Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the Impact of Synthetic Training Data in the Industrial Application of Terminal Strip Object Detection

In industrial manufacturing, numerous tasks of visually inspecting or detecting specific objects exist that are currently performed manually or by classical image processing methods. Therefore, introducing recent deep learning models to industrial environments holds the potential to increase productivity and enable new applications. However, gathering and labeling sufficient data is often intractable, complicating the implementation of such projects. Hence, image synthesis methods are commonly used to generate synthetic training data from 3D models and annotate them automatically, although it results in a sim-to-real domain gap. In this paper, we investigate the sim-to-real generalization performance of standard object detectors on the complex industrial application of terminal strip object detection. Combining domain randomization and domain knowledge, we created an image synthesis pipeline for automatically generating the training data. Moreover, we manually annotated 300 real images of terminal strips for the evaluation. The results show the cruciality of the objects of interest to have the same scale in either domain. Nevertheless, under optimized scaling conditions, the sim-to-real performance difference in mean average precision amounts to 2.69 % for RetinaNet and 0.98 % for Faster R-CNN, qualifying this approach for industrial requirements.
Evaluating Physics Informed Neural Network Performance for Seismic Discrimination Between Earthquakes and Explosions

Combining physics with machine learning models has advanced the performance of machine learning models in many different applications. In this paper, we evaluate adding a weak physics constraint, i.e., a physics-based empirical relationship, to the loss function (the Physics Informed manner) in local distance explosion discrimination in the hope of improving the generalization capability of the machine learning model. We compare the proposed model to the two-branch model we previously developed, as well as to a pure data-driven model. Unexpectedly, the proposed model did not consistently outperform the pure data-driven model. By varying the level of inconsistency in the training data, we find this approach is modulated by the strength of the physics relationship. This result has important implications for how to best incorporate physical constraints in Machine Learning models.
LVIC: Multi-modality segmentation by Lifting Visual Info as Cue

Multi-modality fusion is proven an effective method for 3d perception for autonomous driving. However, most current multi-modality fusion pipelines for LiDAR semantic segmentation have complicated fusion mechanisms. Point painting is a quite straight forward method which directly bind LiDAR points with visual information. Unfortunately, previous point painting like methods suffer from projection error between camera and LiDAR. In our experiments, we find that this projection error is the devil in point painting. As a result of that, we propose a depth aware point painting mechanism, which significantly boosts the multi-modality fusion. Apart from that, we take a deeper look at the desired visual feature for LiDAR to operate semantic segmentation. By Lifting Visual Information as Cue, LVIC ranks 1st on nuScenes LiDAR semantic segmentation benchmark. Our experiments show the robustness and effectiveness. Codes would be make publicly available soon.
APPLE: Adversarial Privacy-aware Perturbations on Latent Embedding for Unfairness Mitigation

Ensuring fairness in deep-learning-based segmentors is crucial for health equity. Much effort has been dedicated to mitigating unfairness in the training datasets or procedures. However, with the increasing prevalence of foundation models in medical image analysis, it is hard to train fair models from scratch while preserving utility. In this paper, we propose a novel method, Adversarial Privacy-aware Perturbations on Latent Embedding (APPLE), that can improve the fairness of deployed segmentors by introducing a small latent feature perturber without updating the weights of the original model. By adding perturbation to the latent vector, APPLE decorates the latent vector of segmentors such that no fairness-related features can be passed to the decoder of the segmentors while preserving the architecture and parameters of the segmentor. Experiments on two segmentation datasets and five segmentors (three U-Net-like and two SAM-like) illustrate the effectiveness of our proposed method compared to several unfairness mitigation methods.
Bass numbers and endomorphism rings of Gorenstein injective modules

Let $R$ be a commutative noetherian ring admitting a dualizing complex and let $\mathfrak p$ be a prime ideal of $R$. In this paper we investigate when $G(R/\frak p)$ is an $R_{\frak p}$-module. We give some necessary and sufficient conditions under which $G(R/\frak p)$ is an $R_{\frak p}$-module. We also study the Bass numbers of $G(R/\frak p)$ and we show that if ${\rm Gid}_RR/\frak p$ is finite, then $\mu^i(\frak q,G(R/\frak p))$ is finite for all $i\geq 0$ and all $\frak q\in{\rm Spec} R$. If ${\rm Gpd}_RR/\frak p$ is finite, then $\mu^i(\frak p,G(R/\frak p))$ is finite for all $i\geq 0$. We define a subring $S(\frak p)_{\frak p}$ of ${\rm End}_{R_{\frak p}}(G(R_{\frak p}/\frak pR_{\frak p}))$ and we show that it is noetherian and contains a subring which is a quotient of $\widehat{R_{\frak p}}$.
LightSword: A Customized Virtual Reality Exergame for Long-Term Cognitive Inhibition Training in Older Adults

The decline of cognitive inhibition significantly impacts older adults' quality of life and well-being, making it a vital public health problem in today's aging society. Previous research has demonstrated that Virtual reality (VR) exergames have great potential to enhance cognitive inhibition among older adults. However, existing commercial VR exergames were unsuitable for older adults' long-term cognitive training due to the inappropriate cognitive activation paradigm, unnecessary complexity, and unbefitting difficulty levels. To bridge these gaps, we developed a customized VR cognitive training exergame (LightSword) based on Dual-task and Stroop paradigms for long-term cognitive inhibition training among healthy older adults. Subsequently, we conducted an eight-month longitudinal user study with 12 older adults aged 60 years and above to demonstrate the effectiveness of LightSword in improving cognitive inhibition. After the training, the cognitive inhibition abilities of older adults were significantly enhanced, with benefits persisting for 6 months. This result indicated that LightSword has both short-term and long-term effects in enhancing cognitive inhibition. Furthermore, qualitative feedback revealed that older adults exhibited a positive attitude toward long-term training with LightSword, which enhanced their motivation and compliance.
Elections in the Post-Quantum Era: Is the Complexity Shield Strong Enough?

The election, a cornerstone of democracy, is one of the best-recognizable symbols of democratic governance. Voters' confidence in elections is essential, and these days, we can watch practically in live broadcast what consequences distrust in the fairness of elections may have. From the times of the celebrated Gibbard-Satterthwaite theorem, it is well-known in the social-choice community that most voting systems are vulnerable to the efforts of various players to influence elections. Luckily for us, computing such influence to affect election outcomes is a hard problem from the computational complexity perspective. This intractability is regarded as a ``complexity shield'' that secures voting rules against this malicious behavior. In this work, we consider quantum computers to be a new threat to the complexity shield described above, as they break out of standard computing paradigms and unlock additional computational resources. To this end, we provide an overview of possible attacks on election, discuss the abilities of quantum computing, and chart possible directions for future research in this area.
ViboPneumo: A Vibratory-Pneumatic Finger-Worn Haptic Device for Altering Perceived Texture Roughness in Mixed Reality

Extensive research has been done in haptic feedback for texture simulation in virtual reality (VR). However, it is challenging to modify the perceived tactile texture of existing physical objects which usually serve as anchors for virtual objects in mixed reality (MR). In this paper, we present ViboPneumo, a finger-worn haptic device that uses vibratory-pneumatic feedback to modulate (i.e., increase and decrease) the perceived roughness of the material surface contacted by the user's fingerpad while supporting the perceived sensation of other haptic properties (e.g., temperature or stickiness) in MR. Our device includes a silicone-based pneumatic actuator that can lift the user's fingerpad on the physical surface to reduce the contact area for roughness decreasing, and an on-finger vibrator for roughness increasing. Our user-perception experimental results showed that the participants could perceive changes in roughness, both increasing and decreasing, compared to the original material surface. We also observed the overlapping roughness ratings among certain haptic stimuli (i.e., vibrotactile and pneumatic) and the originally perceived roughness of some materials without any haptic feedback. This suggests the potential to alter the perceived texture of one type of material to another in terms of roughness (e.g., modifying the perceived texture of ceramics as glass). Lastly, a user study of MR experience showed that ViboPneumo could significantly improve the MR user experience, particularly for visual-haptic matching, compared to the condition of a bare finger. We also demonstrated a few application scenarios for ViboPneumo.
Closely piling up of multiple adhesive fronts in adhesive friction due to re-attachment

To understand why the adhesive frictional force was in linear proportion to the real contact area in experiments, we investigate the adhesive friction generated by sliding elastic solids adhered to a rigid surface via multiple adhesive springs. Our results indicate that the shear-off force of the interface increases with the energetically guided re-attachment rate of adhesive springs, reaching saturation at high re-attachment rates. Remarkably, this shear-off force can surpass the predictions made by the fracture theory. By plotting the adhesive forces along the interface, we observe substantial high adhesive forces distributed throughout the interface, based on which we identify multiple adhesive fronts closely piling up along the interface. These regions can exhibit similar force profiles, and their number appears to increase with the size of the interface, leading to a linear increase in the calculated shear-off force with the size of the interface. We then suggest that multiple adhesive fronts closely pile up to back up each other in adhesive friction due to re-attachments, which may provide profound insights into understanding the observed phenomena associated with adhesive friction along an interface.
Greedy feature selection: Classifier-dependent feature selection via greedy methods

The purpose of this study is to introduce a new approach to feature ranking for classification tasks, called in what follows greedy feature selection. In statistical learning, feature selection is usually realized by means of methods that are independent of the classifier applied to perform the prediction using that reduced number of features. Instead, greedy feature selection identifies the most important feature at each step and according to the selected classifier. In the paper, the benefits of such scheme are investigated theoretically in terms of model capacity indicators, such as the Vapnik-Chervonenkis (VC) dimension or the kernel alignment, and tested numerically by considering its application to the problem of predicting geo-effective manifestations of the active Sun.
Liftings of point-wise finite dimensional persistence modules over local commutative Artinian rings

Let $\mathbf{k}$ be a field and let $V: \mathscr{C} \to \mathbf{k}\textup{-Mod}$ be a point-wise finite dimensional persistence modules, where $\mathscr{C}$ is a small category. Assume that for all local Artinian $\mathbf{k}$-algebras $R$ with residue field isomorphic to $\mathbf{k}$, there is a generalized persistence module $M: \mathscr{C} \to R\textup{-Mod}$, such that for all $x\in \mathrm{Ob}(\mathscr{C})$, $M(x)$ is free over $R$ with finite rank and $\mathbf{k}\otimes_R M(x)\cong V(x)$. If $V$ is a direct sum of indecomposable persistence modules $V_I: \mathscr{C}\to \mathbf{k}\textup{-Mod}$ with endomorphism ring isomorphic to $\mathbf{k}$, then $M$ is a direct sum of indecomposables $M_I:\mathscr{C}\to R\textup{-Mod}$ with endomorphism ring isomorphic to $R$
Predicting Single-cell Drug Sensitivity by Adaptive Weighted Feature for Adversarial Multi-source Domain Adaptation

The development of single-cell sequencing technology had promoted the generation of a large amount of single-cell transcriptional profiles, providing valuable opportunities to explore drug-resistant cell subpopulations in a tumor. However, the drug sensitivity data in single-cell level is still scarce to date, pressing an urgent and highly challenging task for computational prediction of the drug sensitivity to individual cells. This paper proposed scAdaDrug, a multi-source adaptive weighting model to predict single-cell drug sensitivity. We used an autoencoder to extract domain-invariant features related to drug sensitivity from multiple source domains by exploiting adversarial domain adaptation. Especially, we introduced an adaptive weight generator to produce importance-aware and mutual independent weights, which could adaptively modulate the embedding of each sample in dimension-level for both source and target domains. Extensive experimental results showed that our model achieved state-of-the-art performance in predicting drug sensitivity on sinle-cell datasets, as well as on cell line and patient datasets.
Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning

Adaptations of Transformer models, such as BERT4Rec and SASRec, achieve state-of-the-art performance in the sequential recommendation task according to accuracy-based metrics, such as NDCG. These models treat items as tokens and then utilise a score-and-rank approach (Top-K strategy), where the model first computes item scores and then ranks them according to this score. While this approach works well for accuracy-based metrics, it is hard to use it for optimising more complex beyond-accuracy metrics such as diversity. Recently, the GPTRec model, which uses a different Next-K strategy, has been proposed as an alternative to the Top-K models. In contrast with traditional Top-K recommendations, Next-K generates recommendations item-by-item and, therefore, can account for complex item-to-item interdependencies important for the beyond-accuracy measures. However, the original GPTRec paper focused only on accuracy in experiments and needed to address how to optimise the model for complex beyond-accuracy metrics. Indeed, training GPTRec for beyond-accuracy goals is challenging because the interaction training data available for training recommender systems typically needs to be aligned with beyond-accuracy recommendation goals. To solve the misalignment problem, we train GPTRec using a 2-stage approach: in the first stage, we use a teacher-student approach to train GPTRec, mimicking the behaviour of traditional Top-K models; in the second stage, we use Reinforcement Learning to align the model for beyond-accuracy goals. In particular, we experiment with increasing recommendation diversity and reducing popularity bias. Our experiments on two datasets show that in 3 out of 4 cases, GPTRec's Next-K generation approach offers a better tradeoff between accuracy and secondary metrics than classic greedy re-ranking techniques.
Paving the Way for Pass Disturb Free Vertical NAND Storage via A Dedicated and String-Compatible Pass Gate

In this work, we propose a dual-port cell design to address the pass disturb in vertical NAND storage, which can pass signals through a dedicated and string-compatible pass gate. We demonstrate that: i) the pass disturb-free feature originates from weakening of the depolarization field by the pass bias at the high-${V}_{TH}$ (HVT) state and the screening of the applied field by channel at the low-${V}_{TH}$ (LVT) state; ii) combined simulations and experimental demonstrations of dual-port design verify the disturb-free operation in a NAND string, overcoming a key challenge in single-port designs; iii) the proposed design can be incorporated in a highly scaled vertical NAND FeFET string and the pass gate can be incorporated into the existing 3D NAND with the negligible overhead of the pass gate interconnection through a global bottom pass gate contact in the substrate.
Bayesian Preference Elicitation with Language Models

Aligning AI systems to users' interests requires understanding and incorporating humans' complex values and preferences. Recently, language models (LMs) have been used to gather information about the preferences of human users. This preference data can be used to fine-tune or guide other LMs and/or AI systems. However, LMs have been shown to struggle with crucial aspects of preference learning: quantifying uncertainty, modeling human mental states, and asking informative questions. These challenges have been addressed in other areas of machine learning, such as Bayesian Optimal Experimental Design (BOED), which focus on designing informative queries within a well-defined feature space. But these methods, in turn, are difficult to scale and apply to real-world problems where simply identifying the relevant features can be difficult. We introduce OPEN (Optimal Preference Elicitation with Natural language) a framework that uses BOED to guide the choice of informative questions and an LM to extract features and translate abstract BOED queries into natural language questions. By combining the flexibility of LMs with the rigor of BOED, OPEN can optimize the informativity of queries while remaining adaptable to real-world domains. In user studies, we find that OPEN outperforms existing LM- and BOED-based methods for preference elicitation.
Using Fiber Optic Bundles to Miniaturize Vision-Based Tactile Sensors

Vision-based tactile sensors have recently become popular due to their combination of low cost, very high spatial resolution, and ease of integration using widely available miniature cameras. The associated field of view and focal length, however, are difficult to package in a human-sized finger. In this paper we employ optical fiber bundles to achieve a form factor that, at 15 mm diameter, is smaller than an average human fingertip. The electronics and camera are also located remotely, further reducing package size. The sensor achieves a spatial resolution of 0.22 mm and a minimum force resolution 5 mN for normal and shear contact forces. With these attributes, the DIGIT Pinki sensor is suitable for applications such as robotic and teleoperated digital palpation. We demonstrate its utility for palpation of the prostate gland and show that it can achieve clinically relevant discrimination of prostate stiffness for phantom and ex vivo tissue.
PEMODELAN KARAKTERISTIK ANTENA SLOTTED BOW-TIE 27 GHz KE 2,7 GHz MENGGUNAKAN TEKNIK PENSKALAAN

Wireless communication technology has grown very quickly to meet the needs of users. The 5G technology uses higher frequency bands to provide large data capabilities to support multi-Gbps data rates. The frequency allocation of this 5G technology occurs at a frequency of 24.25 GHz to 27.5 GHz. Bow-Tie antenna is a development of biconical antenna. The bow-tie antenna has bandwidth and wide impedance that makes this antenna work on ultra wide band (UWB) frequencies. For antennas to work on ultra wide band (UWB) frequencies, scaling techniques are required. Scaling techniques can be performed to perform antenna measurements associated with very large structures. Scaling technique aims to minimize the cost of making the antenna on a very large structure and get the antenna parameter results in accordance with a more affordable frequency measurement. In the study of this Final Project, a 27 GHz planar bow-tie antenna is designed, realized and evaluated to measure the desired resonance, which is set at a 2.7 GHz frequency by scaling down using Rogers 5880 substrate material / DUROID (Ԑr = 2.2 and h = 1.6 mm). From the simulation result through CST Microwave Suite 2017 and the realization result obtained the comparison of both frequencies with VSWR value ≤ 1,3; return loss ≤ -10 dB and omni-directional radiation pattern.
Algorithm-Hardware Co-Design of Distribution-Aware Logarithmic-Posit Encodings for Efficient DNN Inference

Traditional Deep Neural Network (DNN) quantization methods using integer, fixed-point, or floating-point data types struggle to capture diverse DNN parameter distributions at low precision, and often require large silicon overhead and intensive quantization-aware training. In this study, we introduce Logarithmic Posits (LP), an adaptive, hardware-friendly data type inspired by posits that dynamically adapts to DNN weight/activation distributions by parameterizing LP bit fields. We also develop a novel genetic-algorithm based framework, LP Quantization (LPQ), to find optimal layer-wise LP parameters while reducing representational divergence between quantized and full-precision models through a novel global-local contrastive objective. Additionally, we design a unified mixed-precision LP accelerator (LPA) architecture comprising of processing elements (PEs) incorporating LP in the computational datapath. Our algorithm-hardware co-design demonstrates on average<1% drop in top-1 accuracy across various CNN and ViT models. It also achieves ~ 2x improvements in performance per unit area and 2.2x gains in energy efficiency compared to state-of-the-art quantization accelerators using different data types.
Matching under Imperfectly Transferable Utility

In this paper, we examine matching models with imperfectly transferable utility (ITU). We provide motivating examples, discuss the theoretical foundations of ITU matching models and present methods for estimating them. We also explore connected topics and provide an overview of the related literature. This paper has been submitted as a draft chapter for the Handbook of the Economics of Matching, edited by Che, Chiappori and Salani\'e.
Social Orientation: A New Feature for Dialogue Analysis

There are many settings where it is useful to predict and explain the success or failure of a dialogue. Circumplex theory from psychology models the social orientations (e.g., Warm-Agreeable, Arrogant-Calculating) of conversation participants and can be used to predict and explain the outcome of social interactions. Our work is novel in its systematic application of social orientation tags to modeling conversation outcomes. In this paper, we introduce a new data set of dialogue utterances machine-labeled with social orientation tags. We show that social orientation tags improve task performance, especially in low-resource settings, on both English and Chinese language benchmarks. We also demonstrate how social orientation tags help explain the outcomes of social interactions when used in neural models. Based on these results showing the utility of social orientation tags for dialogue outcome prediction tasks, we release our data sets, code, and models that are fine-tuned to predict social orientation tags on dialogue utterances.