r/UofT

Yeah, just as it says in the title. Oh and go do your readings cause I know you haven’t started that sh!t 😂🤣😂🤣 WATCHU DOING READING MY POST?! GWAN READ YOUR BOOK DEM 😂😂😂😂😂😂 that way you can go to class feeling like 🤓🤓🤓 and then leave class going 😪😪😪 and maybe have a couple 🚬🍃💨 to brighten your week.

AND GO MAKE FRIENDS 😡

That is all. K thanks bye 🐺😘🫶🏼🇨🇦

im in first year and i received an email earlier today from a prof saying that they suspect me of using AI on a quiz (a very small one from quercus). They said I either have the option of booking a meeting with them, or if they still believe that I used AI they will report it to the chair and may result in me getting a zero.

Okay guys ima keep it 100 and admit that i did use it... I'm willing to just take the zero because the quiz is barely worth anything, but im more so worried about gaining an academic offence on my record.

If i don't book the meeting, will I just receive a zero, or will my case escalate more and I will eventually need to meet with the chair?

1. [tsLLM-SG: Time Series Forecasting by Fine-Tuning LLMs with Dynamic Soft Prompts and Gated Frequency Transformation Adapters](https://link.springer.com/content/pdf/10.1007/978-981-92-0372-7\_32.pdf)

2. [SMART: A Statistic-Driven Semantic Alignment and Routing Framework for Multivariate Long-Term Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-38410-2\_31.pdf)

3. [Dual-MoE: Decoupled Cross-Modal Calibration with Scenario-Aware Expert Routing for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_41.pdf)

4. [TimeGFD: A Lightweight Client-Side Time-Series Forecasting Framework Based on Generative Federated Distillation](https://link.springer.com/content/pdf/10.1007/978-981-92-2497-5\_10.pdf)

5. [PSformer: Parameter-Efficient Transformer with Segment Shared Attention for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-31335-5\_32.pdf)

6. [SA-MoE: Spectral-Aware Mixture of Experts for Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_10.pdf)

7. [PCR-Patch: Prompt-Calibrate-Refine Architecture for Short-Term Electricity Load Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_30.pdf)

8. [TFMoE: Bridging Dual Domains for Long-Term Time Series Forecasting via Adaptive Time–Frequency Modeling](https://link.springer.com/content/pdf/10.1007/978-981-92-0375-8\_5.pdf)

9. [DiSRet: Directional Structure-Aware Retentive Network for Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0375-8\_6.pdf)

10. [SFAT-Net: Non-stationary Time Series Forecasting with Multi-resolution Temporal Embedding and Adaptive Alignment](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_4.pdf)

11. [SymMamba: A Symmetric Dual-Stream Framework for Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-31930-2\_20.pdf)

12. [Balancing Temporal and Inter-variable Dependencies in Multivariate Time-Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0372-7\_17.pdf)

13. [WMENet: A Multi-resolution Wavelet Mamba Enhancement Network for Long Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_2.pdf)

14. [STMixMamba: A Lightweight Hybrid Model for User Trajectory Forecasting in Location-Based Services](https://link.springer.com/content/pdf/10.1007/978-981-92-5699-0\_16.pdf)

15. [Dynamic Redundancy-Aware Variate Pruning: Striking the Efficiency-Accuracy Balance in Long-Term MTS Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3447-9\_36.pdf)

16. [R2Net: Recurrent Residual Network Model for Time Series Prediction](https://link.springer.com/content/pdf/10.1007/978-3-032-37933-7\_6.pdf)

17. [SCFT: A Dual-Domain Orthogonal Representation Framework for Efficient Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3551-3\_22.pdf)

18. [FW-Aug: Fourier-Wavelet Input Augmentation for Transformer-Based Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-2480-7\_11.pdf)

19. [ClusterPatchTST: Uncertainty-Aware Causal Clustering for Heterogeneous Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0372-7\_31.pdf)

20. [S2DBM: Series-to-Series Brownian Bridge for Time Series Point Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3551-3\_25.pdf)

21. [Revisiting Long-Term Time Series Forecasting: An Empirical Perspective on Recent Approaches](https://link.springer.com/content/pdf/10.1007/978-3-032-21625-0\_43.pdf)

22. [NuTS: Non-uniform Sampling with Distance-Dependent Adaptive Fusion for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0369-7\_28.pdf)

23. [WaveFi-Patch: Strengthening Channel-Independent Long-Term Time Series Forecasting with Wavelet-Inspired Decomposition and Time-Aware Modulation](https://link.springer.com/content/pdf/10.1007/978-981-92-3551-3\_28.pdf)

24. [A Temporal-Structure-Aware Enhancement Method for Industrial Process Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_29.pdf)

25. [Adaptive Wavelet Decomposition-Based Dual-Path Transformer for Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3394-6\_2.pdf)

26. [Chaoslinear: A Time Series Forecasting Method Integrating Sequence Uncertainty with Deep Learning](https://link.springer.com/content/pdf/10.1007/978-981-92-3554-4\_21.pdf)

27. [TFDF-Net: Time-Frequency Decomposition for Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-38401-0\_6.pdf)

28. [Gaussian Process Conformal Prediction for Risk-Conditional Coverage Consistency](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_33.pdf)

29. [TS-Unlearn: A Dual-Objective Unlearning Framework for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-1462-4\_27.pdf)

30. [UniMixer: unified patch-wise and global inter-series dependency modeling for multivariate time series forecasting](https://link.springer.com/content/pdf/10.1007/978-981-95-4149-2\_30.pdf)

31. [SparseCycleTransformer: Dual-Pathway Sparse Gating with Periodic Semantic Guidance for Efficient Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3447-9\_24.pdf)

32. [Point-Patch Transformer for Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0378-9\_25.pdf)

33. [Periodicity-Aware Time Series Forecasting for Electric Load](https://link.springer.com/content/pdf/10.1007/978-981-95-9207-4\_4.pdf)

34. [(model name missing in Scholar): A Multi-scale Multi-level Decomposition Transformer for Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-38404-1\_20.pdf)

35. [Multi-domain Time Series Reconstruction with Frequency-Enhanced Transformers](https://link.springer.com/content/pdf/10.1007/978-981-92-2480-7\_33.pdf)

36. [Frequency Energy Adaptive Normalization for Non-Stationary Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3391-5\_17.pdf)

37. [DynaRoute: Dynamics-Conditioned Routing for Universal Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_48.pdf)

38. [XML with XPath, XMLStarlet, and Python](https://link.springer.com/content/pdf/10.1007/979-8-8688-2077-9\_11.pdf) (off-topic)

39. [GRFM: Graph-Aware Residual Forecasting via Periodic Decomposition and Residual Graph Modeling](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_9.pdf)

40. [AMGNet: Dual-Domain Scale-Specific Graph Learning for Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_42.pdf)

41. [Multivariate Time Series Forecasting with Frequency-Domain Similarity Clustering and Hierarchical Feature Interaction](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_47.pdf)

42. [AEFTS-Net: Period-Aware Spectral-Temporal Co-design for Efficient Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-2759-4\_22.pdf)

43. [DPFT: A Framework for Decomposed Probabilistic Forecasting with Transformers](https://link.springer.com/content/pdf/10.1007/978-981-92-3450-9\_22.pdf)

44. [DBHE: Dynamic Basis-Driven Hypernetwork Experts for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-38404-1\_38.pdf)

45. [DDCG: Dual-granularity Dual-domain Collaborative Graph Neural Networks for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-1300-9\_42.pdf)

46. [SSM-Crossformer: Attention Mechanisms Based on Spatial-Spectral Masking in Crossformer for Multidimensional Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-08820-8\_7.pdf)

47. [PAPT: Periodic-Aware Transformer with Polynomial Trend Fitting for General Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0375-8\_1.pdf)

48. [MKAT: Mutual Kolmogorov-Arnold Transformer for Reliable Information Flow in Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-37676-3\_27.pdf)

49. [PyraTSG: A Hierarchical Scale Transition Framework for Dependency Modeling in Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0372-7\_16.pdf)

50. [DPC-Net: A Decouple-Predict-Correct Framework for Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0366-6\_40.pdf)

51. [DMNet: Dual-Decomposition and Multi-frequency Differentiated Learning Architecture for Long-term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3438-7\_35.pdf)

52. [EC-Flow: Entropy-Enhanced Contrastive Learning for Fixed-Point Constrained Time-Series Flow Matching](https://link.springer.com/content/pdf/10.1007/978-3-032-37676-3\_17.pdf)

53. [MoE-TAPE: Mixture of Experts with Task-Adaptive Parameter-Efficient Module for Time Series Representation Learning](https://link.springer.com/content/pdf/10.1007/978-981-92-3551-3\_23.pdf)

54. [NeuroCEA: Cross-Domain Embedding Augmentation with Contrastive Knowledge Distillation for Electrophysiological Time Series Representation](https://link.springer.com/content/pdf/10.1007/978-981-92-0369-7\_15.pdf)

Like ok I understand u have many students nit just me! But I have other classes + jobs too and I seem to manage it fine, the least I can expect is to get the marks back 1 month later pleaseeee

every day I end up stressing about the same thing: what I want to study at this uni and do in the future. this question is eating me from inside out. alot of people say that as you progress in ur degree it will come to u but its not and i dont have a fvking degree cuz im technically not enrolled in a program as i got rejected from the program i wanted to take and am trying to apply and get in after my second year but there is no guarantee that i will get in and if i dont im absolutely cooked. i got diagnosed w adhd and am going to start medication soon and i wonder if then i would be able to study well but even if i get in that program i still dont know if i want to study that cuz idk i cant even imagine what kind of profession i would enjoy and would be good at. i have a new interest in something every 2 days and then it wears off. but i do need a stable job and career and provide my family with a good life but i dont know if i can do corporate. my whole life i have done science but now i am too dumb to undeestand it and i wonder if i just didnt focus enough but i also dont know if i want a career in science. i like the idea of being a therapist but ugh it seems kinda boring and dk if it pays well enough. ugghhh i am absolutely crashing out im gonna lose my mind plzz i need helpppp!!!!

edit: not to mention that i also have chronic anxiety and cant do well at any jobbb

Hi, I recently graduated with BSc double majoring CS and Econ. After my degree, I am having no idea what to do. My experience is mostly food services part-time job. I am trying to get into banking industry by applying to entry level roles (customer experience). What other entry level job can I apply to? Thanks!