Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpretable Machine Learning for Weather and Climate Prediction: A Survey

Advanced machine learning models have recently achieved high predictive accuracy for weather and climate prediction. However, these complex models often lack inherent transparency and interpretability, acting as"black boxes"that impede user trust and hinder further model improvements. As such, interpretable machine learning techniques have become crucial in enhancing the credibility and utility of weather and climate modeling. In this survey, we review current interpretable machine learning approaches applied to meteorological predictions. We categorize methods into two major paradigms: 1) Post-hoc interpretability techniques that explain pre-trained models, such as perturbation-based, game theory based, and gradient-based attribution methods. 2) Designing inherently interpretable models from scratch using architectures like tree ensembles and explainable neural networks. We summarize how each technique provides insights into the predictions, uncovering novel meteorological relationships captured by machine learning. Lastly, we discuss research challenges around achieving deeper mechanistic interpretations aligned with physical principles, developing standardized evaluation benchmarks, integrating interpretability into iterative model development workflows, and providing explainability for large foundation models.
UniDepth: Universal Monocular Metric Depth Estimation

Accurate monocular metric depth estimation (MMDE) is crucial to solving downstream tasks in 3D perception and modeling. However, the remarkable accuracy of recent MMDE methods is confined to their training domains. These methods fail to generalize to unseen domains even in the presence of moderate domain gaps, which hinders their practical applicability. We propose a new model, UniDepth, capable of reconstructing metric 3D scenes from solely single images across domains. Departing from the existing MMDE methods, UniDepth directly predicts metric 3D points from the input image at inference time without any additional information, striving for a universal and flexible MMDE solution. In particular, UniDepth implements a self-promptable camera module predicting dense camera representation to condition depth features. Our model exploits a pseudo-spherical output representation, which disentangles camera and depth representations. In addition, we propose a geometric invariance loss that promotes the invariance of camera-prompted depth features. Thorough evaluations on ten datasets in a zero-shot regime consistently demonstrate the superior performance of UniDepth, even when compared with methods directly trained on the testing domains. Code and models are available at: https://github.com/lpiccinelli-eth/unidepth
Diagnostic Endoscopy and Clinical Characteristics of Gastrointestinal Bleeding in Children: a 10-Year Retrospective Study

Background Upper gastrointestinal (UGI) endoscopy is a tool used frequently in the evaluation of patients presented with hematemesis. Objectives The purpose of this study was to evaluate clinical presentations and features of UGI bleeding (UGIB) in children. Methods/Patients and Materials A descriptive retrospective analysis was carried out on the medical records of the patients. Children and adolescents aging 0 – 18 years who were presented with UGIB were recruited in the survey over a period of 10 years (2000 to 2010) in Children's Hospital, Tabriz, Iran. Results There were 447 patients included in this study and they were all evaluated by upper endoscopy. Clinical manifestations included hematemesis (120 cases, 26.85%), melena (60 cases, 13.42%), and hematochezia (11 cases, 2.46%). Age-related analysis showed that erosive esophagities was a more common cause of hematemesis in the younger age group (under 1 year of age) with a prevalence of 37% when compared with elder children and adolescents (40%). Peptic ulcer disease was seen in only 7.4% of infants under the age of one. Overall, Esophagitis and erosive esophagitis was the most common endoscopic finding (179, 40%) . Conclusions There were 80 (17.90%) patients receiving endoscopic hemostatic therapy. In children with UGIB, upper endoscopy is a diagnostic procedure for the categorization of underlying causes of upper gastrointestinal bleeding in children and various endoscopic lesions may be found in a patient with the impression of UGIB.
LocCa: Visual Pretraining with Location-aware Captioners

Image captioning has been shown as an effective pretraining method similar to contrastive pretraining. However, the incorporation of location-aware information into visual pretraining remains an area with limited research. In this paper, we propose a simple visual pretraining method with location-aware captioners (LocCa). LocCa uses a simple image captioner task interface, to teach a model to read out rich information, i.e. bounding box coordinates, and captions, conditioned on the image pixel input. Thanks to the multitask capabilities of an encoder-decoder architecture, we show that an image captioner can easily handle multiple tasks during pretraining. Our experiments demonstrate that LocCa outperforms standard captioners significantly on localization downstream tasks while maintaining comparable performance on holistic tasks.
Directed Criteria Citation Recommendation and Ranking Through Link Prediction

We explore link prediction as a proxy for automatically surfacing documents from existing literature that might be topically or contextually relevant to a new document. Our model uses transformer-based graph embeddings to encode the meaning of each document, presented as a node within a citation network. We show that the semantic representations that our model generates can outperform other content-based methods in recommendation and ranking tasks. This provides a holistic approach to exploring citation graphs in domains where it is critical that these documents properly cite each other, so as to minimize the possibility of any inconsistencies
Ages of"singles"versus"multis": Predictions for dynamical sculpting over Gyr in the Kepler Sample

The sample of host stars with multiple transiting planets has illuminated the orbital architectures of exoplanetary systems. These architectures may be shaped mostly by formation conditions, be continually sculpted by ongoing dynamical processes, or both. As more studies place planet occurrence within a galactic context, evidence has emerged for variable planet multiplicity over time. In this manuscript, we investigate the use of transit multiplicity as a tool to constrain longer-timescale (>1 Gyr) dynamical sculpting. First, with a suite of injection-and-recovery tests, we quantify sensitivity to sculpting laws across different regimes. We employ a forward modeling framework in which we generate synthetic planetary systems, according to a prescribed sculpting speed and timescale, around the FGK dwarfs studied by the Kepler Mission. Some sculpting scenarios are hypothetically detectable in the Kepler sample, while others can be disfavored from Kepler transit statistics alone. Secondly, we apply our analysis to reverse-engineer the sculpting laws consistent with the true yield from Kepler. We confirm the present-day fraction of host stars containing dynamically cool"systems with tightly-packed inner planets"(STIPs) is 4-13%. A variety of Gyr-timescale sculpting laws successfully predict the transit multiplicity of the Kepler sample, but none of these laws succeeds in also producing a detectable trend with transit multiplicity and stellar age. An improvement to measured stellar age precision may help uncover such a sculpting law, but nevertheless reflects limitations in transit multiplicity as an observable. Therefore other phenomena, apart from Gyr-timescale dynamical sculpting, may be required to explain the Kepler yield.
Empirical Analysis for Unsupervised Universal Dependency Parse Tree Aggregation

Dependency parsing is an essential task in NLP, and the quality of dependency parsers is crucial for many downstream tasks. Parsers' quality often varies depending on the domain and the language involved. Therefore, it is essential to combat the issue of varying quality to achieve stable performance. In various NLP tasks, aggregation methods are used for post-processing aggregation and have been shown to combat the issue of varying quality. However, aggregation methods for post-processing aggregation have not been sufficiently studied in dependency parsing tasks. In an extensive empirical study, we compare different unsupervised post-processing aggregation methods to identify the most suitable dependency tree structure aggregation method.
Co-Designing Statistical MIMO Radar and In-band Full-Duplex Multi-User MIMO Communications -- Part II: Joint Precoder, Radar Code, and Receive Filters Design

We address the challenge of spectral sharing between a statistical multiple-input multiple-output (MIMO) radar and an in-band full-duplex (IBFD) multi-user MIMO (MU-MIMO) communications system operating simultaneously in the same frequency band. Existing research on joint MIMO-radar-MIMO-communications (MRMC) systems has limitations, such as focusing on colocated MIMO radars, half-duplex MIMO communications, single-user scenarios, neglecting practical constraints, or employing separate transmit/receive units for MRMC coexistence. This paper, along with companion papers (Part I and III), proposes a comprehensive MRMC framework that addresses all these challenges. In the previous companion paper (Part I), we presented signal processing techniques for a distributed IBFD MRMC system. In this paper, we introduce joint design of statistical MIMO radar codes, uplink/downlink precoders, and corresponding receive filters using a novel metric called compounded-and-weighted sum mutual information. To solve the resulting highly non-convex problem, we employ a combination of block coordinate descent (BCD) and alternating projection methods. Numerical experiments show convergence of our algorithm, mitigation of uplink interference, and stable data rates under varying noise levels, channel estimate imperfections, and self-interference. The subsequent companion paper (Part III) extends the discussion to multiple targets and evaluates the tracking performance of our MRMC system.
Dealing with Imbalanced Classes in Bot-IoT Dataset

With the rapidly spreading usage of Internet of Things (IoT) devices, a network intrusion detection system (NIDS) plays an important role in detecting and protecting various types of attacks in the IoT network. To evaluate the robustness of the NIDS in the IoT network, the existing work proposed a realistic botnet dataset in the IoT network (Bot-IoT dataset) and applied it to machine learning-based anomaly detection. This dataset contains imbalanced normal and attack packets because the number of normal packets is much smaller than that of attack ones. The nature of imbalanced data may make it difficult to identify the minority class correctly. In this thesis, to address the class imbalance problem in the Bot-IoT dataset, we propose a binary classification method with synthetic minority over-sampling techniques (SMOTE). The proposed classifier aims to detect attack packets and overcome the class imbalance problem using the SMOTE algorithm. Through numerical results, we demonstrate the proposed classifier's fundamental characteristics and the impact of imbalanced data on its performance.
NaijaHate: Evaluating Hate Speech Detection on Nigerian Twitter Using Representative Data

To address the global issue of hateful content proliferating in online platforms, hate speech detection (HSD) models are typically developed on datasets collected in the United States, thereby failing to generalize to English dialects from the Majority World. Furthermore, HSD models are often evaluated on curated samples, raising concerns about overestimating model performance in real-world settings. In this work, we introduce NaijaHate, the first dataset annotated for HSD which contains a representative sample of Nigerian tweets. We demonstrate that HSD evaluated on biased datasets traditionally used in the literature largely overestimates real-world performance on representative data. We also propose NaijaXLM-T, a pretrained model tailored to the Nigerian Twitter context, and establish the key role played by domain-adaptive pretraining and finetuning in maximizing HSD performance. Finally, we show that in this context, a human-in-the-loop approach to content moderation where humans review 1% of Nigerian tweets flagged as hateful would enable to moderate 60% of all hateful content. Taken together, these results pave the way towards robust HSD systems and a better protection of social media users from hateful content in low-resource settings.
Locational Marginal Prices Obey DC Circuit Laws

Electricity markets often utilize the DC approximation of the AC power flow equations to facilitate solving an otherwise complex nonconvex optimization problem. These DC power flow equations have analogies to DC circuit laws such as Kirchhoff's Laws, resulting in an intuitive understanding of power flows under this model. Variables derived from the Lagrangian dual of the DC optimal power flow problem, such as locational marginal prices (LMPs) and congestion cost, are less intuitive without an understanding of optimization theory. Even with this understanding, LMP behavior, such as the conditions in which negative prices occur or the impact of individual congested lines on network-wide LMPs, remain somewhat mysterious. In this paper, we show that prices also obey DC circuit laws, which can help facilitate an intuitive understanding of their behavior and relationships throughout a network without explicitly understanding duality. In particular, prices can be modeled as voltages, and their differences can be modeled as flows, allowing for a physical interpretation of prices. This analogy also lends itself to the use of well-understood DC circuit concepts such as superposition and Kirchhoff's Laws, which can further facilitate a clearer understanding of price behavior.
Surface variation analysis of freeform optical systems over surface frequency bands for prescribed wavefront errors

The surface errors of freeform surfaces reflect the manufacturing complexities and significantly impact the feasibility of processing designed optical systems. With multiple degrees of freedom, freeform surfaces pose challenges in surface tolerance analysis in the field. Nevertheless, current research has neglected the influence of surface slopes on the directions of ray propagation. A sudden alteration in the surface slope will lead to a corresponding abrupt shift in the wavefront, even when the change in surface sag is minimal. Moreover, within the realm of freeform surface manufacturing, variation in surface slope across different frequency bands may give rise to unique surface variation. Within the context of this study, we propose a tolerance analysis method to analyze surface variation in freeform surfaces considering surface frequency band slopes based on real ray data. This approach utilizes real ray data to rapidly evaluate surface variation within a specified frequency band of surface slopes. Crucially, our proposed method yields the capability to obtain system surface variation with significant wavefront aberration, in contrast to previous methodologies. The feasibility and advantages of this framework are assessed by analyzing a single-mirror system with a single field and an off-axis two-mirror system. We expect to integrate the proposed methodology with freeform surface design and manufacturing, thereby expanding the scope of freeform optics.
Exploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (EMA) Forecasting

In the evolving field of psychopathology, the accurate assessment and forecasting of data derived from Ecological Momentary Assessment (EMA) is crucial. EMA offers contextually-rich psychopathological measurements over time, that practically lead to Multivariate Time Series (MTS) data. Thus, many challenges arise in analysis from the temporal complexities inherent in emotional, behavioral, and contextual EMA data as well as their inter-dependencies. To address both of these aspects, this research investigates the performance of Recurrent and Temporal Graph Neural Networks (GNNs). Overall, GNNs, by incorporating additional information from graphs reflecting the inner relationships between the variables, notably enhance the results by decreasing the Mean Squared Error (MSE) to 0.84 compared to the baseline LSTM model at 1.02. Therefore, the effect of constructing graphs with different characteristics on GNN performance is also explored. Additionally, GNN-learned graphs, which are dynamically refined during the training process, were evaluated. Using such graphs showed a similarly good performance. Thus, graph learning proved also promising for other GNN methods, potentially refining the pre-defined graphs.
Modulation of Inflammatory Responses by Wnt/β-Catenin Signaling in Dendritic Cells: A Novel Immunotherapy Target for Autoimmunity and Cancer

The Wnt/β-catenin pathway is an evolutionarily conserved signaling pathway critical for several biological processes. An aberrant Wnt/β-catenin signaling is linked to several human diseases. Emerging studies have highlighted the regulatory role of the Wnt/β-catenin signaling pathway in normal physiological processes of parenchymal and hematopoietic cells. Recent studies have shown that the activation of Wnt/β-catenin pathway in dendritic cells (DCs) play a critical role in mucosal tolerance and suppression of chronic autoimmune pathologies. Alternatively, tumors activate Wnt/β-catenin pathway in DCs to induce immune tolerance and thereby evade antitumor immunity through suppression of effector T cell responses and promotion of regulatory T cell responses. Here, we review our work and current understanding of how Wnt/β-catenin signaling in DCs shapes the immune response in cancer and autoimmunity and discuss how Wnt/β-catenin pathway can be targeted for successful therapeutic interventions in various human diseases.
One-Handed Touch Typing on a QWERTY keyboard

"Half-QWERTY" is a new, one-handed typing technique designed to facilitate the transfer of two-handed touch-typing skill to the one-handed condition. It is performed on a standard keyboard with modified software or on a special half-keyboard with full-size keys. In an experiment using touch typists, hunt-and-peck typing speeds were surpassed after 3 to 4 hr of practice. Subjects reached 50% of their two-handed typing speed after about 8 hr. After 10 hr, all subjects typed between 41% and 73% of their two-handed speed, ranging from 23.8 to 42.8 words per minute (wpm). In extended testing, subjects achieved average one-handed speeds as high as 60 wpm and 83% of their two-handed rate. These results are important for providing access to disabled users and for designing compact computers.
SMOF: Streaming Modern CNNs on FPGAs with Smart Off-Chip Eviction

Convolutional Neural Networks (CNNs) have demonstrated their effectiveness in numerous vision tasks. However, their high processing requirements necessitate efficient hardware acceleration to meet the application's performance targets. In the space of FPGAs, streaming-based dataflow architectures are often adopted by users, as significant performance gains can be achieved through layer-wise pipelining and reduced off-chip memory access by retaining data on-chip. However, modern topologies, such as the UNet, YOLO, and X3D models, utilise long skip connections, requiring significant on-chip storage and thus limiting the performance achieved by such system architectures. The paper addresses the above limitation by introducing weight and activation eviction mechanisms to off-chip memory along the computational pipeline, taking into account the available compute and memory resources. The proposed mechanism is incorporated into an existing toolflow, expanding the design space by utilising off-chip memory as a buffer. This enables the mapping of such modern CNNs to devices with limited on-chip memory, under the streaming architecture design approach. SMOF has demonstrated the capacity to deliver competitive and, in some cases, state-of-the-art performance across a spectrum of computer vision tasks, achieving up to 10.65 X throughput improvement compared to previous works.
A Localization Toolkit for Sentic Net

SenticNet is a popular resource for concept-level sentiment analysis. Because SenticNet was created specifically for opinion mining in English language, however, its localization can be very laborious. In this work, a toolkit for creating non-English versions of SenticNet in a time- and cost-effective way is proposed. This is achieved by exploiting online facilities such as Web dictionaries and translation engines. The challenging issues are three: firstly, when a Web lexicon is used, one sentiment concept in English can usually be mapped to multiple concepts in the local language. In this work, we develop a concept disambiguation algorithm to discover context within texts in the target language. Secondly, the polarity of some concepts in the local language may be different from the counterpart in English, which is referred to as language-dependent sentiment concepts. An algorithm is developed to detect sentiment conflict using sentiment annotation corpora in the two languages. Lastly, some sentiment concepts are not included in the local language after dictionary consulting and online translation. In this work, we develop a tool to extract these concepts from sentiment dictionary in the local language. Our practice and evaluation in constructing the Chinese version of SenticNet indicate that the proposed algorithms represent an effective toolkit for localizing SenticNet.
Forecast of CMB TB and EB correlations for AliCPT-1

The correlations between T, E modes and B modes in cosmic microwave background (CMB) radiation, which are expected to vanish under parity symmetry, have become a sensitive probe of the new physics beyond the standard model. In this paper, we forecast the estimation of TB and EB cross power spectra using NILC and cILC on AliCPT-1 simulations. We find that, NILC performs better than cILC on TB and EB correlations in light of its lower uncertainties. In terms of the birefringence angle estimation without assuming systematic errors, the combination of CMB TB and EB spectrum from NILC cleaned simulations could reach a sensitivity of $-0.049^\circ<\beta<0.056^\circ$ (95% CL).
Robust In-Hand Manipulation with Extrinsic Contacts

We present in-hand manipulation tasks where a robot moves an object in grasp, maintains its external contact mode with the environment, and adjusts its in-hand pose simultaneously. The proposed manipulation task leads to complex contact interactions which can be very susceptible to uncertainties in kinematic and physical parameters. Therefore, we propose a robust in-hand manipulation method, which consists of two parts. First, an in-gripper mechanics model that computes a na\"ive motion cone assuming all parameters are precise. Then, a robust planning method refines the motion cone to maintain desired contact mode regardless of parametric errors. Real-world experiments were conducted to illustrate the accuracy of the mechanics model and the effectiveness of the robust planning framework in the presence of kinematics parameter errors.