`Commerciality` in International Commercial Arbitration

Enterprises, the world over, now conduct business on a dramatically more international scale. The growth of world economies is directly connected with millions of commercial contracts, which are becoming more international in character owing to global integration. Commercial arbitration has been hailed as the most efficient form of dispute settlement available to participants in international trade. As the purpose of the commercial arbitration is to resolve commercial disputes, often issues have been raised whether a particular dispute is commercial or not. With globalisation and seamless trade the aspirations of global business community, it would be of immense importance to understand the meaning of ‘commercial’ as construed in ‘international commercial arbitration’ in some of the major jurisdictions of the world.

Intrinsic nonlinear thermal Hall transport of magnons: A Quantum kinetic theory approach

We present a systematic study of the nonlinear thermal Hall responses in bosonic systems using the quantum kinetic theory framework. We demonstrate the existence of an intrinsic nonlinear boson thermal current, arising from the quantum metric which is a wavefunction dependent band geometric quantity. In contrast to the nonlinear Drude and nonlinear anomalous Hall contributions, the intrinsic nonlinear thermal conductivity is independent of the scattering timescale. We demonstrate the dominance of this intrinsic thermal Hall response in topological magnons in a two-dimensional ferromagnetic honeycomb lattice without Dzyaloshinskii-Moriya interaction. Our findings highlight the significance of band geometry induced nonlinear thermal transport and motivate experimental probe of the intrinsic nonlinear thermal Hall response with implications for quantum magnonics.Comment: 17 pages, and 5 figures; Feedback and criticism are invite

Shapes of Emotions: Multimodal Emotion Recognition in Conversations via Emotion Shifts

Emotion Recognition in Conversations (ERC) is an important and active research area. Recent work has shown the benefits of using multiple modalities (e.g., text, audio, and video) for the ERC task. In a conversation, participants tend to maintain a particular emotional state unless some stimuli evokes a change. There is a continuous ebb and flow of emotions in a conversation. Inspired by this observation, we propose a multimodal ERC model and augment it with an emotion-shift component that improves performance. The proposed emotion-shift component is modular and can be added to any existing multimodal ERC model (with a few modifications). We experiment with different variants of the model, and results show that the inclusion of emotion shift signal helps the model to outperform existing models for ERC on MOSEI and IEMOCAP datasets.Comment: 13 pages, Accepted at Workshop on Performance and Interpretability Evaluations of Multimodal, Multipurpose, Massive-Scale Models, COLING 202

DeePhy: On Deepfake Phylogeny

Deepfake refers to tailored and synthetically generated videos which are now prevalent and spreading on a large scale, threatening the trustworthiness of the information available online. While existing datasets contain different kinds of deepfakes which vary in their generation technique, they do not consider progression of deepfakes in a "phylogenetic" manner. It is possible that an existing deepfake face is swapped with another face. This process of face swapping can be performed multiple times and the resultant deepfake can be evolved to confuse the deepfake detection algorithms. Further, many databases do not provide the employed generative model as target labels. Model attribution helps in enhancing the explainability of the detection results by providing information on the generative model employed. In order to enable the research community to address these questions, this paper proposes DeePhy, a novel Deepfake Phylogeny dataset which consists of 5040 deepfake videos generated using three different generation techniques. There are 840 videos of one-time swapped deepfakes, 2520 videos of two-times swapped deepfakes and 1680 videos of three-times swapped deepfakes. With over 30 GBs in size, the database is prepared in over 1100 hours using 18 GPUs of 1,352 GB cumulative memory. We also present the benchmark on DeePhy dataset using six deepfake detection algorithms. The results highlight the need to evolve the research of model attribution of deepfakes and generalize the process over a variety of deepfake generation techniques. The database is available at: http://iab-rubric.org/deephy-databaseComment: Accepted at 2022, International Joint Conference on Biometrics (IJCB 2022

SICKLE: A Multi-Sensor Satellite Imagery Dataset Annotated with Multiple Key Cropping Parameters

The availability of well-curated datasets has driven the success of Machine Learning (ML) models. Despite greater access to earth observation data in agriculture, there is a scarcity of curated and labelled datasets, which limits the potential of its use in training ML models for remote sensing (RS) in agriculture. To this end, we introduce a first-of-its-kind dataset called SICKLE, which constitutes a time-series of multi-resolution imagery from 3 distinct satellites: Landsat-8, Sentinel-1 and Sentinel-2. Our dataset constitutes multi-spectral, thermal and microwave sensors during January 2018 - March 2021 period. We construct each temporal sequence by considering the cropping practices followed by farmers primarily engaged in paddy cultivation in the Cauvery Delta region of Tamil Nadu, India; and annotate the corresponding imagery with key cropping parameters at multiple resolutions (i.e. 3m, 10m and 30m). Our dataset comprises 2,370 season-wise samples from 388 unique plots, having an average size of 0.38 acres, for classifying 21 crop types across 4 districts in the Delta, which amounts to approximately 209,000 satellite images. Out of the 2,370 samples, 351 paddy samples from 145 plots are annotated with multiple crop parameters; such as the variety of paddy, its growing season and productivity in terms of per-acre yields. Ours is also one among the first studies that consider the growing season activities pertinent to crop phenology (spans sowing, transplanting and harvesting dates) as parameters of interest. We benchmark SICKLE on three tasks: crop type, crop phenology (sowing, transplanting, harvesting), and yield predictionComment: Accepted as an oral presentation at WACV 202

Natural Products as Prominent Source of Bioactive Components with Anti-diabetic Potential

Type 2 diabetes (T2DM) is a chronic metabolic condition characterized by elevated blood sugar levels. It is caused by a combination of insulin resistance and insulin production impairment. The nuclear transcription factor peroxisome proliferator-activated receptor gamma (PPAR-gamma) is essential for glucose homeostasis and lipid metabolism. PPAR-g agonists are a family of medicines used to manage type 2 diabetes by improving blood sugar management and enhancing insulin sensitivity. For decades, natural materials have been utilized as traditional remedies, and many of them have been demonstrated to have anti-diabetic properties. Some natural compounds have been proven in recent investigations to activate PPAR-g. We employed molecular docking and physicochemical screening in this investigation to discover natural compounds with the potential to be developed as novel anti-diabetic medicines. A library of more than 50 natural compounds was tested against the PPAR-g ligand binding domain. We also assessed the ADMET and physicochemical features of the compounds found to determine that they are drug-like. Our study shows the Drug-likeness, bioactivity score along with good ADMEt profile of various phytoconstituents with their high binding affinity toward PPAR-g (PDB ID: 2XKW) as a major target for T2DM. Physicochemical properties of selected compounds were done with SWISS ADME server while ADMEt screening was done by pkCSM server. The binding affinity and molecular interaction study of natural compounds with PPAR-g was done by using Molegro Virtual Docker (MVD). The MolDock and Rerank score of the top one compound of each category was taken to check the binding interaction with tubulin

Multiple cysticerci as an unusual cause of mesenteric lymph node enlargement: a case report

<p>Abstract</p> <p>Introduction</p> <p>Cysticercosis is a disease caused by infestation with the larval stage of the intestinal cestode <it>Taenia solium</it>. The parasite usually localizes to subcutaneous tissues and muscles causing palpable or visible nodules, to the brain leading to epileptic attacks, and to the eyes with visible nodules leading to blindness and atrophy.</p> <p>Case presentation</p> <p>Here we present the case of a 15-year-old girl who was incidentally detected as having mesenteric lymph node enlargement caused by multiple cysticerci. This is the second case report of lymph node enlargement due to cysticercus infestation.</p> <p>Conclusion</p> <p>This rare mode of presentation of cysticercus infestation highlights the importance of parasites as a cause of treatable lymph node enlargement.</p