63 research outputs found
Learning to Hash-tag Videos with Tag2Vec
User-given tags or labels are valuable resources for semantic understanding
of visual media such as images and videos. Recently, a new type of labeling
mechanism known as hash-tags have become increasingly popular on social media
sites. In this paper, we study the problem of generating relevant and useful
hash-tags for short video clips. Traditional data-driven approaches for tag
enrichment and recommendation use direct visual similarity for label transfer
and propagation. We attempt to learn a direct low-cost mapping from video to
hash-tags using a two step training process. We first employ a natural language
processing (NLP) technique, skip-gram models with neural network training to
learn a low-dimensional vector representation of hash-tags (Tag2Vec) using a
corpus of 10 million hash-tags. We then train an embedding function to map
video features to the low-dimensional Tag2vec space. We learn this embedding
for 29 categories of short video clips with hash-tags. A query video without
any tag-information can then be directly mapped to the vector space of tags
using the learned embedding and relevant tags can be found by performing a
simple nearest-neighbor retrieval in the Tag2Vec space. We validate the
relevance of the tags suggested by our system qualitatively and quantitatively
with a user study
Antidiabetic activities of Cassia occidentalis
The present investigation was carried out to evaluate the anti-diabetic activities of Cassia occidentalis (Seena, coffee weed), a well known medicinal plant commonly found in India and other tropical countries. Various medicinal properties have been attributed to this plant in the traditional system of Indian medicine. The aqueous and methanolic extracts of aerial parts, viz. leaves, stem and seeds of the plant, Cassia occidentalis possessed anti-hyperglycemic/ anti-diabetic activity against alloxan-induced animal model. All aqueous-treated rats showed no discernible behavioral changes up to 3000 mg/kg by oral route. No mortality was observed at this dose during 72 h observation period. Amongst all the extracts, potent anti-diabetic activity was observed in aqueous extracts of leaves of C. occidentalis followed by aqueous extracts of seeds and aqueous extracts of stem. In normal animals, significant (p<0.05) reduction in the blood glucose level was observed by the aqueous extracts as compared to the control and methanolic extracts. However, treatment of methanolic extracts of aerial parts of C. occidentalis could not bring back the sugar to normal levels. Acute and chronic treatment of the aqueous extract of aerial parts of C. occidentalis (3000 mg/kg) in alloxan-induced diabetic rats resulted in a significant (p<0.05) decrease in the elevated blood glucose levels as compared to the control, there was significant reduction in blood glucose level in the group treated with glibenclamide at 0.5 mg/kg. The results showed that blood glucose level gets decreased after varying the dose level. Thus the findings confirmed that level of blood glucose gets normal in dose-dependent manner
Concept Distillation: Leveraging Human-Centered Explanations for Model Improvement
Humans use abstract concepts for understanding instead of hard features.
Recent interpretability research has focused on human-centered concept
explanations of neural networks. Concept Activation Vectors (CAVs) estimate a
model's sensitivity and possible biases to a given concept. In this paper, we
extend CAVs from post-hoc analysis to ante-hoc training in order to reduce
model bias through fine-tuning using an additional Concept Loss. Concepts were
defined on the final layer of the network in the past. We generalize it to
intermediate layers using class prototypes. This facilitates class learning in
the last convolution layer, which is known to be most informative. We also
introduce Concept Distillation to create richer concepts using a pre-trained
knowledgeable model as the teacher. Our method can sensitize or desensitize a
model towards concepts. We show applications of concept-sensitive training to
debias several classification problems. We also use concepts to induce prior
knowledge into IID, a reconstruction problem. Concept-sensitive training can
improve model interpretability, reduce biases, and induce prior knowledge.
Please visit https://avani17101.github.io/Concept-Distilllation/ for code and
more details.Comment: Neurips 202
Interactive Segmentation of Radiance Fields
Radiance Fields (RF) are popular to represent casually-captured scenes for
new view generation and have been used for applications beyond it.
Understanding and manipulating scenes represented as RFs have to naturally
follow to facilitate mixed reality on personal spaces. Semantic segmentation of
objects in the 3D scene is an important step for that. Prior segmentation
efforts using feature distillation show promise but don't scale to complex
objects with diverse appearance. We present a framework to interactively
segment objects with fine structure. Nearest neighbor feature matching
identifies high-confidence regions of the objects using distilled features.
Bilateral filtering in a joint spatio-semantic space grows the region to
recover accurate segmentation. We show state-of-the-art results of segmenting
objects from RFs and compositing them to another scene, changing appearance,
etc., moving closer to rich scene manipulation and understanding.
Project Page: https://rahul-goel.github.io/isrf/Comment: Project Page: https://rahul-goel.github.io/isrf
Molecular Analysis of a Leprosy Immunotherapeutic Bacillus Provides Insights into Mycobacterium Evolution
BACKGROUND: Evolutionary dynamics plays a central role in facilitating the mechanisms of species divergence among pathogenic and saprophytic mycobacteria. The ability of mycobacteria to colonize hosts, to proliferate and to cause diseases has evolved due to its predisposition to various evolutionary forces acting over a period of time. Mycobacterium indicus pranii (MIP), a taxonomically unknown 'generalist' mycobacterium, acts as an immunotherapeutic against leprosy and is approved for use as a vaccine against it. The large-scale field trials of this MIP based leprosy vaccine coupled with its demonstrated immunomodulatory and adjuvant property has led to human clinical evaluations of MIP in interventions against HIV-AIDS, psoriasis and bladder cancer. MIP, commercially available as 'Immuvac', is currently the focus of advanced phase III clinical trials for its antituberculosis efficacy. Thus a comprehensive analysis of MIP vis-à-vis evolutionary path, underpinning its immanent immunomodulating properties is of the highest desiderata. PRINCIPAL FINDINGS: Genome wide comparisons together with molecular phylogenetic analyses by fluorescent amplified fragment length polymorphism (FAFLP), enterobacterial repetitive intergenic consensus (ERIC) based genotyping and candidate orthologues sequencing revealed that MIP has been the predecessor of highly pathogenic Mycobacterium avium intracellulare complex (MAIC) that did not resort to parasitic adaptation by reductional gene evolution and therefore, preferred a free living life-style. Further analysis suggested a shared aquatic phase of MAIC bacilli with the early pathogenic forms of Mycobacterium, well before the latter diverged as 'specialists'. CONCLUSIONS/SIGNIFICANCE: This evolutionary paradigm possibly affirms to marshall our understanding about the acquisition and optimization of virulence in mycobacteria and determinants of boundaries therein
Investigation of charge carrier dynamics in Ti3C2Tx MXene for ultrafast photonics applications
The rapid advancement of nanomaterials has paved the way for various
technological breakthroughs, and MXenes, in particular, have gained substantial
attention due to their unique properties such as high conductivity,
broad-spectrum absorption strength, and tunable band gap. This article presents
the impact of the process parameters on the structural and optical properties
of Ti3C2Tx MXene for application in ultrafast dynamics. XRD along with Raman
spectroscopy studies, confirmed the synthesis of a single phase from their MAX
phase Ti3AlC2. The complete etching of Al and increase in the interplanar
distance is also observed on centrifugation at very high speed. The ultrafast
transient absorption spectroscopy used to understand the effect of centrifuge
speed on the charge carrier dynamics and ultrafast spectrum of MXene displayed
that the carrier lifetime is critically influenced by rotation per minute (rpm)
e.g. faster decay lifetime at 10k rpm than 7k rpm. The electronic relaxation
probed using the time-resolved photoluminescence (TRPL) technique exhibits an
average decay time of 5.13 ns and 5.35 ns at the 7k and 10k rpm, respectively,
which confirms that the optical properties of the MXene are strongly affected
by the centrifuge speed. The synthesized MXene at 10k rpm typically suggests
that radiative processes due to longer decay lifetime and experiences fewer
nonradiative losses, resulting in enhanced luminescence properties.Comment: 21 pages , 6 figure
Polyphasic Taxonomic Analysis Establishes Mycobacterium indicus pranii as a Distinct Species
that integrate its phenotypic, chemotaxonomic and molecular phylogenetic attributes..
Investigating Flavonoid Extracts from Medicinal Plants: Evaluating their Anti-Cancer Potential, Mechanisms, and Synergistic Impact on Colon Cancer
Colon cancer, the leading cause of global cancer-related mortality, demands innovative therapeutic approaches to combat its formidable impact. This empirical study embarks on a quest to unlock novel avenues for colon cancer treatment by investigating the anti-cancer potential of flavonoid extracts sourced from medicinal plants. Our research journey commences with an in-depth examination of the staggering global burden imposed by colon cancer and the inherent limitations of current therapeutic regimens. In response to this pressing challenge, we spotlight the emerging enthusiasm for natural compounds, specifically flavonoids, as transformative agents within the realm of cancer research and therapy. In our pursuit of innovative solutions, we meticulously select medicinal plants celebrated for their flavonoid-rich content and extract these bioactive compounds with precision. Rigorous phytochemical analyses unveil the specific flavonoids at play. In a series of in vitro experiments employing colon cancer cell lines, we uncover a compelling narrative of concentration-dependent cytotoxicity, underscoring the remarkable anti-proliferative attributes of these extracts. Moreover, our investigations reveal that flavonoid extracts possess the remarkable capability to induce apoptosis, substantiated through Annexin V/PI staining and caspase activation assays. As we delve deeper into mechanistic insights, a rich tapestry unfolds, elucidating the intricate modulation of pivotal apoptosis-related pathways by these natural compounds. This study not only furnishes compelling evidence of flavonoid extracts' anti-cancer potential against colon cancer but also underscores the pivotal role of natural compounds in the ever-evolving landscape of cancer research, offering a beacon of hope for pioneering therapeutic strategies. The journey has only begun, and further investigations, alongside rigorous clinical trials, are warranted to harness the full therapeutic potential of flavonoid-based interventions in colon cancer management, potentially redefining the paradigm of cancer treatment
The 3-Point Method: A Fast, Accurate and Robust Solution to Vanishing Point Estimation
Vanishing points can provide information about the 3D world and hence are of great interest for machine vision
applications. In this paper, we present a single point perspectivity based method for robust and accurate estimation
of Vanishing Points (VPs). It utilizes location of 3 collinear points in image space and their distance ratio in the
world frame for VP estimation. We present an algebraic derivation for the proposed 3-Point (3-P) method. It
provides us a non-iterative, closed-form solution. The 3-P results are compared with ground truth of VP and it is
shown to be accurate. Its robustness to point selection and image noise is proved through extensive simulations.
Computational time requirement for 3-P method is shown to be much less than least squares based method. The
3-P method is extremely useful for accurate VP estimation in structured and well-defined environments
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