. In the structured category, on the other hand, a tight relation is defined between the content and its host, such that any peer in the overlay, given the content to be searched, can compute the identity of the peer hosting the content. Therefore, while in an unstructured P2P overlay, data storage is random and data search is blind or at best heuristic, in a structured P2P overlay, data storage is controlled and, as a result, data search is structured. The way we see it, the structured category can be characterized by a couple of components - the mapping component that maps the data items to one or more overlay nodes and the overlay nodes to the underlay peers, i.e., one that takes care of distribution/re-distribution of the data to be hosted on the overlay among the overlay nodes and the routing component that creates the overlay routing topology in a way that every peer in the overlay can deterministically figure out which outgoing link must be chosen to reach a given destination peer in the overlay. So, for any data search, while the mapping component tells us where - on which overlay node - the data resides, the routing component tells us how to get there. In this work, we formalize the routing component of structured P2P overlay networks and mathematically analyze the overlay properties determined/affected by it. Unless explicitly qualified, throughout thiswork, by structured overlay networks (short hand SONs), or structured overlays or simply overlays, we shall mean structured P2P overlay networks.

Abstract

In the recent past, the Web has not only become the predominant source of information, but also a crucial player in the evolution of events. People reporting information about real time events has made it a significant indicator to detect and estimate the pulse of various communities. Currently the most amount of real-time information about events is generated by social networks like Blogs, Twitter and Facebook. In view of this, it is very important to have a system that can condense the data and can extract meaningful information without human intervention. A new area of research in information retrieval (IR) has developed over the past few years called Topic Detection and Tracking (TDT). Topic detection involves detecting the occurrence of a new event such as earthquake, election results, a new movie released, or a political scam in a stream of news stories from multiple sources. Topic tracking is the process of monitoring a stream of news stories to find those that report or discuss the same event as one specified by a user. Previous approaches for Topic detection mainly focused on sources like news streams, web pages, documents, reviews and other structured and semi-structured information sources. In this thesis, we focus on detecting the underlying semantic aspects (Sub-topics) of an event and extract structured information from the sub-topics found. Topics or events on Twitter relate to major events in the real world; sub-topics on the other hand are fine-grained aspects of such events. The velocity, volume and variety with which Twitter generates text is increasing exponentially. Mining sub-topics from entities helps in trend analysis, social monitoring, topic tracking and reputation mining. In the recent past, Twitter has been widely used for spreading the social pulse about real world entities. Fresh latent sub-topics identified from Twitter feeds at any given point of time could be extremely useful in providing better topic-wise search results relevant to users’ informational needs. Also, extraction of Structured information from tweets related to a sub-topic could provide a nice summary for the subtopic. The two main challenges in mining sub-topics from tweets in real-time are (1) understanding the semantic and the conceptual representation of the tweets, and (2) the ability to determine when a new sub-topic (or cluster) appears in the tweet stream. We address these challenges by proposing two unsupervised clustering approaches. In the first approach (SSR), for each tweet, we generate a semantic space representation by keyword expansion and keyphrase identication. In the second approach (CSR), we transform each tweet into a conceptual space that represents the latent concepts of the tweet. For our evaluation, we use RepLab (CLEF) 2013 dataset. The dataset contains tweets for 61 entities. Each entity has about 700 tweets for training and 1500 tweets for testing. The SSR approach gave an F1 measure of 0.259 and CSR an F1 measure of 0.339. We show a significant increase in the F1 measure value by 16.9% as compared to the Baseline and 1% as compared to the best system in RepLab 2013, which indicates that the proposed CSR approach performs better than the state-of-the-art methods. In extension to the previous approaches, we propose a more sophisticated approach which also considers semantic similarity between concepts and a statistical way to determine a threshold for creating a new sub-topic. We initially try to detect Sub-topics from targetted entities and later try to extract structured information from them. We generate a rich semantic representation of tweets using external knowledge bases, and combine it with relevant similarity metrics to train a sub-topic detection classifier. Further, we train a Conditional Random Field (CRF) model to identify subject-predicate-object fact triplets from tweets. Comparisons with various state-of-the-art mechanisms for sub-topic detection on a dataset of 134K tweets show that the proposed approach outperforms others by a significant margin providing a best F1 score of 0.417. The proposed CRF-based fact triplets extraction approach also beats the baselines significantly providing an overall F1 of 0.43. We also try to map the extracted information to manually generated event schemas. For this task we specifically focus on the natural disaster events on twitter. As soon as natural disaster events happen, users are eager to know more about them. However, search engines currently provide a ten blue links interface for queries related to such events. Relevance of results for such queries can be significantly improved if users are shown a structured summary of the fresh events related to such queries. This would not just reduce the number of user clicks to get the relevant information but would also help users get updated with more fine grained attribute-level information. Twitter is a great source that can be exploited for obtaining su

Abstract

The huge growth in digital libraries, WWW (world wide web) and other text resources has created the demand of techniques which can work intelligently on limited linguistic resources and fulfills the growing demand of text analysis, pattern discovery and information management related operations. The graph based automated text analysis and text mining methods have received a great deal of attention in solving these issues. Actually, an important aspect of graph-based method is that it does not require deep linguistic knowledge, nor domain or language specific annotated corpora, which makes it highly portable to other domains, genres, or languages. The development of advanced graph theoretical techniques for social media mining has also enriched this area. However, the development of effective core techniques to mine the information, from text document(s) by using limited linguistic resources is a challenging task of significant interest. Still The huge growth in digital libraries, WWW (world wide web) and other text resources has created the demand of techniques which can work intelligently on limited linguistic resources and fulfills the growing demand of text analysis, pattern discovery and information management related operations. The graph based automated text analysis and text mining methods have received a great deal of attention in solving these issues. Actually, an important aspect of graph-based method is that it does not require deep linguistic knowledge, nor domain or language specific annotated corpora, which makes it highly portable to other domains, genres, or languages. The development of advanced graph theoretical techniques for social media mining has also enriched this area. However, the development of effective core techniques to mine the information, from text document(s) by using limited linguistic resources is a challenging task of significant interest. Still sincere attentions for improvements in a lot of explored core areas are required. Additionally, the exploring of unexplored/less explored areas can be very useful and key ingredient of several text mining applications. Based on the above discussed facts, we have identified some core issues (and techniques for them) like: (i) meaningful phrase identification (ii) differentiating role and sense of words, preferably via a single measure, (iii) handling information gap at the phrase level by using unsupervised scheme, (iv) integrating the importance of words as a core feature and (v) identifying group semantics and/or logically related features, (vi) sentence abstraction and so on. These techniques are very useful for multiple text mining applications like: (a) Document summarization, (b) Summarization Evaluation, (c) Document Clustering, (d) Key phrase Extraction and (e) Automatic Question Answering. The effective improvement in the results of our devised applications, over state-of-the-arts supervised, unsupervised applications, which use linguistic support and domain knowledge etc., prove the effectiveness of the proposed techniques. Our contributions related to the development in the core techniques for domain independent traditional text mining include: (i) Phrase Identification: Identifying useful phrases are still an open and challenging task, as, available linguistic and statistical state-of the art techniques, still suffer a lot of problems. The performance of several text mining tasks depends upon the quality of the identified phrases. We introduce the centrality measures based technique for phrase identification having accuracy more than 90%. We effectively apply this technique to key-phrase extraction (which is itself treated as core task for several applications like: document categorization, indexing, skimming etc.) and document clustering. (ii) Differentiating between role and sense of words: Often times words having same sense may have different roles in document(s). Neglecting such information may misguide us, especially when we compare two different texts having matching words, like: summarization evaluation, and evaluation of descriptive answers and essays, etc. We introduce the use of graph based mapping of co-occurring words and closeness centrality score for this. (iii) Reducing information gap between documents: We introduce the Wikipedia anchor text community detection based scheme, to reduce the information gap between N-grams that are conceptually-related, despite not having a match owing to differences in writing scheme or strategies. We successfully applied this scheme in document clustering. (iv)Integrating the importance of words as a core feature: Most of the traditional text mining techniques treat word/phrases as point/ “raw data” and thus ignore their importance. We believe that treating every word or phrase as a separate data point by language independent text mining techniques may be inappropriate and even known data mining techniques may not be applicable straightaway. This is becau

Abstract

One of the main challenges that prevent the large-scale deployment of Wireless Sensor Networks (WSNs) is providing the applications with the required quality of service (QoS) given the sensor nodes limited energy supplies. WSNs are an important tool in supporting applications ranging from environmental and industrial monitoring, to battlefield surveillance and traffic control, among others. Most of these applications require sensors to have a longer lifetime without human intervention and battery replacement. Therefore energy conservation is one of the main goals for implementing WSNs. To maximize the network lifetime, approaches such as duty cycling and low power listening modes have been proposed. In this thesis, I explore the possibility of further energy savings through the applying the mobility patterns in the network. In this thesis as part of my research, I propose a new mobility patterns in the network, which increase the lifetime of the network as well as reduces the random flooding of the messages in the network, thus improving the bandwidth of the entire network. Hence to validate my proposal, I conclude my thesis with the results obtained through the simulations. The simulation is carried on open source network simulator called “Network Simulator 2 (NS2)”. NS2 is popular simulator for wired, wireless network based on IEEE 802.11 and also for Wireless Sensor Network based on IEEE 802.15.4. The results obtained from the simulations closely mirror the results obtained theoretically.