Miracle-dz/UniFAR-FTUs huggingface dataset:
Large Language Model-Powered Smart Contract Vulnerability Detection: New Perspectives

This paper provides a systematic analysis of the opportunities, challenges, and potential solutions of harnessing Large Language Models (LLMs) such as GPT-4 to dig out vulnerabilities within smart contracts based on our ongoing research. For the task of smart contract vulnerability detection, achieving practical usability hinges on identifying as many true vulnerabilities as possible while minimizing the number of false positives. Nonetheless, our empirical study reveals contradictory yet interesting findings: generating more answers with higher randomness largely boosts the likelihood of producing a correct answer but inevitably leads to a higher number of false positives. To mitigate this tension, we propose an adversarial framework dubbed GPTLENS that breaks the conventional one-stage detection into two synergistic stages - generation and discrimination, for progressive detection and refinement, wherein the LLM plays dual roles, i.e., AUDITOR and CRITIC, respectively. The goal of AUDITOR is to yield a broad spectrum of vulnerabilities with the hope of encompassing the correct answer, whereas the goal of CRITIC that evaluates the validity of identified vulnerabilities is to minimize the number of false positives. Experimental results and illustrative examples demonstrate that AUDITOR and CRITIC work together harmoniously to yield pronounced improvements over the conventional one-stage detection. GPTLENS is intuitive, strategic, and entirely LLM-driven without relying on specialist expertise in smart contracts, showcasing its methodical generality and potential to detect a broad spectrum of vulnerabilities. Our code is available at: https://github.com/git-disl/GPTLens.
GraphText: Graph Reasoning in Text Space

Large Language Models (LLMs) have gained the ability to assimilate human knowledge and facilitate natural language interactions with both humans and other LLMs. However, despite their impressive achievements, LLMs have not made significant advancements in the realm of graph machine learning. This limitation arises because graphs encapsulate distinct relational data, making it challenging to transform them into natural language that LLMs understand. In this paper, we bridge this gap with a novel framework, GraphText, that translates graphs into natural language. GraphText derives a graph-syntax tree for each graph that encapsulates both the node attributes and inter-node relationships. Traversal of the tree yields a graph text sequence, which is then processed by an LLM to treat graph tasks as text generation tasks. Notably, GraphText offers multiple advantages. It introduces training-free graph reasoning: even without training on graph data, GraphText with ChatGPT can achieve on par with, or even surpassing, the performance of supervised-trained graph neural networks through in-context learning (ICL). Furthermore, GraphText paves the way for interactive graph reasoning, allowing both humans and LLMs to communicate with the model seamlessly using natural language. These capabilities underscore the vast, yet-to-be-explored potential of LLMs in the domain of graph machine learning.
A comparison of stream bank erosion processes on forested and moorland streams in the Balquhidder Catchments, central Scotland

Stream bank erosion rates measured over a two-year period on a moorland and a forested stream in the Institute of Hydrology's Balquhidder Paired Catchments in central Scotland were compared. Bank erosion rates are generally higher on the mainstream of the moorland catchment and highest in wintger on both streams. Bank erosion is correlated with the incidence of frost: minimum temperatures measured on stream banks of the forested stream were an average of 3·7°C higher than on stream banks both outside the forest and on the moorland stream. This makes the incidence of frost on forested stream banks half as frequent. Volumes of material eroded from the mainstreams were combined with bulk density measurements and it is estimated that erosion of the mainstream banks is contributing 1·5 and 7·3 per cent of the sediment yield of the forested and moorland catchments, respectively. Analysis of the vertical distribution of erosion on the banks of both streams suggests an undercutting mechanism which is more pronounced in the moorland stream. The influence of trees on bank erosion and possible implications for the management of forest streams are discussed. © 1997 by John Wiley & Sons, Ltd.
Investigating ground movements caused by the construction of multiple tunnels in soft ground using laboratory model tests

The prediction of the ground movements above single tunnels in soft ground is well established and can be estimated using semi-empirical methods based on the Gaussian curve. However, the prediction of ground movements associated with closely spaced multiple tunnels, in particular side-by-side (sbs) tunnels, is not as well understood, and therefore simple predictive methods for this application are currently quite limited in terms of their accuracy. This paper describes results from a series of small-scale (1/50) laboratory model tests (conducted at 1g) carried out in Speswhite kaolin clay. These tests have been conducted to gain a greater understanding of the short-term ground movements associated with closely spaced multiple (sbs) tunnels. The observed ground movement results from these tests have shown many of the characteristics observed at full-scale in the published case studies. These results are compared to the commonly used Gaussian curve prediction method and demonstrate the potential inaccuracy .
Paucity of rational points on fibrations with multiple fibres

Given a family of varieties over the projective line, we study the density of fibres that are everywhere locally soluble in the case that components of higher multiplicity are allowed. We use log geometry to formulate a new sparsity criterion for the existence of everywhere locally soluble fibres and formulate new conjectures that generalise previous work of Loughran-Smeets. These conjectures involve geometric invariants of the associated multiplicity orbifolds on the base of the fibration in the spirit of Campana. We give evidence for the conjectures using Chebotarev's theorem and sieve methods.
Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy

Sparsely activated Mixture-of-Experts (SMoE) has shown promise to scale up the learning capacity of neural networks, however, they have issues like (a) High Memory Usage, due to duplication of the network layers into multiple copies as experts; and (b) Redundancy in Experts, as common learning-based routing policies suffer from representational collapse. Therefore, vanilla SMoE models are memory inefficient and non-scalable, especially for resource-constrained downstream scenarios. In this paper, we ask: Can we craft a compact SMoE model by consolidating expert information? What is the best recipe to merge multiple experts into fewer but more knowledgeable experts? Our pilot investigation reveals that conventional model merging methods fail to be effective in such expert merging for SMoE. The potential reasons are: (1) redundant information overshadows critical experts; (2) appropriate neuron permutation for each expert is missing to bring all of them in alignment. To address this, we propose M-SMoE, which leverages routing statistics to guide expert merging. Specifically, it starts with neuron permutation alignment for experts; then, dominant experts and their"group members"are formed; lastly, every expert group is merged into a single expert by utilizing each expert's activation frequency as their weight for merging, thus diminishing the impact of insignificant experts. Moreover, we observed that our proposed merging promotes a low dimensionality in the merged expert's weight space, naturally paving the way for additional compression. Hence, our final method, MC-SMoE (i.e., Merge, then Compress SMoE), further decomposes the merged experts into low-rank and structural sparse alternatives. Extensive experiments across 8 benchmarks validate the effectiveness of MC-SMoE. For instance, our MC-SMoE achieves up to 80% memory and a 20% FLOPs reduction, with virtually no loss in performance.
Suspended sediment dynamics in the morteratsch pro‐glacial zone, bernina alps, switzerland

Abstract. Suspended sediment concentration (SSC) in the Ova da Morteratsch, Switzerland, measured during July 2007 was closely associated with discharge (Q) and showed statistically significant relationships at the p < 0.001 level at the proximal and distal ends of the 600 m pro‐glacial zone. SSC predicted from 10‐minute turbidity records gives a much more detailed insight into SSC fluctuations and identified SSC peaks which do not coincide with discharge peaks. Net (proximal – distal) 10‐minute suspended sediment loads (SSL) are predominantly positive (i.e. suspended sediment is being stored in the reach) for most of the 7–19 July 2007 record. Net (proximal – distal) SSLs correlate closely with discharge for the first part of the record (7–13 July) but from 14 to 19 July suspended sediment exhaustion is in evidence and discrete phases of negative net SSL (i.e. sediment flushing) are likely for up to six hours on three separate days which coincide with phases of high discharge and exhaustion of the glacial suspended sediment sources. Analysis of Q at the Berninabach–Pontresina gauging station (5 km downstream) for the past five years revealed that maximum monthly discharges capable of generating sediment flushing events occur in an average of four months each year. The study emphasises the rapid change in suspended sediment transport and yields with distance from the glacier snout and highlights the importance of measurements as close to the glacier snout as possible if data are to be representative of the glaciated land up‐valley. A better understanding of the processes of sediment exchange and the colonisation and stabilisation of sediment stores by vegetation in such pro‐glacial zones is essential if we are to improve predictions of the impacts of climate change on river sediment dynamics and the subsequent effects on aquatic ecology.
Higher Analogues of Discrete Topological Complexity

In this paper, we introduce the n-th discrete topological complexity and study its properties such as its relation with simplicial Lusternik-Schnirelmann category and how the higher dimensions of discrete topological complexity relate with each other. Moreover, we find a lower bound of $n-$discrete topological complexity which is given by the n-th usual topological complexity of the geometric realisation of that complex. Furthermore, we give an example for the strict case of that lower bound.
Target-Aware Contextual Political Bias Detection in News

Media bias detection requires comprehensive integration of information derived from multiple news sources. Sentence-level political bias detection in news is no exception, and has proven to be a challenging task that requires an understanding of bias in consideration of the context. Inspired by the fact that humans exhibit varying degrees of writing styles, resulting in a diverse range of statements with different local and global contexts, previous work in media bias detection has proposed augmentation techniques to exploit this fact. Despite their success, we observe that these techniques introduce noise by over-generalizing bias context boundaries, which hinders performance. To alleviate this issue, we propose techniques to more carefully search for context using a bias-sensitive, target-aware approach for data augmentation. Comprehensive experiments on the well-known BASIL dataset show that when combined with pre-trained models such as BERT, our augmentation techniques lead to state-of-the-art results. Our approach outperforms previous methods significantly, obtaining an F1-score of 58.15 over state-of-the-art bias detection task.
Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning

Large language models (LLMs) have demonstrated impressive reasoning abilities in complex tasks. However, they lack up-to-date knowledge and experience hallucinations during reasoning, which can lead to incorrect reasoning processes and diminish their performance and trustworthiness. Knowledge graphs (KGs), which capture vast amounts of facts in a structured format, offer a reliable source of knowledge for reasoning. Nevertheless, existing KG-based LLM reasoning methods only treat KGs as factual knowledge bases and overlook the importance of their structural information for reasoning. In this paper, we propose a novel method called reasoning on graphs (RoG) that synergizes LLMs with KGs to enable faithful and interpretable reasoning. Specifically, we present a planning-retrieval-reasoning framework, where RoG first generates relation paths grounded by KGs as faithful plans. These plans are then used to retrieve valid reasoning paths from the KGs for LLMs to conduct faithful reasoning. Furthermore, RoG not only distills knowledge from KGs to improve the reasoning ability of LLMs through training but also allows seamless integration with any arbitrary LLMs during inference. Extensive experiments on two benchmark KGQA datasets demonstrate that RoG achieves state-of-the-art performance on KG reasoning tasks and generates faithful and interpretable reasoning results.
CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction

Open-vocabulary dense prediction tasks including object detection and image segmentation have been advanced by the success of Contrastive Language-Image Pre-training (CLIP). CLIP models, particularly those incorporating vision transformers (ViTs), have exhibited remarkable generalization ability in zero-shot image classification. However, when transferring the vision-language alignment of CLIP from global image representation to local region representation for the open-vocabulary dense prediction tasks, CLIP ViTs suffer from the domain shift from full images to local image regions. In this paper, we embark on an in-depth analysis of the region-language alignment in CLIP models, which is essential for downstream open-vocabulary dense prediction tasks. Subsequently, we propose an approach named CLIPSelf, which adapts the image-level recognition ability of CLIP ViT to local image regions without needing any region-text pairs. CLIPSelf empowers ViTs to distill itself by aligning a region representation extracted from its dense feature map with the image-level representation of the corresponding image crop. With the enhanced CLIP ViTs, we achieve new state-of-the-art performance on open-vocabulary object detection, semantic segmentation, and panoptic segmentation across various benchmarks. Models and code are released at https://github.com/wusize/CLIPSelf.
RA-DIT: Retrieval-Augmented Dual Instruction Tuning

Retrieval-augmented language models (RALMs) improve performance by accessing long-tail and up-to-date knowledge from external data stores, but are challenging to build. Existing approaches require either expensive retrieval-specific modifications to LM pre-training or use post-hoc integration of the data store that leads to suboptimal performance. We introduce Retrieval-Augmented Dual Instruction Tuning (RA-DIT), a lightweight fine-tuning methodology that provides a third option by retrofitting any LLM with retrieval capabilities. Our approach operates in two distinct fine-tuning steps: (1) one updates a pre-trained LM to better use retrieved information, while (2) the other updates the retriever to return more relevant results, as preferred by the LM. By fine-tuning over tasks that require both knowledge utilization and contextual awareness, we demonstrate that each stage yields significant performance improvements, and using both leads to additional gains. Our best model, RA-DIT 65B, achieves state-of-the-art performance across a range of knowledge-intensive zero- and few-shot learning benchmarks, significantly outperforming existing in-context RALM approaches by up to +8.9% in 0-shot setting and +1.4% in 5-shot setting on average.
Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models

Text generation models are notoriously vulnerable to errors in the training data. With the wide-spread availability of massive amounts of web-crawled data becoming more commonplace, how can we enhance the robustness of models trained on a massive amount of noisy web-crawled text? In our work, we propose Error Norm Truncation (ENT), a robust enhancement method to the standard training objective that truncates noisy data. Compared to methods that only uses the negative log-likelihood loss to estimate data quality, our method provides a more accurate estimation by considering the distribution of non-target tokens, which is often overlooked by previous work. Through comprehensive experiments across language modeling, machine translation, and text summarization, we show that equipping text generation models with ENT improves generation quality over standard training and previous soft and hard truncation methods. Furthermore, we show that our method improves the robustness of models against two of the most detrimental types of noise in machine translation, resulting in an increase of more than 2 BLEU points over the MLE baseline when up to 50% of noise is added to the data.
Towards high resolution data assimilation and ensemble forecasting

Due the increase in computational power of supercomputers the grid resolution of high resolution numerical weather prediction models is now reaching the 1 km scale. As a result, mesoscale processes related to high impact weather (such as deep convection) can now explicitly be resolved by the models. Compared to synoptic scale systems (~1000 km) nonlinearity plays a much more prominent role at the mesoscale. Furthermore the relation between observations of these mesoscale processes and the model state is strongly nonlinear. The most successful synoptical scale data assimilation method is known as incremental 4D-VAR and makes use of linearised versions of both the model dynamics and the observation operator that maps the model state to the observed values. Due to the use of these linear approximations there are concerns that the incremental 4D-VAR approach will be defeated by these nonlinearities when the resolution of the model is increased to the km scale. This thesis introduces a new method to account for nonlinearities in the estimation of the optimal state of the atmosphere. Due to the success of ensemble prediction systems (EPSs) for the medium-range several numerical weather prediction consortia have developed, or are in the process of developing, EPSs for the short-range (SREPS). The main goal of these short-range ensemble prediction systems is to aid forecasters in situations of severe weather. In medium-range EPSs several methods are being used to create initial condition perturbations. At the European Centre for Medium-Range Weather Forecasts (ECMWF) part of the initial condition perturbations are created by taking linear combination of singular vectors. This thesis introduces a new final time norm for the singular vector calculation based on the convective available potential energy (CAPE) and describes the properties of these singular vectors
COVID-19 Pandemic and Internet Addiction in Young Adults: A Pilot Study on Positive and Negative Psychosocial Correlates

Objective During and after the lockdowns of the COVID-19 pandemic period, a strain was put on young adults who have taken refuge in the problematic use of internet, social media, gaming, and gambling. This paper aims to investigate possible correlates of Internet Addiction Disorder (IAD), hypothesizing anxiety, depression and perceived stress would promote addiction, while mindfulness skills, resilience and socialization would, conversely, hamper IAD, and promote, consequently, a more functional internet use. Method A pilot study was carried out with a sample of 31 young adults, recruited through a snowball sampling using social networks. Participants filled out an online questionnaire including the following measures: Internet Use, Abuse and Addiction (UADI), Beck Depression Inventory (BDI-II); State-Trait Anxiety Inventory (STAI-Y); Perceived Stress Scale (PSS); Five Facet Mindfulness Questionnaire (FFMQ); Mindfulness Attention and Awareness Scale (MAAS); Resilience Scale (RS-14). It was also administered an ad hoc questionnaire for the assessment of socialization behaviour (6 items) and gambling (2 items). Results All the measures, but socialization, showed adequate reliability. Our sample showed high levels of anxiety, stress, IA and gambling, while presenting low levels of resilience, the mindful skill of no-reactivity and socialization. The first hypothesis was confirmed, finding positive and significant correlations between Internet Addiction on one side and PSS, STAI-Y1, STAY-2, and BDI II on the other side. We also partially confirmed the second hypothesis about UADI negatively correlated with both RS-14 and Mindful Acting. Finally, no correlations were found between Gambling and IA. Conclusions In conclusion, the more one perceives an emotional overload with less stress-control, high anxiety, and depression, and the less one can leverage on the skills of mindful acting and resilience, the more one uses the internet as a strategy to escape from a threatening reality.
Demographic profile, risk stratification, clinical characteristics, and treatment outcome of patients with multiple myeloma: A five-year retrospective study at a tertiary hospital in Ethiopia

Background Patients with multiple myeloma are being seen at an increasing frequency in different indigenous African population. However, local data regarding the demographic profile, clinical characteristics, risk stratification, and treatment outcome of these patients is lacking. This study was designed to fill this existing gap in our setup. Hence, it will aid in the revision of treatment guidelines based on local data on the efficacy of existing treatment regimens and risk stratification of patients with a newly diagnosed multiple myeloma. Methods A single centered Hospital-based retrospective Cohort study was conducted from January 2015 to December 2019. Eighty patients with newly diagnosed MM who received non-proteasome inhibitor-based therapy at TASH, Addis Ababa, Ethiopia were analyzed in the study. Results Out of the 80 patients in this cohort, 51(63.8%) of the patients were males (M: F ratio 1.76:1) and the median age at diagnosis was 52 years. The commonest complications identified were anemia (56.3%) and pathologic fracture (55%). The commonest comorbid conditions were; systemic hypertension (24%), CKD (6.3%), and diabetes (5%). The median Progression-Free Survival (PFS) and Overall Survival (OS) of patients were found to be 17.5 and 20 months respectively. This study also identified factors like advanced DS stage, presence of plasmacytoma, renal dysfunction, elevated serum LDH, high levels of serum protein, and monoclonal M-protein to have adverse implication on the OS and PFS of patients. Conclusion In our setup, Multiple Myeloma is more common in the male population group and our patients are younger than the western population. Myeloma treatment regimens like CP and CPT are found to be less effective in our patients than in patients elsewhere. This is likely to be due to the advanced stage at presentation. In resource-limited setups, where determination of cytogenetic features of myeloma is difficult, different clinical and laboratory parameters can still serve as prognostic markers of treatment outcome & patient survival.