A Randomized Algorithm for Large Scale Support Vector Learning

We propose a randomized algorithm for large scale SVM learning which solves the problem by iterating over random subsets of the data. Crucial to the algorithm for scalability is the size of the subsets chosen. In the context of text classification we show that, by using ideas from random projections, a sample size of O(log n) can be used to obtain a solution which is close to the optimal with a high probability. Experiments done on synthetic and real life data sets demonstrate that the algorithm scales up SVM learners, without loss in accuracy. 1

An (O)over-bar(m(2)n) randomized algorithm to compute a minimum cycle basis of a directed graph

We consider the problem of computing a minimum cycle basis in a directed graph G. The input to this problem is a directed graph whose arcs have positive weights. In this problem a {- 1, 0, 1} incidence vector is associated with each cycle and the vector space over Q generated by these vectors is the cycle space of G. A set of cycles is called a cycle basis of G if it forms a basis for its cycle space. A cycle basis where the sum of weights of the cycles is minimum is called a minimum cycle basis of G. The current fastest algorithm for computing a minimum cycle basis in a directed graph with m arcs and n vertices runs in O(m(w+1)n) time (where w < 2.376 is the exponent of matrix multiplication). If one allows randomization, then an (O) over tilde (m(3)n) algorithm is known for this problem. In this paper we present a simple (O) over tilde (m(2)n) randomized algorithm for this problem. The problem of computing a minimum cycle basis in an undirected graph has been well-studied. In this problem a {0, 1} incidence vector is associated with each cycle and the vector space over F-2 generated by these vectors is the cycle space of the graph. The fastest known algorithm for computing a minimum cycle basis in an undirected graph runs in O(m(2)n + mn(2) logn) time and our randomized algorithm for directed graphs almost matches this running time.

Near-optimal large-MIMO detection using randomized MCMC and randomized search algorithms

Low-complexity near-optimal detection of signals in MIMO systems with large number (tens) of antennas is getting increased attention. In this paper, first, we propose a variant of Markov chain Monte Carlo (MCMC) algorithm which i) alleviates the stalling problem encountered in conventional MCMC algorithm at high SNRs, and ii) achieves near-optimal performance for large number of antennas (e.g., 16×16, 32×32, 64×64 MIMO) with 4-QAM. We call this proposed algorithm as randomized MCMC (R-MCMC) algorithm. Second, we propose an other algorithm based on a random selection approach to choose candidate vectors to be tested in a local neighborhood search. This algorithm, which we call as randomized search (RS) algorithm, also achieves near-optimal performance for large number of antennas with 4-QAM. The complexities of the proposed R-MCMC and RS algorithms are quadratic/sub-quadratic in number of transmit antennas, which are attractive for detection in large-MIMO systems. We also propose message passing aided R-MCMC and RS algorithms, which are shown to perform well for higher-order QAM.

Investigational drugs for schizophrenia targeting the dopamine receptor: phase II trials

Introduction: For over half a century now, the dopamine hypothesis has provided the most widely accepted heuristic model linking pathophysiology and treatment in schizophrenia. Despite dopaminergic drugs being available for six decades, this system continues to represent a key target in schizophrenia drug discovery. The present article reviews the scientific rationale for dopaminergic medications historically and the shift in our thinking since, which is clearly reflected in the investigational drugs detailed. Areas covered: We searched for investigational drugs using the key words `dopamine,' `schizophrenia,' and `Phase II' in American and European clinical trial registers (clinicaltrials. gov; clinicaltrialsregister.eu), published articles using National Library of Medicine's PubMed database, and supplemented results with a manual search of cross-references and conference abstracts. We provide a brief description of drugs targeting dopamine synthesis, release or metabolism, and receptors (agonists/partial agonists/antagonists). Expert opinion: There are prominent shifts in how we presently conceptualize schizophrenia and its treatment. Current efforts are not as much focused on developing better antipsychotics but, instead, on treatments that can improve other symptom domains, in particular cognitive and negative. This new era in the pharmacotherapy of schizophrenia moves us away from the older `magic bullet' approach toward a strategy fostering polypharmacy and a more individualized approach shaped by the individual's specific symptom profile.

Targeting the dopamine receptor in schizophrenia: investigational drugs in Phase III trials

Introduction: Antipsychotic drugs date back to the 1950s and chlorpromazine. Soon after, it was established that blockade of dopamine and, in particular, the D-2 receptor was central to this effect. Dopamine continues to represent a critical line of investigation, although much of the work now focuses on its potential in other symptom domains. Areas covered: A search was carried out for investigational drugs using the key words `dopamine', `schizophrenia' and `Phase III' in an American clinical trial registry (clinicaltrials.gov), published articles using the National Library of Medicine's PubMed database, and supplemented results with a manual search of cross-references and conference abstracts. Drugs were excluded that were already FDA approved. Expert opinion: There remains interest, albeit diminished, in developing better antipsychotic compounds. The greatest enthusiasm currently centres on dopamine's role in negative and cognitive symptom domains. With theories conceptualising hypodopaminergic activity as underlying these deficits, considerable effort is focused on drug strategies that will enhance dopamine activity. Finally, a small body of research is investigating dopaminergic compounds vis-a-vis side-effect treatments. In domains beyond psychosis, however, dopamine arguably is not seen as so central, reflected in considerable research following other lines of investigation.

DNA binders in clinical trials and chemotherapy

Cancer has always been a dreadful disease and continues to attract extensive research investigations. Various targets have been identified to restrain cancer. Among these DNA happens to be the most explored one. A wide variety of small molecules, often referred to as `ligands', has been synthesized to target numerous structural features of DNA. The sole purpose of such molecular design has been to interfere with the transcriptional machinery in order to drive the cancer cell toward apoptosis. The mode of action of the DNA targeting ligands focuses either on the sequence-specificity by groove binding and strand cleavage, or by identifying the morphologically distinct higher order structures like that of the G-quadruplex DNA. However, in spite of the extensive research, only a tiny fraction of the molecules have been able to reach clinical trials and only a handful are used in chemotherapy. This review attempts to record the journey of the DNA binding small molecules from its inception to cancer therapy via various modifications at the molecular level. Nevertheless, factors like limited bioavailability, severe toxicities, unfavorable pharmacokinetics etc. still prove to be the major impediments in the field which warrant considerable scope for further research investigations. (C) 2014 Published by Elsevier Ltd.

Effect of amplitude correlations on coherence in the local field potential

Neural activity across the brain shows both spatial and temporal correlations at multiple scales, and understanding these correlations is a key step toward understanding cortical processing. Correlation in the local field potential (LFP) recorded from two brain areas is often characterized by computing the coherence, which is generally taken to reflect the degree of phase consistency across trials between two sites. Coherence, however, depends on two factors-phase consistency as well as amplitude covariation across trials-but the spatial structure of amplitude correlations across sites and its contribution to coherence are not well characterized. We recorded LFP from an array of microelectrodes chronically implanted in the primary visual cortex of monkeys and studied correlations in amplitude across electrodes as a function of interelectrode distance. We found that amplitude correlations showed a similar trend as coherence as a function of frequency and interelectrode distance. Importantly, even when phases were completely randomized between two electrodes, amplitude correlations introduced significant coherence. To quantify the contributions of phase consistency and amplitude correlations to coherence, we simulated pairs of sinusoids with varying phase consistency and amplitude correlations. These simulations confirmed that amplitude correlations can significantly bias coherence measurements, resulting in either over-or underestimation of true phase coherence. Our results highlight the importance of accounting for the correlations in amplitude while using coherence to study phase relationships across sites and frequencies.

Understanding Medical Device Patenting and Clinical Trials for Product Leadership

Unmet clinical needs remain the primary driving force for innovations in medical devices. While appropriate mechanisms to protect these innovative outcomes are essential, the performance of clinical trials to ensure safety is also mandated before the invention is ready for public use. Literature explaining the relationship between patenting activities and clinical trials of medical devices is scarce. Linking patent ownership to clinical trials may imply product leadership and value chain control. In this paper, we use patent data from Indian Patent Office (IPO), PCT, and data from Clinical Trials Registry of India (CTRI) to identify whether patent assignees have any role in leading as primary sponsors of clinical trials. A total of 42 primary sponsors are identified from the CTRI database in India. Number of patents awarded to these primary sponsors in the particular medical device, total number of patents awarded to the primary sponsor in all technologies, total number of patents in the specific medical device technology provides an indication of leadership and control in the value chain.

RD-TDMA: A Randomized and Distributed TDMA Scheduling for Correlated Contention in WSNs

In wireless sensor networks (WSNs), contention occurs when two or more nodes in a proximity simultaneously try to access the channel. The contention causes collisions, which are very likely to occur when traffic is correlated. The excessive collision not only affects the reliability and the QoS of the application, but also the lifetime of the network. It is well-known that random access mechanisms do not efficiently handle correlated-contention, and therefore, suffer from high collision rate. Most of the existing TDMA scheduling techniques try to find an optimal or a sub-optimal schedule. Usually, the situation of correlated-contention persists only for a short duration, and therefore, it is not worthwhile to take a long time to generate an optimal or a sub-optimal schedule. We propose a randomized distributed TDMA scheduling (RD-TDMA) algorithm to quickly generate a feasible schedule (not necessarily optimal) to handle correlated-contention in WSNs. In RD-TDMA, a node in the network negotiates a slot with its neighbors using the message exchange mechanism. The proposed protocol has been simulated using the Castalia simulator to evaluate its runtime performance. Simulation results show that the RD-TDMA algorithm considerably reduces the time required to schedule.