Common Representation Learning Using Step-based Correlation Multi-Modal CNN

Deep learning techniques have been successfully used in learning a common representation for multi-view data, wherein the different modalities are projected onto a common subspace. In a broader perspective, the techniques used to investigate common representation learning falls under the categories of canonical correlation-based approaches and autoencoder based approaches. In this paper, we investigate the performance of deep autoencoder based methods on multi-view data. We propose a novel step-based correlation multi-modal CNN (CorrMCNN) which reconstructs one view of the data given the other while increasing the interaction between the representations at each hidden layer or every intermediate step. Finally, we evaluate the performance of the proposed model on two benchmark datasets - MNIST and XRMB. Through extensive experiments, we find that the proposed model achieves better performance than the current state-of-the-art techniques on joint common representation learning and transfer learning tasks.Comment: Accepted in Asian Conference of Pattern Recognition (ACPR-2017

Deepfakes in India: regulation and privacy

While the use of deepfake videos are relatively rare in Indian politics, Simran Jain and Piyush Jha (Independent Researchers, India) argue that the potential of their misuse in other domains, and subsequent infringement of individual privacy, cannot be underestimated. Only rapid governmental intervention in the form of new legislative and regulatory frameworks can help the country deal with this rapidly evolving technology

OppropBERT: An Extensible Graph Neural Network and BERT-style Reinforcement Learning-based Type Inference System

Built-in type systems for statically-typed programming languages (e.g., Java) can only prevent rudimentary and domain-specific errors at compile time. They do not check for type errors in other domains, e.g., to prevent null pointer exceptions or enforce owner-as-modifier encapsulation discipline. Optional Properties (Opprop) or Pluggable type systems, e.g., Checker Framework, provide a framework where users can specify type rules and guarantee the particular property holds with the help of a type checker. However, manually inserting these user-defined type annotations for new and existing large projects requires a lot of human effort. Inference systems like Checker Framework Inference provide a constraint-based whole-program inference framework. However, to develop such a system, the developer must thoroughly understand the underlying framework (Checker Framework) and the accompanying helper methods, which is time-consuming. Furthermore, these frameworks make expensive calls to SAT and SMT solvers, which increases the runtime overhead during inference. The developers write test cases to ensure their framework covers all the possible type rules and works as expected. Our core idea is to leverage only these manually written test cases to create a Deep Learning model to learn the type rules implicitly using a data-driven approach. We present a novel model, OppropBERT, which takes as an input the raw code along with its Control Flow Graphs to predict the error heatmap or the type annotation. The pre-trained BERT-style Transformer model helps encode the code tokens without specifying the programming language's grammar including the type rules. Moreover, using a custom masked loss function, the Graph Convolutional Network better captures the Control Flow Graphs. Suppose a sound type checker is already provided, and the developer wants to create an inference framework. In that case, the model, as mentioned above, can be refined further using a Proximal Policy Optimization (PPO)-based reinforcement learning (RL) technique. The RL agent enables the model to use a more extensive set of publicly available code (not written by the developer) to create training data artificially. The RL feedback loop reduces the effort of manually creating additional test cases, leveraging the feedback from the type checker to predict the annotation better. Extensive and comprehensive experiments are performed to establish the efficacy of OppropBERT for nullness error prediction and annotation prediction tasks by comparing against state-of-the-art tools like Spotbugs, Eclipse, IntelliJ, and Checker Framework on publicly available Java projects. We also demonstrate the capability of zero and few-shot transfer learning to a new type system. Furthermore, to illustrate the model's extensibility, we evaluate the model for predicting type annotations in TypeScript and errors in Python by comparing it against the state-of-the-art models (e.g., BERT, CodeBERT, GraphCodeBERT, etc.) on standard benchmarks datasets (e.g., ManyTypes4TS)

Unruptured unilateral twin ectopic pregnancy: a rare case report

Unilateral twin ectopic pregnancy is a rare entity with an incidence of 1 in 125,000 pregnancies. This is a case of a 26-year-old primigravida with a spontaneous unilateral twin ectopic gestation, diagnosed on transvaginal ultrasound, treated laparoscopically by doing unilateral salpingectomy and confirmed with histopathology. The doubt for ectopic pregnancy was raised when the serum β-HCG level was constantly >1500 mIU/ml and serum progesterone level was <5 pg/ml and no intrauterine pregnancy was seen. On a follow-up scan, twin gestational sac was noted in right adnexa along with a large haemorrhagic cyst in the right ovary. On post-surgery follow-up, patient was found to have had complete recovery. This case report discusses the incidence and rarity, yet possibility of twin ectopic gestations, the need for early diagnosis and its management

PHASE CHANGE UNDER STATIC ELECTRICAL FIELD; IN THE CASE OF LIPIDS

International audiencePhase change in biological tissues may be affected by electrical and magnetic disturbances. Freezing under static electric field of water, aqueous solution and pork meat has been investigated by the authors, showing the ability of this process to refine ice crystals in frozen matrices. SEF affects the supercooling, which is usually reduced with SEF. SEF also triggers the nucleation. The use of radiofrequencies and microwaves has also been used recently by researchers to promote refined ice crystallization in food systems. A focus is proposed on recent experiments done on solidification of a vegetable fat mix (Vegetaline ® – France) under static electric field (SEF). Results showed that SEF affects the supercooling and the phase change temperature of the fat mix indicating a possible impact on the crystalline structure of the solidified fat

Attention, Compilation, and Solver-based Symbolic Analysis are All You Need

In this paper, we present a Java-to-Python (J2P) and Python-to-Java (P2J) back-to-back code translation method, and an associated tool called CoTran, based on large language models (LLMs). Our method leverages the attention mechanism of LLMs, compilation, and symbolic execution-based test generation for equivalence testing between the input and output programs. More precisely, we modify the typical LLM training loop to incorporate compiler and symbolic execution loss. Via extensive experiments comparing CoTran with 12 other transpilers and LLM-based translation tools over a benchmark of more than 57,000 Java-Python equivalent pairs, we show that CoTran outperforms them on relevant metrics such as compilation and runtime equivalence accuracy. For example, our tool gets 97.43% compilation accuracy and 49.66% runtime equivalence accuracy for J2P translation, whereas the nearest competing tool only gets 92.84% and 40.95% respectively

Cracking of enigma of Evans: a rare association with Sjogren and systemic lupus erythematosus

Evans syndrome (ES) is characterized by the simultaneous or consecutive occurrence of warm autoimmune hemolytic anemia (AIHA) along with immune thrombocytopenia (ITP), and less commonly, autoimmune neutropenia. It may manifest spontaneously or as a result of autoimmune, malignancy or lymphoproliferative disease. Clinical manifestations may be associated with hemolysis and thrombocytopenia, potentially leading to life-threatening outcomes. ES is a rare diagnosis of exclusion. Due to its infrequency, the treatment is typically empirical, relying largely on intravenous corticosteroids or immunoglobulins. We are presenting case of a 46-year-old- female with bleeding from the mouth and gums and rashes all over the body with no prior diagnosis of rheumatological disorder. This case is pivotal as it highlights a key factor contributing to ES and presents a pragmatic method for addressing the condition

Drought risk assessment in central Nepal: temporal and spatial analysis

This paper presents temporal and spatial pattern of drought phenomena in central Nepal using standardized precipitation index (SPI) at multiple time scales. The study is based on 32 years of monthly precipitation data from 40 meteorological stations from 1981 to 2012. Results indicate that, while there is no distinct trend in regional precipitation, interannual variation is large. Trend analysis of drought index shows that most stations are characterized by increases in both severity and frequency of drought and trend is stronger for longer drought time scales. Over the study period, the summer season of 2004, 2005, 2006, 2009 and winters 2006, 2008 and 2009 were the worst widespread droughts. These dry periods have a serious impact on agriculture–livestock production of central Nepal. Better understanding of these SPI dynamics could help in understanding the characteristics of droughts and also to develop effective mitigation strategies