Birefringence-Induced Trains of High-Rate Pulses in a Mode-Locked Fiber Laser

The output of a mode-locked erbium-doped ring fiber laser incorporating a section of a polarization-maintaining (PM) fiber is investigated in both numerical simulations and experiments. With proper inline polarization control, the laser can be set to emit a train of pulses, separated by the differential group delay of the PM section. Repetition rates as high as 500 GHz are experimentally observed. The results provide an added insight into the role of birefringence in mode-locked lasers based on nonlinear polarization rotation

Do firms buy their stock at bargain prices? : Evidence from actual stock repurchase disclosure

We use new data from SEC filings to investigate how S&P 500 firms execute their open market repurchase programs. We find that smaller S&P 500 firms repurchase less frequently than larger firms, and at a price which is significantly lower than the average market price. Their repurchase activity is followed by a positive and significant abnormal return which lasts up to three months after the repurchase. These findings do not hold for large S&P 500 firms. Our interpretation is that small firms repurchase strategically, whereas the repurchase activity of large firms is more focused on the disbursement of free cash. JEL Classification: G14, G30, G35 Keywords: Stock Repurchases, Stock Buybacks, Payout Policy, Timing, Bid-Ask Spread, Liquidit

Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering

The integration of multi-document pre-training objectives into language models has resulted in remarkable improvements in multi-document downstream tasks. In this work, we propose extending this idea by pre-training a generic multi-document model from a novel cross-document question answering pre-training objective. To that end, given a set (or cluster) of topically-related documents, we systematically generate semantically-oriented questions from a salient sentence in one document and challenge the model, during pre-training, to answer these questions while "peeking" into other topically-related documents. In a similar manner, the model is also challenged to recover the sentence from which the question was generated, again while leveraging cross-document information. This novel multi-document QA formulation directs the model to better recover cross-text informational relations, and introduces a natural augmentation that artificially increases the pre-training data. Further, unlike prior multi-document models that focus on either classification or summarization tasks, our pre-training objective formulation enables the model to perform tasks that involve both short text generation (e.g., QA) and long text generation (e.g., summarization). Following this scheme, we pre-train our model -- termed QAmden -- and evaluate its performance across several multi-document tasks, including multi-document QA, summarization, and query-focused summarization, yielding improvements of up to 7%, and significantly outperforms zero-shot GPT-3.5 and GPT-4.Comment: Accepted at ACL 2023; camera-ready versio

Anatomical Data Augmentation For CNN based Pixel-wise Classification

In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network. The extended labeled data is used to train a U-net network for a pixel-wise classification into different hepatic lesions and normal liver tissues. Our dataset contains CT examinations from 140 patients with 333 CT images annotated by an expert radiologist. We tested our approach and compared it to the conventional training process. Results indicate superiority of our method. Using the anatomical data augmentation we achieved an improvement of 3% in the success rate, 5% in the classification accuracy, and 4% in Dice.Comment: To be presented at IEEE ISBI 201

Genome-wide profiling of chromosome interactions in Plasmodium falciparum characterizes nuclear architecture and reconfigurations associated with antigenic variation.

Spatial relationships within the eukaryotic nucleus are essential for proper nuclear function. In Plasmodium falciparum, the repositioning of chromosomes has been implicated in the regulation of the expression of genes responsible for antigenic variation, and the formation of a single, peri-nuclear nucleolus results in the clustering of rDNA. Nevertheless, the precise spatial relationships between chromosomes remain poorly understood, because, until recently, techniques with sufficient resolution have been lacking. Here we have used chromosome conformation capture and second-generation sequencing to study changes in chromosome folding and spatial positioning that occur during switches in var gene expression. We have generated maps of chromosomal spatial affinities within the P. falciparum nucleus at 25 Kb resolution, revealing a structured nucleolus, an absence of chromosome territories, and confirming previously identified clustering of heterochromatin foci. We show that switches in var gene expression do not appear to involve interaction with a distant enhancer, but do result in local changes at the active locus. These maps reveal the folding properties of malaria chromosomes, validate known physical associations, and characterize the global landscape of spatial interactions. Collectively, our data provide critical information for a better understanding of gene expression regulation and antigenic variation in malaria parasites