Miracle-dz/UniFAR-FTUs huggingface dataset:
"There Has To Be a Lot That We're Missing": Moderating AI-Generated Content on Reddit

Generative AI threatens to disrupt how we work, learn, communicate, and participate in online communities. We performed a qualitative interview study to understand how moderators of online communities on the social sharing site Reddit are challenged by AI-generated content (AIGC) and how they are adapting. We conducted fifteen in-depth, semi-structured interviews with subreddit moderators about their experiences with AIGC. Though our participants see both legitimate and illegitimate motivations for using AIGC, on the whole they view it as detrimental to their communities, with a level of concern that is dependent on the purpose and size of their subreddits. Moderators reported developing rules and using a variety of strategies that may help communities prevent or curb AIGC, but without foolproof detection tools, enforcement is challenging and relies on heuristics. AIGC poses a unique threat to online communities who value authenticity, in that even well-intentioned community members may inadvertently cause disruption. Overall, for online communities, the threat of Generative AI is not speculative: the disruption has already begun.
Gene expression in growing cells: A biophysical primer

Cell growth and gene expression, two essential elements of all living systems, have long been the focus of biophysical interrogation. Advances in experimental single-cell methods have invigorated theoretical studies into these processes. However, until recently, there was little dialog between the two areas of study. In particular, most theoretical models for gene regulation assumed gene activity to be oblivious to the progression of the cell cycle between birth and division. But, in fact, there are numerous ways in which the periodic character of all cellular observables can modulate gene expression. The molecular factors required for transcription and translation—RNA polymerase, transcription factors, ribosomes—increase in number during the cell cycle, but are also diluted due to the continuous increase in cell volume. The replication of the genome changes the dosage of those same cellular players but also provides competing targets for regulatory binding. Finally, cell division reduces their number again, and so forth. Stochasticity is inherent to all these biological processes, manifested in fluctuations in the synthesis and degradation of new cellular components as well as the random partitioning of molecules at each cell division event. The notion of gene expression as stationary is thus hard to justify. In this review, we survey the emerging paradigm of cell-cycle regulated gene expression, with an emphasis on the global expression patterns rather than gene-specific regulation. We discuss recent experimental reports where cell growth and gene expression were simultaneously measured in individual cells, providing first glimpses into the coupling between the two, and motivating several questions. How do the levels of gene expression products – mRNA and protein – scale with the cell volume and cell-cycle progression? What are the molecular origins of the observed scaling laws, and when do they break down to yield non-canonical behavior? What are the consequences of cell-cycle dependence for the heterogeneity (“noise”) in gene expression within a cell population? While the experimental findings, not surprisingly, differ among genes, organisms, and environmental conditions, several theoretical models have emerged that attempt to reconcile these differences and form a unifying framework for understanding gene expression in growing cells.
Fast Inner-Product Algorithms and Architectures for Deep Neural Network Accelerators

We introduce a new algorithm called the Free-pipeline Fast Inner Product (FFIP) and its hardware architecture that improve an under-explored fast inner-product algorithm (FIP) proposed by Winograd in 1968. Unlike the unrelated Winograd minimal filtering algorithms for convolutional layers, FIP is applicable to all machine learning (ML) model layers that can mainly decompose to matrix multiplication, including fully-connected, convolutional, recurrent, and attention/transformer layers. We implement FIP for the first time in an ML accelerator then present our FFIP algorithm and generalized architecture which inherently improve FIP's clock frequency and, as a consequence, throughput for a similar hardware cost. Finally, we contribute ML-specific optimizations for the FIP and FFIP algorithms and architectures. We show that FFIP can be seamlessly incorporated into traditional fixed-point systolic array ML accelerators to achieve the same throughput with half the number of multiply-accumulate (MAC) units, or it can double the maximum systolic array size that can fit onto devices with a fixed hardware budget. Our FFIP implementation for non-sparse ML models with 8 to 16-bit fixed-point inputs achieves higher throughput and compute efficiency than the best-in-class prior solutions on the same type of compute platform.
PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics

We introduce PhysGaussian, a new method that seamlessly integrates physically grounded Newtonian dynamics within 3D Gaussians to achieve high-quality novel motion synthesis. Employing a custom Material Point Method (MPM), our approach enriches 3D Gaussian kernels with physically meaningful kinematic deformation and mechanical stress attributes, all evolved in line with continuum mechanics principles. A defining characteristic of our method is the seamless integration between physical simulation and visual rendering: both components utilize the same 3D Gaussian kernels as their discrete representations. This negates the necessity for triangle/tetrahedron meshing, marching cubes,"cage meshes,"or any other geometry embedding, highlighting the principle of"what you see is what you simulate (WS$^2$). "Our method demonstrates exceptional versatility across a wide variety of materials--including elastic entities, metals, non-Newtonian fluids, and granular materials--showcasing its strong capabilities in creating diverse visual content with novel viewpoints and movements. Our project page is at: https://xpandora.github. io/PhysGaussian/
Deep learning-based detection of morphological features associated with hypoxia in H&E breast cancer whole slide images

Hypoxia occurs when tumour cells outgrow their blood supply, leading to regions of low oxygen levels within the tumour. Calculating hypoxia levels can be an important step in understanding the biology of tumours, their clinical progression and response to treatment. This study demonstrates a novel application of deep learning to evaluate hypoxia in the context of breast cancer histomorphology. More precisely, we show that Weakly Supervised Deep Learning (WSDL) models can accurately detect hypoxia associated features in routine Hematoxylin and Eosin (H&E) whole slide images (WSI). We trained and evaluated a deep Multiple Instance Learning model on tiles from WSI H&E tissue from breast cancer primary sites (n=240) obtaining on average an AUC of 0.87 on a left-out test set. We also showed significant differences between features of hypoxic and normoxic tissue regions as distinguished by the WSDL models. Such DL hypoxia H&E WSI detection models could potentially be extended to other tumour types and easily integrated into the pathology workflow without requiring additional costly assays.
The DURel Annotation Tool: Human and Computational Measurement of Semantic Proximity, Sense Clusters and Semantic Change

We present the DURel tool implementing the annotation of semantic proximity between word uses into an online, open source interface. The tool supports standardized human annotation as well as computational annotation, building on recent advances with Word-in-Context models. Annotator judgments are clustered with automatic graph clustering techniques and visualized for analysis. This allows to measure word senses with simple and intuitive micro-task judgments between use pairs, requiring minimal preparation efforts. The tool offers additional functionalities to compare the agreement between annotators to guarantee the inter-subjectivity of the obtained judgments and to calculate summary statistics over the annotated data giving insights into sense frequency distributions, semantic variation or changes of senses over time.
Ice formation and its elimination in cryopreservation of bovine oocytes

Damage from ice and potential toxicity of ice-inhibiting cryoprotective agents (CPAs) are key issues in assisted reproduction using cryopreserved oocytes and embryos. We use synchrotron-based time-resolved x-ray diffraction and tools from protein cryocrystallography to characterize ice formation within bovine oocytes after cooling at rates between ∼1000 °C/min and ∼600,000°C /min and during warming at rates between 20,000 and 150,000 °C /min. Maximum crystalline ice diffraction intensity, maximum ice volume, and maximum ice grain size are always observed during warming. All decrease with increasing CPA concentration, consistent with the decreasing free water fraction. With the cooling rates, warming rates and CPA concentrations of current practice, oocytes may show no ice after cooling but always develop substantial ice fractions on warming, and modestly reducing CPA concentrations causes substantial ice to form during cooling. With much larger cooling and warming rates achieved using cryocrystallography tools, oocytes soaked as in current practice remain essentially ice free during both cooling and warming, and when soaked in half-strength CPA solution oocytes remain ice free after cooling and develop small grain ice during warming. These results clarify the roles of cooling, warming, and CPA concentration in generating ice in oocytes, establish the character of ice formed, and suggest that substantial further improvements in warming rates are feasible. Ice formation can be eliminated as a factor affecting post-thaw oocyte viability and development, allowing other deleterious effects of the cryopreservation cycle to be studied, and osmotic stress and CPA toxicity reduced. Significance Statement Cryopreservation of oocytes and embryos is critical in assisted reproduction of humans and domestic animals and in preservation of endangered species. Success rates are limited by damage from crystalline ice, toxicity of cryoprotective agents (CPAs), and damage from osmotic stress. Time-resolved x-ray diffraction of bovine oocytes shows that ice forms much more readily during warming than during cooling, that maximum ice fractions always occur during warming, and that the tools and large CPA concentrations of current protocols can at best only prevent ice formation during cooling. Using tools from cryocrystallography that give dramatically larger cooling and warming rates, ice formation can be completely eliminated and required CPA concentrations substantially reduced, expanding the scope for species-specific optimization of post-thaw reproductive outcomes.
MMDRP: drug response prediction and biomarker discovery using multi-modal deep learning

A major challenge in cancer care is that patients with similar demographics, tumor types, and medical histories can respond quite differently to the same drug regimens. This difference is largely explained by genetic and other molecular variabilities among the patients and their cancers. Efforts in the pharmacogenomics field are underway to understand better the relationship between the genome of the patient’s healthy and tumor cells and their response to therapy. To advance this goal, research groups and consortia have undertaken large-scale systematic screening of panels of drugs across multiple cancer cell lines that have been molecularly profiled by genomics, proteomics, and similar techniques. These large data drug screening sets have been applied to the problem of drug response prediction (DRP), the challenge of predicting the response of a previously untested drug/cell-line combination. Although deep learning algorithms outperform traditional methods, there are still many challenges in DRP that ultimately result in these models’ low generalizability and hampers their clinical application. In this paper, we describe a novel algorithm that addresses the major shortcomings of current DRP methods by combining multiple cell line characterization data, addressing drug response data skewness, and improving chemical compound representation. The result is an open-source, Python-based, command-line program available at https://github.com/LincolnSteinLab/MMDRP.
NeuroVelo: interpretable learning of cellular dynamics from single-cell transcriptomic data

Reconstructing temporal cellular dynamics from static single-cell transcriptomics remains a major challenge. Methods based on RNA velocity, often in combination with non-linear dimensionality reduction, have been proposed. However, interpreting their results in the light of the underlying biology remains difficult, and their predictive power is limited. Here we propose NeuroVelo, a method that couples learning of an optimal linear projection with a non-linear low-dimensional dynamical system. Using dynamical systems theory, NeuroVelo can then identify genes and biological processes driving temporal cellular dynamics. We benchmark NeuroVelo against several current methods using single-cell multi-omic data, demonstrating that NeuroVelo is superior to competing methods in terms of identifying biological pathways and reconstructing evolutionary dynamics.
Deep learning and direct sequencing of labeled RNA captures transcriptome dynamics

Quantification of the dynamics of RNA metabolism is essential for understanding gene regulation in health and disease. Existing methods rely on metabolic labeling of nascent RNAs and physical separation or inference of labeling through PCR-generated mutations, followed by short-read sequencing. However, these methods are limited in their ability to identify transient decay intermediates or co-analyze RNA decay with cis-regulatory elements of RNA stability such as poly(A) tail length and modification status, at single molecule resolution. Here we use 5-ethynyl uridine (5EU) to label nascent RNA followed by direct RNA sequencing with nanopores. We developed RNAkinet, a deep convolutional and recurrent neural network that processes the electrical signal produced by nanopore sequencing to identify 5EU-labeled nascent RNA molecules. RNAkinet demonstrates generalizability to distinct cell types and organisms and reproducibly quantifies RNA kinetic parameters allowing the combined interrogation of RNA metabolism and cis-acting RNA regulatory elements.
Reversible histone deacetylase activity catalyzes lysine acylation

Starvation and low carbohydrate diets lead to the accumulation of the ketone body, β-hydroxybutyrate (BHB), whose blood concentrations increase more than 10-fold into the millimolar range. In addition to providing a carbon source, BHB accumulation triggers lysine β-hydroxybutyrylation (Kbhb) of proteins via unknown mechanisms. As with other lysine acylation events, Kbhb marks can be removed by histone deacetylases (HDACs). Here, we report that class I HDACs unexpectedly catalyze protein lysine modification with β-hydroxybutyrate (BHB). Mutational analyses of the HDAC2 active site reveal a shared reliance on key amino acids for classical deacetylation and non-canonical HDAC-catalyzed β-hydroxybutyrylation. Also consistent with reverse HDAC activity, Kbhb formation is driven by mass action and substrate availability. This reverse HDAC activity is not limited to BHB but also extends to multiple short-chain fatty acids. The reversible activity of class I HDACs described here represents a novel mechanism of PTM deposition relevant to metabolically-sensitive proteome modifications.
Practical Aspects of Using Fully Homomorphic Encryption Systems to Protect Cloud Computing (short paper)

Fully homomorphic encryption schemes are the most promising area of cryptographic information security, particularly in cloud computing. Over the last ten years, Fully Homomorphic Encryption (FHE) has moved from a theoretical idea to practical implementation in real-world cryptographic applications. The concept of homomorphic encryption is ideal for providing secure cloud computing, where user data will never be in plaintext at any stage of its processing. However, there are still many problems related to the performance and complexity of computing that need to be overcome. To confirm the effectiveness of homomorphic encryption in the cloud, a cryptographic cloud computing protection application based on homomorphic encryption was developed. Based on a detailed analysis of existing FHEs, studying their mathematical apparatus, and classifying them according to various criteria, two schemes were selected for implementation in the application — CKKS and BFV, which allow to performance of homomorphic processing of encrypted data. The proposed solution demonstrates a new approach to the design of FHE applications, where the user independently chooses the parameters for implementing the FHE scheme, according to his requirements. The proposed test local server allows to testing of selected scheme parameters by combining the execution of various homomorphic computations. Based on the tests, it is possible to customize the proposed applic ation according to one’ s tasks, sacrificing performance and security for the ability to perform more complex homomorphic computations, or vice versa, or even to maintain a balance between them.
CMM PLN at MentalRiskES: A Traditional Machine Learning Approach for Detection of Eating Disorders and Depression in Chat Messages

This paper describes our approaches to solving the MentalRiskES task, which belongs to the IberLEF (Iberian Languages Evaluation Forum) shared task. The task aims to identify the eating disorders and depression of a user using a series of Telegram messages. Our proposed system uses the traditional TFiDF method to represent the messages and then utilizes these representations as input for machine learning models. The best results for classification were obtained using the Naive Bayes classifier, while in the regression task, the best models were Gradient Boots Regressor and Linear Regressor. Despite its simplicity, we demonstrated that our traditional approaches can still achieve competitive results in recent NLP tasks, obtaining the best results in the case of detecting depression and eating disorders
Analyzing Sentiment, Attraction Type, and Country in Spanish Language TripAdvisor Reviews Using Language Models

This paper describes our participation in the Rest-Mex 2023 Sentiment Analysis Task. We proposed an ensemble of (i) a cascade of transformer-based two-class classifiers biased to lowering the Mean Average Error in Polarity, and (ii) multi-class transformer-based classifiers for the prediction of the Type and Location of the messages. Our system achieved a sentiment track score of 0 . 719 .
Verkle Tree-based Post-Quantum Digital Signature Scheme using Stateless Updatable Vector Commitment

In recent years, work on quantum computers has made substantial progress. Many of the current public key cryptosystems can be broken if humans ever develop a powerful quantum computer. There are currently several commercial products that use these cryptosystems. Though we have developed defenses against quantum attacks, they are too risky and ineffective to be applied in daily life. The study analyzes hash-based digital signature methods. The evaluation of a digital signature using a Merkle tree. The paper investigates unique ideas using vector commitments and a Verkle tree. The authors of this study describe a novel, Verkle tree technology-based method for creating a digital signature system. This is accomplished using the Verkle tree, vector commitments, and vector commitments based on lattices for post-quantum aspects. This work also provides the theories behind post-quantum signature design using Verkle Tree.
LinkMed: Entity Recognition and Relation Extraction from Clinical Notes in Spanish

Relation extraction is an essential component of Natural Language Processing (NLP) and significantly influences information retrieval and structured information extraction. Within clinical notes, the task is needed to establish connections among illnesses, therapies, indications, and other medical concepts. Motivated by the above, in this work, we propose a two-step model approach for entity linking; in the first step, we solve entity recognition, and in the second, a relation classification approach. We evaluated our approach in a Spanish corpus of the TESTLINK challenge in IberLEF2023 (Iberian Languages Evaluation Forum), comprising 81 clinical notes to train and 80 clinical notes to test. Our results show competitive performance with a precision of 0.47, recall of 0.43, and F1-score of 0.45, presenting an effective strategy for relation extraction from clinical notes in Spanish.
Investigating Propaganda Considering the Discursive Context of Utterances

The following system description presents our approach to the detection of propagandistic techniques in tweets. The given task has been framed as a multi-label classification problem. In a multi-label classification problem, each input chunk—in this case tweet—is assigned one of several class labels. In order to assign class labels to the given utterances, we opted for RoBERTa (A Robustly Optimized BERT Pretraining Approach) for sequence classification. Starting off with a pre-trained model for language representation, we fine-tuned this model on the given classification task with the provided annotated data in supervised training steps. In addition to the content of the message, further features describing the general communication context is taken into account.
Streaming city councils' meetings on social media before, during and after the pandemic

The main objectives of this article are to understand the extent to which the COVID-19 pandemic affected the streaming of city council meetings on social media networks and whether, having this effect existed, it has persisted over time. A complementary objective is to find if there are particular characteristics of municipalities that favor the streaming of city council meetings on social media networks. To the best of the author's knowledge, this is the first study to address these objectives. To achieve them, data were collected and analyzed from all 308 Portuguese municipalities. These included the registration of 4905 streams published on Facebook and YouTube, corresponding to 2639 city council meetings from 74 municipalities, held between January 1, 2019, and December 31, 2022. The main findings are: the number of Portuguese municipalities that streamed their city councils in each month more than doubled during the acute phase of the pandemic; this effect started to fade immediately after the easing of the pandemic; and it started to grow again immediately after the September 2022 municipal elections. The main theoretical implication is that disruptive events such as sanitarian crises and local elections can trigger the adoption of innovative forms of the use of social media networks by municipalities, namely those related to transparency, such as streaming city councils. The study also contributes to confirm population size, voter turnout, and margin of victory as relevant determinants of online transparency at the local level.